II. Data Management with Pandas.ipynb

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   "source": [
    "# In This Notebook:\n",
    "\n",
    "- [Pandas Data Structures: DataFrame and Series](#Pandas-Data-Structures:-DataFrame-and-Series)\n",
    "    - [Pandas Overview](#Pandas-Overview)\n",
    "    - [DataFrame and Series Objects](#DataFrame-and-Series-Objects)\n",
    "    - [Working with Data by Index and Columns](#Working-with-Data-by-Index-and-Column)\n",
    "    - [Removing Data](#Removing-Data)\n",
    "    - [Multi-Level Indexing](#Multi-Level-Indexing)\n",
    "    - [Common Series Methods](#Common-Series-Methods)\n",
    "    - [Filtering and Sorting Data](#Filtering-and-Sorting-Data)\n",
    "- [Getting Data](#Getting-Data)\n",
    "    - [From Files](#From-Files)\n",
    "    - [From User Generated Data](#From-User-Generated-Data)\n",
    "    - [Webscraping](#Webscraping)\n",
    "    - [Writing and Saving Data](#Writing-and-Saving-Data)\n",
    "- [Cleaning Data](#Cleaning-Data)\n",
    "    - [Handling Nulls](#Handling-Nulls)\n",
    "    - [Using Where Functions](#Using-Where-Functions)\n",
    "    - [String Matching and Cleaning](#String-Matching-and-Cleaning)\n",
    "- [Data Wrangling and Manipulation](#Data-Wrangling-and-Manipulation)\n",
    "    - [Applying Functions and Transformations](#Applying-Functions-and-Transformations)\n",
    "    - [Joining/Merging Data Sets](#Joining/Merging-Data-Sets)\n",
    "    - [Aggregating Data: Groupby and Pivot Tables](#Aggregating-Data:-Groupby-and-Pivot-Tables)\n",
    "- [Handling Dates and Times](#Handling-Dates-and-Times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Data Structures: DataFrame and Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Overview"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pandas library provides a wealth of tools and data structures to facilitate reading, cleaning, manipulating, processing, and analyzing data, and is largely responsible for the popularity of python as a data analysis platform.  Since becoming an open source project in 2010, pandas has matured and now integrates well with many other python packages such as matplotlib for data visualization, scikit-learn for machine learning, statsmodels for statistical analysis, and many others.  \n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "This notebook covers the main functionality of the pandas library.  It is certainly not an exhaustive treatment of all the features of the library, but highlights some of the commonly used aspects.  See the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/) for a full reference.  \n",
    "\n",
    "Pandas is built on the numpy library and incorporates many of numpy's functionality.  However, it's still useful to import both libraries since numpy will provide some extra functionality to help run computations and process numerical data.  Hence, every data analysis notebook I ever use will start with the following two imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame and Series Objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two main pandas data structures are DataFrame (for tabular data) and Series (single column/vector of data).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in and view the first 5 rows of a DataFrame\n",
    "#This is some fake Physical Training (PT) Test scores including scores (0-100) for Pushups, Situps, and 2Mile run\n",
    "df = pd.read_csv('Data/ptScores.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A DataFrame is basically a collection of Series objects.  We can access a single column with `df[\"col_name\"]`, or if the column name has no spaces we can also access it with `df.col_name`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18\n",
       "1    30\n",
       "2    36\n",
       "3    48\n",
       "4    27\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Each column of the DataFrame is a Series\n",
    "ser = df.age\n",
    "ser.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df),type(ser))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the DataFrame is essentially a tabular, spreadsheet-like data structure.  Unlike numpy arrays which must be a single data type, DataFrames can hold multiple different data types.  We can see the data types of each column with the info() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   SoldierInitials  200 non-null    object \n",
      " 1   gender           200 non-null    object \n",
      " 2   rank             200 non-null    object \n",
      " 3   age              200 non-null    int64  \n",
      " 4   PU               200 non-null    int64  \n",
      " 5   SU               200 non-null    int64  \n",
      " 6   MI2              200 non-null    int64  \n",
      " 7   Average          200 non-null    float64\n",
      "dtypes: float64(1), int64(4), object(3)\n",
      "memory usage: 12.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#View basic info of DataFrame, including data type of each column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are two numerical types for float and int data.  There are also other pandas data types for boolean or datetime data.  Everything else, including string data, is classified as \"object\" data type.\n",
    "\n",
    "It's also helpful to get a quick summary of any numerical columns with the describe() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.0000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.785000</td>\n",
       "      <td>75.2900</td>\n",
       "      <td>69.675000</td>\n",
       "      <td>78.545000</td>\n",
       "      <td>74.503333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.535376</td>\n",
       "      <td>9.4585</td>\n",
       "      <td>8.135126</td>\n",
       "      <td>5.136134</td>\n",
       "      <td>4.649690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>70.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>75.0000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>82.0000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>98.0000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>86.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        PU          SU         MI2     Average\n",
       "count  200.000000  200.0000  200.000000  200.000000  200.000000\n",
       "mean    33.785000   75.2900   69.675000   78.545000   74.503333\n",
       "std      9.535376    9.4585    8.135126    5.136134    4.649690\n",
       "min     18.000000   46.0000   50.000000   67.000000   63.000000\n",
       "25%     25.000000   68.0000   65.000000   74.000000   70.670000\n",
       "50%     34.000000   75.0000   70.000000   79.000000   75.000000\n",
       "75%     42.000000   82.0000   75.000000   82.000000   78.000000\n",
       "max     50.000000   98.0000   92.000000   91.000000   86.330000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get quick summary of numerical fields\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Data by Index and Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed that DataFrames labels all rows and columns.  Row labels are the DataFrame index while column labels are the DataFrame columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Understanding how to effectively use the Index and Columns is essential to being able to view, explore, interact with, and manipulate your data. Here are a few commonly used techniques for using index and column that may be helpful.\n",
    "\n",
    "Quick note on terminology.  While *index* refers to the row labels and *columns* refers to the column labels, both index and column are pandas *Index* objects.  Hence, sometimes the term \"index\" refers specifically to the row index values, and sometimes the term \"index\" can refer generally to either index or column.  For example, the function `df.reindex()` can re-assign the labels for either the rows or column depending on the arguments you pass in.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=200, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SoldierInitials', 'gender', 'rank', 'age', 'PU', 'SU', 'MI2',\n",
       "       'Average'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can quickly view various subsets of a DataFrame with head(), tail() or sample() methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View first n rows\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>BF</td>\n",
       "      <td>F</td>\n",
       "      <td>E9</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "      <td>76.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>GE</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>IG</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "196              BF      F   E9   45  90  62   77    76.33\n",
       "197              OH      M   O6   26  67  60   73    66.67\n",
       "198              GE      M   O5   22  67  77   78    74.00\n",
       "199              IG      M   O5   38  87  81   91    86.33"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View last n rows\n",
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NU</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>79</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HD</td>\n",
       "      <td>M</td>\n",
       "      <td>O3</td>\n",
       "      <td>46</td>\n",
       "      <td>70</td>\n",
       "      <td>62</td>\n",
       "      <td>81</td>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>VC</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>23</td>\n",
       "      <td>68</td>\n",
       "      <td>63</td>\n",
       "      <td>81</td>\n",
       "      <td>70.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>PO</td>\n",
       "      <td>F</td>\n",
       "      <td>E1</td>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "      <td>74</td>\n",
       "      <td>83</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>HD</td>\n",
       "      <td>M</td>\n",
       "      <td>O4</td>\n",
       "      <td>50</td>\n",
       "      <td>89</td>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "      <td>80.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SH</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>32</td>\n",
       "      <td>63</td>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "47               NU      M   E3   40  56  57   79    64.00\n",
       "29               HD      M   O3   46  70  62   81    71.00\n",
       "166              VC      M   O5   23  68  63   81    70.67\n",
       "185              PO      F   E1   25  83  74   83    80.00\n",
       "169              HD      M   O4   50  89  75   77    80.33\n",
       "22               SH      M   O5   32  63  65   72    66.67"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View random sample\n",
    "df.sample(n=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify a set or range of rows to display by referencing the index of each row, either using `df.loc[]` to locate rows by index name, or using `df.iloc[]` to locate rows by index numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DP</td>\n",
       "      <td>M</td>\n",
       "      <td>E5</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>74</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JO</td>\n",
       "      <td>F</td>\n",
       "      <td>O4</td>\n",
       "      <td>46</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>88</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JF</td>\n",
       "      <td>M</td>\n",
       "      <td>O3</td>\n",
       "      <td>36</td>\n",
       "      <td>91</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>75.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JQ</td>\n",
       "      <td>M</td>\n",
       "      <td>E1</td>\n",
       "      <td>35</td>\n",
       "      <td>87</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "      <td>77.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KE</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>19</td>\n",
       "      <td>68</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>73.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "5              DP      M   E5   26  78  70   74    74.00\n",
       "6              JO      F   O4   46  61  61   88    70.00\n",
       "7              JF      M   O3   36  91  66   70    75.67\n",
       "8              JQ      M   E1   35  87  70   75    77.33\n",
       "9              KE      F   E8   19  68  70   83    73.67"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View specific range of rows\n",
    "df.iloc[5:10] #View rows number 5, 6, 7, 8, and 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DP</td>\n",
       "      <td>M</td>\n",
       "      <td>E5</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>74</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000\n",
       "5              DP      M   E5   26  78  70   74  74.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Leave off start index to default to start of df\n",
    "df.iloc[:6] #rows 0 to 5, same as df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>BF</td>\n",
       "      <td>F</td>\n",
       "      <td>E9</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "      <td>76.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>GE</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>IG</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "196              BF      F   E9   45  90  62   77    76.33\n",
       "197              OH      M   O6   26  67  60   73    66.67\n",
       "198              GE      M   O5   22  67  77   78    74.00\n",
       "199              IG      M   O5   38  87  81   91    86.33"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Leave off end index to go through end of df\n",
    "df.iloc[len(df)-4:] #Same as df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SoldierInitials         LB\n",
       "gender                   M\n",
       "rank                    E8\n",
       "age                     18\n",
       "PU                      71\n",
       "SU                      71\n",
       "MI2                     79\n",
       "Average            73.6667\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also specify a single row, in which case iloc returns a Series object with the data of that row\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DF</td>\n",
       "      <td>M</td>\n",
       "      <td>O2</td>\n",
       "      <td>28</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>HI</td>\n",
       "      <td>M</td>\n",
       "      <td>E4</td>\n",
       "      <td>37</td>\n",
       "      <td>53</td>\n",
       "      <td>68</td>\n",
       "      <td>84</td>\n",
       "      <td>68.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>LP</td>\n",
       "      <td>M</td>\n",
       "      <td>E1</td>\n",
       "      <td>38</td>\n",
       "      <td>63</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>70.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0                LB      M   E8   18  71  71   79  73.666667\n",
       "10               DF      M   O2   28  76  74   84  78.000000\n",
       "50               HI      M   E4   37  53  68   84  68.330000\n",
       "100              LP      M   E1   38  63  70   79  70.670000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can feed in any collection of row numbers\n",
    "df.iloc[[0,10,50,100]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>LB</td>\n",
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       "      <td>18</td>\n",
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       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>BF</td>\n",
       "      <td>F</td>\n",
       "      <td>E9</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "      <td>76.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>GE</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>IG</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>86.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0                LB      M   E8   18  71  71   79  73.666667\n",
       "1                SW      M   E3   30  84  70   79  77.670000\n",
       "2                EV      F   E3   36  97  64   77  79.330000\n",
       "3                ML      F   E8   48  77  62   75  71.330000\n",
       "196              BF      F   E9   45  90  62   77  76.330000\n",
       "197              OH      M   O6   26  67  60   73  66.670000\n",
       "198              GE      M   O5   22  67  77   78  74.000000\n",
       "199              IG      M   O5   38  87  81   91  86.330000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unfortunately we can't feed in a combination of multiple different ranges\n",
    "#df.iloc[[0:4,10:15]] #This won't work\n",
    "\n",
    "#One trick to view multiple ranges is with the np.r_ function to get a custom range\n",
    "#E.g., here's a trick I use often to view first few and last few rows of a DataFrame\n",
    "df.iloc[np.r_[0:4,-4:0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned, we can also view rows by index name with df.loc[].  However, currently the *name* of each df row is the same as the *number* of each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
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       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
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       "      <th>0</th>\n",
       "      <td>LB</td>\n",
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       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:3] #Should give similar result as df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the distinction between df.iloc and df.loc clearer, lets rename the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=200, step=1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can get the current df index with df.index, which returns a pandas RangeIndex object\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This may seem scary, but we can always convert any index range to a list\n",
    "list(df.index)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "200              LB      M   E8   18  71  71   79  73.666667\n",
       "201              SW      M   E3   30  84  70   79  77.670000\n",
       "202              EV      F   E3   36  97  64   77  79.330000\n",
       "203              ML      F   E8   48  77  62   75  71.330000\n",
       "204              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can set index to new values by providing a sequence the same length as df\n",
    "df.index = np.arange(200,200+len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>gender</th>\n",
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       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "200              LB      M   E8   18  71  71   79  73.666667\n",
       "201              SW      M   E3   30  84  70   79  77.670000\n",
       "202              EV      F   E3   36  97  64   77  79.330000\n",
       "203              ML      F   E8   48  77  62   75  71.330000\n",
       "204              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Or we can set index to be a current column of the df\n",
    "df.set_index('SoldierInitials')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, the index didn't actually get changed.  What happened?  Turns out most pandas funtions only return a new value of the dataframe for the user to use or discard; they don't actually change the dataframe object in place.  Hence, it's usually needed to re-assign the updated dataframe to itself to actually update it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2    Average\n",
       "SoldierInitials                                         \n",
       "LB                   M   E8   18  71  71   79  73.666667\n",
       "SW                   M   E3   30  84  70   79  77.670000\n",
       "EV                   F   E3   36  97  64   77  79.330000\n",
       "ML                   F   E8   48  77  62   75  71.330000\n",
       "DW                   M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('SoldierInitials')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That did the trick.  Two points worth noting about the index:\n",
    "1. Index doesn't have to be an integer.  Here it's a string, and we'll see later it can also be a datetime.  \n",
    "2. The index can have a name.  This can be a useful way to keep track of what the index name refers to.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender           M\n",
       "rank            E8\n",
       "age             18\n",
       "PU              71\n",
       "SU              71\n",
       "MI2             79\n",
       "Average    73.6667\n",
       "Name: LB, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we can locate df rows by index name with df.loc[]\n",
    "df.loc['LB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2    Average\n",
       "SoldierInitials                                         \n",
       "LB                   M   E8   18  71  71   79  73.666667\n",
       "EV                   F   E3   36  97  64   77  79.330000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['LB','EV']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another point about Index is that the values don't have to be unique.  We can use the value_counts() pandas method to see that several of the initial are repeated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UT    3\n",
       "TS    3\n",
       "ML    3\n",
       "NU    3\n",
       "RB    2\n",
       "Name: SoldierInitials, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the 5 most repeated values in the index\n",
    "df.index.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NU</th>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>79</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU</th>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>32</td>\n",
       "      <td>77</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>73.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU</th>\n",
       "      <td>M</td>\n",
       "      <td>O4</td>\n",
       "      <td>22</td>\n",
       "      <td>68</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS</th>\n",
       "      <td>M</td>\n",
       "      <td>O3</td>\n",
       "      <td>20</td>\n",
       "      <td>74</td>\n",
       "      <td>57</td>\n",
       "      <td>77</td>\n",
       "      <td>69.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS</th>\n",
       "      <td>M</td>\n",
       "      <td>E1</td>\n",
       "      <td>21</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>69.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS</th>\n",
       "      <td>M</td>\n",
       "      <td>E7</td>\n",
       "      <td>39</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>68</td>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2  Average\n",
       "SoldierInitials                                       \n",
       "NU                   M   E3   40  56  57   79    64.00\n",
       "NU                   M   E3   32  77  68   75    73.33\n",
       "NU                   M   O4   22  68  71   80    73.00\n",
       "TS                   M   O3   20  74  57   77    69.33\n",
       "TS                   M   E1   21  71  66   72    69.67\n",
       "TS                   M   E7   39  72  73   68    71.00"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Locating any repeated index will return all matching rows\n",
    "df.loc[['NU','TS']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also view a subset of the DataFrame columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'rank', 'age', 'PU', 'SU', 'MI2', 'Average'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns #Returns Index object, a sequence of column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 PU  SU  MI2\n",
       "SoldierInitials             \n",
       "LB               71  71   79\n",
       "SW               84  70   79\n",
       "EV               97  64   77\n",
       "ML               77  62   75\n",
       "DW               91  75   82"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View subset of columns\n",
    "df[['PU','SU','MI2']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SoldierInitials\n",
       "LB    73.666667\n",
       "SW    77.670000\n",
       "EV    79.330000\n",
       "ML    71.330000\n",
       "DW    82.670000\n",
       "Name: Average, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If we grab a single column, it will return a Series\n",
    "df['Average'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Average\n",
       "SoldierInitials           \n",
       "LB               73.666667\n",
       "SW               77.670000\n",
       "EV               79.330000\n",
       "ML               71.330000\n",
       "DW               82.670000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can grab a single column but remain a DataFrame by feeding in a list of a single element\n",
    "df[['Average']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use df.iloc[] or df.loc[] to re-assign values in a dataframe row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2    Average\n",
       "SoldierInitials                                         \n",
       "LB                   M   E8   18  71  71   79  73.666667\n",
       "SW                   M   E3   30  84  70   79  77.670000\n",
       "EV                   F   E3   36  97  64   77  79.330000\n",
       "ML                   F   E8   48  77  62   75  71.330000\n",
       "DW                   M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age   PU   SU  MI2  Average\n",
       "SoldierInitials                                         \n",
       "LB                   M   O5   50  100  100  100   100.00\n",
       "SW                   M   E3   30   84   70   79    77.67\n",
       "EV                   F   E3   36   97   64   77    79.33"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Must feed in a sequence that matches the number of columns\n",
    "df.iloc[0] = ['M','O5',50,100,100,100,100]\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>gender</th>\n",
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       "      <th>PU</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
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       "      <td>77.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2  Average\n",
       "SoldierInitials                                       \n",
       "LB                   M   O5   50  90  90   90    90.00\n",
       "SW                   M   E3   30  84  70   79    77.67\n",
       "EV                   F   E3   36  97  64   77    79.33"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Works for loc as well\n",
    "df.loc['LB'] = ['M','O5',50,90,90,90,90]\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can **add** new rows with the loc[] method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>OH</th>\n",
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       "      <td>O6</td>\n",
       "      <td>26</td>\n",
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       "      <td>73</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>74.00</td>\n",
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       "    <tr>\n",
       "      <th>IG</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2  Average\n",
       "SoldierInitials                                       \n",
       "OH                   M   O6   26  67  60   73    66.67\n",
       "GE                   M   O5   22  67  77   78    74.00\n",
       "IG                   M   O5   38  87  81   91    86.33"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also feed in an index name that doesn't exist to df.loc[] and it will creat a new row\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZ</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90.00</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU  MI2  Average\n",
       "SoldierInitials                                       \n",
       "GE                   M   O5   22  67  77   78    74.00\n",
       "IG                   M   O5   38  87  81   91    86.33\n",
       "ZZ                   M   O5   50  90  90   90    90.00"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['ZZ'] = ['M','O5',50,90,90,90,90]\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you only specify a single value when adding a new row, pandas will \"Broadcast\" that value, i.e. repeat it enough times to fill the entire row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZ</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank age  PU  SU MI2 Average\n",
       "SoldierInitials                                    \n",
       "OH                   M   O6  26  67  60  73   66.67\n",
       "GE                   M   O5  22  67  77  78      74\n",
       "IG                   M   O5  38  87  81  91   86.33\n",
       "ZZ                   M   O5  50  90  90  90      90\n",
       "ZZZ                  5    5   5   5   5   5       5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If you only specify a single value, pandas will \"Braodcast\" that value\n",
    "df.loc['ZZZ'] = '5'\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>IG</th>\n",
       "      <td>M</td>\n",
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       "      <td>86.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZ</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU MI2 Average\n",
       "SoldierInitials                                     \n",
       "OH                   M   O6   26  67  60  73   66.67\n",
       "GE                   M   O5   22  67  77  78      74\n",
       "IG                   M   O5   38  87  81  91   86.33\n",
       "ZZ                   M   O5  100  90  90  90      90\n",
       "ZZZ                  5    5    5   5   5   5       5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unfortunately we can't use iloc or loc to change a single entry in the df\n",
    "df.loc['ZZ']['age'] = 100 #This wont' update the age of the ZZ entry\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>IG</th>\n",
       "      <td>M</td>\n",
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       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZ</th>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZZZ</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank  age  PU  SU MI2 Average\n",
       "SoldierInitials                                     \n",
       "OH                   M   O6   26  67  60  73   66.67\n",
       "GE                   M   O5   22  67  77  78      74\n",
       "IG                   M   O5   38  87  81  91   86.33\n",
       "ZZ                   M   O5  100  90  90  90      90\n",
       "ZZZ                  5    5    5   5   5   5       5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pandas provides an at functionality for this purpose\n",
    "#Feed in index name followed by column name\n",
    "df.at['ZZ','age'] = 100\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add new Columns in the same way, either by assigning it to a vector of the correct length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>M</td>\n",
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      ],
      "text/plain": [
       "                gender rank age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                                         \n",
       "LB                   M   O5  50  90  90  90      90  Yes\n",
       "SW                   M   E3  30  84  70  79   77.67  Yes\n",
       "EV                   F   E3  36  97  64  77   79.33  Yes\n",
       "ML                   F   E8  48  77  62  75   71.33  Yes\n",
       "DW                   M   O6  27  91  75  82   82.67  Yes"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pass'] = ['Yes']*len(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      <td>No</td>\n",
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       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.67</td>\n",
       "      <td>No</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                                         \n",
       "LB                   M   O5  50  90  90  90      90   No\n",
       "SW                   M   E3  30  84  70  79   77.67   No\n",
       "EV                   F   E3  36  97  64  77   79.33   No\n",
       "ML                   F   E8  48  77  62  75   71.33   No\n",
       "DW                   M   O6  27  91  75  82   82.67   No"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Or we can again specify one value, and pandas will broadcast it across the entire column\n",
    "df['Pass'] = 'No'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also change the column or index names with rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.33</td>\n",
       "      <td>No</td>\n",
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       "      <th>DW</th>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.67</td>\n",
       "      <td>No</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                gender rank age Pushups  SU 2MileRun Average Pass\n",
       "SoldierInitials                                                  \n",
       "LB                   M   O5  50      90  90       90      90   No\n",
       "SW                   M   E3  30      84  70       79   77.67   No\n",
       "EV                   F   E3  36      97  64       77   79.33   No\n",
       "ML                   F   E8  48      77  62       75   71.33   No\n",
       "DW                   M   O6  27      91  75       82   82.67   No"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'MI2':'2MileRun','PU':'Pushups'}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Removing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We've seen how to *keep* or even rearrange a subset of columns or indices, e.g. with `df[cols_to_keep]` or `df.loc[rows_to_keep]`.  We can also specify what index values to remove."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <td>No</td>\n",
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       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
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      ],
      "text/plain": [
       "                gender rank age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                                         \n",
       "LB                   M   O5  50  90  90  90      90   No\n",
       "SW                   M   E3  30  84  70  79   77.67   No\n",
       "EV                   F   E3  36  97  64  77   79.33   No\n",
       "ML                   F   E8  48  77  62  75   71.33   No\n",
       "DW                   M   O6  27  91  75  82   82.67   No"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall current value of df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "      <td>No</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                gender rank age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                                         \n",
       "EV                   F   E3  36  97  64  77   79.33   No\n",
       "ML                   F   E8  48  77  62  75   71.33   No\n",
       "DW                   M   O6  27  91  75  82   82.67   No\n",
       "DP                   M   E5  26  78  70  74      74   No\n",
       "JO                   F   O4  46  61  61  88      70   No"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove given entries with df.drop()\n",
    "#Default is to remove entries from Index (axis=0)\n",
    "df_new = df.drop(['LB','SW'])\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "                age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                             \n",
       "LB               50  90  90  90      90   No\n",
       "SW               30  84  70  79   77.67   No\n",
       "EV               36  97  64  77   79.33   No\n",
       "ML               48  77  62  75   71.33   No\n",
       "DW               27  91  75  82   82.67   No"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set axis argument to 1 to remove values from columns\n",
    "df_new = df.drop(['gender','rank'],axis=1)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Instead of the confusing \"axis=1\" notation, we can directly specify the list of items to remove from the index or column, which can be more intuitive.  It's always great to make your code more intuitive, especially if someone else will look at your code later, such as another data analyst, or even future you!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <td>61</td>\n",
       "      <td>88</td>\n",
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       "      <td>No</td>\n",
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      ],
      "text/plain": [
       "                gender rank age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                                         \n",
       "EV                   F   E3  36  97  64  77   79.33   No\n",
       "ML                   F   E8  48  77  62  75   71.33   No\n",
       "DW                   M   O6  27  91  75  82   82.67   No\n",
       "DP                   M   E5  26  78  70  74      74   No\n",
       "JO                   F   O4  46  61  61  88      70   No"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(index=['LB','SW']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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      "text/plain": [
       "                age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                             \n",
       "LB               50  90  90  90      90   No\n",
       "SW               30  84  70  79   77.67   No\n",
       "EV               36  97  64  77   79.33   No\n",
       "ML               48  77  62  75   71.33   No\n",
       "DW               27  91  75  82   82.67   No"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['gender','rank']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
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       "      <td>No</td>\n",
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      "text/plain": [
       "                age  PU  SU MI2 Average Pass\n",
       "SoldierInitials                             \n",
       "EV               36  97  64  77   79.33   No\n",
       "ML               48  77  62  75   71.33   No\n",
       "DW               27  91  75  82   82.67   No\n",
       "DP               26  78  70  74      74   No\n",
       "JO               46  61  61  88      70   No"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can even use this notation to drop both index and column labels\n",
    "df.drop(index=['LB','SW'],columns=['gender','rank']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "One tricky part of the drop() method is that if we feed in a value that is not in the index, it will throw an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#This will throw an error, since 'Pass' is not in the columns\n",
    "#df.drop(['gender','rank','Pass'],axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Another tool we can use, which helps get around this issue, is to use the difference() method of pandas Index objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true,
    "scrolled": true
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   "outputs": [
    {
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      ],
      "text/plain": [
       "                Average MI2  PU  SU age\n",
       "SoldierInitials                        \n",
       "LB                   90  90  90  90  50\n",
       "SW                77.67  79  84  70  30\n",
       "EV                79.33  77  97  64  36\n",
       "ML                71.33  75  77  62  48\n",
       "DW                82.67  82  91  75  27"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This will only keep columns that are not in the list ['gender','rank','Pass']\n",
    "cols_to_keep = df.columns.difference(['gender','rank','Pass'])\n",
    "#This will run just fine even though 'Pass' is not in the columns\n",
    "df[cols_to_keep].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Yet another method we can use to remove and narrow down data (there's always about 10 ways to do any task in python/pandas) is with the reindex() method, where we can specify the new set of indices or columns to keep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>MI2</th>\n",
       "      <th>SU</th>\n",
       "      <th>PU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LB</th>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW</th>\n",
       "      <td>30</td>\n",
       "      <td>79</td>\n",
       "      <td>70</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>36</td>\n",
       "      <td>77</td>\n",
       "      <td>64</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <td>48</td>\n",
       "      <td>75</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <td>27</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age MI2  SU  PU\n",
       "SoldierInitials                \n",
       "LB               50  90  90  90\n",
       "SW               30  79  70  84\n",
       "EV               36  77  64  97\n",
       "ML               48  75  62  77\n",
       "DW               27  82  75  91"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use reindex() to specify which index or column values to keep, and the order\n",
    "df.reindex(columns=['age','MI2','SU','PU']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "One advantage of reindex is that we can pass in values that are currently not in the index, and it will initialize a new row/column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>MI2</th>\n",
       "      <th>SU</th>\n",
       "      <th>PU</th>\n",
       "      <th>Pass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>50</td>\n",
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       "      <td>30</td>\n",
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       "      <td>84</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>36</td>\n",
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       "      <td>64</td>\n",
       "      <td>97</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <td>48</td>\n",
       "      <td>75</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <td>27</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "      <td>91</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age MI2  SU  PU Pass\n",
       "SoldierInitials                     \n",
       "LB               50  90  90  90   No\n",
       "SW               30  79  70  84   No\n",
       "EV               36  77  64  97   No\n",
       "ML               48  75  62  77   No\n",
       "DW               27  82  75  91   No"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pass is not in the current columns, so this will initialize a new 'Pass' column, with all null values for now\n",
    "df.reindex(columns=['age','MI2','SU','PU','Pass']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Multi-Level Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pandas supports mutli-level indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
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       "  </thead>\n",
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       "      <td>71</td>\n",
       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
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      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/ptScores.csv')\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>79</td>\n",
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       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <th>F</th>\n",
       "      <th>E3</th>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ML</th>\n",
       "      <th>F</th>\n",
       "      <th>E8</th>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DW</th>\n",
       "      <th>M</th>\n",
       "      <th>O6</th>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             age  PU  SU  MI2    Average\n",
       "SoldierInitials gender rank                             \n",
       "LB              M      E8     18  71  71   79  73.666667\n",
       "SW              M      E3     30  84  70   79  77.670000\n",
       "EV              F      E3     36  97  64   77  79.330000\n",
       "ML              F      E8     48  77  62   75  71.330000\n",
       "DW              M      O6     27  91  75   82  82.670000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi = df.set_index(['SoldierInitials','gender','rank'])\n",
    "df_multi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can access rows by specifying the index values in order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
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       "             age  PU  SU  MI2  Average\n",
       "gender rank                           \n",
       "F      E8     48  77  62   75    71.33\n",
       "M      O5     31  84  74   79    79.00\n",
       "       E4     33  76  63   78    72.33"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_multi.loc['ML']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-674541609852>:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  df_multi.loc['ML','M','O5']\n"
     ]
    },
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       "                             age  PU  SU  MI2  Average\n",
       "SoldierInitials gender rank                           \n",
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    "df_multi.loc['ML','M','O5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
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       "      <td>75</td>\n",
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       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
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       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
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      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can use reset_index() to \"unpack\" indices back into columns\n",
    "df_multi.reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  PU  SU  MI2    Average\n",
       "0   18  71  71   79  73.666667\n",
       "1   30  84  70   79  77.670000\n",
       "2   36  97  64   77  79.330000\n",
       "3   48  77  62   75  71.330000\n",
       "4   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also drop (wipe out) the index values with reset_index(drop=True)\n",
    "df_multi.reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Common Series Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has many built-in Series methods that are useful for summarizing or evaluating the data in that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 50, 33.785, 9.535375816390195)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute variety of statistics of any numerical column\n",
    "df.age.min(), df.age.max(), df.age.mean(), df.age.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    183\n",
       "1    303\n",
       "2    363\n",
       "3    483\n",
       "4    273\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perform arithmatic on any series\n",
    "(10*df.age+3).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "Name: age, dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute Boolean Series wherever value satisfies some condition\n",
    "(df.age<30).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For any string columns, we can access any standard python string methods using df.col_name.str.method().  This will typically return a new series where the string method is applied element-wise to each entry in the original series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "Name: SoldierInitials, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get new Series with length of strings in SoldierInitials column\n",
    "df.SoldierInitials.str.len().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "Name: SoldierInitials, dtype: bool"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get new Boolean Series which is True for any entries that contain an \"L\"\n",
    "df.SoldierInitials.str.contains('L').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some more useful Series methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['E8', 'E3', 'O6', 'E5', 'O4', 'O3', 'E1', 'O2', 'E6', 'E2', 'O1',\n",
       "       'E9', 'O5', 'E4', 'E7'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get array of unique values in any column\n",
    "df['rank'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: Why did we run `df['rank'].unique()` instead of `df.rank.unique()`?  Turns out that rank is a DataFrame method, so when we type `df.rank`, pandas first tries to execute the rank() method (which retruns a new dataframe where each element is replaced by its ranking within that column, try it out below to see).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E1    21\n",
       "O1    20\n",
       "O5    17\n",
       "E2    16\n",
       "O3    15\n",
       "O4    14\n",
       "E3    13\n",
       "E9    12\n",
       "E6    12\n",
       "E8    12\n",
       "E5    11\n",
       "E4    11\n",
       "E7    11\n",
       "O6     8\n",
       "O2     7\n",
       "Name: rank, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get list of value counts in any Series\n",
    "df['rank'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>LS</td>\n",
       "      <td>M</td>\n",
       "      <td>E9</td>\n",
       "      <td>26</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>81</td>\n",
       "      <td>69.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>BF</td>\n",
       "      <td>F</td>\n",
       "      <td>E9</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "      <td>76.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>GE</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>IG</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>86.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0                LB      M   E8   18  71  71   79  73.666667\n",
       "1                SW      M   E3   30  84  70   79  77.670000\n",
       "2                EV      F   E3   36  97  64   77  79.330000\n",
       "3                ML      F   E8   48  77  62   75  71.330000\n",
       "4                DW      M   O6   27  91  75   82  82.670000\n",
       "..              ...    ...  ...  ...  ..  ..  ...        ...\n",
       "195              LS      M   E9   26  61  66   81  69.330000\n",
       "196              BF      F   E9   45  90  62   77  76.330000\n",
       "197              OH      M   O6   26  67  60   73  66.670000\n",
       "198              GE      M   O5   22  67  77   78  74.000000\n",
       "199              IG      M   O5   38  87  81   91  86.330000\n",
       "\n",
       "[200 rows x 8 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace values by some mapping\n",
    "gender_map = {'M':'Male','F':'Female'}\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Filtering and Sorting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We just saw that we can evaluate Boolean Series indicating where the values of a Series satisfy some condition.  We can use this to quickly filter our DataFrames to only show data satisfying some condition, a simple but powerful technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KE</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>19</td>\n",
       "      <td>68</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>73.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NQ</td>\n",
       "      <td>M</td>\n",
       "      <td>O3</td>\n",
       "      <td>19</td>\n",
       "      <td>64</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PA</td>\n",
       "      <td>F</td>\n",
       "      <td>O5</td>\n",
       "      <td>18</td>\n",
       "      <td>67</td>\n",
       "      <td>69</td>\n",
       "      <td>89</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>RH</td>\n",
       "      <td>F</td>\n",
       "      <td>O1</td>\n",
       "      <td>18</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>79</td>\n",
       "      <td>81.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0               LB      M   E8   18  71  71   79  73.666667\n",
       "9               KE      F   E8   19  68  70   83  73.670000\n",
       "35              NQ      M   O3   19  64  80   90  78.000000\n",
       "39              PA      F   O5   18  67  69   89  75.000000\n",
       "48              RH      F   O1   18  82  83   79  81.330000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only view Soldiers under 20\n",
    "df[df.age<20].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>JO</td>\n",
       "      <td>F</td>\n",
       "      <td>O4</td>\n",
       "      <td>46</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>88</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JF</td>\n",
       "      <td>M</td>\n",
       "      <td>O3</td>\n",
       "      <td>36</td>\n",
       "      <td>91</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>75.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DF</td>\n",
       "      <td>M</td>\n",
       "      <td>O2</td>\n",
       "      <td>28</td>\n",
       "      <td>76</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MC</td>\n",
       "      <td>M</td>\n",
       "      <td>O4</td>\n",
       "      <td>49</td>\n",
       "      <td>89</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>79.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "4               DW      M   O6   27  91  75   82    82.67\n",
       "6               JO      F   O4   46  61  61   88    70.00\n",
       "7               JF      M   O3   36  91  66   70    75.67\n",
       "10              DF      M   O2   28  76  74   84    78.00\n",
       "11              MC      M   O4   49  89  75   75    79.67"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only view Officers\n",
    "df[df['rank'].str.startswith('O')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To *negate* a boolean Series, we have to use the ~ symbol rather than the not keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DP</td>\n",
       "      <td>M</td>\n",
       "      <td>E5</td>\n",
       "      <td>26</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>74</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "5              DP      M   E5   26  78  70   74  74.000000"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~ df['rank'].str.startswith('O')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>WX</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>52</td>\n",
       "      <td>78</td>\n",
       "      <td>67.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MW</td>\n",
       "      <td>F</td>\n",
       "      <td>O1</td>\n",
       "      <td>20</td>\n",
       "      <td>56</td>\n",
       "      <td>67</td>\n",
       "      <td>83</td>\n",
       "      <td>68.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>XA</td>\n",
       "      <td>M</td>\n",
       "      <td>O2</td>\n",
       "      <td>32</td>\n",
       "      <td>67</td>\n",
       "      <td>52</td>\n",
       "      <td>73</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>IF</td>\n",
       "      <td>M</td>\n",
       "      <td>E5</td>\n",
       "      <td>28</td>\n",
       "      <td>79</td>\n",
       "      <td>53</td>\n",
       "      <td>82</td>\n",
       "      <td>71.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NU</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>79</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "17              WX      M   E8   47  73  52   78    67.67\n",
       "25              MW      F   O1   20  56  67   83    68.67\n",
       "32              XA      M   O2   32  67  52   73    64.00\n",
       "43              IF      M   E5   28  79  53   82    71.33\n",
       "47              NU      M   E3   40  56  57   79    64.00"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can combine multiple conditions\n",
    "#E.g. see who failed at least one event (scored less than 60 points)\n",
    "df[(df.PU<60) | (df.SU < 60) | (df.MI2 < 60)].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As a warning on using multiple conditions, pandas may not be able to parse multiple boolean conditions if they are not contained in parentheses.  E.g., `df[df.PU<60 | df.SU < 60 | df.MI2 < 60]` will throw an error, so always good practice to include parentheses around each condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also sort our dataframe, a great way to quickly view or rearrange the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>AG</td>\n",
       "      <td>M</td>\n",
       "      <td>E5</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "      <td>75.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>AH</td>\n",
       "      <td>M</td>\n",
       "      <td>E4</td>\n",
       "      <td>34</td>\n",
       "      <td>89</td>\n",
       "      <td>61</td>\n",
       "      <td>84</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>AI</td>\n",
       "      <td>F</td>\n",
       "      <td>O4</td>\n",
       "      <td>23</td>\n",
       "      <td>66</td>\n",
       "      <td>63</td>\n",
       "      <td>78</td>\n",
       "      <td>69.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>AK</td>\n",
       "      <td>F</td>\n",
       "      <td>E4</td>\n",
       "      <td>21</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "      <td>79.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>AT</td>\n",
       "      <td>M</td>\n",
       "      <td>E4</td>\n",
       "      <td>43</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>70.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "97               AG      M   E5   46  75  71   80    75.33\n",
       "60               AH      M   E4   34  89  61   84    78.00\n",
       "157              AI      F   O4   23  66  63   78    69.00\n",
       "78               AK      F   E4   21  85  80   74    79.67\n",
       "189              AT      M   E4   43  68  65   79    70.67"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort alphabetical by initials\n",
    "df.sort_values(by='SoldierInitials').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KT</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>50</td>\n",
       "      <td>77</td>\n",
       "      <td>73</td>\n",
       "      <td>70</td>\n",
       "      <td>73.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>SS</td>\n",
       "      <td>M</td>\n",
       "      <td>E9</td>\n",
       "      <td>50</td>\n",
       "      <td>82</td>\n",
       "      <td>67</td>\n",
       "      <td>74</td>\n",
       "      <td>74.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>LM</td>\n",
       "      <td>F</td>\n",
       "      <td>O3</td>\n",
       "      <td>50</td>\n",
       "      <td>94</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>85.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>HD</td>\n",
       "      <td>M</td>\n",
       "      <td>O4</td>\n",
       "      <td>50</td>\n",
       "      <td>89</td>\n",
       "      <td>75</td>\n",
       "      <td>77</td>\n",
       "      <td>80.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>CC</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>50</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>79.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "21               KT      M   O5   50  77  73   70    73.33\n",
       "184              SS      M   E9   50  82  67   74    74.33\n",
       "150              LM      F   O3   50  94  80   82    85.33\n",
       "169              HD      M   O4   50  89  75   77    80.33\n",
       "173              CC      M   O6   50  82  75   82    79.67"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort from oldest to youngest\n",
    "df.sort_values(by='age',ascending=False).head() #ascending=True is the default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>FV</td>\n",
       "      <td>F</td>\n",
       "      <td>O6</td>\n",
       "      <td>20</td>\n",
       "      <td>77</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>77.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>CM</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>21</td>\n",
       "      <td>77</td>\n",
       "      <td>70</td>\n",
       "      <td>73</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>23</td>\n",
       "      <td>77</td>\n",
       "      <td>61</td>\n",
       "      <td>70</td>\n",
       "      <td>69.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>EM</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>73.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VX</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>49</td>\n",
       "      <td>71</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>78.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>CC</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>50</td>\n",
       "      <td>82</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>79.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>PA</td>\n",
       "      <td>F</td>\n",
       "      <td>O5</td>\n",
       "      <td>18</td>\n",
       "      <td>67</td>\n",
       "      <td>69</td>\n",
       "      <td>89</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>DX</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>69</td>\n",
       "      <td>84</td>\n",
       "      <td>81</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "52               FV      F   O6   20  77  72   83    77.33\n",
       "168              CM      M   O6   21  77  70   73    73.33\n",
       "125              PP      M   O6   23  77  61   70    69.33\n",
       "197              OH      M   O6   26  67  60   73    66.67\n",
       "4                DW      M   O6   27  91  75   82    82.67\n",
       "187              EM      M   O6   32  80  71   70    73.67\n",
       "30               VX      M   O6   49  71  85   80    78.67\n",
       "173              CC      M   O6   50  82  75   82    79.67\n",
       "39               PA      F   O5   18  67  69   89    75.00\n",
       "119              DX      M   O5   22  69  84   81    78.00"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Do multiple sorts\n",
    "df.sort_values(by=['rank','age'],ascending=[False,True]).head(10)\n",
    "#This will do a primary sort on rank in descedning order, and a secondary sort on age in ascending order\n",
    "#Hence, we start off with some high speed 20 yr old O6's....recall this is fake data :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "It's worth noting that none of the above commands actually *changed* the `df` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
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       "      <th>Average</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
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       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To change the dataframe itself, we can either apply the update back to the object, `df = df.sort_values(..)`, or we can use the inplace argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>FV</td>\n",
       "      <td>F</td>\n",
       "      <td>O6</td>\n",
       "      <td>20</td>\n",
       "      <td>77</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>77.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>CM</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>21</td>\n",
       "      <td>77</td>\n",
       "      <td>70</td>\n",
       "      <td>73</td>\n",
       "      <td>73.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>PP</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>23</td>\n",
       "      <td>77</td>\n",
       "      <td>61</td>\n",
       "      <td>70</td>\n",
       "      <td>69.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average\n",
       "52               FV      F   O6   20  77  72   83    77.33\n",
       "168              CM      M   O6   21  77  70   73    73.33\n",
       "125              PP      M   O6   23  77  61   70    69.33\n",
       "197              OH      M   O6   26  67  60   73    66.67\n",
       "4                DW      M   O6   27  91  75   82    82.67"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['rank','age'],ascending=[False,True],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### From Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As we've seen, we can grab data from a csv file with the read_csv method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/ptScores.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pandas also has built in methods to read in data from a variety of other data formats, including excel, json, and many others.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Type TAB after pd.read to see the available read in methods\n",
    "#pd.read_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### From User Generated Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Sometimes we want to initialize a DataFrame with some existing data.  For example, maybe we ran a set of simulations, and want to load these into a DataFrame so that we can use pandas tools to wrangle and evaluate the data we collected.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student_ID</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>69</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>84</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>82</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>58</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student_ID Grade\n",
       "0          79     A\n",
       "1          26     C\n",
       "2          70     A\n",
       "3          65     B\n",
       "4           5     F\n",
       "5          69     D\n",
       "6          84     C\n",
       "7          68     F\n",
       "8          82     D\n",
       "9          58     A"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate or simulate some data\n",
    "grades = ['A','B','C','D','F']\n",
    "student_IDs = np.random.randint(1,100,size=10) #10 random student ID #'s\n",
    "student_grades = np.random.choice(grades,size=10) #10 random student grades\n",
    "\n",
    "#Feed in data as a dict mapping keys to data sequences\n",
    "#Keys become column names\n",
    "df_grades = pd.DataFrame(data={'Student_ID':student_IDs,'Grade':student_grades})\n",
    "df_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.093818</td>\n",
       "      <td>1.923535</td>\n",
       "      <td>0.806059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.574233</td>\n",
       "      <td>-0.846529</td>\n",
       "      <td>-1.776405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.230863</td>\n",
       "      <td>-1.222368</td>\n",
       "      <td>-1.420609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.615811</td>\n",
       "      <td>-1.090196</td>\n",
       "      <td>0.280880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.380137</td>\n",
       "      <td>-0.137315</td>\n",
       "      <td>1.015275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.045558</td>\n",
       "      <td>-0.210294</td>\n",
       "      <td>-1.454391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.407784</td>\n",
       "      <td>-0.296308</td>\n",
       "      <td>0.302918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.537780</td>\n",
       "      <td>0.065760</td>\n",
       "      <td>1.051008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.093818  1.923535  0.806059\n",
       "1  1.574233 -0.846529 -1.776405\n",
       "2 -1.230863 -1.222368 -1.420609\n",
       "3  0.615811 -1.090196  0.280880\n",
       "4  0.380137 -0.137315  1.015275\n",
       "5  0.045558 -0.210294 -1.454391\n",
       "6  1.407784 -0.296308  0.302918\n",
       "7 -0.537780  0.065760  1.051008"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also fill in a 2D array\n",
    "x = np.random.randn(8,3)\n",
    "df_data = pd.DataFrame(data=x)\n",
    "df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sim1</th>\n",
       "      <th>Sim2</th>\n",
       "      <th>Sim3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.093818</td>\n",
       "      <td>1.923535</td>\n",
       "      <td>0.806059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.574233</td>\n",
       "      <td>-0.846529</td>\n",
       "      <td>-1.776405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.230863</td>\n",
       "      <td>-1.222368</td>\n",
       "      <td>-1.420609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.615811</td>\n",
       "      <td>-1.090196</td>\n",
       "      <td>0.280880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.380137</td>\n",
       "      <td>-0.137315</td>\n",
       "      <td>1.015275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.045558</td>\n",
       "      <td>-0.210294</td>\n",
       "      <td>-1.454391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.407784</td>\n",
       "      <td>-0.296308</td>\n",
       "      <td>0.302918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.537780</td>\n",
       "      <td>0.065760</td>\n",
       "      <td>1.051008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sim1      Sim2      Sim3\n",
       "0  0.093818  1.923535  0.806059\n",
       "1  1.574233 -0.846529 -1.776405\n",
       "2 -1.230863 -1.222368 -1.420609\n",
       "3  0.615811 -1.090196  0.280880\n",
       "4  0.380137 -0.137315  1.015275\n",
       "5  0.045558 -0.210294 -1.454391\n",
       "6  1.407784 -0.296308  0.302918\n",
       "7 -0.537780  0.065760  1.051008"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If you don't like column labels of 0, 1, and 2, can feed in column labels\n",
    "df_data = pd.DataFrame(data=x,columns=['Sim1','Sim2','Sim3'])\n",
    "df_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Webscraping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Python has powerful tools to search and extract data from the web.  Consider the following url, which contains the results from the 2019 Masters golf tournamnet: https://www.espn.com/golf/leaderboard?tournamentId=401056527.  How could we extract the data from that website and get it into a DataFrame to capture the results and/or do further analysis?\n",
    "\n",
    "We can use the urllib library to read in raw data from url's.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'\\n        <!doctype html>\\n        <html lang=\"en\">\\n            <head>\\n                <meta charSet=\"utf-8\" />\\n\\n                <!-- ESPNFITT | 26f56d53a190 | 4246 | babc358209d168f27af15ab10d4edf2d81dea738 | Tue, 10 Nov 2020 22:31:24 GMT -->\\n                <script type=\\'text/javascript\\' >\\n        ;(function(){\\n            function gc(n){var r=document.cookie.match(\"(^|;) ?\"+n+\"=([^;]*)(;|$)\");return r?r[2]:null}function sc(n){document.cookie=n}function smpl(n){var r=n/100;return!!r\n"
     ]
    }
   ],
   "source": [
    "#Now use the request module to request the raw html code from our url\n",
    "url = 'https://www.espn.com/golf/leaderboard?tournamentId=401056527'\n",
    "\n",
    "#Open the url, and read in the raw html code\n",
    "html = str(urllib.request.urlopen(url).read())\n",
    "\n",
    "print(html[:500]) #Print first 500 characters of html code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "If, like me, you are a human and not a computer, that probably looks like a hot mess.  If we wanted, we could try to parse through the html code to try and extract the data, perhaps with some sophisticated regex pattern searching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table style=\"border-collapse:collapse;border-spacing:0\" class=\"Table Table--align-right\"><colgroup class=\"Table__Colgroup\"><col class=\"Table__Column\"/><col class=\"Table__Column\"/><col class=\"Table__Column Table--sorted\"/><col class=\"Table__Column\"/><col class=\"Table__Column\"/><col class=\"Table__Column\"/><col class=\"Table__Column\"/><col class=\"Table__Column\"/><col class=\"Table__Column\"/><col class=\"Table__Column\"/></colgroup><thead class=\"Table__THEAD\"><tr class=\"Table__TR Table__even\"><th title=\"\" class=\"pos tl Table__TH\"><a class=\"AnchorLink clr-gray-04\" tabindex=\"0\" data-sort-key=\"pos\">POS</a></th><th title=\"\" class=\"name tl Table__TH\"><a class=\"AnchorLink clr-gray-04\" tabindex=\"0\" data-sort-key=\"name\">PLAYER</a></th><th title=\"\" class=\"toPar Table__TH\"><a class=\"AnchorLink clr-gray-04\" tabindex=\"0\" data-sort-key=\"toPar\">TO PAR</a><span class=\"dib arrow-icon_cont\" style=\"width:10px;height:10px\"><svg class=\"w-70 icon__svg\" viewBox=\"0 0 24 24\"><use xlink:href=\"#icon__caret__down\"></us'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_of_table = html.find('<table')\n",
    "html[start_of_table:start_of_table+1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Thankfully, python has a package BeautifulSoup which helps parse through the mess that is html code.  Here's how we could use it to extract the table data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup as BS\n",
    "soup = BS(html, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bs4.element.Tag'>\n"
     ]
    }
   ],
   "source": [
    "#Find the table tag\n",
    "tables = soup('table')\n",
    "table = tables[0]\n",
    "#table is now a beautiful soup Tag object\n",
    "print(type(table))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We could now try and parse through and grab the contents.  For example, to get the column names, we could sift through all `<th>` tags (table header) and grab their contents (text attribute) as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>TO PAR</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>TOT</th>\n",
       "      <th>EARNINGS</th>\n",
       "      <th>FEDEX PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [POS, PLAYER, TO PAR, R1, R2, R3, R4, TOT, EARNINGS, FEDEX PTS]\n",
       "Index: []"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grab column names from all <th> (table header) tags\n",
    "column_tags = table('th') #list of all <th> tags within the table tag\n",
    "col_labels = []\n",
    "for col in column_tags:\n",
    "    col_labels.append(col.text)\n",
    "df_masters = pd.DataFrame(columns=col_labels)\n",
    "df_masters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>TO PAR</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>TOT</th>\n",
       "      <th>EARNINGS</th>\n",
       "      <th>FEDEX PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tiger Woods</td>\n",
       "      <td>-13</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>275</td>\n",
       "      <td>$2,070,000</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T2</td>\n",
       "      <td>Dustin Johnson</td>\n",
       "      <td>-12</td>\n",
       "      <td>68</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>276</td>\n",
       "      <td>$858,667</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T2</td>\n",
       "      <td>Xander Schauffele</td>\n",
       "      <td>-12</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>276</td>\n",
       "      <td>$858,667</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T2</td>\n",
       "      <td>Brooks Koepka</td>\n",
       "      <td>-12</td>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>276</td>\n",
       "      <td>$858,667</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T5</td>\n",
       "      <td>Jason Day</td>\n",
       "      <td>-11</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>277</td>\n",
       "      <td>$403,938</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  POS             PLAYER TO PAR  R1  R2  R3  R4  TOT    EARNINGS FEDEX PTS\n",
       "0   1        Tiger Woods    -13  70  68  67  70  275  $2,070,000       600\n",
       "1  T2     Dustin Johnson    -12  68  70  70  68  276    $858,667       230\n",
       "2  T2  Xander Schauffele    -12  73  65  70  68  276    $858,667       230\n",
       "3  T2      Brooks Koepka    -12  66  71  69  70  276    $858,667       230\n",
       "4  T5          Jason Day    -11  70  67  73  67  277    $403,938       106"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And then we could go through all rows (<tr> tags) and extract the data for each table entry in that row (<td> tags)\n",
    "for tr in table('tr'):\n",
    "    curr_row_data = []\n",
    "    for td in tr('td'):\n",
    "        curr_row_data.append(td.text)\n",
    "    #Now add current row data to dataframe\n",
    "    if (len(curr_row_data) == len(col_labels)):\n",
    "        df_masters.loc[len(df_masters)] = curr_row_data\n",
    "\n",
    "#Lets see if that worked\n",
    "df_masters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POS</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>TO PAR</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>TOT</th>\n",
       "      <th>EARNINGS</th>\n",
       "      <th>FEDEX PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tiger Woods</td>\n",
       "      <td>-13</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>275</td>\n",
       "      <td>$2,070,000</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T2</td>\n",
       "      <td>Dustin Johnson</td>\n",
       "      <td>-12</td>\n",
       "      <td>68</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>276</td>\n",
       "      <td>$858,667</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T2</td>\n",
       "      <td>Xander Schauffele</td>\n",
       "      <td>-12</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>68</td>\n",
       "      <td>276</td>\n",
       "      <td>$858,667</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T2</td>\n",
       "      <td>Brooks Koepka</td>\n",
       "      <td>-12</td>\n",
       "      <td>66</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>276</td>\n",
       "      <td>$858,667</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T5</td>\n",
       "      <td>Jason Day</td>\n",
       "      <td>-11</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>277</td>\n",
       "      <td>$403,938</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  POS             PLAYER TO PAR  R1  R2  R3  R4  TOT    EARNINGS  FEDEX PTS\n",
       "0   1        Tiger Woods    -13  70  68  67  70  275  $2,070,000        600\n",
       "1  T2     Dustin Johnson    -12  68  70  70  68  276    $858,667        230\n",
       "2  T2  Xander Schauffele    -12  73  65  70  68  276    $858,667        230\n",
       "3  T2      Brooks Koepka    -12  66  71  69  70  276    $858,667        230\n",
       "4  T5          Jason Day    -11  70  67  73  67  277    $403,938        106"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Thankfully, for this particular task of grabbing data from html tables, pandas has built in function\n",
    "df_masters = pd.read_html(url)[0]\n",
    "#pd.read_html returns list of all tables.  Use [0] to grab the first table.\n",
    "df_masters.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Of course, things may not always be that simple.  Often times the content in web pages is not simply embedded in the html code but is generated through some javascript calls, in which case this approach won't work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Can't scrape table data from this page\n",
    "url2 = 'https://www.masters.com/en_US/scores/index.html'\n",
    "# df_masters2 = pd.read_html(url2)[0]\n",
    "# df_masters2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This only scratches the surface of what you can do with webscraping in python.  Often times a website will have an API to facilitate extraction of the key data, which python can handle well.  Python also has a good parser for XML content called [ElementTree](https://docs.python.org/2/library/xml.etree.elementtree.html).  Also can see the [urllib](https://pythonspot.com/urllib-tutorial-python-3/) and [BeautifulSoup](https://pypi.org/project/beautifulsoup4/) documentation for more information on those packages.  I would also recommend the Coursera Course [Using Python to Access Web Data](https://www.coursera.org/learn/python-network-data/home/welcome) which covers all these topics and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Writing and Saving Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also use pandas to write and save a dataframe to a new file.  This can be useful way to save updated results of any data analysis or manipulations.  For example we could save off the masters data we just read in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df_masters.to_csv('Data/2019_Masters.csv',index=False) #Set index=False to avoid getting extra column for index in csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "To see other possible file formats, type `df_masters.to_[TAB]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often reported that data analysts will spend up to 80% of their time just getting, cleaning, and wrangling data, with only 20% left over for analyzing and gaining insights from data.  With data often pulled in from many different data sources, in many different formats and lots of missing values, data cleaning is a crucial skill for data analysis.  Pandas provides many fast and flexible tools to clean data and get it into the right form."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Nulls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas handles null values with the NaN notation (for Not a Number).  Lets read in some sample CDC health data, in which some entries are missing and come through as NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>genhlth</th>\n",
       "      <th>exerany</th>\n",
       "      <th>hlthplan</th>\n",
       "      <th>smoke100</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wtdesire</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire   age  \\\n",
       "0   1       good      0.0       1.0       0.0    70.0   175.0     175.0  77.0   \n",
       "1   2       good      0.0       1.0       1.0    64.0   125.0     115.0  33.0   \n",
       "2   3       good      1.0       1.0       1.0    60.0   105.0     105.0  49.0   \n",
       "3   4       good      1.0       1.0       0.0    66.0   132.0     124.0  42.0   \n",
       "4   5  very good      0.0       1.0       0.0    61.0   150.0     130.0  55.0   \n",
       "5   6  very good      1.0       1.0       0.0    64.0   114.0     114.0  55.0   \n",
       "6   7  very good      1.0       1.0       0.0    71.0   194.0     185.0  31.0   \n",
       "7   8  very good      0.0       1.0       0.0     NaN   170.0     160.0  45.0   \n",
       "8   9       good      0.0       1.0       1.0    65.0   150.0     130.0  27.0   \n",
       "9  10       good      1.0       1.0       0.0    70.0   180.0     170.0   NaN   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  \n",
       "5      f  \n",
       "6      m  \n",
       "7      m  \n",
       "8      f  \n",
       "9      m  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/cdcSmall.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7     True\n",
       "8    False\n",
       "9    False\n",
       "Name: height, dtype: bool"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can use isnull() on a Series to see any entries where that row is null\n",
    "df.height.isnull().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>genhlth</th>\n",
       "      <th>exerany</th>\n",
       "      <th>hlthplan</th>\n",
       "      <th>smoke100</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wtdesire</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>fair</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>210</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>excellent</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>372</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "7      8  very good      0.0       1.0       0.0     NaN   170.0     160.0   \n",
       "87    88       fair      0.0       1.0       0.0     NaN   115.0     115.0   \n",
       "209  210       good      0.0       1.0       0.0     NaN   112.0     105.0   \n",
       "210  211  excellent      1.0       1.0       1.0     NaN   116.0     110.0   \n",
       "371  372  very good      1.0       1.0       1.0     NaN   170.0     145.0   \n",
       "\n",
       "      age gender  \n",
       "7    45.0      m  \n",
       "87   27.0      f  \n",
       "209  45.0      f  \n",
       "210   NaN      f  \n",
       "371  66.0      F  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Look at any entries of entire DataFrame where height is null\n",
    "df[df.height.isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>X1</th>\n",
       "      <th>genhlth</th>\n",
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       "      <th>smoke100</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      X1  genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "0  False    False    False     False     False   False   False     False   \n",
       "1  False    False    False     False     False   False   False     False   \n",
       "2  False    False    False     False     False   False   False     False   \n",
       "3  False    False    False     False     False   False   False     False   \n",
       "4  False    False    False     False     False   False   False     False   \n",
       "5  False    False    False     False     False   False   False     False   \n",
       "6  False    False    False     False     False   False   False     False   \n",
       "7  False    False    False     False     False    True   False     False   \n",
       "8  False    False    False     False     False   False   False     False   \n",
       "9  False    False    False     False     False   False   False     False   \n",
       "\n",
       "     age  gender  \n",
       "0  False   False  \n",
       "1  False   False  \n",
       "2  False   False  \n",
       "3  False   False  \n",
       "4  False   False  \n",
       "5  False   False  \n",
       "6  False   False  \n",
       "7  False   False  \n",
       "8  False   False  \n",
       "9   True   False  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can also use isnull() on entire dataframe to find Nulls in any column\n",
    "df.isnull().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>45.0</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>59.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "7    8  very good      0.0       1.0       0.0     NaN   170.0     160.0   \n",
       "9   10       good      1.0       1.0       0.0    70.0   180.0     170.0   \n",
       "22  23  very good      0.0       1.0       1.0    73.0   160.0     160.0   \n",
       "41  42  very good      NaN       1.0       0.0    66.0   143.0     130.0   \n",
       "45  46        NaN      0.0       1.0       0.0    59.0   145.0     110.0   \n",
       "\n",
       "     age gender  \n",
       "7   45.0      m  \n",
       "9    NaN      m  \n",
       "22  43.0    NaN  \n",
       "41  45.0      f  \n",
       "45  36.0      f  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can use df.isnull() along with any() or all() methods to find rows with any (at least one) null or all nulls\n",
    "#View any rows with AT LEAST ONE null\n",
    "df[df.isnull().any(axis=1)].head() #Axis=1 to go across columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
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       "      <td>175.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>m</td>\n",
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       "      <th>1</th>\n",
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       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire   age  \\\n",
       "0   1       good      0.0       1.0       0.0    70.0   175.0     175.0  77.0   \n",
       "1   2       good      0.0       1.0       1.0    64.0   125.0     115.0  33.0   \n",
       "2   3       good      1.0       1.0       1.0    60.0   105.0     105.0  49.0   \n",
       "3   4       good      1.0       1.0       0.0    66.0   132.0     124.0  42.0   \n",
       "4   5  very good      0.0       1.0       0.0    61.0   150.0     130.0  55.0   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To return all entries where height is NOT null, we could either negate the boolean Series with the ~ symbol\n",
    "df[~ df.height.isnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>genhlth</th>\n",
       "      <th>exerany</th>\n",
       "      <th>hlthplan</th>\n",
       "      <th>smoke100</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wtdesire</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
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       "      <td>125.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>good</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>60.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>very good</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
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       "      <td>f</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire   age  \\\n",
       "0   1       good      0.0       1.0       0.0    70.0   175.0     175.0  77.0   \n",
       "1   2       good      0.0       1.0       1.0    64.0   125.0     115.0  33.0   \n",
       "2   3       good      1.0       1.0       1.0    60.0   105.0     105.0  49.0   \n",
       "3   4       good      1.0       1.0       0.0    66.0   132.0     124.0  42.0   \n",
       "4   5  very good      0.0       1.0       0.0    61.0   150.0     130.0  55.0   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Or we could use the notnull() method on a Series or DataFrame\n",
    "df[df.height.notnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>X1</th>\n",
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       "      <th>exerany</th>\n",
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       "      <td>61</td>\n",
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       "      <td>64</td>\n",
       "      <td>114</td>\n",
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       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>very good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>194</td>\n",
       "      <td>185</td>\n",
       "      <td>31</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very good</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>UNKONWN</td>\n",
       "      <td>170</td>\n",
       "      <td>160</td>\n",
       "      <td>45</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>10</td>\n",
       "      <td>good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>180</td>\n",
       "      <td>170</td>\n",
       "      <td>UNKONWN</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1    genhlth exerany hlthplan smoke100   height weight wtdesire      age  \\\n",
       "0   1       good       0        1        0       70    175      175       77   \n",
       "1   2       good       0        1        1       64    125      115       33   \n",
       "2   3       good       1        1        1       60    105      105       49   \n",
       "3   4       good       1        1        0       66    132      124       42   \n",
       "4   5  very good       0        1        0       61    150      130       55   \n",
       "5   6  very good       1        1        0       64    114      114       55   \n",
       "6   7  very good       1        1        0       71    194      185       31   \n",
       "7   8  very good       0        1        0  UNKONWN    170      160       45   \n",
       "8   9       good       0        1        1       65    150      130       27   \n",
       "9  10       good       1        1        0       70    180      170  UNKONWN   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  \n",
       "5      f  \n",
       "6      m  \n",
       "7      m  \n",
       "8      f  \n",
       "9      m  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can fill in any null values with the fillna() method\n",
    "df.fillna('UNKONWN').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common value for column X1 is 2047\n",
      "Most common value for column genhlth is very good\n",
      "Most common value for column exerany is 1.0\n",
      "Most common value for column hlthplan is 1.0\n",
      "Most common value for column smoke100 is 0.0\n",
      "Most common value for column height is 66.0\n",
      "Most common value for column weight is 160.0\n",
      "Most common value for column wtdesire is 150.0\n",
      "Most common value for column age is 40.0\n",
      "Most common value for column gender is f\n"
     ]
    }
   ],
   "source": [
    "#We could also apply fillna to each column\n",
    "#Here's how we could replace each Null by the most common value within that column\n",
    "for c in df.columns:\n",
    "    #Find most common entry in this column\n",
    "    most_common = df[c].value_counts().index[0]\n",
    "    print('Most common value for column {} is {}'.format(c,most_common))\n",
    "    df[c] = df[c].fillna(most_common)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>very good</td>\n",
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       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire   age  \\\n",
       "0   1       good      0.0       1.0       0.0    70.0   175.0     175.0  77.0   \n",
       "1   2       good      0.0       1.0       1.0    64.0   125.0     115.0  33.0   \n",
       "2   3       good      1.0       1.0       1.0    60.0   105.0     105.0  49.0   \n",
       "3   4       good      1.0       1.0       0.0    66.0   132.0     124.0  42.0   \n",
       "4   5  very good      0.0       1.0       0.0    61.0   150.0     130.0  55.0   \n",
       "5   6  very good      1.0       1.0       0.0    64.0   114.0     114.0  55.0   \n",
       "6   7  very good      1.0       1.0       0.0    71.0   194.0     185.0  31.0   \n",
       "7   8  very good      0.0       1.0       0.0    66.0   170.0     160.0  45.0   \n",
       "8   9       good      0.0       1.0       1.0    65.0   150.0     130.0  27.0   \n",
       "9  10       good      1.0       1.0       0.0    70.0   180.0     170.0  40.0   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  \n",
       "5      f  \n",
       "6      m  \n",
       "7      m  \n",
       "8      f  \n",
       "9      m  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And resulting df\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, the value you fill in will be highly dependent on the problem at hand.  For example, you don't want to inject representative but fake data and have it be interpreted as real. \n",
    "\n",
    "We can also drop any entires with null data with dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
       "      <td>very good</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>61.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>very good</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>160.0</td>\n",
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       "      <td>180.0</td>\n",
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      ],
      "text/plain": [
       "   X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire   age  \\\n",
       "0   1       good      0.0       1.0       0.0    70.0   175.0     175.0  77.0   \n",
       "1   2       good      0.0       1.0       1.0    64.0   125.0     115.0  33.0   \n",
       "2   3       good      1.0       1.0       1.0    60.0   105.0     105.0  49.0   \n",
       "3   4       good      1.0       1.0       0.0    66.0   132.0     124.0  42.0   \n",
       "4   5  very good      0.0       1.0       0.0    61.0   150.0     130.0  55.0   \n",
       "5   6  very good      1.0       1.0       0.0    64.0   114.0     114.0  55.0   \n",
       "6   7  very good      1.0       1.0       0.0    71.0   194.0     185.0  31.0   \n",
       "7   8  very good      0.0       1.0       0.0     NaN   170.0     160.0  45.0   \n",
       "8   9       good      0.0       1.0       1.0    65.0   150.0     130.0  27.0   \n",
       "9  10       good      1.0       1.0       0.0    70.0   180.0     170.0   NaN   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  \n",
       "5      f  \n",
       "6      m  \n",
       "7      m  \n",
       "8      f  \n",
       "9      m  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Re-read original df with null values\n",
    "df = pd.read_csv('Data/cdcSmall.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "      <td>61.0</td>\n",
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       "      <td>130.0</td>\n",
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       "      <td>64.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>m</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
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       "      <th>10</th>\n",
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       "      <td>excellent</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>m</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>m</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "0    1       good      0.0       1.0       0.0    70.0   175.0     175.0   \n",
       "1    2       good      0.0       1.0       1.0    64.0   125.0     115.0   \n",
       "2    3       good      1.0       1.0       1.0    60.0   105.0     105.0   \n",
       "3    4       good      1.0       1.0       0.0    66.0   132.0     124.0   \n",
       "4    5  very good      0.0       1.0       0.0    61.0   150.0     130.0   \n",
       "5    6  very good      1.0       1.0       0.0    64.0   114.0     114.0   \n",
       "6    7  very good      1.0       1.0       0.0    71.0   194.0     185.0   \n",
       "8    9       good      0.0       1.0       1.0    65.0   150.0     130.0   \n",
       "10  11  excellent      1.0       1.0       1.0    69.0   186.0     175.0   \n",
       "11  12       fair      1.0       1.0       1.0    69.0   168.0     148.0   \n",
       "\n",
       "     age gender  \n",
       "0   77.0      m  \n",
       "1   33.0      f  \n",
       "2   49.0      f  \n",
       "3   42.0      f  \n",
       "4   55.0      f  \n",
       "5   55.0      f  \n",
       "6   31.0      m  \n",
       "8   27.0      f  \n",
       "10  46.0      m  \n",
       "11  62.0      m  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop any rows with at least one null\n",
    "df.dropna().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
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       "      <td>130.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "0   NaN        NaN      NaN       NaN       NaN     NaN     NaN       NaN   \n",
       "1   2.0       good      0.0       1.0       1.0    64.0   125.0     115.0   \n",
       "2   3.0       good      1.0       1.0       1.0    60.0   105.0     105.0   \n",
       "3   4.0       good      1.0       1.0       0.0    66.0   132.0     124.0   \n",
       "4   5.0  very good      0.0       1.0       0.0    61.0   150.0     130.0   \n",
       "5   6.0  very good      1.0       1.0       0.0    64.0   114.0     114.0   \n",
       "6   7.0  very good      1.0       1.0       0.0    71.0   194.0     185.0   \n",
       "7   8.0  very good      0.0       1.0       0.0     NaN   170.0     160.0   \n",
       "8   9.0       good      0.0       1.0       1.0    65.0   150.0     130.0   \n",
       "9  10.0       good      1.0       1.0       0.0    70.0   180.0     170.0   \n",
       "\n",
       "    age gender  \n",
       "0   NaN    NaN  \n",
       "1  33.0      f  \n",
       "2  49.0      f  \n",
       "3  42.0      f  \n",
       "4  55.0      f  \n",
       "5  55.0      f  \n",
       "6  31.0      m  \n",
       "7  45.0      m  \n",
       "8  27.0      f  \n",
       "9   NaN      m  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We could also only drop rows with ALL values are null\n",
    "df.loc[0] = [np.nan]*len(df.columns)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>genhlth</th>\n",
       "      <th>exerany</th>\n",
       "      <th>hlthplan</th>\n",
       "      <th>smoke100</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wtdesire</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>105.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>excellent</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "1    2.0       good      0.0       1.0       1.0    64.0   125.0     115.0   \n",
       "2    3.0       good      1.0       1.0       1.0    60.0   105.0     105.0   \n",
       "3    4.0       good      1.0       1.0       0.0    66.0   132.0     124.0   \n",
       "4    5.0  very good      0.0       1.0       0.0    61.0   150.0     130.0   \n",
       "5    6.0  very good      1.0       1.0       0.0    64.0   114.0     114.0   \n",
       "6    7.0  very good      1.0       1.0       0.0    71.0   194.0     185.0   \n",
       "7    8.0  very good      0.0       1.0       0.0     NaN   170.0     160.0   \n",
       "8    9.0       good      0.0       1.0       1.0    65.0   150.0     130.0   \n",
       "9   10.0       good      1.0       1.0       0.0    70.0   180.0     170.0   \n",
       "10  11.0  excellent      1.0       1.0       1.0    69.0   186.0     175.0   \n",
       "\n",
       "     age gender  \n",
       "1   33.0      f  \n",
       "2   49.0      f  \n",
       "3   42.0      f  \n",
       "4   55.0      f  \n",
       "5   55.0      f  \n",
       "6   31.0      m  \n",
       "7   45.0      m  \n",
       "8   27.0      f  \n",
       "9    NaN      m  \n",
       "10  46.0      m  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna(how='all')\n",
    "df.head(10)\n",
    "#Now this will drop the first row will all nulls, but not delete the rows with a single null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Using Where Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>wtdesire</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>33.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>66.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>very good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  \\\n",
       "1  2.0       good      0.0       1.0       1.0    64.0   125.0     115.0   \n",
       "2  3.0       good      1.0       1.0       1.0    60.0   105.0     105.0   \n",
       "3  4.0       good      1.0       1.0       0.0    66.0   132.0     124.0   \n",
       "4  5.0  very good      0.0       1.0       0.0    61.0   150.0     130.0   \n",
       "5  6.0  very good      1.0       1.0       0.0    64.0   114.0     114.0   \n",
       "\n",
       "    age gender  \n",
       "1  33.0      f  \n",
       "2  49.0      f  \n",
       "3  42.0      f  \n",
       "4  55.0      f  \n",
       "5  55.0      f  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall current dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The pandas where() function returns a new dataframe where some condition is met.  Where the condition is met, the original dataframe value are unchanged, and wherever condition is not met the original value is replaced by somethign else (default being NaN).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>exerany</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1 genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire  age  \\\n",
       "1 NaN     NaN      NaN       NaN       NaN     NaN     NaN       NaN  NaN   \n",
       "2 NaN     NaN      NaN       NaN       NaN     NaN     NaN       NaN  NaN   \n",
       "3 NaN     NaN      NaN       NaN       NaN     NaN     NaN       NaN  NaN   \n",
       "4 NaN     NaN      NaN       NaN       NaN     NaN     NaN       NaN  NaN   \n",
       "5 NaN     NaN      NaN       NaN       NaN     NaN     NaN       NaN  NaN   \n",
       "\n",
       "  gender  \n",
       "1    NaN  \n",
       "2    NaN  \n",
       "3    NaN  \n",
       "4    NaN  \n",
       "5    NaN  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Return df where gender is 'm'\n",
    "df.where(df.gender=='m').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wtdesire</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "      <td>Not m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      X1 genhlth exerany hlthplan smoke100 height weight wtdesire    age  \\\n",
       "1  Not m   Not m   Not m    Not m    Not m  Not m  Not m    Not m  Not m   \n",
       "2  Not m   Not m   Not m    Not m    Not m  Not m  Not m    Not m  Not m   \n",
       "3  Not m   Not m   Not m    Not m    Not m  Not m  Not m    Not m  Not m   \n",
       "4  Not m   Not m   Not m    Not m    Not m  Not m  Not m    Not m  Not m   \n",
       "5  Not m   Not m   Not m    Not m    Not m  Not m  Not m    Not m  Not m   \n",
       "\n",
       "  gender  \n",
       "1  Not m  \n",
       "2  Not m  \n",
       "3  Not m  \n",
       "4  Not m  \n",
       "5  Not m  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Specify fill/replacement values\n",
    "df.where(df.gender=='m',other='Not m').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As you can see, df.where() applies the same condition everywhere, and frankly I don't use it a whole lot.  However, I do frequently use the np.where() method.  One key reason is that np.where allows you to specify one value to return where condition is True, and another value to return if condition is False.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Consider PT Scores again\n",
    "df_pt = pd.read_csv('Data/ptScores.csv')\n",
    "df_pt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Say we wan't to add a column for Pass which is 'Yes' wherever PU, SU and MI are ALL >=60, and \"No\" otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PU    SU   MI2\n",
       "0  True  True  True\n",
       "1  True  True  True\n",
       "2  True  True  True\n",
       "3  True  True  True\n",
       "4  True  True  True"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First, notice how we can get a boolean Series\n",
    "#Start by getting dataframe replacing all raw scores with booleans\n",
    "(df_pt[['PU','SU','MI2']]>=60).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "4    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Then convert to single Boolean Series with all() method\n",
    "(df_pt[['PU','SU','MI2']]>=60).all(axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "      <th>Pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>DT</td>\n",
       "      <td>M</td>\n",
       "      <td>E1</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "      <td>80</td>\n",
       "      <td>80.33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>RS</td>\n",
       "      <td>M</td>\n",
       "      <td>E9</td>\n",
       "      <td>47</td>\n",
       "      <td>79</td>\n",
       "      <td>52</td>\n",
       "      <td>78</td>\n",
       "      <td>69.67</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>BA</td>\n",
       "      <td>M</td>\n",
       "      <td>E7</td>\n",
       "      <td>30</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>86</td>\n",
       "      <td>78.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>JS</td>\n",
       "      <td>M</td>\n",
       "      <td>E5</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>73</td>\n",
       "      <td>80</td>\n",
       "      <td>76.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>WP</td>\n",
       "      <td>M</td>\n",
       "      <td>O1</td>\n",
       "      <td>27</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>79</td>\n",
       "      <td>80.33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>LS</td>\n",
       "      <td>M</td>\n",
       "      <td>E9</td>\n",
       "      <td>26</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>81</td>\n",
       "      <td>69.33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>BF</td>\n",
       "      <td>F</td>\n",
       "      <td>E9</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>62</td>\n",
       "      <td>77</td>\n",
       "      <td>76.33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>OH</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>73</td>\n",
       "      <td>66.67</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>GE</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>22</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>74.00</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>IG</td>\n",
       "      <td>M</td>\n",
       "      <td>O5</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>86.33</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SoldierInitials gender rank  age  PU  SU  MI2  Average Pass\n",
       "190              DT      M   E1   27  77  84   80    80.33  Yes\n",
       "191              RS      M   E9   47  79  52   78    69.67   No\n",
       "192              BA      M   E7   30  77  71   86    78.00  Yes\n",
       "193              JS      M   E5   20  75  73   80    76.00  Yes\n",
       "194              WP      M   O1   27  81  81   79    80.33  Yes\n",
       "195              LS      M   E9   26  61  66   81    69.33  Yes\n",
       "196              BF      F   E9   45  90  62   77    76.33  Yes\n",
       "197              OH      M   O6   26  67  60   73    66.67  Yes\n",
       "198              GE      M   O5   22  67  77   78    74.00  Yes\n",
       "199              IG      M   O5   38  87  81   91    86.33  Yes"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now feed this boolean series into np.where\n",
    "df_pt['Pass'] = np.where((df_pt[['PU','SU','MI2']]>=60).all(axis=1),\n",
    "                        'Yes',\n",
    "                        'No')\n",
    "df_pt.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### String Matching and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As mentioned previously, python provides many powerful tools for analyzing and working with string data.  Pandas incorporates these capabilities to enable fast and vectorized manipulation and searching of string data within DataFrames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Lets read in some worldwide country data.  There are two commonly used python repositories of country data, [geopandas](https://geopandas.org/index.html) and [pycountry](https://pypi.org/project/pycountry/) (links have some good examples of how you can use the datasets to create maps).  Sometimes you might need information from both places, but they give differnet names to some countries, so it requires some string cleaning to match them up.\n",
    "\n",
    "First lets install both packages into the local environment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#May take a couple minutes\n",
    "# !pip install geopandas\n",
    "# !pip install pycountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'geopandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-118-a82263455219>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpycountry\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'"
     ]
    }
   ],
   "source": [
    "import geopandas\n",
    "import pycountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Grab world data from geopandas\n",
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Grab country names from pycountry\n",
    "pycountry_names = [country.name for country in pycountry.countries]\n",
    "pycountry_names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "What if we want to replace all the geopandas country names by the name that appears in pycountry?  First, note that there are a different number of entries in each set, so there may be entires in geopandas that are not in pycountry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "len(world.name),len(pycountry_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also see that the country names are listed differently.  For example look at the Koreas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "[c for c in world.name if c.find('Korea')>=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "[c for c in pycountry_names if c.find('Korea')>=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Python has a get_close_matches function (part of the [difflib](https://docs.python.org/2/library/difflib.html) library) we can use to match the geopandas names to the closest matches in pycountry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from difflib import get_close_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#If there is a good match, get_close_matches will return a list of best matches\n",
    "name = 'United States of America'\n",
    "get_close_matches(name,pycountry_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#If there's no match, we'll get an empty string\n",
    "get_close_matches('xyz',pycountry_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For now, lets do a for loop to go through each row and update name using get_close_matches.  Later we'll see how to create functions that automatically apply functions to update each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Initialize new column in world\n",
    "world['new_name'] = 'UNK'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for i,name in enumerate(world.name):\n",
    "    new_name_list = get_close_matches(name,pycountry_names)\n",
    "    if len(new_name_list)>0:\n",
    "        new_name = new_name_list[0]\n",
    "        if new_name != name:\n",
    "            print('geopandas name {} replaced with pycountry name {}'.format(name,new_name))\n",
    "            world.at[i,'new_name'] = new_name\n",
    "    else:\n",
    "        print('No match in pycountry found for geopandas name {}'.format(name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We see that there was no match found for several countries, and several countries were assigned to the wrong new name.  E.g. Syria matched to Serbia!  In reality string matching can be tricky and require significant fine tuning.  For example, we can adjust the get_close_matches cutoff argument, but may result in more false positives.\n",
    "\n",
    "For this particular problem, many of the country names are given in different orders.  Hence, we may have better luck with a token sort method from the fuzzywuzzy package rather than using get_close_matches, which only uses strict Levenstein distance measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#May have to install fuzzywuzzy into local environment\n",
    "#!pip install fuzzywuzzy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from fuzzywuzzy import process\n",
    "from fuzzywuzzy import fuzz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We see that fuzzywuzzy can give better matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "get_close_matches('Iran',pycountry_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "process.extractOne('Iran',pycountry_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#We can play with the scoring function to do even better for this particular country, but might do worse for other countires\n",
    "process.extractOne('Iran',pycountry_names,scorer=fuzz.partial_token_set_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Now use fuzzy matching to find country\n",
    "for i,name in enumerate(world.name):\n",
    "    new_name_list = process.extractOne(name,pycountry_names)\n",
    "    if len(new_name_list)>0:\n",
    "        new_name = new_name_list[0]\n",
    "        if new_name != name:\n",
    "            print('geopandas name {} replaced with pycountry name {}'.format(name,new_name))\n",
    "            world.at[i,'new_name'] = new_name\n",
    "    else:\n",
    "        print('No match in pycountry found for geopandas name {}'.format(name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As this shows, string matching is as much art as science, and often requires careful selection and tuning of the appropriate matching method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling and Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Applying Functions and Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SoldierInitials</th>\n",
       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>79</td>\n",
       "      <td>77.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EV</td>\n",
       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average\n",
       "0              LB      M   E8   18  71  71   79  73.666667\n",
       "1              SW      M   E3   30  84  70   79  77.670000\n",
       "2              EV      F   E3   36  97  64   77  79.330000\n",
       "3              ML      F   E8   48  77  62   75  71.330000\n",
       "4              DW      M   O6   27  91  75   82  82.670000"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pt = pd.read_csv('Data/ptScores.csv')\n",
    "df_pt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can apply functions to each row or column of a DataFrame with the apply() method.  For example, say we wanted a new column that contained a tuple ID of (SoldierInitials,gender,rank) for each Soldier.  We could use apply the following function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>gender</th>\n",
       "      <th>rank</th>\n",
       "      <th>age</th>\n",
       "      <th>PU</th>\n",
       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
       "      <th>Average</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LB</td>\n",
       "      <td>M</td>\n",
       "      <td>E8</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "      <td>(LB, M, E8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
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       "      <td>(SW, M, E3)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>F</td>\n",
       "      <td>E3</td>\n",
       "      <td>36</td>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "      <td>79.330000</td>\n",
       "      <td>(EV, F, E3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "      <td>(ML, F, E8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "      <td>(DW, M, O6)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average           ID\n",
       "0              LB      M   E8   18  71  71   79  73.666667  (LB, M, E8)\n",
       "1              SW      M   E3   30  84  70   79  77.670000  (SW, M, E3)\n",
       "2              EV      F   E3   36  97  64   77  79.330000  (EV, F, E3)\n",
       "3              ML      F   E8   48  77  62   75  71.330000  (ML, F, E8)\n",
       "4              DW      M   O6   27  91  75   82  82.670000  (DW, M, O6)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define a function that will act on each row of the dataframe\n",
    "def add_ID(row):\n",
    "    #The function will treat each row as its own Series object, meaning we can access its values as follows\n",
    "    initials = row['SoldierInitials']\n",
    "    gender = row['gender']\n",
    "    rank = row['rank']\n",
    "    new_ID = (initials,gender,rank)\n",
    "    #Now create a new entry in the row\n",
    "    row['ID'] = new_ID\n",
    "    #And return updated row\n",
    "    return row\n",
    "\n",
    "#Now apply this function to df, using axis=columns to apply it to the columns\n",
    "df_pt = df_pt.apply(add_ID,axis='columns')\n",
    "df_pt.head()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "It is also common to apply lambda functions.  We could accomplish the same task as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>SU</th>\n",
       "      <th>MI2</th>\n",
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       "      <th>0</th>\n",
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       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>73.666667</td>\n",
       "      <td>(LB, M, E8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SW</td>\n",
       "      <td>M</td>\n",
       "      <td>E3</td>\n",
       "      <td>30</td>\n",
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       "      <td>EV</td>\n",
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       "      <td>E3</td>\n",
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       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
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       "      <td>(EV, F, E3)</td>\n",
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       "      <th>3</th>\n",
       "      <td>ML</td>\n",
       "      <td>F</td>\n",
       "      <td>E8</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>71.330000</td>\n",
       "      <td>(ML, F, E8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DW</td>\n",
       "      <td>M</td>\n",
       "      <td>O6</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>82.670000</td>\n",
       "      <td>(DW, M, O6)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SoldierInitials gender rank  age  PU  SU  MI2    Average           ID\n",
       "0              LB      M   E8   18  71  71   79  73.666667  (LB, M, E8)\n",
       "1              SW      M   E3   30  84  70   79  77.670000  (SW, M, E3)\n",
       "2              EV      F   E3   36  97  64   77  79.330000  (EV, F, E3)\n",
       "3              ML      F   E8   48  77  62   75  71.330000  (ML, F, E8)\n",
       "4              DW      M   O6   27  91  75   82  82.670000  (DW, M, O6)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pt['ID'] = df_pt.apply(lambda x: (x['SoldierInitials'],x['gender'],x['rank']),axis='columns')\n",
    "df_pt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also apply a function to each individual element of a dataframe with applymap()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>64</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PU  SU  MI2\n",
       "0  71  71   79\n",
       "1  84  70   79\n",
       "2  97  64   77\n",
       "3  77  62   75\n",
       "4  91  75   82"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores = df_pt[['PU','SU','MI2']]\n",
    "df_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>79.00</td>\n",
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       "      PU     SU    MI2\n",
       "0  71.00  71.00  79.00\n",
       "1  84.00  70.00  79.00\n",
       "2  97.00  64.00  77.00\n",
       "3  77.00  62.00  75.00\n",
       "4  91.00  75.00  82.00"
      ]
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     "execution_count": 123,
     "metadata": {},
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   ],
   "source": [
    "df_scores = df_scores.applymap(lambda x: \"{:.2f}\".format(x)).head()\n",
    "df_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There is also the map() method, which can be applied to a Series (single column) to use a function or a dict to map current value to new values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PU</th>\n",
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       "      <th>MI2</th>\n",
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       "  <tbody>\n",
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       "      <td>77</td>\n",
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       "      <td>77.00</td>\n",
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      "text/plain": [
       "      PU     SU  MI2\n",
       "0  71.00  71.00   79\n",
       "1  84.00  70.00   79\n",
       "2  97.00  64.00   77\n",
       "3  77.00  62.00   75\n",
       "4  91.00  75.00   82"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Convert each elemetn of MI2 back to int\n",
    "df_scores.MI2 = df_scores.MI2.map(lambda x: int(float(x)))\n",
    "df_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Joining/Merging Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The `pd.merge()` method allows us to quickly combine two datasets based on some common info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <td>Charlie</td>\n",
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      "text/plain": [
       "      name  age\n",
       "0    Alice   20\n",
       "1      Bob   27\n",
       "2  Charlie   32\n",
       "3   Dwayne   37\n",
       "4     Erin   27"
      ]
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     "execution_count": 125,
     "metadata": {},
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   ],
   "source": [
    "names1 = ['Alice','Bob','Charlie','Dwayne','Erin']\n",
    "df1 = pd.DataFrame(data={'name':names1,'age':np.random.randint(low=20,high=40,size=len(names1))})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "hidden": true
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   "outputs": [
    {
     "data": {
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       "      <th>name</th>\n",
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      "text/plain": [
       "     name  height\n",
       "0  Dwayne      59\n",
       "1    Erin      57\n",
       "2   Frank      66\n",
       "3    Gina      55"
      ]
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     "metadata": {},
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   "source": [
    "names2 = ['Dwayne','Erin','Frank','Gina']\n",
    "df2 = pd.DataFrame(data={'name':names2,'height':np.random.randint(low=55,high=70,size=len(names2))})\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The two main steps to merging data:\n",
    "1. Specify what column or columns to merge on\n",
    "2. Specify what data to keep with the how argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     name  age  height\n",
       "0  Dwayne   37      59\n",
       "1    Erin   27      57"
      ]
     },
     "execution_count": 127,
     "metadata": {},
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   "source": [
    "#Merge on name column, and only keep entries found in BOTH datasets with how='inner'\n",
    "pd.merge(df1,df2,on='name',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Alice</td>\n",
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       "      <td>Erin</td>\n",
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      "text/plain": [
       "      name  age  height\n",
       "0    Alice   20     NaN\n",
       "1      Bob   27     NaN\n",
       "2  Charlie   32     NaN\n",
       "3   Dwayne   37    59.0\n",
       "4     Erin   27    57.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only keep data from the first/left dataframe with how='left'\n",
    "#For records not found in seoncd/right dataframe, it will return NaN\n",
    "pd.merge(df1,df2,on='name',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     name   age  height\n",
       "0  Dwayne  37.0      59\n",
       "1    Erin  27.0      57\n",
       "2   Frank   NaN      66\n",
       "3    Gina   NaN      55"
      ]
     },
     "execution_count": 129,
     "metadata": {},
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   ],
   "source": [
    "#Can also keep just right entries\n",
    "pd.merge(df1,df2,on='name',how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <td>66.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
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      "text/plain": [
       "      name   age  height\n",
       "0    Alice  20.0     NaN\n",
       "1      Bob  27.0     NaN\n",
       "2  Charlie  32.0     NaN\n",
       "3   Dwayne  37.0    59.0\n",
       "4     Erin  27.0    57.0\n",
       "5    Frank   NaN    66.0\n",
       "6     Gina   NaN    55.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can also keep all records with how='outer'\n",
    "pd.merge(df1,df2,on='name',how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also specify different field names if the same field is called 2 different things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dwayne</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Erin</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Frank</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gina</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NAME  height\n",
       "0  Dwayne      59\n",
       "1    Erin      57\n",
       "2   Frank      66\n",
       "3    Gina      55"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.rename(columns={'name':'NAME'})\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>NAME</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dwayne</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Dwayne</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Erin</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Erin</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Frank</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gina</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name   age    NAME  height\n",
       "0    Alice  20.0     NaN     NaN\n",
       "1      Bob  27.0     NaN     NaN\n",
       "2  Charlie  32.0     NaN     NaN\n",
       "3   Dwayne  37.0  Dwayne    59.0\n",
       "4     Erin  27.0    Erin    57.0\n",
       "5      NaN   NaN   Frank    66.0\n",
       "6      NaN   NaN    Gina    55.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df3,left_on='name',right_on='NAME',how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Aggregating Data: Groupby and Pivot Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Grouping and pivoting data are some of the most useful methods for summarizing and extracting insights from data sets.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>genhlth</th>\n",
       "      <th>exerany</th>\n",
       "      <th>hlthplan</th>\n",
       "      <th>smoke100</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>wtdesire</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>good</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>very good</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1    genhlth  exerany  hlthplan  smoke100  height  weight  wtdesire   age  \\\n",
       "0   1       good      0.0       1.0       0.0    70.0   175.0     175.0  77.0   \n",
       "1   2       good      0.0       1.0       1.0    64.0   125.0     115.0  33.0   \n",
       "2   3       good      1.0       1.0       1.0    60.0   105.0     105.0  49.0   \n",
       "3   4       good      1.0       1.0       0.0    66.0   132.0     124.0  42.0   \n",
       "4   5  very good      0.0       1.0       0.0    61.0   150.0     130.0  55.0   \n",
       "\n",
       "  gender  \n",
       "0      m  \n",
       "1      f  \n",
       "2      f  \n",
       "3      f  \n",
       "4      f  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the CDC data\n",
    "df_cdc = pd.read_csv('Data/cdcSmall.csv')\n",
    "df_cdc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    9989\n",
       "m    8977\n",
       "M     426\n",
       "F     227\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We'll groupby gender, so clean that column up\n",
    "df_cdc.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    10216\n",
       "M     9403\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdc.gender = df_cdc.gender.replace({'f':'F','m':'M'})\n",
    "df_cdc.gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pandas follows the same Split-Apply-Combine methodology found in R and many other data analysis languages where we Split data by some field or set of fields, Apply a function to the data columns from each split, then Combine and aggregate the results.  \n",
    "\n",
    "The basic mechanics is `df.groupby([fields to split on])[fields to apply function to].function_to_apply()`.  Here's how it looks if we want to get average height by gender.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "F    64.368556\n",
       "M    70.251235\n",
       "Name: height, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdc.groupby('gender')['height'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also split by multiple fields, or aggregate on multiple fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  genhlth  \n",
       "F       excellent    64.745245\n",
       "        fair         63.796903\n",
       "        good         64.183782\n",
       "        poor         63.826203\n",
       "        very good    64.513428\n",
       "M       excellent    70.440831\n",
       "        fair         69.429577\n",
       "        good         70.079068\n",
       "        poor         69.435897\n",
       "        very good    70.537405\n",
       "Name: height, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdc.groupby(['gender','genhlth'])['height'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-138-24e2f6289c22>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_cdc.groupby(['gender','genhlth'])['height','weight'].mean()\n"
     ]
    },
    {
     "data": {
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       "      <th>weight</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>excellent</th>\n",
       "      <td>64.745245</td>\n",
       "      <td>142.286655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>63.796903</td>\n",
       "      <td>162.234389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>64.183782</td>\n",
       "      <td>154.798738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
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       "    <tr>\n",
       "      <th>very good</th>\n",
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       "      <td>150.379765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>excellent</th>\n",
       "      <td>70.440831</td>\n",
       "      <td>182.808482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>69.429577</td>\n",
       "      <td>193.278231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>70.079068</td>\n",
       "      <td>193.114903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>69.435897</td>\n",
       "      <td>194.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very good</th>\n",
       "      <td>70.537405</td>\n",
       "      <td>189.016985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     height      weight\n",
       "gender genhlth                         \n",
       "F      excellent  64.745245  142.286655\n",
       "       fair       63.796903  162.234389\n",
       "       good       64.183782  154.798738\n",
       "       poor       63.826203  164.239362\n",
       "       very good  64.513428  150.379765\n",
       "M      excellent  70.440831  182.808482\n",
       "       fair       69.429577  193.278231\n",
       "       good       70.079068  193.114903\n",
       "       poor       69.435897  194.000000\n",
       "       very good  70.537405  189.016985"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdc.groupby(['gender','genhlth'])['height','weight'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We see that the returned object will next all the fields we split on as a multi-level index.  It is also common practice to get these fields back into the resulting dataframe with reset_index() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-139-2d4cd6d8d8c7>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_cdc.groupby(['gender','genhlth'])['height','weight'].mean().reset_index()\n"
     ]
    },
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>good</td>\n",
       "      <td>64.183782</td>\n",
       "      <td>154.798738</td>\n",
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       "      <th>3</th>\n",
       "      <td>F</td>\n",
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       "      <td>63.826203</td>\n",
       "      <td>164.239362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>very good</td>\n",
       "      <td>64.513428</td>\n",
       "      <td>150.379765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>excellent</td>\n",
       "      <td>70.440831</td>\n",
       "      <td>182.808482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M</td>\n",
       "      <td>fair</td>\n",
       "      <td>69.429577</td>\n",
       "      <td>193.278231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M</td>\n",
       "      <td>good</td>\n",
       "      <td>70.079068</td>\n",
       "      <td>193.114903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M</td>\n",
       "      <td>poor</td>\n",
       "      <td>69.435897</td>\n",
       "      <td>194.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M</td>\n",
       "      <td>very good</td>\n",
       "      <td>70.537405</td>\n",
       "      <td>189.016985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender    genhlth     height      weight\n",
       "0      F  excellent  64.745245  142.286655\n",
       "1      F       fair  63.796903  162.234389\n",
       "2      F       good  64.183782  154.798738\n",
       "3      F       poor  63.826203  164.239362\n",
       "4      F  very good  64.513428  150.379765\n",
       "5      M  excellent  70.440831  182.808482\n",
       "6      M       fair  69.429577  193.278231\n",
       "7      M       good  70.079068  193.114903\n",
       "8      M       poor  69.435897  194.000000\n",
       "9      M  very good  70.537405  189.016985"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdc.groupby(['gender','genhlth'])['height','weight'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also apply custom lambda functions using the `agg()` method to feed in a custom function to aggregate on.  E.g. say we want the spread of each field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-140-3932cc6af5b8>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_cdc.groupby(['gender'])['height','weight'].agg(lambda x: x.max() - x.min()).reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  height  weight\n",
       "0      F    30.0   427.0\n",
       "1      M    44.0   422.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Aggregate by custom lambda function\n",
    "df_cdc.groupby(['gender'])['height','weight'].agg(lambda x: x.max() - x.min()).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Note that the column names are now confusing since we are actually showing the range of height and weight.  It is also common to apply the pandas add_suffix() or add_prefix() method before resetting the index to add a clarifying label to each column name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-141-cd93585d83b7>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_cdc.groupby(['gender'])['height','weight'].agg(lambda x: x.max() - x.min()).add_suffix('_range').reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>height_range</th>\n",
       "      <th>weight_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  height_range  weight_range\n",
       "0      F          30.0         427.0\n",
       "1      M          44.0         422.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Re-label columns after groupby\n",
    "df_cdc.groupby(['gender'])['height','weight'].agg(lambda x: x.max() - x.min()).add_suffix('_range').reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Pivot Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Similar to groupby, pivot tables allow us to aggregate data to get insightfully summarize the data.  The main difference is that pivot tables yield a 2-dimensional view, and can give an additional method for reshaping the data.\n",
    "\n",
    "The mechanics of pivot_tables are to specify:\n",
    "1. The index of the resulting dataframe ('index' argument)\n",
    "2. The columns of the resulting dataframe ('columns' argument)\n",
    "3. The fields to aggregate on ('values' argument)\n",
    "4. The function used to do the aggregation ('aggfunc' argument).\n",
    "\n",
    "Here's how it looks to get pivot table showing average height by genhlth and gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genhlth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>64.745245</td>\n",
       "      <td>70.440831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>63.796903</td>\n",
       "      <td>69.429577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>64.183782</td>\n",
       "      <td>70.079068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>63.826203</td>\n",
       "      <td>69.435897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very good</th>\n",
       "      <td>64.513428</td>\n",
       "      <td>70.537405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender             F          M\n",
       "genhlth                        \n",
       "excellent  64.745245  70.440831\n",
       "fair       63.796903  69.429577\n",
       "good       64.183782  70.079068\n",
       "poor       63.826203  69.435897\n",
       "very good  64.513428  70.537405"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cdc.pivot_table(values='height', index='genhlth', columns='gender', aggfunc=np.mean)\n",
    "#Note that 'gender' is now the name of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">height</th>\n",
       "      <th colspan=\"2\" halign=\"left\">weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genhlth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>64.745245</td>\n",
       "      <td>70.440831</td>\n",
       "      <td>142.286655</td>\n",
       "      <td>182.808482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>63.796903</td>\n",
       "      <td>69.429577</td>\n",
       "      <td>162.234389</td>\n",
       "      <td>193.278231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>64.183782</td>\n",
       "      <td>70.079068</td>\n",
       "      <td>154.798738</td>\n",
       "      <td>193.114903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>63.826203</td>\n",
       "      <td>69.435897</td>\n",
       "      <td>164.239362</td>\n",
       "      <td>194.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very good</th>\n",
       "      <td>64.513428</td>\n",
       "      <td>70.537405</td>\n",
       "      <td>150.379765</td>\n",
       "      <td>189.016985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              height                 weight            \n",
       "gender             F          M           F           M\n",
       "genhlth                                                \n",
       "excellent  64.745245  70.440831  142.286655  182.808482\n",
       "fair       63.796903  69.429577  162.234389  193.278231\n",
       "good       64.183782  70.079068  154.798738  193.114903\n",
       "poor       63.826203  69.435897  164.239362  194.000000\n",
       "very good  64.513428  70.537405  150.379765  189.016985"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also speficy multiple fields to aggregate on\n",
    "#The result is a multi-level column\n",
    "df_cdc.pivot_table(values=['height','weight'], index='genhlth', columns='gender', aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">amax</th>\n",
       "      <th colspan=\"4\" halign=\"left\">amin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">height</th>\n",
       "      <th colspan=\"2\" halign=\"left\">weight</th>\n",
       "      <th colspan=\"2\" halign=\"left\">height</th>\n",
       "      <th colspan=\"2\" halign=\"left\">weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genhlth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>78.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>74.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>78.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>78.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very good</th>\n",
       "      <td>77.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            amax                       amin                    \n",
       "          height       weight        height       weight       \n",
       "gender         F     M      F      M      F     M      F      M\n",
       "genhlth                                                        \n",
       "excellent   78.0  83.0  350.0  400.0   54.0  60.0   85.0   94.0\n",
       "fair        74.0  79.0  495.0  400.0   51.0  49.0   78.0   78.0\n",
       "good        78.0  84.0  400.0  385.0   50.0  58.0   68.0   86.0\n",
       "poor        78.0  80.0  371.0  500.0   53.0  58.0   79.0  100.0\n",
       "very good   77.0  93.0  350.0  360.0   48.0  55.0   82.0   90.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can even specify multiple aggregation functions\n",
    "df_cdc.pivot_table(values=['height','weight'], index='genhlth', columns='gender', aggfunc=[np.max,np.min])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Handling Dates and Times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pandas has built in functionality to handle dates and times.  Before looking at the pandas functionality, it helps to first look at the python datetime module, which is the bases for how pandas deals with dates and times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 11, 10, 14, 42, 57, 153798)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#datetime object contains tuple of (Year, Month, Day, Hour, Minute, Microseconds)\n",
    "now = datetime.now()\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2020, 42)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can access components of a datetime object\n",
    "now.year, now.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=314, seconds=52977, microseconds=153798)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#timedelta object gives difference between two times\n",
    "td = now - datetime(2020,1,1) #Time since start of the year\n",
    "td\n",
    "#timedelta ojbect is given as tuple of (days,seconds,microseconds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "It is also common to build custom strings from datetime objects.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-11-10'"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert datetime to string with strftime(), \"string from time\"\n",
    "now.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 5, 10, 0, 0)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert string to datetime with strptime()\n",
    "#Note that we have to feed in the exact datetime format, which can be annoying.\n",
    "#Thankfully, as we'll see, pandas can automatially interpret a range of datetime strings\n",
    "datetime.strptime(\"2019-05-10\",'%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Pandas incorporates datetime and other functionality to be able to handle and filter by date and time data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>TempF</th>\n",
       "      <th>humid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5/1/2017 0:15</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5/1/2017 0:35</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5/1/2017 0:55</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5/1/2017 1:15</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/1/2017 1:35</td>\n",
       "      <td>69.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  TempF  humid\n",
       "0  5/1/2017 0:15   68.0   69.0\n",
       "1  5/1/2017 0:35   68.0   69.0\n",
       "2  5/1/2017 0:55   67.0   74.0\n",
       "3  5/1/2017 1:15   67.0   74.0\n",
       "4  5/1/2017 1:35   69.0   78.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in exampel weather data\n",
    "df_weather = pd.read_csv('Data/weather.csv')\n",
    "df_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21488 entries, 0 to 21487\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   DATE    21488 non-null  object \n",
      " 1   TempF   21174 non-null  float64\n",
      " 2   humid   21485 non-null  float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 503.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#Notice that DATE column is currently a string (object dtype)\n",
    "df_weather.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>TempF</th>\n",
       "      <th>humid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-01 00:15:00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-01 00:35:00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-01 00:55:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-01 01:15:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-01 01:35:00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DATE  TempF  humid\n",
       "0 2017-05-01 00:15:00   68.0   69.0\n",
       "1 2017-05-01 00:35:00   68.0   69.0\n",
       "2 2017-05-01 00:55:00   67.0   74.0\n",
       "3 2017-05-01 01:15:00   67.0   74.0\n",
       "4 2017-05-01 01:35:00   69.0   78.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can convert the column to a pandas datetime object with pd.to_datetime()\n",
    "df_weather.DATE = pd.to_datetime(df_weather.DATE)\n",
    "df_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21488 entries, 0 to 21487\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   DATE    21488 non-null  datetime64[ns]\n",
      " 1   TempF   21174 non-null  float64       \n",
      " 2   humid   21485 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(2)\n",
      "memory usage: 503.8 KB\n"
     ]
    }
   ],
   "source": [
    "#Now see the updated dtype\n",
    "df_weather.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TempF</th>\n",
       "      <th>humid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-01 00:15:00</th>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01 00:35:00</th>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01 00:55:00</th>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01 01:15:00</th>\n",
       "      <td>67.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01 01:35:00</th>\n",
       "      <td>69.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TempF  humid\n",
       "DATE                             \n",
       "2017-05-01 00:15:00   68.0   69.0\n",
       "2017-05-01 00:35:00   68.0   69.0\n",
       "2017-05-01 00:55:00   67.0   74.0\n",
       "2017-05-01 01:15:00   67.0   74.0\n",
       "2017-05-01 01:35:00   69.0   78.0"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can se the DATE as the index, in which case it becomes a DateTimeIndex object\n",
    "df_weather.set_index('DATE',inplace=True)\n",
    "df_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-05-01 00:15:00', '2017-05-01 00:35:00',\n",
       "               '2017-05-01 00:55:00', '2017-05-01 01:15:00',\n",
       "               '2017-05-01 01:35:00', '2017-05-01 01:55:00',\n",
       "               '2017-05-01 02:15:00', '2017-05-01 02:35:00',\n",
       "               '2017-05-01 02:55:00', '2017-05-01 03:15:00',\n",
       "               ...\n",
       "               '2018-02-23 18:15:00', '2018-02-23 18:35:00',\n",
       "               '2018-02-23 18:55:00', '2018-02-23 19:15:00',\n",
       "               '2018-02-23 19:35:00', '2018-02-23 19:55:00',\n",
       "               '2018-02-23 20:15:00', '2018-02-23 20:35:00',\n",
       "               '2018-02-23 20:55:00', '2018-02-23 21:15:00'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=21488, freq=None)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TempF</th>\n",
       "      <th>humid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01 00:15:00</th>\n",
       "      <td>50.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 00:35:00</th>\n",
       "      <td>50.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 00:55:00</th>\n",
       "      <td>50.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01 01:15:00</th>\n",
       "      <td>50.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31 22:55:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31 23:15:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31 23:35:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31 23:55:00</th>\n",
       "      <td>16.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TempF  humid\n",
       "DATE                             \n",
       "2017-12-01 00:15:00   50.0   77.0\n",
       "2017-12-01 00:35:00   50.0   72.0\n",
       "2017-12-01 00:55:00   50.0   72.0\n",
       "2017-12-01 01:15:00   50.0   72.0\n",
       "2017-12-31 22:55:00    NaN   58.0\n",
       "2017-12-31 23:15:00    NaN   58.0\n",
       "2017-12-31 23:35:00    NaN   58.0\n",
       "2017-12-31 23:55:00   16.0   58.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grab just data for DEC of 2017\n",
    "df_DEC = df_weather['2017-12']\n",
    "df_DEC.iloc[np.r_[0:4,-4:0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The pandas period object is also a useful way to specify a PERIOD of time rather than a specific point in time, which is what you get with a single timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-11-10 14:42:57.153798')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(now) #Timestamp object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2020-11', 'M')"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period(now,freq='M') #Monthly period, which will specify the entire time period of the current month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('7 days 00:00:00')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a pandas Timedelta object to any Timestamp or Period\n",
    "td = pd.Timedelta('7D') #7 Day Timedelta\n",
    "td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-11-17 14:42:57.153798')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(now) + td #Timestamp for 7 days from now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2021-01-09', 'D')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Move Period ahead by given Timedelta\n",
    "pd.Period(now,freq='D') + pd.Timedelta('60D') #60 days ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-10-02', '2016-10-16', '2016-10-30', '2016-11-13',\n",
       "               '2016-11-27', '2016-12-11', '2016-12-25', '2017-01-08',\n",
       "               '2017-01-22'],\n",
       "              dtype='datetime64[ns]', freq='2W-SUN')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also create a custome date range with pd.date_range()\n",
    "#Ex: Get range of every other Sunday for 9 perios\n",
    "dates = pd.date_range('10-01-2016', periods=9, freq='2W-SUN')\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also create a PeriodIndex\n",
    "pd.period_range('2000-01-01', '2000-06-30', freq='M')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For much more on datetime functionality, see the python [datetime documentation](https://docs.python.org/3/library/datetime.html) and the [pandas datetime documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html).  I'd also recommend the datetime chapter of McKinney's [Python for Data Analysis](https://learning.oreilly.com/library/view/python-for-data/9781491957653/ch11.html) book on O'Reily.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
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