Python-03-Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Data Science\n",
    "\n",
    "\n",
    "### Pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Intro Video - Wrangling Data with Pandas - Google Cloud Platform\n",
    "\n",
    "<a href=\"http://www.youtube.com/watch?feature=player_embedded&v=XDAnFZqJDvI\n",
    "\" target=\"_blank\"><img src=\"http://img.youtube.com/vi/XDAnFZqJDvI/0.jpg\" \n",
    "alt=\"IMAGE ALT TEXT HERE\" width=\"240\" height=\"180\" border=\"10\" /></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.23.0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a `Series` by passing a list of values, letting pandas create a default integer index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a `DataFrame` by passing a NumPy array, with a datetime index and labeled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('20180101', periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.161521</td>\n",
       "      <td>-0.176777</td>\n",
       "      <td>-1.053566</td>\n",
       "      <td>0.055093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.750852</td>\n",
       "      <td>-0.630708</td>\n",
       "      <td>0.233918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>0.296357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.170012</td>\n",
       "      <td>-0.025409</td>\n",
       "      <td>-0.056600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-1.416954</td>\n",
       "      <td>-0.677444</td>\n",
       "      <td>1.301718</td>\n",
       "      <td>1.061319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>-0.428887</td>\n",
       "      <td>0.056360</td>\n",
       "      <td>-0.836003</td>\n",
       "      <td>-0.906351</td>\n",
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       "                   A         B         C         D\n",
       "2018-01-01  0.161521 -0.176777 -1.053566  0.055093\n",
       "2018-01-02 -0.355685 -0.750852 -0.630708  0.233918\n",
       "2018-01-03 -0.561194  0.850295 -1.190817  0.296357\n",
       "2018-01-04  0.508058  0.170012 -0.025409 -0.056600\n",
       "2018-01-05 -1.416954 -0.677444  1.301718  1.061319\n",
       "2018-01-06 -0.428887  0.056360 -0.836003 -0.906351"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a `DataFrame` by passing a dict of objects that can be converted to series-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({ 'A' : 1.,\n",
    "            'B' : pd.Timestamp('20180102'),\n",
    "            'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "            'D' : np.array([3] * 4,dtype='int32'),\n",
    "            'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "            'F' : 'foo' })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
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       "      <th>1</th>\n",
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       "      <td>3</td>\n",
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       "      <td>foo</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>test</td>\n",
       "      <td>foo</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>foo</td>\n",
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       "     A          B    C  D      E    F\n",
       "0  1.0 2018-01-02  1.0  3   test  foo\n",
       "1  1.0 2018-01-02  1.0  3  train  foo\n",
       "2  1.0 2018-01-02  1.0  3   test  foo\n",
       "3  1.0 2018-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The columns of the resulting DataFrame have different dtypes.\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-16-a9b614e70a2e>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-16-a9b614e70a2e>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    df2.<TAB>\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#Tab completion for column names\n",
    "df2.<TAB>\n",
    "df2.A                  df2.bool\n",
    "df2.abs                df2.boxplot\n",
    "df2.add                df2.C\n",
    "df2.add_prefix         df2.clip\n",
    "df2.add_suffix         df2.clip_lower\n",
    "df2.align              df2.clip_upper\n",
    "df2.all                df2.columns\n",
    "df2.any                df2.combine\n",
    "df2.append             df2.combine_first\n",
    "df2.apply              df2.compound\n",
    "df2.applymap           df2.consolidate\n",
    "df2.D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing Data\n",
    "Here is how to view the top and bottom rows of the frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-01-02</td>\n",
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       "      <td>foo</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>foo</td>\n",
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       "      <td>3</td>\n",
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       "      <td>foo</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>foo</td>\n",
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2018-01-02  1.0  3   test  foo\n",
       "1  1.0 2018-01-02  1.0  3  train  foo\n",
       "2  1.0 2018-01-02  1.0  3   test  foo\n",
       "3  1.0 2018-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-01-02</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>foo</td>\n",
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       "     A          B    C  D      E    F\n",
       "0  1.0 2018-01-02  1.0  3   test  foo\n",
       "1  1.0 2018-01-02  1.0  3  train  foo\n",
       "2  1.0 2018-01-02  1.0  3   test  foo\n",
       "3  1.0 2018-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the index, columns, and the underlying NumPy data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1615208 , -0.17677725, -1.05356561,  0.05509325],\n",
       "       [-0.3556852 , -0.75085224, -0.6307075 ,  0.23391823],\n",
       "       [-0.56119445,  0.85029505, -1.19081697,  0.29635654],\n",
       "       [ 0.5080582 ,  0.17001176, -0.02540858, -0.05659981],\n",
       "       [-1.416954  , -0.67744376,  1.30171821,  1.06131927],\n",
       "       [-0.42888651,  0.0563596 , -0.83600288, -0.90635132]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`describe()` shows a quick statistic summary of your data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.348857</td>\n",
       "      <td>-0.088068</td>\n",
       "      <td>-0.405797</td>\n",
       "      <td>0.113956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.661386</td>\n",
       "      <td>0.593718</td>\n",
       "      <td>0.930787</td>\n",
       "      <td>0.635412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.416954</td>\n",
       "      <td>-0.750852</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>-0.906351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.528117</td>\n",
       "      <td>-0.552277</td>\n",
       "      <td>-0.999175</td>\n",
       "      <td>-0.028677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.392286</td>\n",
       "      <td>-0.060209</td>\n",
       "      <td>-0.733355</td>\n",
       "      <td>0.144506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.032219</td>\n",
       "      <td>0.141599</td>\n",
       "      <td>-0.176733</td>\n",
       "      <td>0.280747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>1.301718</td>\n",
       "      <td>1.061319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.348857 -0.088068 -0.405797  0.113956\n",
       "std    0.661386  0.593718  0.930787  0.635412\n",
       "min   -1.416954 -0.750852 -1.190817 -0.906351\n",
       "25%   -0.528117 -0.552277 -0.999175 -0.028677\n",
       "50%   -0.392286 -0.060209 -0.733355  0.144506\n",
       "75%    0.032219  0.141599 -0.176733  0.280747\n",
       "max    0.508058  0.850295  1.301718  1.061319"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>A</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A    C    D\n",
       "count  4.0  4.0  4.0\n",
       "mean   1.0  1.0  3.0\n",
       "std    0.0  0.0  0.0\n",
       "min    1.0  1.0  3.0\n",
       "25%    1.0  1.0  3.0\n",
       "50%    1.0  1.0  3.0\n",
       "75%    1.0  1.0  3.0\n",
       "max    1.0  1.0  3.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing your data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>2018-01-01 00:00:00</th>\n",
       "      <th>2018-01-02 00:00:00</th>\n",
       "      <th>2018-01-03 00:00:00</th>\n",
       "      <th>2018-01-04 00:00:00</th>\n",
       "      <th>2018-01-05 00:00:00</th>\n",
       "      <th>2018-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.161521</td>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.508058</td>\n",
       "      <td>-1.416954</td>\n",
       "      <td>-0.428887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.176777</td>\n",
       "      <td>-0.750852</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>0.170012</td>\n",
       "      <td>-0.677444</td>\n",
       "      <td>0.056360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-1.053566</td>\n",
       "      <td>-0.630708</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>-0.025409</td>\n",
       "      <td>1.301718</td>\n",
       "      <td>-0.836003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.055093</td>\n",
       "      <td>0.233918</td>\n",
       "      <td>0.296357</td>\n",
       "      <td>-0.056600</td>\n",
       "      <td>1.061319</td>\n",
       "      <td>-0.906351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2018-01-01  2018-01-02  2018-01-03  2018-01-04  2018-01-05  2018-01-06\n",
       "A    0.161521   -0.355685   -0.561194    0.508058   -1.416954   -0.428887\n",
       "B   -0.176777   -0.750852    0.850295    0.170012   -0.677444    0.056360\n",
       "C   -1.053566   -0.630708   -1.190817   -0.025409    1.301718   -0.836003\n",
       "D    0.055093    0.233918    0.296357   -0.056600    1.061319   -0.906351"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by an axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.413071</td>\n",
       "      <td>-0.988640</td>\n",
       "      <td>0.978263</td>\n",
       "      <td>0.476278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.563323</td>\n",
       "      <td>0.238888</td>\n",
       "      <td>0.262722</td>\n",
       "      <td>1.495812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.574882</td>\n",
       "      <td>0.294734</td>\n",
       "      <td>1.254385</td>\n",
       "      <td>0.324630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2.302291</td>\n",
       "      <td>1.263080</td>\n",
       "      <td>0.523339</td>\n",
       "      <td>1.417684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.873408</td>\n",
       "      <td>-0.576795</td>\n",
       "      <td>1.163846</td>\n",
       "      <td>1.627111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>0.788913</td>\n",
       "      <td>-0.801073</td>\n",
       "      <td>-0.021522</td>\n",
       "      <td>0.556139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2018-01-01 -0.413071 -0.988640  0.978263  0.476278\n",
       "2018-01-02 -0.563323  0.238888  0.262722  1.495812\n",
       "2018-01-03 -0.574882  0.294734  1.254385  0.324630\n",
       "2018-01-04  2.302291  1.263080  0.523339  1.417684\n",
       "2018-01-05  0.873408 -0.576795  1.163846  1.627111\n",
       "2018-01-06  0.788913 -0.801073 -0.021522  0.556139"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>0.556139</td>\n",
       "      <td>-0.021522</td>\n",
       "      <td>-0.801073</td>\n",
       "      <td>0.788913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>1.495812</td>\n",
       "      <td>0.262722</td>\n",
       "      <td>0.238888</td>\n",
       "      <td>-0.563323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>1.417684</td>\n",
       "      <td>0.523339</td>\n",
       "      <td>1.263080</td>\n",
       "      <td>2.302291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.476278</td>\n",
       "      <td>0.978263</td>\n",
       "      <td>-0.988640</td>\n",
       "      <td>-0.413071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>1.627111</td>\n",
       "      <td>1.163846</td>\n",
       "      <td>-0.576795</td>\n",
       "      <td>0.873408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.324630</td>\n",
       "      <td>1.254385</td>\n",
       "      <td>0.294734</td>\n",
       "      <td>-0.574882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-06  0.556139 -0.021522 -0.801073  0.788913\n",
       "2018-01-02  1.495812  0.262722  0.238888 -0.563323\n",
       "2018-01-04  1.417684  0.523339  1.263080  2.302291\n",
       "2018-01-01  0.476278  0.978263 -0.988640 -0.413071\n",
       "2018-01-05  1.627111  1.163846 -0.576795  0.873408\n",
       "2018-01-03  0.324630  1.254385  0.294734 -0.574882"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection - Getting\n",
    "Selecting a single column, which yields a Series, equivalent to df.A:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01    0.161521\n",
       "2018-01-02   -0.355685\n",
       "2018-01-03   -0.561194\n",
       "2018-01-04    0.508058\n",
       "2018-01-05   -1.416954\n",
       "2018-01-06   -0.428887\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting via [], which slices the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.161521</td>\n",
       "      <td>-0.176777</td>\n",
       "      <td>-1.053566</td>\n",
       "      <td>0.055093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.750852</td>\n",
       "      <td>-0.630708</td>\n",
       "      <td>0.233918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>0.296357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01  0.161521 -0.176777 -1.053566  0.055093\n",
       "2018-01-02 -0.355685 -0.750852 -0.630708  0.233918\n",
       "2018-01-03 -0.561194  0.850295 -1.190817  0.296357"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.750852</td>\n",
       "      <td>-0.630708</td>\n",
       "      <td>0.233918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>0.296357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.170012</td>\n",
       "      <td>-0.025409</td>\n",
       "      <td>-0.056600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-02 -0.355685 -0.750852 -0.630708  0.233918\n",
       "2018-01-03 -0.561194  0.850295 -1.190817  0.296357\n",
       "2018-01-04  0.508058  0.170012 -0.025409 -0.056600"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20180102':'20180104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selection by Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.161521\n",
       "B   -0.176777\n",
       "C   -1.053566\n",
       "D    0.055093\n",
       "Name: 2018-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.161521</td>\n",
       "      <td>-0.176777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.750852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.170012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-1.416954</td>\n",
       "      <td>-0.677444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>-0.428887</td>\n",
       "      <td>0.056360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2018-01-01  0.161521 -0.176777\n",
       "2018-01-02 -0.355685 -0.750852\n",
       "2018-01-03 -0.561194  0.850295\n",
       "2018-01-04  0.508058  0.170012\n",
       "2018-01-05 -1.416954 -0.677444\n",
       "2018-01-06 -0.428887  0.056360"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.750852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.170012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2018-01-02 -0.355685 -0.750852\n",
       "2018-01-03 -0.561194  0.850295\n",
       "2018-01-04  0.508058  0.170012"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20180102':'20180104',['A','B']] #Showing label slicing, both endpoints are included:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.355685\n",
       "B   -0.750852\n",
       "Name: 2018-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20180102',['A','B']] #Reduction in the dimensions of the returned object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16152079623063323"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'A'] #For getting a scalar value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16152079623063323"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'A'] #For getting fast access to a scalar (equivalent to the prior method):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selection by Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.508058\n",
       "B    0.170012\n",
       "C   -0.025409\n",
       "D   -0.056600\n",
       "Name: 2018-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.170012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-1.416954</td>\n",
       "      <td>-0.677444</td>\n",
       "    </tr>\n",
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       "</div>"
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      "text/plain": [
       "                   A         B\n",
       "2018-01-04  0.508058  0.170012\n",
       "2018-01-05 -1.416954 -0.677444"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.630708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>-1.190817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-1.416954</td>\n",
       "      <td>1.301718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2018-01-02 -0.355685 -0.630708\n",
       "2018-01-03 -0.561194 -1.190817\n",
       "2018-01-05 -1.416954  1.301718"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,2]] #By lists of integer position locations, similar to the numpy/python style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
       "      <td>-0.750852</td>\n",
       "      <td>-0.630708</td>\n",
       "      <td>0.233918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>0.296357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-02 -0.355685 -0.750852 -0.630708  0.233918\n",
       "2018-01-03 -0.561194  0.850295 -1.190817  0.296357"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:] #For slicing rows explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-0.176777</td>\n",
       "      <td>-1.053566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.750852</td>\n",
       "      <td>-0.630708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.850295</td>\n",
       "      <td>-1.190817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.170012</td>\n",
       "      <td>-0.025409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-0.677444</td>\n",
       "      <td>1.301718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>0.056360</td>\n",
       "      <td>-0.836003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2018-01-01 -0.176777 -1.053566\n",
       "2018-01-02 -0.750852 -0.630708\n",
       "2018-01-03  0.850295 -1.190817\n",
       "2018-01-04  0.170012 -0.025409\n",
       "2018-01-05 -0.677444  1.301718\n",
       "2018-01-06  0.056360 -0.836003"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3] #For slicing columns explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7508522352909704"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1] #For getting a value explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7508522352909704"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1] #For getting fast access to a scalar (equivalent to the prior method):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection - Setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting a new column automatically aligns the data by the indexes.\n",
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20180102', periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-02    1\n",
       "2018-01-03    2\n",
       "2018-01-04    3\n",
       "2018-01-05    4\n",
       "2018-01-06    5\n",
       "2018-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.at[dates[0],'A'] = 0 #Setting values by label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iat[0,1] = 0 #Setting values by position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2018-01-01</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>-0.355685</td>\n",
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       "      <td>-0.630708</td>\n",
       "      <td>0.233918</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.561194</td>\n",
       "      <td>0.850295</td>\n",
       "      <td>-1.190817</td>\n",
       "      <td>0.296357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.508058</td>\n",
       "      <td>0.170012</td>\n",
       "      <td>-0.025409</td>\n",
       "      <td>-0.056600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-1.416954</td>\n",
       "      <td>-0.677444</td>\n",
       "      <td>1.301718</td>\n",
       "      <td>1.061319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>-0.428887</td>\n",
       "      <td>0.056360</td>\n",
       "      <td>-0.836003</td>\n",
       "      <td>-0.906351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2018-01-01  0.000000  0.000000 -1.053566  0.055093\n",
       "2018-01-02 -0.355685 -0.750852 -0.630708  0.233918\n",
       "2018-01-03 -0.561194  0.850295 -1.190817  0.296357\n",
       "2018-01-04  0.508058  0.170012 -0.025409 -0.056600\n",
       "2018-01-05 -1.416954 -0.677444  1.301718  1.061319\n",
       "2018-01-06 -0.428887  0.056360 -0.836003 -0.906351"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting by assigning with a NumPy array:\n",
    "df.loc[:,'D'] = np.array([5] * len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}