08-DA_Pandas_import_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Importing/export, basic plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen in the previous chapter what structures are offered by Pandas and how to create them. Another very common way of \"creating\" a Pandas Dataframe is by importing a table from another format like CSV or Excel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Simple import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An Excel table containing the same information as we had in [Chapter 1](01-Pandas_structures.ipynb) is provided in [composers.xlsx](composers.xlsx) and can be read with the ```read_excel``` function. There are many more readers for other types of data (csv, json, html etc.) but we focus here on Excel."
   ]
  },
  {
   "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",
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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>composer</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahler</td>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beethoven</td>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puccini</td>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shostakovich</td>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       composer  birth  death              city\n",
       "0        Mahler   1860   1911           Kaliste\n",
       "1     Beethoven   1770   1827              Bonn\n",
       "2       Puccini   1858   1924           Lucques\n",
       "3  Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('Data/composers.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reader automatically recognized the heaers of the file. However it created a new index. If needed we can specify which column to use as header:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              birth  death              city\n",
       "composer                                    \n",
       "Mahler         1860   1911           Kaliste\n",
       "Beethoven      1770   1827              Bonn\n",
       "Puccini        1858   1924           Lucques\n",
       "Shostakovich   1906   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('Data/composers.xlsx', index_col = 'composer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we open the file in Excel, we see that it is composed of more than one sheet. Clearly, when not specifying anything, the reader only reads the first sheet. However we can specify a sheet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "specific_sheet = pd.read_excel('Data/composers.xlsx', index_col = 'composer',sheet_name='Sheet2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>10.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Röhrau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth    death              city\n",
       "composer                                       \n",
       "Mahler        1860.0     1911           Kaliste\n",
       "Beethoven     1770.0     1827              Bonn\n",
       "Puccini       1858.0     1924           Lucques\n",
       "Shostakovich  1906.0     1975  Saint-Petersburg\n",
       "Sibelius        10.0  unknown           unknown\n",
       "Haydn            NaN      NaN            Röhrau"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specific_sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each reader, there is a long list of options to specify how the file should be read. We can see all these options using the help (see below). Imagine that our tables contains a title and unnecessary rows: we can use the ```skiprows``` argument. Imagine you have dates in your table: you can use the ```date_parser``` argument to specify how to format them etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use shift+tab within the parenthesis to see optional arguemnts\n",
    "#pd.read_excel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Handling unknown values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, some information is missing. Some missing values are marked as \"unknown\" while other are NaN. NaN is the standard symbol for unknown/missing values and is understood by Pandas while \"unknown\" is just seen as text. This is impractical as now we have e.g. columns with a mix of numbers and text which will make later computations difficult. What we would like to do is to replace all \"irrelevant\" values with the standard NaN symbol that says \"no information\".\n",
    "\n",
    "Let's first do a regular import:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>10.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Röhrau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth    death              city\n",
       "composer                                       \n",
       "Mahler        1860.0     1911           Kaliste\n",
       "Beethoven     1770.0     1827              Bonn\n",
       "Puccini       1858.0     1924           Lucques\n",
       "Shostakovich  1906.0     1975  Saint-Petersburg\n",
       "Sibelius        10.0  unknown           unknown\n",
       "Haydn            NaN      NaN            Röhrau"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import1 = pd.read_excel('Data/composers.xlsx', index_col = 'composer',sheet_name='Sheet2')\n",
    "import1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look now at one column, we can see that columns have been imported in different ways. One column is an object, i.e. mixed types, the other contains floats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Sibelius          10.0\n",
       "Haydn              NaN\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import1.birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler             1911\n",
       "Beethoven          1827\n",
       "Puccini            1924\n",
       "Shostakovich       1975\n",
       "Sibelius        unknown\n",
       "Haydn               NaN\n",
       "Name: death, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import1.death"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to do calculations, for example getting summary information using ```describe()``` we have a problem: the ```death``` column is skipped because no calculation can be done with strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1480.800000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>823.674207</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1770.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1858.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1906.000000</td>\n",
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      ],
      "text/plain": [
       "             birth\n",
       "count     5.000000\n",
       "mean   1480.800000\n",
       "std     823.674207\n",
       "min      10.000000\n",
       "25%    1770.000000\n",
       "50%    1858.000000\n",
       "75%    1860.000000\n",
       "max    1906.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we specify that 'unknown' should be a NaN value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Röhrau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth   death              city\n",
       "composer                                      \n",
       "Mahler        1860.0  1911.0           Kaliste\n",
       "Beethoven     1770.0  1827.0              Bonn\n",
       "Puccini       1858.0  1924.0           Lucques\n",
       "Shostakovich  1906.0  1975.0  Saint-Petersburg\n",
       "Sibelius        10.0     NaN               NaN\n",
       "Haydn            NaN     NaN            Röhrau"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2 = pd.read_excel('Data/composers.xlsx', index_col = 'composer',\n",
    "                        sheet_name='Sheet2', na_values=['unknown'])\n",
    "import2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now computations are again possible, as Pandas knows how to deal with NaNs:"
   ]
  },
  {
   "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",
       "    .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>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1480.800000</td>\n",
       "      <td>1909.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>823.674207</td>\n",
       "      <td>61.396933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1827.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1770.000000</td>\n",
       "      <td>1890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1858.000000</td>\n",
       "      <td>1917.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1860.000000</td>\n",
       "      <td>1936.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1906.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             birth        death\n",
       "count     5.000000     4.000000\n",
       "mean   1480.800000  1909.250000\n",
       "std     823.674207    61.396933\n",
       "min      10.000000  1827.000000\n",
       "25%    1770.000000  1890.000000\n",
       "50%    1858.000000  1917.500000\n",
       "75%    1860.000000  1936.750000\n",
       "max    1906.000000  1975.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Handling bad or missing values is a very important part of data science**. Taking care of the most common occurrences at import is a good solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3 Column types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see above that the birth column has been \"classified\" as a float. However we know that this is not the case, it's just an integer. Here again, we can specify the column type already at import time using the dtype option and a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import2 = pd.read_excel('Data/composers.xlsx', index_col = 'composer',sheet_name='Sheet1', na_values=['unknown'],\n",
    "                       dtype={'composer':np.str,'birth':np.int32,'death':np.int32,'city':np.str})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860\n",
       "Beethoven       1770\n",
       "Puccini         1858\n",
       "Shostakovich    1906\n",
       "Name: birth, dtype: int32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2.birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.4 Modifications after import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course we don't have to do all these adjustement at import time. We can also do a default import and check what has to be corrected afterward."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4.1 Create NaNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we missed some bad values at import we can just replace all those directly in the dataframe. We can achieve that by using the ```replace()``` method and specifying what should be replaced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sibelius</th>\n",
       "      <td>10.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haydn</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Röhrau</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth    death              city\n",
       "composer                                       \n",
       "Mahler        1860.0     1911           Kaliste\n",
       "Beethoven     1770.0     1827              Bonn\n",
       "Puccini       1858.0     1924           Lucques\n",
       "Shostakovich  1906.0     1975  Saint-Petersburg\n",
       "Sibelius        10.0  unknown           unknown\n",
       "Haydn            NaN      NaN            Röhrau"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Sibelius          10.0\n",
       "Haydn              NaN\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_nans = import1.replace('unknown', np.nan)\n",
    "import_nans.birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when we fix \"bad\" values, e.g. here the \"unknown\" text value with NaNs, Pandas automatically adjust the type of the column, allowing us for exampel to later do mathemtical operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import1.death.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import_nans.death.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4.2 Changing the type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also change the type of a column on an existing Dataframe with the same command as in Numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860\n",
       "Beethoven       1770\n",
       "Puccini         1858\n",
       "Shostakovich    1906\n",
       "Name: birth, dtype: int32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2.birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2.birth.astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look again at import2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860\n",
       "Beethoven       1770\n",
       "Puccini         1858\n",
       "Shostakovich    1906\n",
       "Name: birth, dtype: int32"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2.birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see that we didn't actually change the type. Changes on a Dataframe are only effective if we reassign the column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import2.birth = import2.birth.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "composer\n",
       "Mahler          1860.0\n",
       "Beethoven       1770.0\n",
       "Puccini         1858.0\n",
       "Shostakovich    1906.0\n",
       "Name: birth, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import2.birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.5 Export\n",
    "\n",
    "You can easily export a Dataframe that you worked on. Most commonly you will export it in a common format like CSV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import2.to_csv('mydataframe.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have a complex dataframe that e.g. contains lists, you can save it as a *pickle* object, a specific Python format that allows one to save complex data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import2.to_pickle('Data/my_dataframe.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can reload this type of data via the pickle loading function of Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import3 = pd.read_pickle('Data/my_dataframe.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mahler</th>\n",
       "      <td>1860.0</td>\n",
       "      <td>1911</td>\n",
       "      <td>Kaliste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beethoven</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>1827</td>\n",
       "      <td>Bonn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puccini</th>\n",
       "      <td>1858.0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Lucques</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shostakovich</th>\n",
       "      <td>1906.0</td>\n",
       "      <td>1975</td>\n",
       "      <td>Saint-Petersburg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               birth  death              city\n",
       "composer                                     \n",
       "Mahler        1860.0   1911           Kaliste\n",
       "Beethoven     1770.0   1827              Bonn\n",
       "Puccini       1858.0   1924           Lucques\n",
       "Shostakovich  1906.0   1975  Saint-Petersburg"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.6 Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will learn more about plotting later, but let's see here some possibilities offered by Pandas. Pandas builds on top of Matplotlib but exploits the knowledge included in Dataframes to improve the default output. Let's see with a simple dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "composers = pd.read_excel('Data/composers.xlsx', sheet_name='Sheet5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass Series to Matplotlib which manages to understand them. Here's a default scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(composers.birth, composers.death, 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we look at the default Pandas output. Different types of plots are accessible when using the ```data_frame.plot``` function via the ```kind``` option. The variables to plot are column names passed as keywords instead of whole series like in Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers.plot(x = 'birth', y = 'death', kind = 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the plot automatically gets axis labels. Another gain is that some obvious options like setting a title are directly accesible when creating the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers.plot(x = 'birth', y = 'death', kind = 'scatter',\n",
    "               title = 'Composer birth and death', grid = True, fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can add even more information on the plot by using more arguments used in a similar way as a grammar of graphics. For example we can color the scatter plot by periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers.plot(x = 'birth', y = 'death',kind = 'scatter',\n",
    "               c = composers.period.astype('category').cat.codes, colormap = 'Reds', title = 'Composer birth and death', grid = True, fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you see already a limitation of the plotting library. To color dots by the peiod category, we had to turn the latter into a series of numbers. We could then rename those to improve the plot, but it's better to use more specialized packages such as Seaborn which allow to realize this kind of plot easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = composers, x = 'birth', y = 'death', hue = 'period')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some additional plotting options are available in the ```plot()``` module. For example histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers.plot.hist(alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you see again the gain from using Pandas: without specifying anything, Pandas made a histogram of the two columns containing numbers, labelled the axis and even added a legend to the plot.\n",
    "\n",
    "All these features are very nice and very helpful when exploring a dataset. When anaylzing data in depth and creating complex plots, Pandas's plotting might however be limiting and other options such as Seaborn or Plotnine can be used.\n",
    "\n",
    "Finally, all plots can be \"styled\" down to the smallest detail, either by using Matplotlib options or by directly applying a style e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "composers.plot.hist(alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}