{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie Chart\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import all the dependencies first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# primary data structure library\n",
    "import pandas as pd       \n",
    "\n",
    "# primary plotting library\n",
    "import matplotlib as mpl       \n",
    "\n",
    "# importing the pyplot layer of matplotlib for easy usage\n",
    "import matplotlib.pyplot as plt        \n",
    "\n",
    "# optional: for ggplot-like style of plots\n",
    "mpl.style.use(['ggplot'])        \n",
    "\n",
    "# using the inline backend\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: Immigration to Canada from 1980 to 2013 - International migration flows to and from selected countries - The 2015 revision from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration 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",
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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>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. \n",
    "\n",
    "Step 1: Gather data. \n",
    "\n",
    "We will use *pandas* `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
    "\n",
    "1. **Split:** Splitting the data into groups based on some criteria.\n",
    "2. **Apply:** Applying a function to each group independently:\n",
    "       .sum()\n",
    "       .count()\n",
    "       .mean() \n",
    "       .std() \n",
    "       .aggregate()\n",
    "       .apply()\n",
    "       .etc..\n",
    "3. **Combine:** Combining the results into a data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>1942</td>\n",
       "      <td>1839</td>\n",
       "      <td>1675</td>\n",
       "      <td>1018</td>\n",
       "      <td>878</td>\n",
       "      <td>920</td>\n",
       "      <td>904</td>\n",
       "      <td>1200</td>\n",
       "      <td>1181</td>\n",
       "      <td>1539</td>\n",
       "      <td>...</td>\n",
       "      <td>1585</td>\n",
       "      <td>1473</td>\n",
       "      <td>1693</td>\n",
       "      <td>1834</td>\n",
       "      <td>1860</td>\n",
       "      <td>1834</td>\n",
       "      <td>1548</td>\n",
       "      <td>1679</td>\n",
       "      <td>1775</td>\n",
       "      <td>55174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "Oceania                           1942   1839   1675   1018    878    920   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "Oceania                            904   1200   1181   1539  ...    1585   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "Oceania                            1473    1693    1834    1860    1834   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "Oceania                            1548    1679    1775    55174  \n",
       "\n",
       "[6 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "- `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this looks pretty nice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thanks for reading :)\n",
    "Created by [Tarun Kamboj](https://www.linkedin.com/in/kambojtarun/)."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}