DV0101EN-2-3-1-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py-v2.0.ipynb

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    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Pie Charts, Box Plots, Scatter Plots, and Bubble Plots\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Explore Matplotlib library further\n",
    "*   Create pie charts, box plots, scatter plots and bubble charts\n"
   ]
  },
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1.  [Exploring Datasets with *p*andas](#0)<br>\n",
    "2.  [Downloading and Prepping Data](#2)<br>\n",
    "3.  [Visualizing Data using Matplotlib](#4) <br>\n",
    "4.  [Pie Charts](#6) <br>\n",
    "5.  [Box Plots](#8) <br>\n",
    "6.  [Scatter Plots](#10) <br>\n",
    "7.  [Bubble Plots](#12) <br>\n",
    "\n",
    "</div>\n"
   ]
  },
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   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and [**Numpy**](http://www.numpy.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) 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.\n"
   ]
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Import primary modules.\n"
   ]
  },
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   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
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   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas*'s `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires reading in Excel files. This module was **openpyxl** (formerlly **xlrd**). For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **openpyxl** module:\n",
    "\n",
    "```\n",
    "! pip3 install openpyxl\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Collecting openpyxl\n",
      "  Downloading openpyxl-3.0.9-py2.py3-none-any.whl (242 kB)\n",
      "     |████████████████████████████████| 242 kB 10.6 MB/s            \n",
      "\u001b[?25hCollecting et-xmlfile\n",
      "  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\n",
      "Installing collected packages: et-xmlfile, openpyxl\n",
      "Successfully installed et-xmlfile-1.1.0 openpyxl-3.0.9\n"
     ]
    }
   ],
   "source": [
    "! pip3 install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel(\n",
    "    'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
    "    sheet_name='Canada by Citizenship',\n",
    "    skiprows=range(20),\n",
    "    skipfooter=2\n",
    "    )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
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   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
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       "\n",
       "    .dataframe thead th {\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>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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "         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]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
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   },
   "source": [
    "Let's find out how many entries there are in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:14: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  \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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.5.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circular graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
    "\n",
    "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"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "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.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "jupyter": {
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    },
    "new_sheet": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\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",
       "\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",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 13,
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "\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).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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    "jupyter": {
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   "outputs": [
    {
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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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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 Caribbean) by passing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "Africa                              38543\n",
       "Asia                               155075\n",
       "Europe                              28691\n",
       "Latin America and the Caribbean     24950\n",
       "Northern America                     8503\n",
       "Oceania                              1775\n",
       "Name: 2013, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_con_2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "explode_li = [0.,0.,0.,0.1,0.1,0.1]\n",
    "s_con_2013 = df_can.groupby('Continent', axis=0).sum()['2013']\n",
    "s_con_2013\n",
    "\n",
    "s_con_2013.plot(kind='pie',\n",
    "                figsize=(15,8),\n",
    "                startangle=90,\n",
    "                shadow=True,\n",
    "                labels=None,\n",
    "                autopct='%1.1f%%',\n",
    "                pctdistance=1.1,\n",
    "                explode=explode_li,\n",
    "                )\n",
    "\n",
    "plt.title('Number of immigration from continents in 2013')\n",
    "plt.axis('equal')\n",
    "plt.legend(labels=s_con_2013.index,loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The correct answer is:\n",
    "explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['2013'].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 pie center and start of text label\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 in 2013', y=1.12) \n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "    df_continents['2013'].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 pie center and start of text label\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 in 2013', y=1.12) \n",
    "    plt.axis('equal') \n",
    "\n",
    "    # add legend\n",
    "    plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions:\n",
    "\n",
    "*   **Minimum:** The smallest number in the dataset excluding the outliers.\n",
    "*   **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "*   **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "*   **Third quartile:** Middle number between `median` and `maximum`.\n",
    "*   **Maximum:** The largest number in the dataset excluding the outliers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/boxplot_complete.png\" width=440, align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `boxplot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the subset of the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Republic of Korea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "      <td>1011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "      <td>1456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "      <td>1081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan  Republic of Korea\n",
       "1980       701               1011\n",
       "1981       756               1456\n",
       "1982       598               1572\n",
       "1983       309               1081\n",
       "1984       246                847"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DJ's test\n",
    "df_twocon = df_can.loc[['Japan', 'Republic of Korea'], years].transpose()\n",
    "df_twocon.head()\n",
    "\n",
    "df_twocon.plot(kind='box', figsize=(10,10))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "df_twocon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "\n",
    "1.  The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2.  25% of the years for period 1980 - 2013 had an annual immigrant count of \\~500 or fewer (First quartile).\n",
    "3.  75% of the years for period 1980 - 2013 had an annual immigrant count of \\~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyze these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PP7nc36BBg4y/v785efKkc57MzEzn0ZzSev75502dOnUKTS/NPiQ2NtY0bNjQ5ajM008/bcLDw122w9TUVCPJJCYmOp+Dkp7f+++/39xyyy0u88yfP7/II2Mff/yxc55Dhw4ZSc6jY6Xdli51ZKyo8ZVGcUfGcnNzTb169UyvXr3MkSNHTG5urnnrrbeMw+EwVapUcc6Xn59vpkyZYtzd3Y2Hh4eRZEaMGGHy8/Od8zRs2NBMmjTJZf3Z2dlGkvnwww9LPdYC27dvN9WqVTOzZ892TnvxxRdNzZo1C817zz33mF69ehWaXtLfs88++8z4+voaNzc3U716dfPZZ5+VeZxldc0fGWvbtq1SU1OVmpqqlJQUPfvssxo0aJBWrFghSdq+fbskKSYmxmW5mJgYpaWlOW8PGjRIvXv31vPPP6/nn39ebdq0KXRf7du3d7ndsWNH5/ovlpaWpqCgIDVt2tQ5rWrVqmrbtq3L/RZl/PjxGj58uDp37qzJkycrJSXlkvOnpaUVenyxsbEyxhQ7vqKY/3tHXNrPhERFRbncDg8P15EjRy65zI033qiqVasWu8ypU6c0ceJENWvWTIGBgfLz89OXX36pn3766ZLr/eSTTxQTE6NatWrJz89PAwYMUE5Ojg4fPuycx+Fw6KabbnK5b0nO+9++fbvatGnjcvTvlltuueT9Fqei7mv16tXq0aOHateurWrVqjmXKen5uVh4eLjLO9OwsDCFhYUpJCTEZdrRo0ddlrvxxhtdPrMTFhamxo0bu7ymRS1X4MSJEzp48KDatWvnMv3i3y1JqlGjhm644QaXaceOHdMjjzyiJk2aqHr16vLz81NaWlqZH39aWpqaNm3q8hzUqFFDjRs3dvn9LOl1LE5J23lpORwORUZGOm8XfO6tRYsWLtMyMjJcPlAdEhLi8o3KRo0aKTg42Lk/SEpK0syZM52fJ/Tz83Puqy78zM+NN97o8nnDgIAADR8+XD169FDPnj01depU/fDDD2V+XAVKsw9p1aqVy1GZtLQ0tWvXzmU7vOmmm+Tv71/ivvVC27dvL9V2ePE4w8LC5O7u7hxnabel35KHh4eWLl2qo0ePqkaNGvLy8tKbb76pAQMGuHzu8p///Kfmz5+vt956SykpKfroo4/01Vdf6emnn77k+gv+RpT184O7du1S9+7ddd999+lPf/pTqZa5nM8o3nrrrUpNTdU333yjESNG6MEHHyzx7+iVuuZjzNvbWxEREYqIiFBUVJT+/Oc/KyYmRi+++OIllzPGuLzI2dnZSklJkbu7u/OUR0nMBYfzi1LURnTx/RblmWee0c6dO9W3b19t27ZN7dq1K/UvR2mnF6Vx48aSVOodyMUfpHU4HMrPzy/zMhc+j0888YQWL16sZ599Vl9//bVSU1PVq1evSx7u37Rpk+69917FxMRo6dKlSklJ0euvvy5JLsu5ubm57IgKnpuCMRf12lzuh5Ur4r7279+vXr16qW7dunr//feVnJysZcuWSVKpT4cU8PT0LHTfRU27+PW83OUKlCX4fX19C00bPHiw1q5dq5dffllr165VamqqoqKiyvz4ixvDxa9LSa9jcUrazt3c3ArtP3Jzcwutp7j7v/A5L5hW0v7owp/n5+drwoQJzjeyBf/t2rVLPXv2dM5X1GuwYMECbd68WbfddpsSExPVvHlz54e9y6o0+5CixlDS/q60z29pf7+L+tLAheMsaVu6MCYvNZ7ydNNNNykpKUknTpzQf//7X23YsEFnz551+WLb448/rscee0wDBw5UZGSk7rnnHv31r3/Vyy+/7DzVWbNmTZc3tZKct8vypZht27YpJiZGd9xxR6HtpWbNmkpPT3d5QyGdf9NzOV+88fX1VUREhFq3bq2XX37Z+b8V6ZqPsaJ4eHjo1KlTks5/s0KSy3ly6fzXbQt+JkmjRo2Su7u7EhIStHjxYr3//vuF1nvx5QE2btyoG2+8scgxNGvWTOnp6S5Hps6ePatvv/3Web8Fv+AXb4CSVL9+fT3yyCP65z//qSlTprh866So+0pMTHSZlpiYKIfD4XJkriQ33XSTIiMj9be//a3Iz8RkZ2eX+FmZK7VmzRoNGDBA/fr100033aT69euXGMfr1q1TcHCwXnjhBbVt21aNGjW6rOsCNWvWTJs2bXJ5PSrqulSXc19JSUk6ffq0Zs6cqY4dO6px48aXdbTFJn9/f9WqVUsbN250mV7aS2+sWbNGjzzyiOLi4hQZGamaNWtqz549LvNUqVKlyN+pCzVr1kxpaWlKT093Tjty5Ih27tzpsl+oKKGhoTp69KjLOMvznfuxY8dcLguwc+dOZWRkOPdX0dHRSktLc76RvfC/0nzztnnz5ho3bpy++uorDRs2rNDnay9UmtejLJo1a6aNGze6BPiWLVt0/Phx52tXmue3adOml70dXjyekral0NBQ5eXlufy+XjyeS/09uBLVqlVTjRo1lJGRoRUrVuiPf/yj82e//vproVB0d3eXMcYZsx07dtS///1vl/hcvny5fHx81LJly1KNISkpSbGxserbt6/mz59fKF47duyo3NxcJSQkOKf98ssv2rRp02WfnbhQfn5+oUtplLdrPsYKTkUdPnxYP/74o+bNm6cVK1borrvukiQ1aNBA9957rx555BGtWLFCO3bs0GOPPaZt27bpiSeekCQtXrxYH330kd5//33FxMTor3/9q0aOHKm9e/e63NfChQv13nvvaefOnXr22We1cePGYj/w2aVLF7Vp00b333+/1q9fr23btunBBx/UmTNnNGrUKElSnTp15Obmpi+//FJHjx7V8ePHlZ2drf/5n/9RQkKC9u7dq++++07Lly+/ZFQ98cQTSklJ0bhx47Rjxw4tX75co0eP1oABAwqd5rkUh8OhRYsW6cCBA2rbtq3i4+O1a9cu7dixQ2+88YZatGih7OzsUq/vcjRu3Fiffvqpvv32W23fvl0PPfSQDh48WOIyx44d08KFC7Vnzx698847mjdvXpnve9SoUTp27Jgeeugh/ec//9GqVav01FNPXe5DKff7atiwoRwOh6ZNm6a9e/cqPj6+yA++V3aPP/64Zs6cqSVLlmjXrl2aOXOm/vWvf5XqKEXjxo21ZMkSff/990pNTVX//v0L/fGqV6+e1q9fr/379ys9Pb3Io1j333+/QkJC1K9fP6WkpGjz5s267777FB4ern79+pXbYy3OrbfeqlOnTumZZ57Rjz/+qI8++khz584tt/X7+PhoyJAh2rx5s5KTkzVo0CBFRkY6L747ZcoUffrppxo7dqxSU1P1448/avny5Ro2bJhOnz5d7Hp3796tCRMmaN26dfrpp5+0ceNGrV279pL7p3r16unw4cPauHGj0tPTnW+UL9ef/vQnnThxQoMHD9a2bdu0bt06DRw4ULfccovzQ9qleX7HjRun9evX69lnn9XOnTu1bNkyTZs2TVLZjoiXZltq06aNqlWrpokTJ2rXrl1avnx5od/dov4eFOfw4cNKTU11vtnfvXu3UlNTXY5gffzxx1q1apX27t2r5cuXq3PnzgoPD3f+3ZOkPn366NVXX9XSpUu1b98+rVixQk8//bR69uzpvJzEqFGjdPz4cY0YMUJpaWlatmyZnnnmGY0ePbrIo5YXW7Nmjbp27arevXtr0qRJOnLkiPNvdoFGjRqpd+/eGjVqlBITE5Wamqr777+/0O/j/v37lZqaqt27d0s6f6o5NTVVmZmZks5H8HPPPadNmzbpp59+0pYtWzRx4kStWrVKDz74YIljvSIV/qm0SmzQoEEuXwX29vY2TZs2Na+88orLBz6PHz/uvLRFlSpVXC5tsWvXLlOtWjUza9Ys5/z5+fnm9ttvN23atDE5OTkul7aIjY01VatWNXXq1Cl0yQWVcGmLmJgY56UtCvztb38ztWrVMm5ubiY2NtacPn3a9O/f39StW9dUrVrVhISEmL59+5r9+/df8rm48NIWwcHB5uGHH3b52nFZvgq8d+9e89BDD5k6deoYT09PU7NmTdOjRw/z6aefOj/YWadOHfP888+7LDds2DCXr2MXd2mLC7377rvmws14//79pnv37sbHx8eEhYWZZ5991gwdOrTEr3k//fTTJjQ01Pj4+JiePXua9957r8jLBFzo4g/QGnP+ixfNmzc3VapUMc2aNTOrVq267EtblMd9XXx7zpw55vrrrzdeXl6mY8eO5quvvirxA9XFXdriQkV9yPqll14y4eHhzttFvX4Xv+bGGDNy5EjTsWPHYpfLy8szEydONEFBQc5LW7z44ovGz8/vkmM0xpitW7ea9u3bGy8vL1OnTh0zd+5c07VrVzNo0CDnPElJSebmm282Xl5eJV7aomfPns4vAN1xxx1luhxBcUqznRtjzMKFC029evWMl5eXuf32283//u//lrjNFrWeguUKvgxS8Ny9++67pk6dOqZKlSrm1ltvNbt373ZZbs2aNaZr167Gz8/PeQmdxx57zLmeoh7HwYMHzV133WXCw8NNlSpVTM2aNc3w4cNdLoFysZycHNO/f38TEBBQ6NIWJe1DYmNjzbBhwwqt88JLW/j7+xe6tEVpnl9j/v+lLapUqWLatWvn/BJTwRckittvuru7u3zJqqRtyRhjPv/8c9OkSRPj5eVlOnToYJYvX15oW7r470Fxirpc0YXPrTHn9xU33HCD8fT0NGFhYebhhx92+ZKBMec/iD9+/HjnJVBq165tRo0aZTIyMgo93+3btzdVq1Y1NWrUMBMnTnS5BMmlXPx3+sL/LnTixAkzbNgwExAQYLy9vU2PHj0KPYfFravgtcjMzDRxcXGmZs2axtPT09SoUcN069bN5fI3FcVhTBkuiY7Lsm/fPtWrV09r164tl0OmAFwNHTpUW7Zs0ebNm20PBdewd955R0OGDFFGRkapL24NSFyBH8BV5uDBg1q6dKluvfVWubu767PPPtM777zjvM4f8Ft59dVXdeuttyowMFBJSUmaMGGC7r33XkIMZUaMAbiquLu766OPPtIzzzyjM2fOKCIiQvPnz9eIESNsDw3XmK1bt2ratGnKzMxU7dq19cADD+gvf/mL7WHhKsRpSgAAAIuu+W9TAgAA2ESMAQAAWESMAQAAWHRVf4C/pIt5AgWCg4NdrnANAOWBfQtKq1atWsX+jCNjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFnnYHgBwufJGxJV63iNlWK/7gmVlHwwAAJeJGMNVqyzRlDcijsgCAFRKnKYEAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwyKO0M+bn52vixIkKDAzUxIkTlZ2drRkzZujYsWMKCQnR2LFj5efnJ0launSpEhIS5ObmpiFDhigqKkqStGfPHs2dO1c5OTlq2bKlhgwZIofDodzcXM2ZM0d79uxRtWrVNGbMGIWGhlbIAwYAAKhMSn1k7Msvv1R4eLjzdnx8vCIjIzVr1ixFRkYqPj5eknTgwAFt2LBB06dP11NPPaWFCxcqPz9fkrRgwQKNHDlSs2bN0uHDh5WamipJSkhIkK+vr2bPnq077rhDS5YsKb9HCAAAUImVKsYyMjKUkpKirl27OqclJSUpNjZWkhQbG6ukpCTn9A4dOsjT01OhoaEKCwvT7t27lZWVpdOnT6tRo0ZyOByKiYlxLpOcnKzOnTtLktq1a6dt27bJGFOejxMAAKBSKlWMLVq0SA888IAcDodz2vHjxxUQECBJCggI0IkTJyRJmZmZCgoKcs4XGBiozMzMQtODgoKUmZlZaBl3d3f5+Pjo5MmTV/jQAAAAKr8SPzO2efNm+fv7q379+kpLSytxhcUd0brUka6ifnZh+BVYuXKlVq5cKUmaOnWqgoODSxwPIElHJLYXAOXOw8ODfQuuWIkx9sMPPyg5OVnfffedcnJydPr0ac2aNUv+/v7KyspSQECAsrKydN1110k6f8QrIyPDuXxmZqYCAwMLTc/IyFBgYKDLMkFBQcrLy9OpU6ecXwa4ULdu3dStWzfn7fT09Mt/5LjmsL0AKG/BwcHsW1AqtWrVKvZnJZ6mvP/++/X6669r7ty5GjNmjJo3b65HH31U0dHRSkxMlCQlJiaqdevWkqTo6Ght2LBBubm5Onr0qA4dOqSIiAgFBATI29tbO3fulDFGa9asUXR0tCSpVatWWr16tSTpm2++UbNmzYo8MgYAAPB7U+pLW1ysT58+mjFjhhISEhQcHKxx48ZJkmrXrq327dtr3LhxcnNz07Bhw+Tmdr75hg8frnnz5iknJ0dRUVFq2bKlJKlLly6aM2eORo8eLT8/P40ZM+bKHxkAAMBVwGGu4q8tHjx40PYQcJXIGxEn9wXLbA8DwO8MpylRWld0mhIAAAAVhxgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwyMP2AAAAqEzyRsSVet4jZViv+4JlZR8MrgnEGAAAFyhLNOWNiCOycMU4TQkAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGARMQYAAGCRh+0BABfLe+x+6VR2+a93RFz5rtDHT+6vvVe+6wQAXHOIMVQ+p7LlvmBZua4yODhY6enp5brOco87AMA1idOUAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFhFjAAAAFnmUNENOTo6ee+45nTt3Tnl5eWrXrp369u2r7OxszZgxQ8eOHVNISIjGjh0rPz8/SdLSpUuVkJAgNzc3DRkyRFFRUZKkPXv2aO7cucrJyVHLli01ZMgQORwO5ebmas6cOdqzZ4+qVaumMWPGKDQ0tEIfOAAAQGVQ4pExT09PPffcc3rllVf08ssvKzU1VTt37lR8fLwiIyM1a9YsRUZGKj4+XpJ04MABbdiwQdOnT9dTTz2lhQsXKj8/X5K0YMECjRw5UrNmzdLhw4eVmpoqSUpISJCvr69mz56tO+64Q0uWLKmwBwwAAFCZlBhjDodDXl5ekqS8vDzl5eXJ4XAoKSlJsbGxkqTY2FglJSVJkpKSktShQwd5enoqNDRUYWFh2r17t7KysnT69Gk1atRIDodDMTExzmWSk5PVuXNnSVK7du20bds2GWMq4vECAABUKiWeppSk/Px8TZgwQYcPH1aPHj3UsGFDHT9+XAEBAZKkgIAAnThxQpKUmZmphg0bOpcNDAxUZmam3N3dFRQU5JweFBSkzMxM5zIFP3N3d5ePj49Onjyp6667rnweJQAAQCVVqhhzc3PTK6+8ol9//VWvvvqq9u/fX+y8xR3RutSRrqJ+5nA4Ck1buXKlVq5cKUmaOnWqgoODSxo6rkJHpHJ/bT08PMp9nRUxTgBXF/YDKA+lirECvr6+atq0qVJTU+Xv76+srCwFBAQoKyvLeRQrKChIGRkZzmUyMzMVGBhYaHpGRoYCAwNdlgkKClJeXp5OnTrl/DLAhbp166Zu3bo5b6enp5ft0eKqUd6vbXBwcIVsL2yDANgPoDRq1apV7M9K/MzYiRMn9Ouvv0o6/83K77//XuHh4YqOjlZiYqIkKTExUa1bt5YkRUdHa8OGDcrNzdXRo0d16NAhRUREKCAgQN7e3tq5c6eMMVqzZo2io6MlSa1atdLq1aslSd98842aNWtW5JExAACA35sSj4xlZWVp7ty5ys/PlzFG7du3V6tWrdSoUSPNmDFDCQkJCg4O1rhx4yRJtWvXVvv27TVu3Di5ublp2LBhcnM733zDhw/XvHnzlJOTo6ioKLVs2VKS1KVLF82ZM0ejR4+Wn5+fxowZU3GPGAAAoBJxmKv4a4sHDx60PQRUgLwRcXJfsKxc11kRpykrYpwAri7sB1BaV3SaEgAAABWHGAMAALCIGAMAALCIGAMAALCIGAMAALCIGAMAALCIGAMAALCIGAMAALCIGAMAALCIGAMAALCIGAMAALCoxH8oHACA34O8x+6XTmWX/3pHxJXvCn385P7ae+W7TlRqxBgA4NpwKrvc/1Hv4OBgpaenl+s6yz3uUOlxmhIAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAij5JmSE9P19y5c/XLL7/I4XCoW7du6tWrl7KzszVjxgwdO3ZMISEhGjt2rPz8/CRJS5cuVUJCgtzc3DRkyBBFRUVJkvbs2aO5c+cqJydHLVu21JAhQ+RwOJSbm6s5c+Zoz549qlatmsaMGaPQ0NAKfeAAAACVQYkx5u7uroEDB6p+/fo6ffq0Jk6cqBYtWmj16tWKjIxUnz59FB8fr/j4eD3wwAM6cOCANmzYoOnTpysrK0vPP/+8XnvtNbm5uWnBggUaOXKkGjZsqJdeekmpqalq2bKlEhIS5Ovrq9mzZ2v9+vVasmSJxo4d+1s8fgDANeLLbu9IH/xSzmst7/VJ6vaO/lD+a0UlVmKMBQQEKCAgQJLk7e2t8PBwZWZmKikpSZMnT5YkxcbGavLkyXrggQeUlJSkDh06yNPTU6GhoQoLC9Pu3bsVEhKi06dPq1GjRpKkmJgYJSUlqWXLlkpOTta9994rSWrXrp3+8Y9/yBgjh8NRQQ8bAHCt6bXyQbkvWFau6wwODlZ6enq5rjNvRJzUr3zHicqtTJ8ZO3r0qPbu3auIiAgdP37cGWkBAQE6ceKEJCkzM1NBQUHOZQIDA5WZmVloelBQkDIzMwst4+7uLh8fH508efLKHhkAAMBVoMQjYwXOnDmjadOmafDgwfLx8Sl2PmNMmaYX97OijoqtXLlSK1eulCRNnTpVwcHBJQ0bV6G3rqJTCUPYBoGrxhGp3P9ueHh4lPs6K2KcqNxKFWPnzp3TtGnT1KlTJ7Vt21aS5O/vr6ysLAUEBCgrK0vXXXedpPNHvDIyMpzLZmZmKjAwsND0jIwMBQYGuiwTFBSkvLw8nTp1yvllgAt169ZN3bp1c94u70PDqByuplMJ6ZxKAK4q5b0fqIh9i8Tft9+jWrVqFfuzEk9TGmP0+uuvKzw8XHfeeadzenR0tBITEyVJiYmJat26tXP6hg0blJubq6NHj+rQoUOKiIhQQECAvL29tXPnThljtGbNGkVHR0uSWrVqpdWrV0uSvvnmGzVr1ozPiwEAgGtCiUfGfvjhB61Zs0Y33HCDnnjiCUlS//791adPH82YMUMJCQkKDg7WuHHjJEm1a9dW+/btNW7cOLm5uWnYsGFyczvffMOHD9e8efOUk5OjqKgotWzZUpLUpUsXzZkzR6NHj5afn5/GjBlTQQ8XAACgcnGYS32Yq5I7ePCg7SGgAuSNiLtqTlOW9zgBVBz2LbDpik5TAgAAoOIQYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABYRYwAAABZ52B4AAAC/lbwRceW6viPlurb/4+NXEWtFJUaMAQCuCe4LlpX7OvNGxFXIenFt4TQlAACARcQYAACARSWeppw3b55SUlLk7++vadOmSZKys7M1Y8YMHTt2TCEhIRo7dqz8/M6f4166dKkSEhLk5uamIUOGKCoqSpK0Z88ezZ07Vzk5OWrZsqWGDBkih8Oh3NxczZkzR3v27FG1atU0ZswYhYaGVtwjBgAAqERKPDLWuXNnPfnkky7T4uPjFRkZqVmzZikyMlLx8fGSpAMHDmjDhg2aPn26nnrqKS1cuFD5+fmSpAULFmjkyJGaNWuWDh8+rNTUVElSQkKCfH19NXv2bN1xxx1asmRJ+T5CAACASqzEGGvatKnzqFeBpKQkxcbGSpJiY2OVlJTknN6hQwd5enoqNDRUYWFh2r17t7KysnT69Gk1atRIDodDMTExzmWSk5PVuXNnSVK7du20bds2GWPK8zECAABUWpf1mbHjx48rICBAkhQQEKATJ05IkjIzMxUUFOScLzAwUJmZmYWmBwUFKTMzs9Ay7u7u8vHx0cmTJy/v0QAAAFxlyvXSFsUd0brUka6ifuZwOIqcd+XKlVq5cqUkaerUqQoODr6MUaKyOyKV+2vr4eFR7uusiHECuLqwH0B5uKwY8/f3V1ZWlgICApSVlaXrrrtO0vkjXhkZGc75MjMzFRgYWGh6RkaGAgMDXZYJCgpSXl6eTp06Vei0aIFu3bqpW7duztvp6emXM3xcBcr7tQ0ODq6Q7YVtEAD7AZRGrVq1iv3ZZZ2mjI6OVmJioiQpMTFRrVu3dk7fsGGDcnNzdfToUR06dEgREREKCAiQt7e3du7cKWOM1qxZo+joaElSq1attHr1aknSN998o2bNmhV7ZAwAAOD3psQjYzNnztT27dt18uRJPfzww+rbt6/69OmjGTNmKCEhQcHBwRo3bpwkqXbt2mrfvr3GjRsnNzc3DRs2TG5u53tv+PDhmjdvnnJychQVFaWWLVtKkrp06aI5c+Zo9OjR8vPz05gxYyru0QIAAFQyDnMVf3Xx4MGDtoeAClAR/7xIRZym5J9BAcB+AKVV7qcpAQAAUD6IMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIuIMQAAAIs8bA8AKEreiLhyXd+Rcl3b//Hxq4i1AgCuMcQYKh33BcvKfZ15I+IqZL0AAFwpTlMCAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABYRIwBAABY5GF7AAAAVCZ5I+IqZH73BcsuZzi4BhBjAABcoCzRFBwcrPT09AocDa4FnKYEAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwiBgDAACwyMP2AIDLlTcirkLmd1+w7HKGAwDAZSHGcNUqSzQFBwcrPT29AkcDAMDl4TQlAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARcQYAACARZXmHwpPTU3VW2+9pfz8fHXt2lV9+vSxPSQAAIAKVymOjOXn52vhwoV68sknNWPGDK1fv14HDhywPSwAAIAKVylibPfu3QoLC1ONGjXk4eGhDh06KCkpyfawAAAAKlyliLHMzEwFBQU5bwcFBSkzM9PiiAAAAH4bleIzY8aYQtMcDkehaStXrtTKlSslSVOnTlVwcHCFjw2/Dx4eHmwvAMod+xaUh0oRY0FBQcrIyHDezsjIUEBAQKH5unXrpm7dujlvp6en/ybjw9UvODiY7QVAuWPfgtKqVatWsT+rFKcpGzRooEOHDuno0aM6d+6cNmzYoOjoaNvDAgAAqHCV4siYu7u7hg4dqhdffFH5+fm69dZbVbt2bdvDAgAAqHCVIsYk6eabb9bNN99sexgAAAC/qUpxmhIAAOBaRYwBAABY5DBFXVcCAAAAvwmOjOGaMHHiRNtDAPA7xL4F5YEYAwAAsIgYAwAAsIgYwzXhwn+5AQDKC/sWlAc+wA8AAGARR8YAAAAsIsZw1fnll180c+ZMjR49WmPHjtVLL72klStXaurUqUXO//rrr+vAgQO/8SgBXI0GDhxYpvnT0tKc+57k5GTFx8dXwKjwe1dp/jkkoDSMMXrllVcUGxurMWPGSJL27dun5OTkYpd5+OGHf6PRAbiWRUdHKzo62vYwcBUixnBVSUtLk4eHh7p37+6cVrduXf3666/atm2bpk2bpp9//ln169fX6NGj5XA4NHnyZA0cOFANGjTQwIED1atXL6WkpKhKlSp64oknVL16dSUnJ+uTTz7RuXPnVK1aNY0ePVrVq1e390ABWJWWlqaPPvpI1apVK7RPSU1N1aJFi1StWjXVq1fPuczq1av1448/atiwYexTUCacpsRVZf/+/S47vwvt3btXgwcP1vTp03XkyBH98MMPheY5e/asGjZsqFdeeUU33nijVq1aJUlq0qSJXnzxRb388svq0KGDli1bVqGPA0DlV9Q+JScnR2+88YYmTJigKVOm6JdffilyWfYpKAuOjOF3IyIiQkFBQZLOHy07evSomjRp4jKPh4eHWrVqJUmqX7++tm7dKknKzMzUzJkzlZWVpXPnzik0NPS3HTyASqeofYqXl5dCQ0NVs2ZNSVJMTIxWrlxZaFn2KSgLYgxXldq1a2vTpk1F/szT09P5/93c3JSfn19oHnd3dzkcDuc8eXl5kqR//OMfuvPOOxUdHe08PQHg2laafUpx2KegLDhNiatK8+bNlZub6/JOdPfu3dq+ffsVrffUqVMKDAyUJCUmJl7RugD8ftWqVUtHjx7V4cOHJUnr1q0rcj72KSgLjozhquJwODR+/HgtWrRIn376qTw9PRUSEqLWrVtf0XrvvfdeTZ8+XYGBgWrYsKGOHj1aTiMG8HtSpUoVjRw5UlOnTlW1atXUpEkT/fzzz4XmY5+CsuAK/AAAABZxmhIAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMAiYgwAAMCi/weJnri4z4lFkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI = df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(10,10))\n",
    "\n",
    "plt.title('Boxplots of China and India immigration numbers throughout 1980˜2013')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "    df_CI.head()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentiles associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "    plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (\\~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**.\n",
    "\n",
    "Typical syntax is : <br>\n",
    "\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "\n",
    "Where\n",
    "\n",
    "*   `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,\n",
    "*   `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` \\* `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Tip regarding subplot convention**\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that a 3-digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced.\n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</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>...</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>Country</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>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\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>...</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>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "# df_top15 = ...\n",
    "# df_top15\n",
    "\n",
    "df_top15 = df_can.sort_values(by='Total', axis=0, ascending=False).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "\n",
    "1.  Create a list of all years in decades 80's, 90's, and 00's.\n",
    "2.  Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "3.  Merge the three series into a new data frame. Call your dataframe **new_df**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "years_80 = list(map(str, range(1980, 1990)))\n",
    "years_90 = list(map(str, range(1990, 2000)))\n",
    "years_00 = list(map(str, range(2000, 2010)))\n",
    "\n",
    "df_top15['80s_total'] = df_top15[years_80].sum(axis=1)\n",
    "df_top15['90s_total'] = df_top15[years_90].sum(axis=1)\n",
    "df_top15['00s_total'] = df_top15[years_00].sum(axis=1)\n",
    "\n",
    "df_top15.head()\n",
    "\n",
    "# merge the three columns only\n",
    "df_decades = df_top15.loc[:, '80s_total':]\n",
    "df_decades\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     1980s   1990s   2000s\n",
       "Country                                                                   \n",
       "India                                                82154  180395  303591\n",
       "China                                                32003  161528  340385\n",
       "United Kingdom of Great Britain and Northern Ir...  179171  261966   83413\n",
       "Philippines                                          60764  138482  172904\n",
       "Pakistan                                             10591   65302  127598"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "#The correct answer is:\n",
    "\n",
    "# create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "# slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "# merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "# display dataframe\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # create a list of all years in decades 80's, 90's, and 00's\n",
    "    years_80s = list(map(str, range(1980, 1990))) \n",
    "    years_90s = list(map(str, range(1990, 2000))) \n",
    "    years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "    # slice the original dataframe df_can to create a series for each decade\n",
    "    df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "    df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "    df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "    # merge the three series into a new data frame\n",
    "    new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "    # display dataframe\n",
    "    new_df.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>80s_total</th>\n",
       "      <th>90s_total</th>\n",
       "      <th>00s_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           80s_total      90s_total      00s_total\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_decades.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_decades.plot(kind='box', figsize=(10,10))\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "    plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "\n",
    "*   larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "*   smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "\n",
    "*   Q1 (25%) = 36,101.5 <br>\n",
    "*   Q3 (75%) = 105,505.5 <br>\n",
    "*   IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 \\* 69,404) <br>\n",
    "Outlier > 209,611.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
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    {
     "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>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>Country</th>\n",
       "      <th>80s_total</th>\n",
       "      <th>90s_total</th>\n",
       "      <th>00s_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   level_0  index Country  80s_total  90s_total  00s_total\n",
       "0        0      0   India      82154     180395     303591\n",
       "1        1      1   China      32003     161528     340385"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "df_decades = df_decades.reset_index()\n",
    "df_decades[df_decades['00s_total'] > 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df=new_df.reset_index()\n",
    "    new_df[new_df['2000s']> 209611.5]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The correct answer is:\n",
    "new_df[new_df['2000s']> 209611.5]\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5.\n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#matplotlib.pyplot.boxplot) on box plots for more information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the data points are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>year</th>\n",
       "      <th>tot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1985</td>\n",
       "      <td>69978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986</td>\n",
       "      <td>86048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>134771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988</td>\n",
       "      <td>139306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1989</td>\n",
       "      <td>164432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1990</td>\n",
       "      <td>188054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1991</td>\n",
       "      <td>207509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992</td>\n",
       "      <td>221687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1993</td>\n",
       "      <td>222049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1994</td>\n",
       "      <td>193665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995</td>\n",
       "      <td>187712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1996</td>\n",
       "      <td>200085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1997</td>\n",
       "      <td>192885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1998</td>\n",
       "      <td>160727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>179818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000</td>\n",
       "      <td>216712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>242643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>223111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2003</td>\n",
       "      <td>217297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004</td>\n",
       "      <td>232083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005</td>\n",
       "      <td>257457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2006</td>\n",
       "      <td>247057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2007</td>\n",
       "      <td>232405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>243047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009</td>\n",
       "      <td>248768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>276956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2011</td>\n",
       "      <td>246194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012</td>\n",
       "      <td>256222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>257537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year     tot\n",
       "0   1980   99137\n",
       "1   1981  110563\n",
       "2   1982  104271\n",
       "3   1983   75550\n",
       "4   1984   73417\n",
       "5   1985   69978\n",
       "6   1986   86048\n",
       "7   1987  134771\n",
       "8   1988  139306\n",
       "9   1989  164432\n",
       "10  1990  188054\n",
       "11  1991  207509\n",
       "12  1992  221687\n",
       "13  1993  222049\n",
       "14  1994  193665\n",
       "15  1995  187712\n",
       "16  1996  200085\n",
       "17  1997  192885\n",
       "18  1998  160727\n",
       "19  1999  179818\n",
       "20  2000  216712\n",
       "21  2001  242643\n",
       "22  2002  223111\n",
       "23  2003  217297\n",
       "24  2004  232083\n",
       "25  2005  257457\n",
       "26  2006  247057\n",
       "27  2007  232405\n",
       "28  2008  243047\n",
       "29  2009  248768\n",
       "30  2010  276956\n",
       "31  2011  246194\n",
       "32  2012  256222\n",
       "33  2013  257537"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "t1.index = map(int, t1.index)\n",
    "t1.reset_index(inplace=True)\n",
    "t1.columns = ['year', 'tot']\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
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   "metadata": {
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    "new_sheet": false,
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   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis.\n"
   ]
  },
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the data  points together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "\n",
    "*   `x`: x-coordinates of the data.\n",
    "*   `y`: y-coordinates of the data.\n",
    "*   `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a * x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1.\n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print out the line of best fit\n",
    "'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
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    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "\n",
    "When compared to the actual from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years.\n"
   ]
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   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?\n"
   ]
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    "**Step 1**: Get the data:\n",
    "\n",
    "1.  Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "2.  Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "3.  Reset the index in place.\n",
    "4.  Rename the columns to **year** and **total**.\n",
    "5.  Display the resulting dataframe.\n"
   ]
  },
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
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       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1985</td>\n",
       "      <td>285</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1986</td>\n",
       "      <td>336</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1987</td>\n",
       "      <td>387</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1988</td>\n",
       "      <td>373</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1989</td>\n",
       "      <td>387</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1990</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1991</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1992</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1993</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1994</td>\n",
       "      <td>345</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1995</td>\n",
       "      <td>352</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1996</td>\n",
       "      <td>301</td>\n",
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       "      <th>17</th>\n",
       "      <td>1997</td>\n",
       "      <td>338</td>\n",
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       "      <th>18</th>\n",
       "      <td>1998</td>\n",
       "      <td>217</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1999</td>\n",
       "      <td>287</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2002</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2003</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004</td>\n",
       "      <td>291</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005</td>\n",
       "      <td>324</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2006</td>\n",
       "      <td>293</td>\n",
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       "      <th>27</th>\n",
       "      <td>2007</td>\n",
       "      <td>363</td>\n",
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       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>339</td>\n",
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       "      <th>29</th>\n",
       "      <td>2009</td>\n",
       "      <td>323</td>\n",
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       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>297</td>\n",
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       "      <th>31</th>\n",
       "      <td>2011</td>\n",
       "      <td>276</td>\n",
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       "      <th>32</th>\n",
       "      <td>2012</td>\n",
       "      <td>287</td>\n",
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       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>280</td>\n",
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      "text/plain": [
       "    year  total\n",
       "0   1980    669\n",
       "1   1981    678\n",
       "2   1982    627\n",
       "3   1983    333\n",
       "4   1984    252\n",
       "5   1985    285\n",
       "6   1986    336\n",
       "7   1987    387\n",
       "8   1988    373\n",
       "9   1989    387\n",
       "10  1990    331\n",
       "11  1991    381\n",
       "12  1992    411\n",
       "13  1993    481\n",
       "14  1994    345\n",
       "15  1995    352\n",
       "16  1996    301\n",
       "17  1997    338\n",
       "18  1998    217\n",
       "19  1999    287\n",
       "20  2000    287\n",
       "21  2001    343\n",
       "22  2002    293\n",
       "23  2003    327\n",
       "24  2004    291\n",
       "25  2005    324\n",
       "26  2006    293\n",
       "27  2007    363\n",
       "28  2008    339\n",
       "29  2009    323\n",
       "30  2010    297\n",
       "31  2011    276\n",
       "32  2012    287\n",
       "33  2013    280"
      ]
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   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden']]\n",
    "df_total = df_countries[years].sum(axis=0)\n",
    "df_total = pd.DataFrame(df_total)\n",
    "df_total.reset_index(inplace=True)\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "df_total"
   ]
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    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # create df_countries dataframe\n",
    "    df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "    # create df_total by summing across three countries for each year\n",
    "    df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "    # reset index in place\n",
    "    df_total.reset_index(inplace=True)\n",
    "\n",
    "    # rename columns\n",
    "    df_total.columns = ['year', 'total']\n",
    "\n",
    "    # change column year from string to int to create scatter plot\n",
    "    df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "    # show resulting dataframe\n",
    "    df_total.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
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   "source": [
    "**Step 2**: Generate the scatter plot by plotting the total versus year in **df_total**.\n"
   ]
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10,8))\n",
    "\n",
    "plt.title('total number of immigration from Norway, Denmark and Sweden throughout the years')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_total['year']\n",
    "y = df_total['total']\n",
    "\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "df_total.plot(kind='scatter', x='year', y='total')\n",
    "plt.plot(x, fit[0]*x + fit[1])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # generate scatter plot\n",
    "    df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "    # add title and label to axes\n",
    "    plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The data points are replaced with bubbles, and the size of the bubble is determined by the third variable `z`, also known as the weight. In `maplotlib`, we can pass in an array or scalar to the parameter `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 to 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002.\n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 1**: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and include it in the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transposed dataframe\n",
    "df_can_t = df_can[years].transpose()\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "**Step 2**: Create the normalized weights.\n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) to bring all values into the range \\[0, 1]. The general formula is:\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where $X$ is the original value, $X'$ is the corresponding normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the data points are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minus</th>\n",
       "      <th>original</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>145</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>114</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>264</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>401</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>436</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>442</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>456</td>\n",
       "      <td>586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>461</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>496</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>520</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>520</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>615</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>709</td>\n",
       "      <td>839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>715</td>\n",
       "      <td>845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>717</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>736</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>747</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>787</td>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>839</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>936</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1051</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1378</td>\n",
       "      <td>1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1512</td>\n",
       "      <td>1642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1584</td>\n",
       "      <td>1714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1616</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008</td>\n",
       "      <td>2138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2379</td>\n",
       "      <td>2509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2468</td>\n",
       "      <td>2598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    minus  original\n",
       "5       0       130\n",
       "3       9       139\n",
       "4      15       145\n",
       "2      62       192\n",
       "6      75       205\n",
       "0      81       211\n",
       "1      90       220\n",
       "7     114       244\n",
       "8     264       394\n",
       "18    401       531\n",
       "14    436       566\n",
       "15    442       572\n",
       "16    456       586\n",
       "17    461       591\n",
       "19    496       626\n",
       "10    520       650\n",
       "9     520       650\n",
       "22    615       745\n",
       "23    709       839\n",
       "20    715       845\n",
       "21    717       847\n",
       "13    736       866\n",
       "11    747       877\n",
       "24    787       917\n",
       "25    839       969\n",
       "12    936      1066\n",
       "26   1051      1181\n",
       "31   1378      1508\n",
       "32   1512      1642\n",
       "33   1584      1714\n",
       "27   1616      1746\n",
       "28   2008      2138\n",
       "29   2379      2509\n",
       "30   2468      2598"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = pd.DataFrame({'minus':(df_can_t['Brazil'] - df_can_t['Brazil'].min()).sort_values(),\n",
    "              'original':df_can_t['Brazil'].sort_values()})\n",
    "t2\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
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    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.032820\n",
       "1     0.036467\n",
       "2     0.025122\n",
       "3     0.003647\n",
       "4     0.006078\n",
       "5     0.000000\n",
       "6     0.030389\n",
       "7     0.046191\n",
       "8     0.106969\n",
       "9     0.210697\n",
       "10    0.210697\n",
       "11    0.302674\n",
       "12    0.379254\n",
       "13    0.298217\n",
       "14    0.176661\n",
       "15    0.179092\n",
       "16    0.184765\n",
       "17    0.186791\n",
       "18    0.162480\n",
       "19    0.200972\n",
       "20    0.289708\n",
       "21    0.290519\n",
       "22    0.249190\n",
       "23    0.287277\n",
       "24    0.318882\n",
       "25    0.339951\n",
       "26    0.425851\n",
       "27    0.654781\n",
       "28    0.813614\n",
       "29    0.963938\n",
       "30    1.000000\n",
       "31    0.558347\n",
       "32    0.612642\n",
       "33    0.641815\n",
       "Name: Brazil, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_brazil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "**Step 3**: Plot the data.\n",
    "\n",
    "*   To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter.\n",
    "*   We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore, we will:\n",
    "    *   multiply weights by 2000 to scale it up on the graph, and,\n",
    "    *   add 10 to compensate for the min value (which has a 0 weight and therefore scale with $\\times 2000$).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f020776c250>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                    )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax=ax0\n",
    "                    )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 to 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble is, the more immigrants are in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 to 1990, just before the onset of 1998 - 2002 great depression.\n",
    "\n",
    "On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "# normalized Chinese data\n",
    "# norm_china = ...\n",
    "# normalized Indian data\n",
    "# norm_india = ...\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # normalized Chinese data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalized Indian data\n",
    "    norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OAIGgwaqPy/lrUS0eb3PRwe9OTOKyb/eN6GLEfj2T+J+7e/L4S0dwuTRstubk4vdrBAMweYLO7KuGRuz1OsveQ3W8/l4ZtfUKh13j+v/qzYh+qe2+vqKqkbue3IdCIz4eiDewWBR+v+JQucZtvyzhpUdiM7FoSrW1dKZ7O3z48GldF6t7TcRq3BC7sXd23FV1bu55Zg/Oah2bvXkLWaXA7YakZIPnfjqQnIzEk677JnEHggbv/PsgRcX1BA3IzjIx69IBUfmQ7Mj77fEFeHDRdnbvMWGxGpjNEDTA69bo18/gmXvat0Pm7Gc/o+ywxlfzucViwe9vnsAQDED/fooX5o3ukPfxTfXu3TvsMWmpCNGNGYbinmd209SoY0/48vujpoHdDj6vzr3P7uP1J0ZitZgi9rpmk86Vk/tz5eSIPWWXcO8L2zlQpmOzfznNzaSDPUFxuELjnmd3sey/89B1DWeDh1ff3UdZhR+LWePib6dyfn5vapu87DugExcX/vu8yQwl+zTqGr0kO2JrG+eoJBWfz8ejjz5KIBAgGAwyfvx4rrnmGhobGyksLOTYsWNkZGQwd+5cHA4HACtWrGDNmjXous7MmTPJz88HoLS0lMWLF+Pz+RgzZgwzZ85E0zT8fj+LFi2itLSUxMRE5syZQ2ZmhDYIECJGrd5cjtNpwh5mf3Jdh/oGnf/78CBXnx/9DZ1iyabdlewtNWFPaHnetMUCFUd0Vm0sY2tJI0XrDTRNYbU2Vwd+bnsNS5OrueK7Cfh8WqtJBZoT/t6Kes4aHFtbHUdl8aPFYuHRRx/l2Wef5ZlnnmHLli3s3r2blStXkpeXx4IFC8jLy2PlypUAlJeXs379ep5//nkeeughli1bhvHFBPilS5dy6623smDBAo4cOcKWLVsAWLNmDQkJCSxcuJBp06bx5ptvRuOtCdGlvbO25qQtdL/Oblf8c31DlCKKXW+9d5R4W+sVHO02xbO/bmDthwZxcSq0yr25Zdhcf+yl33kI+NpXc6vthaRdT1SSiqZpxMc319YJBoMEg0E0TaO4uJgpU6YAMGXKFIqLiwEoLi5m4sSJWCwWMjMzycrKoqSkhJqaGtxuN0OGDEHTNCZPnhy6ZuPGjZx33nkAjB8/nq1bt3KGDxcJgcfbvg8mj1f+VtpS16DaLMBoBKFif4+wiVzXNWx2A2dV27XGbDaDwVEsrxIpURtTMQyDBx54gCNHjnDRRRcxePBg6urqSElJASAlJYX6+noAnE4ngwcPDl2bmpqK0+nEZDKRlpYWejwtLQ2n0xm65vgxk8mE3W6noaGBpKSkE+JYvXo1q1evBuCpp54iPf309qU2m82nfW1nitW4IXZj78y4k5PiaGhQbSaWpETtpBjlfp/I4Yijvo17eazShKbrWFpZBWqxwBFdBywnLBbVNC10nd8PY75lon9O+AHxripqSUXXdZ599lmampp47rnnOHjwYNhzw7UwWmt5tHRMa+H//tSpU5k6dWro59OdJdLZM3pOV6zGDbEbe2fGfdFEO4XL6sKOqQC4XBrfuyjhpBjlfp/o22OsLNvV1Oq9rHfGk5haH5rFFU5mrwBmUxCXSw8lluOzv/x+jYSEIPOuHRZ6H4Ggwd6KOnw+gz6ZCaQ4Oreqcpea/ZWQkMCIESPYsmULycnJ1NTUkJKSQk1NTahVkZaWRnV1degap9NJamrqSY9XV1eTmpp6wjVpaWkEg0FcLldo0F+IM9XUs/rw+v/VUF/f8na5hgFJiQaXT4peafRYddnEvvzhr9sIBlsumqkUmKx+0jLarmtmjVO89N9DeeaNPezYCR6PjtmsYbHC6JGK+384jCR7HIGgwcI/7Wb9Jj8N9TpKgcWqyMkJ8tMb+pGb3SPyb/QbisqYSn19PU1NTUDzTLDPP/+c7OxsCgoKKCoqAqCoqIhx48YBUFBQwPr16/H7/VRWVlJRUUFubi4pKSnYbDZ2796NUop169ZRUFAAwNixY3n//fcB2LBhAyNHjmyxpSLEmUTXNQrvHYzDYeByfblHuVLgcoE1zuD5+wZGdDpxd2W1mHjynr4opfB6T/xs8fmad5D8nwfanv5rGIp+OYqUxHh+dXsev39mGM//vBe/eXIwbz09nMdvzSPJHodhKOYVbmX1B82FOhMcBo7E5gkAR45ozH2qjO37q9t8vWiLyuLHAwcOsHjxYgzDQCnFhAkTuOqqq2hoaKCwsJCqqirS09OZN29eqHWxfPly1q5di67r3HjjjYwZMwaAvXv3smTJEnw+H/n5+dx0001omobP52PRokXs27cPh8PBnDlz6NmzZ5uxyeLH2BGrsXeFuA1D8fePy/hrUS1uD8THwUXfTmL6xPAr6rtC3Kejo+Oua/TyyrulFH/mx+sFqxVGDzdxy2UDSU+2MfvZzzhQpmOxtPzR6nZpPPHTnozJPXGq8Nfjfu29PfzxL96wg/5KNc82e+OJ6C+QbK37S1bUS1KJGbEau8QdXZGI2+ML4PYFSLRZT7mMTb3Ly+ynd3HsWHOZmuOCAfD4NH4wI54fXpTbZtw3PPwZ7jZ2dXQ16Tw+L4OxQ6K7Jq9LjakIIURX9a9Pynnr3WoqKnRAw2JRDB8Kd101gOyM9o3RJtnjWPrwSJZ/cIC/r2ugoVHDrEPuQI2bL+/D4D492nwOnz9IbW3rq+6hufvyXxurop5UWiNJRQghgCXLd/GXf/ix2fXmIo80f6Bv3wl3PL6PZ+7tw9CclHY9l9Vi4toLBnLtBacXi6FU+9fZdbG+JtlOWAhxxtu0u5J3VgWwJ5y8DsVsBpMJHl1chmFE5xM83momOant1/J5NSbktS/RRYskFSHEGe9//3IEmz18CRZNg9panQ8/r4haTOeeHYfHE/64UpDcw+Dbo7KiFlN7SFIRQpzxDh2mzRIsdrvibx9GbwrvrOm59O5l4GuhTphS4PVozPtxzzbjjjZJKkKIM55htP3BrGkQaLlAcYewWkwsvH8kY/KaNzhraNBpbNBxuTR69FD8ck5Pzh7e9rKJaJOBeiHEGS8pUdHY1Hpi8Xo0hvSzRimiZvFWM7/8yUhcXj+b91ThcgcZ2i+JfplJbV/cSSSpCCHOeFMn2fn9Sg/2VrYJ0HXF9d/pH72gvsIeZ2HSqF6d8tqnSrq/hBBnvO+fP4BePQ18vpaPu10a074Th8MW3ZZKLJKkIoQ441ktJhY9OJwhg5u7uTye5vLzLpeGQnH1ZXHcetmQzg4zJkj3lxBC0NzFNP/uPCprXfzlw3JqG4MM7ZvAd8dlS8HNUyBJRQghviKzh51Z06VVcrqk+0sIIUTESFIRQggRMZJUhBBCRIwkFSGEEBEjSUUIIUTESFIRQggRMac0pbi2tpaSkhIaGhpO2EDmggtOcycaIYQQ3Uq7k8rHH3/MwoUL6dWrF2VlZeTk5FBWVsawYcMkqQghhABOIan84Q9/4I477mDChAnMnDmTZ555hrVr11JWVtaR8QkhhIgh7R5TqaqqYsKECSc8NmXKFNatWxfxoIQQQsSmdieVpKQkamtrAcjIyGD37t0cPXoUw4jirjVCCCG6tHZ3f1144YXs3LmT8ePHM23aNH7xi1+gaRrTp0/vyPiEEELEkHYnlRkzZoT+e8qUKYwcORKPx0OfPn06Ii4hhBAx6LSqFBuGQWpqaui/dV2WuwghhDiFpFJaWsqyZcs4ePAgvq9tj/aHP/wh4oEJIYSIPe1OKosXL2bs2LHcfvvtxMXFdWRMQgghYlS7k0pVVRXXXXcdmqZ1ZDxCCCFiWLsHQ8aNG8enn37akbEIIYSIce1uqfj9fp577jmGDRtGjx49Tjh21113RTouIYQQMajdSaVPnz4yfViILsrjC7B+21HqGv0M7pPIqAFpnR2SOEO1O6lcffXVHRmHEOI0BIIGT72+g02fGrhcGrqmoVQTaemH+NGMFC4al9PZIYozTKtJZfv27YwYMQKArVu3hj1v1KhRkY1KCNEmw1DMLdzKvv0acXHgcCigeUsKj0fjhVfqcHsMZpzbr3MDFWeUVpPKsmXLmD9/PgC//vWvWzxH0zQWLVoU+ciEEBiG4p+byln7cS1BA/KH2fnelH7EW8288+8DlJTo2BNUi9fa7YrXltdzyfggVospypGLM1WrSeV4QoHmdSpCiOgp3lnJ0789QmOTjt0GmgZbd7h5+2/buWFGIu8W1WOztz6B0+eF5R8c4NoLBkYpanGmO636KoZhnPCPECKydhx08tjCSgxDI8GuOL48zG5T6LrGsv/noqTEQlvLxmw22LyjqeMDFuILUqZFiC5o0R/KsVoJu9jYZjfYtyuV9HRnq4lFqdP85ijEaZIyLUJ0MXWNXg4caB58b0tDg0ZSUstjKgBut8aUcUkRjE6I1kWlTEtVVRWLFy+mtrYWTdOYOnUql1xyCY2NjRQWFnLs2DEyMjKYO3cuDocDgBUrVrBmzRp0XWfmzJnk5+cDzS2mxYsX4/P5GDNmDDNnzkTTNPx+P4sWLaK0tJTExETmzJlDZmbmKccqRLQYhmLtlsNs2lGHxaxxycQshuakUFnrJhjQIa71ruW0nvXU11hJSvK2eFwpSEgwuKhAphWL6IlKmRaTycQPf/hDCgsLeeKJJ1i1ahXl5eWsXLmSvLw8FixYQF5eHitXrgSgvLyc9evX8/zzz/PQQw+xbNmy0NjN0qVLufXWW1mwYAFHjhxhy5YtAKxZs4aEhAQWLlzItGnTePPNN08rViGiYVVxGd9/8HOeW1rDvz8OsvbfQeY8eYiZv/iUOpcXtPCtj+McSQZ53/Lg9pz8RU8p8HrhgZuz0HWp1yeip9WWysKFC0Mtk29SpiUlJYWUlBQAbDYb2dnZOJ1OiouLeeyxx4Dmjb8ee+wxbrjhBoqLi5k4cSIWi4XMzEyysrIoKSkhIyMDt9vNkCFDAJg8eTLFxcWMGTOGjRs3hhZojh8/nldeeQWllBTAFF3O6k3lvPhqHTabht1+PHkoLBaordV54qVKEhMNgsHWv/PFxxu8cO8wVqwr472iJmqcJhQKsxn69jW465psRvSXlfUiulpNKllZWSf8HIkyLZWVlezbt4/c3Fzq6upCySYlJYX6+noAnE4ngwcPDl2TmpqK0+nEZDKRlvblH0laWhpOpzN0zfFjJpMJu91OQ0MDSUkn9ievXr2a1atXA/DUU0+Rnp5+Wu/DbDaf9rWdKVbjhtiN/atxG4bi9Xd2kJQU/k8vEACT2cDr1bHZWj7H44Vvj7fSP6c3c3/Qm3uuU5RV1tPk8ZOVmkBqUpgLTzPuWCJxd65Wk8rVV1/Nzp072bhxIzfccMNJx9944w3OPvvsdr+Yx+Nh/vz53Hjjjdjt9rDnKdVy0z/c4+GOtdRKmTp1KlOnTg39XFVV1VrIYaWnp5/2tZ0pVuOG2I39q3EX7zxKZaWB3d76eInHrRg7Wmf9xxr2BCM0w0spcLk0Bg00uOeqwSfcjwQzJDjA8DVRVfXNpxF3h/sdS2Ip7t69e4c91uaYyooVK0KlWr5u1KhRLF++vF1BBAIB5s+fz7nnnss555wDQHJyMjU1NQDU1NSEWhVpaWlUV1eHrnU6naSmpp70eHV1dWhb468eCwaDuFyu0KC/EF3FZyV16Hrba7u8Xp1bZgzgoTtT6ZmpQDUnlB7Jip9c72DhvXmySl50SW3O/tq/f39o5tXX5eXlhS3f8lVKKV566SWys7OZPn166PGCggKKioqYMWMGRUVFjBs3LvT4ggULmD59OjU1NVRUVJCbm4uu69hsNnbv3s3gwYNZt24dF198MQBjx47l/fffZ8iQIWzYsIGRI0fKeIrocuw2E4ahcbxGV2virCbOHd2Lc0f36vjAhIiQNpOK2+0mEAhgtVpPOhYMBnG73W2+yK5du1i3bh19+/blvvvuA+C6665jxowZFBYWsmbNGtLT05k3bx4AOTk5TJgwgXnz5qHrOrNmzULXmxtVN998M0uWLMHn85Gfn8+YMWMAuOCCC1i0aBGzZ8/G4XAwZ86cdt8EIaLlOwW9eXP53jbPS0s1SE2Mj0JEQkRWm0klOzubTz/9NNSK+KpPP/2U7OzsNl9k2LBh/PGPf2zx2COPPNLi41deeSVXXnnlSY8PGjTohJpkx1mt1lBSEqKrSk+2kTvIoHS/hsXS8jlut8YVF4UfcxSiK2tzTGXatGn85je/4aOPPgqtFTEMg48++oilS5cybdq0Dg9SiO7kl7cNJTHRwOM58fHjg/BnfUtx/VQpACliU5stlUmTJlFbW8vixYvx+/0kJSVRX1+P1Wrl6quvZtKkSdGIU4huI8kex9JHRvDyyhI2bPbT0KChaZCZrrhhRiIzJvWTBYsiZrWrTMv06dO54IIL2L17N42NjTgcDoYMGdLqtGAhRHj2OAtzvz8cvt+8FbDZpGM2SelHEfvaXfvLbreHnQUmhDh98dZ2/xkK0eXJVyMhhBARI0lFCCFExEhSEUIIETGSVIQQQkSMJBUhhBARI0lFCCFExEhSEUIIETGSVIQQQkSMJBUhhBARI0lFCCFExEhSEUIIETFSdEiIDrLzYA0HjjbSL9tDbk+rFIwUZwRJKiLmVdW5eeXdfRw+EsRsgf/6dirn5/futPLx7310kDfeqaW62oRSYDY1EW/zMelsC/dcPUzK2otuTZKKiFmGoXj2zR18sMFA0xVWa/NGV89tr2FpcjWPz+5LbnaPqMb0p7X7ePWPTdjsGgkJzZvaWSwm/H6N1esCHKnayq9uHyWJRXRb0h4XMavwjzsp+o9BXHxzQgHQNLDbFV4v3PtsGc4GT+tPEkEur583VjZis6sWj8fHw6efaXz4eUXUYhIi2iSpiJhU7/Ly/voANlvLH+C6rmEYGr/+896oxfTWP/fh97feArEnKN76e1WUIhIi+iSpiJj0p/cPEAy2fo7FotiyrY2TImhbiTdskjtO06C6OkoBCdEJZExFxKQDh3zEx7V9ntutYRgq4mMYh6oaqWvykdnDRnqy7ZSu1eSrnOjGJKmImJScaCIQCGKxtN4yMJuJaEJZ8cF+lv+jnqoqHcMAkwl69za46YpMzh6dwM7dTdhayTGGoeiZEbFwhOhy5DuTiEnXXNiHQKD1cwwDBvRvPemcikV/3sVvft+Iy6VhtyscDoXNpnA6NR5fXEW8RSOujdaTy6Xzo+lZEYtJiK5GkoqISTkZieQONFodGPd6NG65ok9EXm9PeS1//YcfewszuzQNbDbFK39q5ObvJ+N2aagWcpnHrXHeJI2CoZkRiUmIrkiSiohZT945jLQ0A7f7xA/xQADcLo2brrUzol9qRF7rt/9XRnx8662eQBAqjnn5n7k9SU9XeNwajY06jY0QF6f4wRU2fvbDkRGJR4iuSsZURMxy2Ky8/PORvLP+IO8VNdDQoGE2K4YP1rnlyr70y0yK2GsdOmxgMrf+HcwWD1t2urn50sH85ucZVNW5qax1k9M7kwSTXxY8ijOCJBUR06wWE1dNGcBVUzr2dRTtTQhfnpee3DwzLD29B1VVsjZFnBmk+0uIdshMb5651RqvV2NQX/meJs5sklSEaIcbpmXhcrX953LjtAFRiEaIrkuSihDtMHZIJuMLNNzulo+7XRpXXhJHiiM+uoEJ0cVIUhGinR6dNYJLLrSiadDQqNH4xT9xVsVN19q56ZLBnR2iEJ1OOoDFGcPl9XPwaCMA/bMSibee2q+/rmvc9b2h3DbDYOOuSqrrffRJt5M3ME1mdgnxBUkqotura/Ty1Ou72bET3J7mD3+73WD0CBP33TAYh816Ss9nNumMHyGr4oVoiXR/iW6tptHDTx7fybYdYDKDw9FcXkXXNTZ/ZnD7kztpdPs6O0whug1JKqJbe/yVPXhcOhbLycesVqit1Xjm9T3RD0yIbkqSiui26hq97N4D5hYSynFWK3y+w8Dja6M6pRCiXSSpiG5r24EavB5Tm+e5XDolh+sA8PmDfF5azabdx6iqCzN/WAgRlgzUi+5Na0/pe4XXG+RXr29j4xaDxobmwXyT+Sj9+hnM+0FfBvfp0aFhCtFdRCWpLFmyhM2bN5OcnMz8+fMBaGxspLCwkGPHjpGRkcHcuXNxOBwArFixgjVr1qDrOjNnziQ/Px+A0tJSFi9ejM/nY8yYMcycORNN0/D7/SxatIjS0lISExOZM2cOmZlSXvxMN6xvD6yWtmtuWa0Gv/5TBUeP6FjjwJH4ZSI6elRj7tNlPPPTICP6p3VkuEJ0C1Hp/jrvvPP4+c9/fsJjK1euJC8vjwULFpCXl8fKlSsBKC8vZ/369Tz//PM89NBDLFu2DMMwAFi6dCm33norCxYs4MiRI2zZsgWANWvWkJCQwMKFC5k2bRpvvvlmNN6W6OJSE+MZ0N8gaIQ/JxAABVQcNmFtYYMtTQOrBX617FCHxSlEdxKVpDJixIhQK+S44uJipkxpLi07ZcoUiouLQ49PnDgRi8VCZmYmWVlZlJSUUFNTg9vtZsiQIWiaxuTJk0PXbNy4kfPOOw+A8ePHs3XrVlRLuySJM87PZw4ERYuJJRAAi6W5ZH28Lfzvi6ZBVZXGp3uPdWCkQnQPnTamUldXR0pKCgApKSnU19cD4HQ6GTz4y3IXqampOJ1OTCYTaWlfdj+kpaXhdDpD1xw/ZjKZsNvtNDQ0kJR08n4aq1evZvXq1QA89dRTpKenn1b8ZrP5tK/tTLEaN5xe7Onp6fzh+TR+vvAz9u5T+LzN4yVxcYphQ3V+cfsovj/ns1b3lQew2aB4l5sLzzn1exer91zijq5YjfvrutxAfbgWRmstj5aOaVrLZTOmTp3K1KlTQz+f7j4X6enpMblHRqzGDacfe5wO8+8ZSlWdm8/3NX8Ryc9NI8URj8fnwu/3Y27jLyEQ0HA1uU7r9WP1nkvc0RVLcffu3TvssU5LKsnJydTU1JCSkkJNTU2oVZGWlkZ1dXXoPKfTSWpq6kmPV1dXk5qaesI1aWlpBINBXC7XSd1tQqQn2zg/P/uEx+KtZnr0UHi9rdfu8vsV546RgXoh2tJp61QKCgooKioCoKioiHHjxoUeX79+PX6/n8rKSioqKsjNzSUlJQWbzcbu3btRSrFu3ToKCgoAGDt2LO+//z4AGzZsYOTIkWFbKkJ83QUTbHjc4X9flIK0VMWY3IwoRiVEbIpKS+WFF15g+/btNDQ0cNttt3HNNdcwY8YMCgsLWbNmDenp6cybNw+AnJwcJkyYwLx589B1nVmzZqHrzbnv5ptvZsmSJfh8PvLz8xkzZgwAF1xwAYsWLWL27Nk4HA7mzJkTjbclOlij28eb/9zH+k1evF5ITLSSP1zjhxf3J8newlSt03TjJYPYuHUrhw5pJ80AUwp8Po1fzg7f3BdCfElTZ/g0qcOHD5/WdbHU//lVsRJ3aUUt9z13ELdHw/bFvlcWi4XGhgAWq+LJudkM75sasdfz+YM8+9ZONn9q0NigowCLWdE3x2DeD7/Z4sdYuedfJ3FHVyzF3SXHVIQIJxA0eLDwIIZBKKEcFxevUAoefrGcN3+VdMp7ooRjtZh46Ecj8fmD7DhYg89v0C/LQWYPe0SeX4gzhdT+El3OO/8+SEODFnbjK00Dj1vnT2v3R/y1rRYT3xqUzrhhmZJQhDgNklREl7P6P/VtrhuJtyk+2OiKTkBCiHaTpCK6HL+/uTXSFp//jB4OFKJLkqTSQWoaPZRW1FHT6OnsUGKOIwEMo/WEoRQk2GXauBBdjQzUR9j6bUd4bUUlhyt0gkHQTYpevQx+eFk6U0Znt/0Egmsv7smjLx4jISF8YnG7Na6cGrnZX0KIyJCWSgT9uWgfjy+qosqpERevsCco4uPB6dR5+qUa/vCv0s4OMSaMG5pJv74GvjBbx/v9kJVlcH6+rB0RoquRpBIhNY0eXvljIza7Omk8QNPAZlP873KX7CbYDrquMX/uMHJyFK4mLVRh2DCgqUmjV5bixfuGhp0dJoToPNL9FSGvvbvvi9Iw4btsTLpi6Tul/OyHI6MXWIxy2KwsuX802w84eeO9QzQ0KdJSTFx5XjqjB8Z+JVchuitJKhGyY6+PuLjWG35WK+zdH4xSRN3DiH6pPHlb89hJLK04FuJMJd1fEaKM9nXFGK3sQiiEELFOkkqE9O6pEwi0fk4gAD0zZBxACNF9SffXKTAMxerN5bz3YQ0mUxzJjgA3XdqP7HQHN8/ox082H8BsDj+m4vXq3Hx5nyhGLIQQ0SVJpZ1KK2r52YsHqK8zYbNpWK2KPS6D/xTvpyBf47GbR3DhZJ21Hxgt7nfudmucOxEGZSd3QvRCCBEdklTaoabRw73PlqGUht3+ZcIwW8BsUWz6VPH4a9t55KaR9HDsZtU6L/X1OrquMAxITDS49LtWbr1sSCe+CyGE6HiSVNph2Tv78Ps1rNaWj8fHw8ebDZxXe7j50iHceInBhu1HqajykJUWx4SRWZhNMnwlhOj+JKm0w8bP/FitrQ+wa5rGG6v2c/dVwzCbdCbl9YpSdLGp3uXl9b/vZ/NWL/6AItGh8f2L0pmU10sWNQoRwySptIPHrWFq405ZrYrDR/3RCSjGbdh2hCdfPoYR1IiL1wCNxkZ48tdO+vWronDecOxxls4OUwhxGqRPph3MlrZLrAcCkOSQ29mWI84mnvj1Mczm5l0cj9M0SEhQVFTo/Hzxjk6MUAjxTcinYDsMy217DYrfp3HdRTJduC0vr9iHpoffL8ViUezeo1N2rCG6gQkhIkKSSjvMmpGDPxC+n9/v1xgwwGBAlkwXbsu23QaWNnq2LFbF6+8djE5AQoiIkjGVdhiQlczcmQ288GoNuk5oFphSzetPsrIMnp49rN3P5/MH+fO6/az6oImmRtBNMLC/xi2X92Vg7+6dmPw+Db2NrzJmM9Q2SD0bIWKRJJV2+k5BH76Vm8qyv+xj++4gmqZITFRcen4Sl5yT0+4pw/UuL3c9tZOqKh2brbn1YwRg+06467MyZl7j5OrzB3TkW+lU1jhFwN/67C6/XyMzxRSliIQQkSRJ5RRk9rCHytafbsXcny3aRW2tjs124uNmM5jNilf+2MTIgU5G9IvtXQ1dXj87D9YSDCpy+ySR4ogH4KxRZj78KBh2zQ9AMKj40SX9ohSpECKSJKlEUdmxBvbt07HZw88mi49XLFtxiPlzumZSOVTVyLJ3DnD4aBBdh7NG2Lh2aj8ctuYs0ej28eT/7mL7DnC5m1tvVutRBg5Q/OzHA7nl8oH8Z+MelGp5sN7jhW/lNSdwIUTskYH6KHrrn2VtTk/WdSg92PYU5mgzDMUzb27nlv/ez6YtBseqNI5Waiz/u4frH9jFvzaX0+j2cevjO9i6vXldT2KiQWKiQVwcHCzXuOOJfbg9AX41LxtdV7jdGuqLtxo0wOXSGD4YfnmLbGImRKySlkoUNTQYmNtxx9sac+gML7+zm/fXGye1smzNvVrM/20NgwY6qa/XsVpPToomvTlxPP5KKb9+YDS//1Uyf/nPQdZuaMAfVKQk68y8NJuhOSnReDtCiA4iSSWKBubEselTL7YWqhh/lT2hc1oqgaDBPzeVs++Qm/RkKxed3ZtkRxw+f5DVH/hCCaQlFotB0ZpkBg8Pv77EpMOBAzpHnE1kpSbwvckD+N7kDngjQohOI0kliq4+vx8rV+1q9RyfD84Za8YwFO99VMby1TXU1DT3UqanK75/cRrn5/c+pfpYgaDB3z4qY9WHdWhaHGaTl+9fnMk5w3qGnufV9/bw19Vumhp1LFZFIODn9RW7yc/TOCcvkcZGDYcjfLIL+E24ahMxjIZWpwwHgxqflFTzX2cntDt+IUTskKQSRQ6blQu+bWHV+4EWWyuG0byifNb0Ady74HO279CxJ+ihAe2qKo1nX67h/bE1/OLmkaGEUO/ysmHbMTy+ICP79zhhz5ayYw3cO7/0hH1gfD6NXy6spm+fSubPG8bv/r6Pv/7Dj82ukfBF4jjehbXlc0XxpnpM7Zjhq1RzuZrWZnYB6OGW0wshYp4klSibfdVQAsZO3v93AKU04uLUF4sodVJTgzw1ZwCv/HUfu/Z8+QF/nKZBgkOxaYvGK+/u4frvDuCXv93J9p3g9WlfJJ8GevU8wF0/6MXI/inMe2YfgYB+wj4wmgZ2u8HhIxp3P7edY5UmbGEmW1mtcOyoFV0HW7Yv7PuyxhmYzIE2C2+azQZjBqe1824JIWKNJJUo03WNn147nJume3j9vf0cPOzHbIbLz0/jnGE9CQQN/rMpQHx8+G/zNpviH+s8rNu4nRqnjtXaXNrkuLoGjf9+8SjnjDmCy6URH2YsxGqFT4qTSElzY00Nv4I9q5ePkh096NVKUtE0jaz+ToIBDVMLA/XQPFDfv7+S6cJCdGOSVDpJiiOeu68+ubTL1r1OGhtMJCa2XqZkz64E0jI8JPc4+TxNg/g4xZ//nETu8PpWnyfo16mpspOS2hj2HJMZHMkuPF6Ij2v5HI9b48n7evCbtyupqdFP6gILBkA3KR69JbfVeIQQsU3WqXQxdU3hWwPHKQWNzsRW17xoGnib4mhsaP1/saYrAv62fw36D/KQP0qjqUnD+Eoe8/nA49G46Vo7k0b34qWHRjAmT8cwoLFBp6FRw+eDAQMULz2SK60UIbo5aal0MQOyEjHpta2eYyhQQR1rXBsLKc0B6uusJCZ5wp6TnObCeSSx1ecJBmDwQJ0nbhvFZ6VV/O4vFRxzGph0jcEDTdxy2UDSk5vrztjjLPzyJyPx+ALsKa8jYBgMzEoi2RGmiSOE6FYkqXQx/bOSyOwZpKFBC7vnCAZYE7xY2vi/50h1tdkKsdkU2f3rw5ZNgeZJALfMyAFg9MB0nrsnvY13AfFWM3kDZUBeiDONdH91QTdflY7H3fInvFLg98PgkdVtPk9Kqo/MLE+oFMrXBfyQ08fg6Qd64vc3t0i+/lquJo1rLos7YZqyEEKEI0mlCzo3rzfzbu4BNNfDUqr5A76pUUPXFY/OzuSqaXZc7vDPYRjQp0+QBQ/3QdfB/ZVzlWp+3pQUxfy5Q8kflMGSR/ozdEjzdR6Phter0TND8fCdacz8r8Ed+4aFEN2GdH91UVPH9uG8/N68+5+DfLS1eWbWlLHJfGdsH3Rdo2BoJp9s/5xduzXiv1ZGPxgANPjF7QPJyUjkjScd/LnoAEUfN6HpYNIVMy7swdSz+oQWUOZkJPL0nXkEggYNbh8JcRasFtnTRAhxajSlwnWOxJ4tW7bw6quvYhgGF154ITNmzGjzmsOHD5/Wa53ufiqRZBiKl/9vN0UfeamrNaFQxMUphuTCT38wkKzUk0uhdIW4T1esxi5xR5fE3fF69+4d9li3aakYhsGyZct4+OGHSUtL42c/+xkFBQX06dOns0PrMLqucfsVQ7n1csWh6kb8AYOsVDv2uDY2gRdCiA7SbZJKSUkJWVlZ9OzZE4CJEydSXFzcrZPKcbqukZPR+rRgIYSIhm6TVJxOJ2lpX05hTUtLY8+ePSedt3r1alavXg3AU089RXp629NjW2I2m0/72s4Uq3FD7MYucUeXxN25uk1SaWloSGth4cXUqVOZOnVq6OfT7cOMpf7Pr4rVuCF2Y5e4o0vi7nitjal0mynFaWlpVFd/uXajurqalBTZRVAIIaKp27RUBg0aREVFBZWVlaSmprJ+/XruvvvuNq9rLeN25LWdKVbjhtiNXeKOLom783SblorJZOKmm27iiSeeYO7cuUyYMIGcnJwOe70HH3yww567I8Vq3BC7sUvc0SVxd65u01IBOOusszjrrLM6OwwhhDhjdZuWihBCiM4nSeU0fXUGWSyJ1bghdmOXuKNL4u5c3apMixBCiM4lLRUhhBARI0lFCCFExHSr2V/f1JIlS9i8eTPJycnMnz8fgP3797N06VI8Hg8ZGRncfffd2O12AoEAL730Evv27cMwDCZPnswVV1wBQGlpKYsXL8bn8zFmzBhmzpzZ4ur+rhb3Y489Rk1NDVarFYCHH36Y5OSO25zrVOP+zW9+w969e9F1nRtvvJGRI0cCXf9+h4s72ve7qqqKxYsXU1tbi6ZpTJ06lUsuuYTGxkYKCws5duwYGRkZzJ07F4fDAcCKFStYs2YNuq4zc+ZM8vPzgeje80jGHc17fqpxNzQ08Pzzz1NSUsJ5553HrFmzQs8V7d/xb0SJkG3btqm9e/eqefPmhR578MEH1bZt25RSSv3rX/9Sb731llJKqQ8++EAVFhYqpZTyeDzqjjvuUEePHg1ds2vXLmUYhnriiSfU5s2bYyLuRx99VJWUlHRorKcb93vvvacWL16slFKqtrZW3X///SoYDIau6ar3u7W4o32/nU6n2rt3r1JKKZfLpe6++25VVlamXn/9dbVixQqllFIrVqxQr7/+ulJKqbKyMnXvvfcqn8+njh49qu66665OueeRjDua9/xU43a73WrHjh1q1apV6re//e0JzxXt3/FvQrq/vmLEiBGhbzrHHT58mOHDhwMwevRoPvroo9Axj8dDMBjE5/NhNpux2+3U1NTgdrsZMmQImqYxefJkiouLu3zcneFU4i4vL2fUqFEAJCcnk5CQQGlpaZe/3+Hi7gwpKSkMHDgQAJvNRnZ2Nk6nk+LiYqZMmQLAlClTQvevuLiYiRMnYrFYyMzMJCsri5KSkqjf80jFHW2nGnd8fDzDhg0LtaKO64zf8W9CkkobcnJy2LhxIwAbNmwI1RcbP3488fHx/OQnP+GOO+7g0ksvxeFwtFgt2el0dvm4j1uyZAn33Xcfb7/9dotFOjsr7v79+7Nx40aCwSCVlZWUlpZSVVXV5e93uLiP66z7XVlZyb59+8jNzaWuri5UJy8lJYX6+nrg5MrfqampOJ3OTr3n3yTu4zrjnrcn7nC6yu94e8mYShtuv/12Xn31Vd5++20KCgowm5tvWUlJCbqu8/LLL9PU1MQjjzxCXl5ep3wQt+RU4+7Zsyd33303qampuN1u5s+fz7p160LfqDo77vPPP5/y8nIefPBBMjIyGDp0KCaTqcvf73BxA512vz0eD/Pnz+fGG29stZUa7t521j3/pnFD59zz9sYdTlf5HW8vSSptyM7O5uGHHwaauzg2b94MwIcffkh+fj5ms5nk5GSGDh3K3r17GT58+EnVklNTU7t83D179gzFabPZmDRpEiUlJVFPKuHiNplM3HjjjaHzHn74YXr16kVCQkKXvt/h4gY65X4HAgHmz5/PueeeyznnnAM0d8vV1NSQkpJCTU0NSUlJwMmVv51OJ6mpqS1WBO/oex6JuCH69/xU4g6nM+73NyHdX22oq6sDmrcrXr58Od/5zneA5r0Ptm7dilIKj8fDnj17yM7OJiUlBZvNxu7du1FKsW7dOgoKCrp83MFgMNQMDwQCbNq0qUMLcp5q3F6vF4/HA8Bnn32GyWSiT58+Xf5+h4u7M+63UoqXXnqJ7Oxspk+fHnq8oKCAoqIiAIqKihg3blzo8fXr1+P3+6msrKSiooLc3Nyo3/NIxR3te36qcYfTVX7H20tW1H/FCy+8wPbt22loaCA5OZlrrrkGj8fDqlWrADj77LO5/vrr0TQNj8fDkiVLKC8vRynF+eefz2WXXQbA3r17WbJkCT6fj/z8fG666aYOnf4Xibg9Hg+PPvoowWAQwzDIy8vjxz/+Mbrecd87TiXuyspKnnjiCXRdJzU1ldtuu42MjAyga9/vcHF3xv3euXMnjzzyCH379g3dn+uuu47BgwdTWFhIVVUV6enpzJs3LzTOtnz5ctauXRuaDj1mzBgguvc8UnFH+56fTtx33nknLpeLQCBAQkICDz/8MH369In67/g3IUlFCCFExEj3lxBCiIiRpCKEECJiJKkIIYSIGEkqQgghIkaSihBCiIiRpCKEECJiJKkIEQULFixgyZIlJzy2fft2brrpJmpqajopKiEiT5KKEFEwc+ZMPvnkEz777DMAfD4fL7/8Mj/60Y9CxQW/iWAw+I2fQ4hIkMWPQkTJf/7zH9544w3mz5/P8uXL2b9/P1dddRW/+93vKC8vJyMj44RNvNauXcs777xDdXU1SUlJXH755aHyL9u2bWPhwoVcfPHFvPvuu4wePZrZs2d35tsTApCCkkJEzYQJE1i/fj0vvvgiu3bt4umnn+aBBx7grrvuIj8/n61btzJ//nxeeOEFkpKSSE5O5oEHHqBnz57s2LGDJ598kkGDBoX26KitraWxsZElS5bEXCVb0X1J95cQUTRr1iy2bt3KVVddxb///W/GjBnDWWedha7rjB49mkGDBoUqHJ911llkZWWhaRojRoxg9OjR7Ny5M/RcmqZxzTXXYLFYTtrYSYjOIi0VIaKoR48eJCUl0adPHz7++GM2bNjApk2bQseDwWCo++uTTz7h7bff5vDhwyil8Hq99O3bN3RuUlKSJBPR5UhSEaKTpKWlce6553LbbbeddMzv9zN//nzuuuuu0KZfzzzzzAnndNUqteLMJt1fQnSSc889l02bNrFlyxYMw8Dn87Ft2zaqq6sJBAL4/X6SkpIwmUwnzBwToiuTlooQnSQ9PZ3777+fN954gxdffBFd18nNzeWWW27BZrMxc+ZMCgsL8fv9jB07tktvzCTEcTKlWAghRMRI95cQQoiIkaQihBAiYiSpCCGEiBhJKkIIISJGkooQQoiIkaQihBAiYiSpCCGEiBhJKkIIISLm/wOmTexFcjctYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "#DJ's practice\n",
    "\n",
    "\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "              x='Year',\n",
    "              y='China',\n",
    "              alpha=0.5, color='green',\n",
    "              s=norm_china + 100)\n",
    "\n",
    "ax2 = df_can_t.plot(kind='scatter',\n",
    "              x='Year',\n",
    "              y='China',\n",
    "              alpha=0.5, color='blue',\n",
    "              s=norm_china + 100, ax=ax1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# China\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_china * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# India\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_india * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "ax0.legend(['China','India'], loc='upper left', fontsize='xx-large')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\n",
    "                       )\n",
    "\n",
    "    # India\n",
    "    ax1 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='India',\n",
    "                        alpha=0.5,\n",
    "                        color=\"blue\",\n",
    "                        s=norm_india * 2000 + 10,\n",
    "                        ax = ax0\n",
    "                       )\n",
    "\n",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Weiqing Wang](https://www.linkedin.com/in/weiqing-wang-641640133?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\n",
    "| 2021-05-29        | 2.6     | Weiqing Wang | Fixed typos and code smells.       |\n",
    "| 2021-01-20        | 2.5     | LakshmiHolla | Changed TOC markdown section       |\n",
    "| 2021-01-05        | 2.4     | LakshmiHolla | Changed markdown for outliers      |\n",
    "| 2020-11-12        | 2.3     | LakshmiHolla | Added example code for outliers    |\n",
    "| 2020-11-03        | 2.2     | LakshmiHolla | Changed URL of excel file          |\n",
    "| 2020-09-29        | 2.1     | LakshmiHolla | Made fix to a boxplot label        |\n",
    "| 2020-08-27        | 2.0     | Lavanya      | Moved lab to course repo in GitLab |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
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