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

## 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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.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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    "## 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",
    "    </div>\n",
    "    <hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
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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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Numpy**](http://www.numpy.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) 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_ `read_excel()` method. Normally, before we can do that, we would need to download a module which _pandas_ requires to read in excel files. This module is **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 **xlrd** module:\n",
    "\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Download the dataset and read it into a _pandas_ dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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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]"
      ]
     },
     "execution_count": 3,
     "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": 4,
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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)"
   ]
  },
  {
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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": 5,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "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": 6,
   "metadata": {
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    "collapsed": false,
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    "new_sheet": false,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.3.2\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": {
    "button": false,
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   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circualr 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",
   "metadata": {
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    "new_sheet": false,
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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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
   ]
  },
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": 7,
     "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": {
     "read_only": false
    }
   },
   "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": 8,
   "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 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 Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 11,
   "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": [
    "### type your answer here\n",
    "\n",
    "explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\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 [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": [
    "<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",
    "-   **Minimun:** Smallest number in the dataset.\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:** Highest number in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/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 `box plot`, 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 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": 12,
   "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": 12,
     "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": 13,
   "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 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": 14,
   "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": 14,
     "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 analyize 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": 19,
   "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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r168bnYvR2L+LuWpOpFm3bh2cnZ3h6emJ69evIyIiAhqNxtBv/vz5KCgowPbt23HgwAEsW7YM8fHx+OKLL0xaTl3vualTpxqN459++qnBeRUXF+ORRx5Bz549sXjx4rtafn26dOmCvLw85OTkYP78+Zg5c6ZJx1MbfS2Ao6OjUUiFhYVh165dWLRoER599NE6p7vzQ628vBwHDx6ERqMx+6zDO+dbn7pe1Ma82E05rzvZ2dnVmmfNG7pmHc1dzvLly7F48WIkJiaiV69ecHV1RVJSUq03hNKy63tdKyoq8Mgjj+Chhx7C2rVrDSc0dO/evdZJDA2tV2MHfXZ2NiorK2vNX4lSn4Y+LOuru+a/+/btg5OTU61+AODi4oLc3Fzs3bsXGRkZWLlyJV555RXs2rULERER0Ov1GDduHF577bVay9ZqtQ2u0+1sbW1r1aDUduc6N3a6O5nyN7zzxJ6mGkN1efTRR/HTTz9h586d2L17N2JjYxEaGopdu3YZhYYplE5KunOdld6jTfX6mmLMmDEYM2YMiouLYW9vD3d3d7Rp0wb9+/cHAPzwww9ITEzE/v370bdvXwBAz5498d1332Hx4sWGM2BtbGyMToYBgF9++QX29vbw9PRUXPbChQuNztj38/Ort9YzZ87g4YcfRmBgID777DOj16Rt27YAbp14VPOFqaaGuk6cqo+dnZ0hr8LDw1FQUIAFCxZg+PDh9U7XpNdB2tjYoKKiAsCtAQ4A2dnZRn2+/vprw3MA8MILL0Cj0SAzMxMff/wx/v73vze4nPPnzxt9Qztx4gRKSkrQtWtXxf7du3dHVlaWUVtWVhZUKhW6desG4NYLWF1d3eCyTZmXpbi7u8Pf3x979+41ar/z8Z2ys7MxbNgwTJw4EeHh4QgMDDR760TJsWPHcP78eSxatAiDBw9G165dUVZW1uCXlTt1794d58+fN6rpwoULDX5hio2NRUVFBRITExWfLysrM6sOc0VERAAAfvrpJwQGBhr969y5s6GfRqNBVFQUFi5ciAMHDqBt27b461//CgCIjIzEoUOH0Llz51rzqOuDqKWpGffffPONoe3mzZu1TslX0lRjCKj7Pezl5YWnn34aq1atwhdffIGsrKwm2duj9FmQnZ0NR0dHdOrU6a7nfzd8fHzg7u6OjIwMFBcX46mnngIAw+fznVvxGo3G8Jrb2dmhd+/ehr2BNXbs2IF+/frV+cXCx8fHaPzWdy32Dz/8gIEDB6Jbt27YvHmz0Z4r4NZ7y97e3qgGvV6PjIyMBveYmUKv1+PGjRsN9mv0FmRlZaXhG8bVq1exc+dO7Ny5EwsWLAAAdO7cGWPGjMG0adOwatUqBAQE4L333sPhw4cNHw4ff/wxNm3ahP379yMsLAxvvfUWpkyZgr59+6Jjx451LtvJyQlxcXFISkqCiCA+Ph6hoaGIiYlR7P/yyy+jV69emDlzJn7/+9/j9OnTiI+Px7PPPmv4dtKxY0ds2rQJR44cQZs2beDq6lrrj2bqvCzppZdewuuvv46QkBD069cPW7duRUZGRr3TdOnSBR999BG++uor+Pv7Y8OGDfjPf/5z1x/AAQEBsLe3R0pKCl566SWcPn0ar732mtlbg0OHDsWDDz6I2NhYpKSkwM7ODq+++mqDNzuIjIzE3LlzMWvWLPz8888YO3YsAgICcO7cOXz66ac4e/YsPv3007tZxXoFBgbi+eefx+TJk7F06VL0798fV69exYEDB3D+/Hm8+uqr+Oc//4mCggJERUXB29sbBw4cwM8//2wIlVmzZqFPnz6IjY3F9OnT4e3tjdOnTyM9PR3Tp0+3+getKYKCgvDEE0/gD3/4A1atWgVvb28sX74cly9fbnAsNNUYAm69h7/66isMHz4cdnZ20Ol0mD17NiIiItC9e3eo1Wps3LgRLi4uTfJe/fOf/4wnnngCS5YswVNPPYW8vDzMnz8fL730kkl7NMyVn5+P8vJyw67LvLw8ALfGYc2lZGlpaejbty88PDywZ88ezJgxA7GxsRg8eDCAW4dRgoOD8cc//hGJiYnw8/PDV199hQ0bNmDRokWGZb3yyisYPXo0+vTpg2HDhuGLL77A5s2b8a9//euu1+Po0aOIiYlBz549kZycjJKSEsNz3t7e0Gg0cHNzw9SpUzFr1iy0bdsWHTt2xLJly3Dt2jVMmTLF0L+oqAhFRUU4d+6c4TVycXGBr6+vYUtz5syZePLJJ9G+fXtcvXoV27Ztw/r167F06dKGizXpSOUdxo8fb3QqtKOjo3Tr1k2WLVtmdAD+0qVLhss87OzsjC7zOHnypLi6uhqdbqzX62XYsGHSp08fo1OVb1dzmcdHH30kAQEBYmdnJ4MHD5b8/HxDn4Yu89DpdDJ16lSjyydKSkpk+PDh4ubmZtZlHkrzupuTdO48OeCNN96QgIAAw+Pq6mr585//LFqtVpycnOQ3v/lNg5d5XLx4UcaMGSOurq7i5eUl06ZNkzlz5hjNV+nSnY8++kgaGiKbNm2SwMBAsbe3l7CwMNm9e7fRyRJ1HTzv3Lmz0enap06dkocffljs7e3F399fVqxY0eBlHjW2bt0qDz/8sHh4eIi9vb0EBwfLCy+8YDiRq66/x50ndUDhJJ3bH4uIDB061Oikj5s3b8rbb78tXbp0EVtbW9FqtRIVFSWffvqpiIhkZWXJ4MGDDZc6BQYGyuLFi43ONDx06JCMHDlSPDw8xMHBQTp37iyTJ0+WkpKSOtfZlLFzZ60iIo8++qg8++yzhsd3no0qUvtvI3Lr5KTZs2fXOd2FCxfkN7/5jTg6Ooq3t7e8/vrrMnr0aKOTfer6ezbVGNq+fbuEhISInZ2dYdwuXLhQunfvLs7OzuLm5iZRUVENnulY10k6d44FkVsnGYaEhIitra34+fnJrFmzal3m0Zi/S1113f65W/Pvq6++MvSJi4sTnU4ntra2EhQUJIsXL6511ukPP/wgY8eOFV9fX3FwcJAuXbrI0qVLa508tW7dOgkKChJbW1sJDg5WXP/GULoUrubf7ZfEVVZWyssvvyxt2rQRe3t7GTBggOTk5Jg0r9vHxdixY+WBBx4QOzs70Wq1MmDAANm4caNJtapEGrEvw4rmz5+Pjz/+GPn5+dYuhYjqUF1djZCQEIwcORLLly+3djlEjXLv37CTiKwuOzsbxcXFCA8Px5UrV5CUlITTp09jwoQJ1i6NqNEYkER016qrq/Hmm28iPz8ftra26NGjB7766iuEhoZauzSiRrvndrESERE1h1b5c1dERER3iwFJRESkgAFJRESk4L4/SafmAlO6ezqdzug+o0QtBcdm02roNnKtBbcgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFDAgiYiIFNz3Nysn8/j7+zdqurNnzzZxJURElsWAJLPUF3TVk0dCs3prM1ZDRGQ53MVKRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkgAFJRESkoFl/7kqv1+O1116Dl5cXXnvtNZSXlyMpKQnnz5+Ht7c3ZsyYARcXFwDAli1bkJmZCbVajbi4OISFhQEACgoKkJaWhsrKSoSHhyMuLg4qlQpVVVVITU1FQUEBXF1dkZCQAB8fn+ZcPSIiakWadQty27ZtRj+4m56ejtDQUCQnJyM0NBTp6ekAgDNnzmDfvn1ITEzE7NmzsWbNGuj1egDA6tWrMWXKFCQnJ6OoqAh5eXkAgMzMTDg7OyMlJQUjRozAxo0bm3PViIiolWm2gCwpKcHBgwcxdOhQQ1tOTg6io6MBANHR0cjJyTG0DxgwALa2tvDx8YGvry/y8/NRVlaGa9euITg4GCqVClFRUYZpcnNzMWjQIABAv379cPjwYYhIc60eERG1Ms22i3X9+vWIjY3FtWvXDG2XLl2Cp6cnAMDT0xOXL18GAJSWliIoKMjQz8vLC6WlpdBoNNBqtYZ2rVaL0tJSwzQ1z2k0Gjg5OeHKlStwc3MzqiMjIwMZGRkAgCVLlkCn01lgbe9PvwB8PalFsrGx4dgkszVLQB44cADu7u7o1KkTjhw50mD/urb86tsiVHpOpVLVaouJiUFMTIzh8YULFxqsh0zH15NaIp1Ox7HZhPz8/KxdQrNoloA8fvw4cnNz8e2336KyshLXrl1DcnIy3N3dUVZWBk9PT5SVlRm29rRaLUpKSgzTl5aWwsvLq1Z7SUkJvLy8jKbRarWorq5GRUWF4YQfIiIiczXLMchnnnkGK1euRFpaGhISEtCjRw+8+OKLiIyMRFZWFgAgKysLvXv3BgBERkZi3759qKqqQnFxMQoLCxEYGAhPT084OjrixIkTEBFkZ2cjMjISABAREYHdu3cDAPbv34/u3bsrbkESERGZolkv87jTqFGjkJSUhMzMTOh0OsycORMA0L59e/Tv3x8zZ86EWq3GxIkToVbfyvJJkybh3XffRWVlJcLCwhAeHg4AGDJkCFJTUxEfHw8XFxckJCRYa7WIiKgVUMl9fqrnuXPnrF1Cq1E9eSQ0q7dauwyiWngMsmndL8cgeScdIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBQxIIiIiBTbWLoCIqKn4+/ubPc3Zs2ctUAm1BgxIImo16gq76skjoVm9tZmroXsdd7ESEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpYEASEREpsLF2AdQyVU9/BqgoN3+6ySNN7+zkAs07fzV7GUREzYEBScoqyqFZvdWsSXQ6HS5cuGByf7PClIiomTVqF2tlZSVu3rzZ1LUQERG1GCYF5IYNG5Cfnw8AOHjwIOLi4jBhwgTk5uZatDgiIiJrMSkg9+zZg/bt2wMAPvvsM8THx+OVV17B3/72N4sWR0REZC0mHYO8ceMG7O3tceXKFfzyyy/o168fAJh1vImIiOheYlJA+vn54euvv0ZRURF69uwJALh8+TLs7OwsWhwREZG1mLSLdeLEidi5cyeOHDmCsWPHAgC+++47Q1gSERG1NiZtQep0Orz55ptGbQMHDkRoaKhJC6msrMS8efNw8+ZNVFdXo1+/fvjtb3+L8vJyJCUl4fz58/D29saMGTPg4uICANiyZQsyMzOhVqsRFxeHsLAwAEBBQQHS0tJQWVmJ8PBwxMXFQaVSoaqqCqmpqSgoKICrqysSEhLg4+NjxktBRET0f0zagpw+fbpi+4wZM0xaiK2tLebNm4dly5Zh6dKlyMvLw4kTJ5Ceno7Q0FAkJycjNDQU6enpAIAzZ85g3759SExMxOzZs7FmzRro9XoAwOrVqzFlyhQkJyejqKgIeXl5AIDMzEw4OzsjJSUFI0aMwMaNG02qjYiISIlJASkitdoqKiqgVpt2GaVKpYKDgwMAoLq6GtXV1VCpVMjJyUF0dDQAIDo6Gjk5OQCAnJwcDBgwALa2tvDx8YGvry/y8/NRVlaGa9euITg4GCqVClFRUYZpcnNzMWjQIABAv379cPjwYcW6iYiITFHvLtYXXngBwK1dpDX/X6O8vBy/+tWvTF6QXq/Hq6++iqKiIjz66KMICgrCpUuX4OnpCQDw9PTE5cuXAQClpaUICgoyTOvl5YXS0lJoNBpotVpDu1arRWlpqWGamuc0Gg2cnJxw5coVuLm5GdWRkZGBjIwMAMCSJUug0+lMXof7yS+A2a+NjY2NWdM0ZhlEjcGxRo1Rb0DGx8dDRLB48WLEx8cbPefh4QE/Pz+TF6RWq7Fs2TJcvXoVf/nLX/DTTz/V2beuLb/6tgiVnlOpVLXaYmJiEBMTY3jMS1XqZu5rY+6t5hqzDKLG4lhrOuZ89t/L6g3Ibt26AQDWrFkDe3v7Jlmgs7MzunXrhry8PLi7u6OsrAyenp4oKyszbO1ptVqUlJQYpiktLYWXl1et9pKSEnh5eRlNo9VqUV1djYqKCsMJP0REROYy6SxWjUaDjIwMnD59GtevXzd67o9//GOD01++fBkajQbOzs6orKzE999/j1//+teIjIxEVlYWRo0ahaysLPTu3RsAEBkZieTkZDz++OMoKytDYWEhAgMDoVar4ejoiBMnTiAoKAjZ2dkYNmwYACAiIgK7d+9GcHAw9u/fj+7duytuQRIREZnCpIBMTU3Fjz/+iIiICLi7u5u9kLKyMqSlpUGv10NE0L9/f0RERCA4OBhJSUnIzMyETqfDzJkzAQDt27dH//79MXPmTKjVakycONFwQtCkSZPw7rvvorKyEmFhYQgPDwcADBkyBKmpqYiPj4eLiwsSEhLMrpOIiKiGSkw41TMuLg6pqalwdnZujpqa1blz56xdQotUPXlks/zclbnLIGoMjrWmdb8cgzTpOg2dToeqqipL10JERNRimLSLNSoqCsuWLcPw4cPh4eFh9FyPHj0sURcREZFVmRSQO3bsAIBaP2+lUqmQmpra9FURERFZmUkBmZaWZuk6iIiIWhSTApKIqCWpnv4MUFFu3jSTR5q3ECcXaN75q3nTUKtiUkBWVFRg06ZNOHr0KK5cuWJ015r33nvPYsURESmqKDfrrNTG3OXJ7EClVseks1g/+OADnDp1CqNHj0Z5eTmef/556HQ6jBgxwtL1ERERWYVJAXno0CG89NJL6N27N9RqNXr37o0ZM2bg66+/tnR9REREVmHyz105OTkBABwcHHD16lV4eHigqKjIosURERFZi0nHIAMCAnD06FGEhoYiJCQEa9asgYODA9q2bWvp+oiIiKzCpC3IKVOmwNvbGwDw/PPPw87ODlevXjXpRuVERET3oga3IPV6PXbv3o2nnnoKAODm5oapU6davDAiIiJranALUq1WY+fOndBoNM1RDxERUYtg0i7W6OhofPnll5auhYiIqMUw6SSd/Px87NixA1u3boVWqzX6IeIFCxZYrDgiIiJrMSkghw4diqFDh1q6FiIiohbDpIAcNGiQhcsgIiJqWUwKyMzMTMV2W1tbaLVaBAUFwdbWtkkLIyIisiaTAjI7OxsnTpyAu7s7tFotSkpKcOnSJXTu3BnFxcUAgFdeeQWdO3e2aLFERETNxaSAbNeuHfr06YPHHnvM0LZjxw6cPXsWCxcuxObNm7F27VosWrTIYoUSERE1J5Mu89i7dy+GDRtm1PbII49gz549UKlUGDlyJM6cOWORAomIiKzBpIB0d3fHgQMHjNoOHjwINzc3AEBVVRVsbPjby0RE1HqYlGpxcXFITEzEAw88YDgG+dNPP2HmzJkAgJMnT9bawiQiIrqXmRSQDz74IFJSUpCXl4fS0lKEh4ejV69ecHV1NTz/4IMPWrRQIiKi5mTyflE3NzdERUVZshYiIqIWo86AXLRoEWbPng0AmDt3rtHt5W7HW80REVFrVGdARkdHG/5/yJAhzVIMERFRS1FnQD700EOG/+et5oiI6H5j8jHIY8eO4dSpU7h+/bpRe80PKRMREbUmJgXk2rVr8c033yAkJAR2dnaG9rqOSxIREd3rTArIr7/+GsuXL4eXl5el6yEiImoRTLqTjk6n4691EBHRfcWkLcipU6di1apV+NWvfgV3d3ej57p162aRwoiIiKzJpIAsKCjAt99+i2PHjhkdgwSA9957zyKFERERWZNJAfm3v/0Nr776Knr27GnpeoiIiFoEk45B2tvbc1cqERHdV0wKyLFjx2L9+vW4ePEi9Hq90T8iIqLWyKRdrDXHGb/88staz33yySdNWxEREVELYFJApqamWroOIiKiFsWkgPT29rZ0HURERC2KSQFZUVGBbdu24fTp07XuxTpnzhyLFEZERGRNJgVkYmIi9Ho9+vTpU+s6SCIiotbIpIA8efIk1qxZAxsbk3/8g4iI6J5m0mUeISEhOHv2rKVrISIiajFM2iScNm0aFi9ejMDAQHh4eBg9N3r0aEvURUREZFUm32qupKQE3t7euHbtmqGdvwdJREStlUkBuW/fPrzzzjvw9PS0dD1EREQtgknHINu0aQONRmPpWoiIiFoMk7YgBw4ciKVLl2LYsGG1jkH26NHDEnURERFZlUkBuXPnTgC3jkXeTqVS8TZ0RETUKpkUkGlpaZaug4iIqEUx6RgkERHR/abeLci5c+c2eCnHggULmrQgIiKilqDegBwyZEhz1UFERNSi1BuQgwYNaqYyiIiIWhYegyQiIlLAgCQiIlLAgCQiIlJQZ0DOnj3b8P+bNm1qlmKIiIhaijoD8ty5c6isrAQAfP75581WEBERUUtQ51msvXv3xvTp0+Hj44PKykrMmzdPsR+vgyQiotaozoCcNm0a/ve//6G4uBj5+fkYPHhwc9ZFRERkVfVeBxkSEoKQkBDcvHmT10QSEdF9xaSblQ8ZMgSHDx9GdnY2ysrK4OnpiaioKJN/6urChQtIS0vDxYsXoVKpEBMTg8ceewzl5eVISkrC+fPn4e3tjRkzZsDFxQUAsGXLFmRmZkKtViMuLg5hYWEAgIKCAqSlpaGyshLh4eGIi4uDSqVCVVUVUlNTUVBQAFdXVyQkJMDHx6dxrwoREd33TLrMY9euXVixYgU8PDzQp08feHp64p133kFGRoZJC9FoNBg3bhySkpKwaNEi7Ny5E2fOnEF6ejpCQ0ORnJyM0NBQpKenAwDOnDmDffv2ITExEbNnz8aaNWug1+sBAKtXr8aUKVOQnJyMoqIi5OXlAQAyMzPh7OyMlJQUjBgxAhs3bjT/1SAiIvr/TNqC3Lp1K+bMmYMOHToY2gYMGIDly5cjJiamwek9PT3h6ekJAHB0dIS/vz9KS0uRk5OD+fPnAwCio6Mxf/58xMbGIicnBwMGDICtrS18fHzg6+uL/Px8eHt749q1awgODgYAREVFIScnB+Hh4cjNzcWYMWMAAP369cPatWshIg3ebJ2I7j3bYjYAn1w0Ywpz+v5/MRvwhPlTUStiUkBeuXIF7dq1M2rz8/NDeXm52QssLi7GqVOnEBgYiEuXLhmC09PTE5cvXwYAlJaWIigoyDCNl5cXSktLodFooNVqDe1arRalpaWGaWqe02g0cHJywpUrV+Dm5mZ2jUTUsj2W8Rw0q7ea3F+n0+HChQtmLaN68khgrOnLoNbHpIAMCQnBhg0b8Oyzz8Le3h7Xr1/HX//6V8OWnKmuX7+O5cuXY8KECXBycqqzn4iY1V7Xc0pbjxkZGYZdw0uWLIFOp2uo7PvSOrO/oQNmf0uP2YA4vv7UCL8AZr13bWxszH6vm7sMan1MCsjJkydjxYoVmDBhAlxcXFBeXo7g4GBMnz7d5AXdvHkTy5cvx8CBA9G3b18AgLu7u+Gkn7KyMsPWnlarRUlJiWHa0tJSeHl51WovKSmBl5eX0TRarRbV1dWoqKgwnPBzu5iYGKPdwuZ+q7xfmPsNHTD/W3r15JG4wG/o1EjmjLXGbEGau4z7iZ+fn7VLaBYmBaSnpycWLFiAkpISQ6DdvquzISKClStXwt/fH48//rihPTIyEllZWRg1ahSysrLQu3dvQ3tycjIef/xxlJWVobCwEIGBgVCr1XB0dMSJEycQFBSE7OxsDBs2DAAQERGB3bt3Izg4GPv370f37t15/JGIiBrNpICsodVqzQrGGsePH0d2djYeeOABvPzyywCAp59+GqNGjUJSUhIyMzOh0+kwc+ZMAED79u3Rv39/zJw5E2q1GhMnToRafeuE20mTJuHdd99FZWUlwsLCEB4eDuDWpSipqamIj4+Hi4sLEhISzK6TiIiohkrqO7B3Hzh37py1S2iRqiePbJZdrOYugwgwf+w09iQdjk9l98suVv7cFRERkYIGA1Kv1+Pw4cO4efNmc9RDRETUIjQYkGq1GkuXLoWNjVmHK4mIiO5pJu1i7dq1K06cOGHpWoiIiFoMkzYLvb29sXjxYkRGRkKr1RpdPjF27FiLFUdERGQtJgVkZWWl4RrFmlu7ERERtWYmBeS0adMsXQcREVGLYvKZN2fOnMH+/ftx6dIlTJw4EefOnUNVVRUCAgIsWR8REZFVmHSSzjfffIN58+ahtLQU2dnZAIBr165hw4YNFi2OiIjIWkzagvz000/x+uuvo0OHDvjmm28AAAEBATh9+rQlayMiIrIak7YgL126VGtXqkql4s3AiYio1TIpIDt16mTYtVpj7969CAwMtEhRRERE1mbSLta4uDi8+eabyMzMxI0bN7Bo0SKcO3cOc+bMsXR9REREVmFSQPr7+2PFihU4cOAAIiIioNVqERERAQcHB0vXR0REZBUmX+Zhb2+PkJAQlJaWwsvLi+FIREStmkkBeeHCBSQnJ+PkyZNwdnbG1atXERgYiBdffBHe3t6WrpGIiKjZmXSSTlpaGjp16oR169bhgw8+wLp169C5c2ekpaVZuj4iIiKrMCkgCwoKEBsba9it6uDggNjYWBQUFFi0OCIiImsxKSCDgoKQn59v1PbDDz8gODjYIkURERFZW53HID/55BPD/7dp0waLFy9Gr169oNVqUVJSgm+//RYPPfRQsxRJRETU3OoMyJKSEqPHffv2BQBcvnwZtra26NOnDyorKy1bHRERkZXUGZD8iSsiIrqfmXwd5I0bN1BUVITr168btXfp0qXJiyIiIrI2kwIyKysLa9euhY2NDezs7Iyee++99yxSGBERkTWZFJAff/wxXnrpJfTs2dPS9RAREbUIJl3mYWNjg27dulm6FiIiohbDpIAcO3YsNmzYgMuXL1u6HiIiohbBpF2sfn5++PTTT7Fz585az91+vSQREVFrYVJApqSkICoqCgMGDKh1kg4REVFrZFJAlpeXY+zYsVCpVJauh4iIqEUw6RjkoEGDkJ2dbelaiIiIWgyTtiDz8/OxY8cObN68GR4eHkbPLViwwBJ1ERERWZVJATl06FAMHTrU0rUQERG1GCYF5KBBgyxcBhERUctiUkBmZmbW+dyQIUOarBgiIqKWwqSA/Prrr40eX7x4EUVFRQgJCWFAEhFRq2RSQM6bN69WW2ZmJs6ePdvkBREREbUEJl3moWTQoEH17nolIiK6l5m0BanX640eV1ZWIjs7G87OzhYpioiIyNpMCsinn366VpuXlxemTJnS5AURERG1BCYFZGpqqtFje3t7uLm5WaQgIiKilsCkgPT29rZ0HURERC1KvQHZ0G3kVCoV5s6d26QFERERtQT1BuTAgQMV20tLS7F9+3bcuHHDIkURERFZW70BeedNAK5cuYItW7Zg165dGDBgAEaPHm3R4oiIiKzFpGOQFRUV2Lp1K3bu3IlevXrh7bffhq+vr6VrIyIispp6A7KyshJffPEFPv/8c3Tr1g0LFy5E+/btm6s2IiIiq6k3IP/whz9Ar9dj5MiR6Ny5My5duoRLly4Z9enRo4dFCyQiIrKGegPSzs4OAPDvf/9b8XmVSlXrGkkiIqLWoN6ATEtLa646iIiIWpRG36yciIioNWNAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKWBAEhERKTDpB5OJiFqa6skjTe77S2MW4OTSmKmoFWmWgHz33Xdx8OBBuLu7Y/ny5QCA8vJyJCUl4fz58/D29saMGTPg4nJrQG7ZsgWZmZlQq9WIi4tDWFgYAKCgoABpaWmorKxEeHg44uLioFKpUFVVhdTUVBQUFMDV1RUJCQnw8fFpjlUjIivQrN5qVv/qySPNnoaoWXaxDho0CLNmzTJqS09PR2hoKJKTkxEaGor09HQAwJkzZ7Bv3z4kJiZi9uzZWLNmDfR6PQBg9erVmDJlCpKTk1FUVIS8vDwAQGZmJpydnZGSkoIRI0Zg48aNzbFaRETUijVLQHbr1s2wdVgjJycH0dHRAIDo6Gjk5OQY2gcMGABbW1v4+PjA19cX+fn5KCsrw7Vr1xAcHAyVSoWoqCjDNLm5uRg0aBAAoF+/fjh8+DBEpDlWjYiIWimrnaRz6dIleHp6AgA8PT1x+fJlAEBpaSm0Wq2hn5eXF0pLS2u1a7ValJaW1ppGo9HAyckJV65caa5VISKiVqjFnaRT15ZffVuESs+pVCrFvhkZGcjIyAAALFmyBDqdrhFVtn6/AGa/NjY2NmZN05hlEDUGxxo1htUC0t3dHWVlZfD09ERZWRnc3NwA3NoyLCkpMfQrLS2Fl5dXrfaSkhJ4eXkZTaPValFdXY2Kiopau3RrxMTEICYmxvD4woULlli9VsHc10an05k9DV9/ai4ca03Hz8/P2iU0C6vtYo2MjERWVhYAICsrC7179za079u3D1VVVSguLkZhYSECAwPh6ekJR0dHnDhxAiKC7OxsREZGAgAiIiKwe/duAMD+/fvRvXv3OrcgiYiITNEsW5ArVqzA0aNHceXKFUydOhW//e1vMWrUKCQlJSEzMxM6nQ4zZ84EALRv3x79+/fHzJkzoVarMXHiRKjVt3J80qRJePfdd1FZWYmwsDCEh4cDAIYMGYLU1FTEx8fDxcUFCQkJzbFaRETUiqnkPj/d89y5c9YuoUVqzHVj5u5i5bVp1Fw41poWd7ESERHdxxiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQREREChiQRERECmysXQC1XNWTR5rV/xdzF+DkYu4URETNhgFJijSrt5o9TfXkkY2ajoioJeIuViIiIgUMSCIiIgUMSCIiIgUMSCIiIgUMSCIiIgUMSCIiIgUMSCIiIgUMSCIiIgW8UQARtRr+/v71PanYfPbsWQtVQ/c6BiQRtRp1hZ1Op8OFCxeauRq613EXKxERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIGJBERkQIbaxdA9xZ/f/+GOig21/VL70RELRUDksxSX9DpdDpcuHChGashIrIc7mIlIiJSwIAkIiJSwIAkIiJSwIAkIiJSwIAkIiJSwIAkIiJS0Kou88jLy8O6deug1+sxdOhQjBo1ytolERHRParVbEHq9XqsWbMGs2bNQlJSEvbu3YszZ85YuywiIrpHtZqAzM/Ph6+vL9q0aQMbGxsMGDAAOTk51i6LiIjuUa1mF2tpaSm0Wq3hsVarxcmTJ2v1y8jIQEZGBgBgyZIl0Ol0zVZja2djY8PXk1okjk1qjFYTkCJSq02lUtVqi4mJQUxMjOExb43WdHirOWqpODablp+fn7VLaBatZherVqtFSUmJ4XFJSQk8PT2tWBEREd3LWk1Adu7cGYWFhSguLsbNmzexb98+REZGWrssIiK6R6lEad/kPergwYP48MMPodfrMXjwYDz11FPWLomIiO5RrSogybpee+01LFmyxNplENXCsUmN0Wp2sRIRETUlBiQREZECBiQ1mdsvnyFqSTg2qTF4DJKIiEgBtyCJiIgUMCBJ0cWLF7FixQrEx8djxowZWLx4MTIyMuo8E3DlypW8OTw1q3HjxpnV/8iRI4bxm5ubi/T0dAtURa1Jq7nVHDUdEcGyZcsQHR2NhIQEAMDp06eRm5tb5zRTp05tpuqI7l5kZCRvJEINYkBSLUeOHIGNjQ0eeeQRQ1uHDh1w9epVHD58GMuXL8fPP/+MTp06IT4+HiqVCvPnz8e4cePQuXNnjBs3Do899hgOHjwIOzs7vPzyy/Dw8EBubi42b96MmzdvwtXVFfHx8fDw8LDeilKrcOTIEWzatAmurq61xmVeXh7Wr18PV1dXdOzY0TDN7t278cMPP2DixIkcl1Qn7mKlWn766SejD5PbnTp1ChMmTEBiYiJ++eUXHD9+vFafGzduICgoCMuWLUPXrl2xa9cuAEBISAgWLVqEpUuXYsCAAdi6datF14PuH0rjsrKyEqtWrcKrr76KhQsX4uLFi4rTclxSXbgFSWYJDAw0/KxYhw4dUFxcjJCQEKM+NjY2iIiIAAB06tQJhw4dAnDrJ8lWrFiBsrIy3Lx5Ez4+Ps1bPLVaSuPSwcEBPj4+aNu2LQAgKirK8FN3t+O4pLowIKmW9u3b4z//+Y/ic7a2tob/V6vV0Ov1tfpoNBrDT42p1WpUV1cDANauXYvHH38ckZGRht1iRE3BlHFZF45Lqgt3sVItPXr0QFVVldG37fz8fBw9evSu5ltRUQEvLy8AQFZW1l3Ni6ghfn5+KC4uRlFREQBgz549iv04Lqku3IKkWlQqFf70pz9h/fr1+Oc//wlbW1t4e3ujd+/edzXfMWPGIDExEV5eXggKCkJxcXETVUxUm52dHaZMmYIlS5bA1dUVISEh+Pnnn2v147ikuvBOOkRERAq4i5WIiEgBA5KIiEgBA5KIiEgBA5KIiEgBA5KIiEgBA5KIiEgBA5KIiEgBA5KIiEjB/wNABLmNdfW9fAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI = df_can.loc[['China','India'],years].transpose()\n",
    "\n",
    "df_CI.plot(kind = 'box',figsize = (6,6))\n",
    "plt.title('Box plot of Indian and Chinese 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": [
    "<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 percentages associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.describe()\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",
    "    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": null,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "\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",
    "    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": 21,
   "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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/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": 22,
   "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 the 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": 25,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\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",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_can.sort_values(['Total'], ascending = False, inplace = True)\n",
    "\n",
    "df_top15 = df_can.iloc[0:16,:].copy()\n",
    "\n",
    "df_top15.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
    "run_control": {
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   },
   "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": {
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   },
   "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": 58,
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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",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\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"
   ]
  },
  {
   "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": 61,
   "metadata": {
    "button": false,
    "collapsed": false,
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    "new_sheet": false,
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    "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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43051.312500</td>\n",
       "      <td>81601.875000</td>\n",
       "      <td>92585.18750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43041.004811</td>\n",
       "      <td>67830.807783</td>\n",
       "      <td>99118.80538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7613.000000</td>\n",
       "      <td>21710.000000</td>\n",
       "      <td>13629.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16917.500000</td>\n",
       "      <td>38073.250000</td>\n",
       "      <td>28862.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27778.000000</td>\n",
       "      <td>55893.000000</td>\n",
       "      <td>57736.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58392.500000</td>\n",
       "      <td>87436.250000</td>\n",
       "      <td>94459.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s         2000s\n",
       "count      16.000000      16.000000      16.00000\n",
       "mean    43051.312500   81601.875000   92585.18750\n",
       "std     43041.004811   67830.807783   99118.80538\n",
       "min      7613.000000   21710.000000   13629.00000\n",
       "25%     16917.500000   38073.250000   28862.75000\n",
       "50%     27778.000000   55893.000000   57736.00000\n",
       "75%     58392.500000   87436.250000   94459.25000\n",
       "max    179171.000000  261966.000000  340385.00000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "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": 62,
   "metadata": {
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    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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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",
    "new_df.plot(kind = 'box' , figsize = (10,10))\n",
    "\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\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",
    "    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": 65,
   "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>Country</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  1980s   1990s   2000s\n",
       "0   India  82154  180395  303591\n",
       "1   China  32003  161528  340385"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df=new_df.reset_index()\n",
    "new_df[new_df['2000s']> 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
    }
   },
   "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#matplotlib.pyplot.boxplot?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) 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 datapoints 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": 66,
   "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></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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 66,
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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 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 datapoints 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": 68,
   "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": 68,
     "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": "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": 69,
   "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": 69,
     "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]) "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "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 actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ), 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
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    },
    "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_countries = df_can.loc[['Denmark','Norway','Sweden'],:]\n",
    "#df_countries.head()\n",
    "df_total = pd.DataFrame(df_countries[years].sum(axis = 0))\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_total.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_total.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "<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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
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   "outputs": [
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      "text/plain": [
       "<Figure size 2520x720 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 = (35,10))\n",
    "\n",
    "plt.title('Scatter Plot of Immigrants from Norway, Denmark and Sweden by year')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\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 datapoints 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 keyword `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 - 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 bring it in the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>India</th>\n",
       "      <th>China</th>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Pakistan</th>\n",
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       "      <th>Sri Lanka</th>\n",
       "      <th>Republic of Korea</th>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "      <td>22045</td>\n",
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       "      <td>1172</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "      <td>24796</td>\n",
       "      <td>5921</td>\n",
       "      <td>972</td>\n",
       "      <td>10030</td>\n",
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       "      <td>371</td>\n",
       "      <td>1456</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5249</td>\n",
       "      <td>1201</td>\n",
       "      <td>9074</td>\n",
       "      <td>1822</td>\n",
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       "      <td>1572</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "      <td>10015</td>\n",
       "      <td>4562</td>\n",
       "      <td>900</td>\n",
       "      <td>7100</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "      <td>10170</td>\n",
       "      <td>3801</td>\n",
       "      <td>668</td>\n",
       "      <td>6661</td>\n",
       "      <td>1977</td>\n",
       "      <td>1086</td>\n",
       "      <td>847</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  India  China  \\\n",
       "0        1980   8880   5123   \n",
       "1        1981   8670   6682   \n",
       "2        1982   8147   3308   \n",
       "3        1983   7338   1863   \n",
       "4        1984   5704   1527   \n",
       "\n",
       "Country  United Kingdom of Great Britain and Northern Ireland  Philippines  \\\n",
       "0                                                    22045            6051   \n",
       "1                                                    24796            5921   \n",
       "2                                                    20620            5249   \n",
       "3                                                    10015            4562   \n",
       "4                                                    10170            3801   \n",
       "\n",
       "Country  Pakistan  United States of America  Iran (Islamic Republic of)  \\\n",
       "0             978                      9378                        1172   \n",
       "1             972                     10030                        1429   \n",
       "2            1201                      9074                        1822   \n",
       "3             900                      7100                        1592   \n",
       "4             668                      6661                        1977   \n",
       "\n",
       "Country  Sri Lanka  Republic of Korea  ...  Kiribati  Vanuatu  \\\n",
       "0              185               1011  ...         0        0   \n",
       "1              371               1456  ...         0        0   \n",
       "2              290               1572  ...         0        0   \n",
       "3              197               1081  ...         1        0   \n",
       "4             1086                847  ...         0        0   \n",
       "\n",
       "Country  Sao Tome and Principe  Tuvalu  American Samoa  San Marino  \\\n",
       "0                            0       0               0           1   \n",
       "1                            0       1               1           0   \n",
       "2                            0       0               0           0   \n",
       "3                            0       0               0           0   \n",
       "4                            0       1               0           0   \n",
       "\n",
       "Country  New Caledonia  Marshall Islands  Western Sahara  Palau  \n",
       "0                    0                 0               0      0  \n",
       "1                    0                 0               0      0  \n",
       "2                    0                 0               0      0  \n",
       "3                    0                 0               0      0  \n",
       "4                    0                 0               0      0  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t = df_can[years].transpose() # transposed dataframe\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
    }
   },
   "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?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where _`X`_ is an original value, _`X'`_ is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "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": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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 x2000).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f80f2930978>"
      ]
     },
     "execution_count": 92,
     "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 - 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, the more immigrants 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 - 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": 93,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# normalize Brazil 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",
    "# normalize Argentina data\n",
    "norm_China = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\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",
    "    # normalize China data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalize India 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": 94,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f817c80ac50>"
      ]
     },
     "execution_count": 94,
     "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": [
    "### type your answer here\n",
    "\n",
    "\n",
    "# India\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_India * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# China\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_China * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from India and China from 1980 - 2013')\n",
    "ax0.legend(['India', 'China'], loc='upper left', fontsize='x-large')\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",
    "    # 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/\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\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",
    "|                   |         |              |                                    |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
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