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

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   "source": [
    "\n",
    "<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Introduction\n",
    "\n",
    "In this lab session, we continue exploring the Matplotlib library. More specificatlly, we will learn how to create pie charts, box plots, scatter plots, and bubble charts."
   ]
  },
  {
   "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/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\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) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Import primary modules."
   ]
  },
  {
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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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",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "text": [
      "Solving environment: done\n",
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.5.11\n",
      "  latest version: 4.7.12\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda/envs/python\n",
      "\n",
      "  added / updated specs: \n",
      "    - xlrd\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    openssl-1.1.1              |       h7b6447c_0         5.0 MB  anaconda\n",
      "    certifi-2019.9.11          |           py36_0         154 KB  anaconda\n",
      "    xlrd-1.2.0                 |           py36_0         188 KB  anaconda\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         5.4 MB\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    certifi: 2019.6.16-py36_1  conda-forge --> 2019.9.11-py36_0 anaconda\n",
      "    openssl: 1.1.1c-h516909a_0 conda-forge --> 1.1.1-h7b6447c_0 anaconda\n",
      "    xlrd:    1.1.0-py37_1                  --> 1.2.0-py36_0     anaconda\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "openssl-1.1.1        | 5.0 MB    | ##################################### | 100% \n",
      "certifi-2019.9.11    | 154 KB    | ##################################### | 100% \n",
      "xlrd-1.2.0           | 188 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "!conda install -c anaconda xlrd --yes"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
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    "new_sheet": false,
    "run_control": {
     "read_only": false
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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://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Let's take a look at the first five items in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "<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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
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       "      <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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      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's find out how many entries there are in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "jupyter": {
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    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
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    "new_sheet": false,
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     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Country', 'Continent', 'Region', 'DevName', '1980', '1981', '1982',\n",
      "       '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
      "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000',\n",
      "       '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n",
      "       '2010', '2011', '2012', '2013'],\n",
      "      dtype='object')\n",
      "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",
    "print(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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
       "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
       "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
       "       '2011', '2012', '2013', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.1.1\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,
    "new_sheet": 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. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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."
   ]
  },
  {
   "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/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
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    "run_control": {
     "read_only": false
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Africa</td>\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",
       "      <td>Asia</td>\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",
       "      <td>Europe</td>\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",
       "      <td>Latin America and the Caribbean</td>\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",
       "      <td>Northern America</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>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": 10,
     "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",
    "- `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "Africa                              618948\n",
       "Asia                               3317794\n",
       "Europe                             1410947\n",
       "Latin America and the Caribbean     765148\n",
       "Northern America                    241142\n",
       "Oceania                              55174\n",
       "Name: Total, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continents['Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "jupyter": {
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   },
   "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": 23,
   "metadata": {
    "button": 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. 0.1 means that corresponding slide will jump out\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "button": 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>2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Africa</td>\n",
       "      <td>38543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asia</td>\n",
       "      <td>155075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Europe</td>\n",
       "      <td>28691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>24950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Northern America</td>\n",
       "      <td>8503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Oceania</td>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   2013\n",
       "Continent                              \n",
       "Africa                            38543\n",
       "Asia                             155075\n",
       "Europe                            28691\n",
       "Latin America and the Caribbean   24950\n",
       "Northern America                   8503\n",
       "Oceania                            1775"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "# get the data\n",
    "df_2013=df_can[['Continent','2013']]\n",
    "df_continent_2013=df_2013.groupby('Continent', axis=0).sum()\n",
    "df_continent_2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list_2013 = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list_2013 = [0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge. 0.1 means that corresponding slice will jump out\n",
    "df_continent_2013['2013'].plot(kind='pie',\n",
    "                      figsize=(15,6),\n",
    "                      autopct='%1.1f%%',\n",
    "                      startangle=90,\n",
    "                       shadow=True,\n",
    "                       labels=None,\n",
    "                       pctdistance=1.3,\n",
    "                       colors=colors_list_2013,\n",
    "                       explode=explode_list_2013\n",
    "                      )\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent in 2013', y=1.3) \n",
    "plt.axis('equal')\n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continent_2013.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "-->\n",
    "\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",
    "\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",
    "\n",
    "<!--\n",
    "\\\\ # add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\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."
   ]
  },
  {
   "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\">"
   ]
  },
  {
   "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."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "button": 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",
       "      <td>1980</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1982</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1983</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1984</td>\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": 42,
     "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'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "button": 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": {
      "needs_background": "light"
     },
     "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",
    "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",
    "2. 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "button": 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",
       "      <td>count</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
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   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</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",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>1</td>\n",
       "      <td>...</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",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</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",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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",
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       "      <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",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \n",
       "Albania            0     0     0     0     1  ...  1223   856   702   560   \n",
       "Algeria           71    69    63    44    69  ...  3626  4807  3623  4005   \n",
       "American Samoa     0     0     0     0     0  ...     0     1     0     0   \n",
       "Andorra            0     0     0     0     2  ...     0     1     1     0   \n",
       "\n",
       "                2009  2010  2011  2012  2013  Total  \n",
       "Country                                              \n",
       "Afghanistan     1746  1758  2203  2635  2004  58639  \n",
       "Albania          716   561   539   620   603  15699  \n",
       "Algeria         5393  4752  4325  3774  4331  69439  \n",
       "American Samoa     0     0     0     0     0      6  \n",
       "Andorra            0     0     0     1     1     15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>1980</td>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
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       "      <td>1981</td>\n",
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       "      <td>1982</td>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
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       "      <td>1983</td>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
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       "      <td>1984</td>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
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       "      <td>1985</td>\n",
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       "      <td>1986</td>\n",
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       "      <td>1987</td>\n",
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       "      <td>10189</td>\n",
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       "      <td>1988</td>\n",
       "      <td>2758</td>\n",
       "      <td>11522</td>\n",
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       "      <td>1989</td>\n",
       "      <td>4323</td>\n",
       "      <td>10343</td>\n",
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       "    <tr>\n",
       "      <td>1990</td>\n",
       "      <td>8076</td>\n",
       "      <td>12041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1991</td>\n",
       "      <td>14255</td>\n",
       "      <td>13734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1992</td>\n",
       "      <td>10846</td>\n",
       "      <td>13673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1993</td>\n",
       "      <td>9817</td>\n",
       "      <td>21496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1994</td>\n",
       "      <td>13128</td>\n",
       "      <td>18620</td>\n",
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       "      <td>1995</td>\n",
       "      <td>14398</td>\n",
       "      <td>18489</td>\n",
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       "      <td>1996</td>\n",
       "      <td>19415</td>\n",
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       "      <td>1997</td>\n",
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       "      <td>22268</td>\n",
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       "      <td>1998</td>\n",
       "      <td>21049</td>\n",
       "      <td>17241</td>\n",
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       "    <tr>\n",
       "      <td>1999</td>\n",
       "      <td>30069</td>\n",
       "      <td>18974</td>\n",
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       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>35529</td>\n",
       "      <td>28572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>36434</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>31961</td>\n",
       "      <td>31889</td>\n",
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       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>36439</td>\n",
       "      <td>27155</td>\n",
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       "      <td>36619</td>\n",
       "      <td>28235</td>\n",
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       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>42584</td>\n",
       "      <td>36210</td>\n",
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       "    <tr>\n",
       "      <td>2006</td>\n",
       "      <td>33518</td>\n",
       "      <td>33848</td>\n",
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       "    <tr>\n",
       "      <td>2007</td>\n",
       "      <td>27642</td>\n",
       "      <td>28742</td>\n",
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       "    <tr>\n",
       "      <td>2008</td>\n",
       "      <td>30037</td>\n",
       "      <td>28261</td>\n",
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       "      <td>2009</td>\n",
       "      <td>29622</td>\n",
       "      <td>29456</td>\n",
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       "      <td>30391</td>\n",
       "      <td>34235</td>\n",
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       "      <td>2011</td>\n",
       "      <td>28502</td>\n",
       "      <td>27509</td>\n",
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       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>33024</td>\n",
       "      <td>30933</td>\n",
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       "      <td>2013</td>\n",
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       "      <td>33087</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704\n",
       "1985      1816   4211\n",
       "1986      1960   7150\n",
       "1987      2643  10189\n",
       "1988      2758  11522\n",
       "1989      4323  10343\n",
       "1990      8076  12041\n",
       "1991     14255  13734\n",
       "1992     10846  13673\n",
       "1993      9817  21496\n",
       "1994     13128  18620\n",
       "1995     14398  18489\n",
       "1996     19415  23859\n",
       "1997     20475  22268\n",
       "1998     21049  17241\n",
       "1999     30069  18974\n",
       "2000     35529  28572\n",
       "2001     36434  31223\n",
       "2002     31961  31889\n",
       "2003     36439  27155\n",
       "2004     36619  28235\n",
       "2005     42584  36210\n",
       "2006     33518  33848\n",
       "2007     27642  28742\n",
       "2008     30037  28261\n",
       "2009     29622  29456\n",
       "2010     30391  34235\n",
       "2011     28502  27509\n",
       "2012     33024  30933\n",
       "2013     34129  33087"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer heredf_can[years]\n",
    "\n",
    "df_CI=df_can.loc[['China','India'], years].transpose()\n",
    "\n",
    "df_CI"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
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       "    }\n",
       "\n",
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       "</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",
       "      <td>count</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.plot(kind='box', figsize=(8,6))\n",
    "\n",
    "plt.title('Box plot of Chinese and Indian Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "button": 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": {
      "needs_background": "light"
     },
     "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",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "Where\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\">"
   ]
  },
  {
   "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "button": 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": {
      "needs_background": "light"
     },
     "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",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \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)."
   ]
  },
  {
   "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`."
   ]
  },
  {
   "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**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "button": 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",
       "      <td>India</td>\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",
       "      <td>China</td>\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",
       "      <td>United Kingdom of Great Britain and Northern Ireland</td>\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",
       "      <td>Philippines</td>\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",
       "      <td>Pakistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United States of America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iran (Islamic Republic of)</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Poland</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>France</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jamaica</td>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Romania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_top15=df_can.sort_values(by='Total', ascending=False).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "   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**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China</td>\n",
       "      <td>32003</td>\n",
       "      <td>161528</td>\n",
       "      <td>340385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United Kingdom of Great Britain and Northern Ireland</td>\n",
       "      <td>179171</td>\n",
       "      <td>261966</td>\n",
       "      <td>83413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Philippines</td>\n",
       "      <td>60764</td>\n",
       "      <td>138482</td>\n",
       "      <td>172904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pakistan</td>\n",
       "      <td>10591</td>\n",
       "      <td>65302</td>\n",
       "      <td>127598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United States of America</td>\n",
       "      <td>76824</td>\n",
       "      <td>56915</td>\n",
       "      <td>75173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iran (Islamic Republic of)</td>\n",
       "      <td>21477</td>\n",
       "      <td>54871</td>\n",
       "      <td>65794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>14796</td>\n",
       "      <td>70421</td>\n",
       "      <td>49678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>16259</td>\n",
       "      <td>38189</td>\n",
       "      <td>68183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Poland</td>\n",
       "      <td>57602</td>\n",
       "      <td>64864</td>\n",
       "      <td>13629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lebanon</td>\n",
       "      <td>24918</td>\n",
       "      <td>49245</td>\n",
       "      <td>30906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>France</td>\n",
       "      <td>17137</td>\n",
       "      <td>30028</td>\n",
       "      <td>41297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jamaica</td>\n",
       "      <td>34328</td>\n",
       "      <td>40329</td>\n",
       "      <td>22733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>30638</td>\n",
       "      <td>37726</td>\n",
       "      <td>21274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Romania</td>\n",
       "      <td>7613</td>\n",
       "      <td>33659</td>\n",
       "      <td>45515</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\n",
       "United States of America                             76824   56915   75173\n",
       "Iran (Islamic Republic of)                           21477   54871   65794\n",
       "Sri Lanka                                            14796   70421   49678\n",
       "Republic of Korea                                    16259   38189   68183\n",
       "Poland                                               57602   64864   13629\n",
       "Lebanon                                              24918   49245   30906\n",
       "France                                               17137   30028   41297\n",
       "Jamaica                                              34328   40329   22733\n",
       "Viet Nam                                             30638   37726   21274\n",
       "Romania                                               7613   33659   45515"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\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))) #1990 is not included in the range\n",
    "years_90s = list(map(str, range(1990, 2000))) # 2000 is not included in the range\n",
    "years_00s = list(map(str, range(2000, 2010))) # 2010 is not included in the range\n",
    "\n",
    "# Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "df_1980s=df_top15.loc[:,years_80s].sum(axis=1)\n",
    "df_1990s=df_top15.loc[:,years_90s].sum(axis=1)\n",
    "df_2000s=df_top15.loc[:,years_00s].sum(axis=1)\n",
    "#print(df_1980s)\n",
    "# Merge the three series into a new data frame. Call your dataframe **new_df**.\n",
    "new_df=pd.DataFrame({\n",
    "    '1980s':df_1980s,\n",
    "    '1990s':df_1990s,\n",
    "    '2000s':df_2000s\n",
    "})\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\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",
    "\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",
    "\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",
    "\n",
    "<!--\n",
    "\\\\ # display dataframe\n",
    "new_df.head()\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>44418.333333</td>\n",
       "      <td>85594.666667</td>\n",
       "      <td>97471.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>44190.676455</td>\n",
       "      <td>68237.560246</td>\n",
       "      <td>100583.204205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>7613.000000</td>\n",
       "      <td>30028.000000</td>\n",
       "      <td>13629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>16698.000000</td>\n",
       "      <td>39259.000000</td>\n",
       "      <td>36101.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>30638.000000</td>\n",
       "      <td>56915.000000</td>\n",
       "      <td>65794.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>59183.000000</td>\n",
       "      <td>104451.500000</td>\n",
       "      <td>105505.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>179171.000000</td>\n",
       "      <td>261966.000000</td>\n",
       "      <td>340385.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980s          1990s          2000s\n",
       "count      15.000000      15.000000      15.000000\n",
       "mean    44418.333333   85594.666667   97471.533333\n",
       "std     44190.676455   68237.560246  100583.204205\n",
       "min      7613.000000   30028.000000   13629.000000\n",
       "25%     16698.000000   39259.000000   36101.500000\n",
       "50%     30638.000000   56915.000000   65794.000000\n",
       "75%     59183.000000  104451.500000  105505.500000\n",
       "max    179171.000000  261966.000000  340385.000000"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.describe()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "button": 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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.plot(kind='box', figsize=(10,6))\n",
    "\n",
    "\n",
    "plt.title('Immigrants from the Top 15 Countries by Decades')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\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",
    "* larger than Q3 by at least 1.5 times the interquartile range (IQR, the first quartile subtracted from the third quartile), 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",
    "* 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>82154</td>\n",
       "      <td>180395</td>\n",
       "      <td>303591</td>\n",
       "    </tr>\n",
       "    <tr>\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": [
       "         1980s   1990s   2000s\n",
       "Country                       \n",
       "India    82154  180395  303591\n",
       "China    32003  161528  340385"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "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) on box plots for more information."
   ]
  },
  {
   "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."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "button": 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",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 117,
     "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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1bNGTJEmL1tDQOkZGxrn11s2MjIwzOLi20yF1FRM9SZK0aE1MbJtxWTMz0ZMkSYtWb+/yGZc1MxM9SZK0aA0Pr2JgoI9DDlnBwEAfw8OrOh1SV3EwhiRJWrT6+3tYs+Y4+vr6GB8f73Q4XccWPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqqGWdDkCSpKVibGwLQ0Pr2Lz5flas2JXh4VX09/d0Oiy1weRnPTGxjd7e5R37rG3RkyRpgQwNrWNkZJxbb93MyMg4g4NrOx2S2mTysx4d3drRz9oWPUmSFsjExLYZl7W4zKdVbrF81rboSZK0QHp7l8+4rMVlPq1yi+WzNtGTJGmBDA+vYmCgj0MOWcHAQB/Dw6s6HZJmMJ9WucnPeuXKno5+1l66lSRpgfT397BmzXH09fUxPj7e6XA0i97e5YyObt1huVGTn3Wn2aInSZI0hcXSKjcftuhJkiRNYbG0ys2HLXqSJEkVZaInSZJUUQty6TbGeBBwMbAv8BBwfkrpAzHGfwBeBfwyb/qWlNKVeZ83A2cADwJnp5SuyuUDwIXA7sCVwGtTSkWMcbf8GgPAr4CTU0rr8z6nA2/Nr/GOlNJFbX3DkiRJi8BCteg9ALwhpfQE4GjgrBjjYXnd+1NKR+bHZJJ3GHAKcDjwfOBDMcZd8vbnAWcCh+bH83P5GcBdKaVDgPcD78nH6gXOAZ4KHAWcE2Pcq63vVpIkaRFYkEQvpbQppfSd/HwL8EPggBl2OQG4NKV0b0ppFLgVOCrGuB+wZ0ppXUqpoGzBe3HNPpMtdZcBz40xBuB44JqU0kRK6S7gGrYnh5IkSZW14KNuY4wHA08Cvgk8HRiMMZ4GfJuy1e8uyiTwhprdNuSy+/Pz+nLyz9sBUkoPxBg3A3vXlk+xT21cZ1K2FJJSoq+vb17vsxHLli1bkNepEuusedZZc6yv5llnzVtqdTY6+j/8+Z9fzfj4PfT17c6FFx7PypUrmjrGUquzVlnQRC/G2AN8FnhdSunXMcbzgLcDRf75r8ArgDDF7sUM5cxxn99KKZ0PnD+5fiEmsnTCzOZZZ82zzppjfTXPOmveUquzU0+9mpGR8v3eeutmTj31i01PW7LU6qwZ+++//7TrFizRizHuSpnkfSKldDlASumOmvUfBr6QFzcAB9XsfiCwMZcfOEV57T4bYozLgBXARC5/Vt0+17fiPUmStFDGxrYwNLSOiYlt9PYuZ3h4Ff39PZ0OqyHzuZWY5mdB+ujlvnIfBX6YUnpfTfl+NZv9CXBTfr4GOCXGuFuMcSXloIsbU0qbgC0xxqPzMU8DrqjZ5/T8/ETgutyP7yrguBjjXnkQxnG5TJKkrjE0tI6RkXFGR7cyMjLO4ODaTofUsPpbhzVzKzHNz0K16D0deDnwgxjjd3PZW4CXxhiPpLyUuh74S4CU0s0xxgTcQjli96yU0oN5v9ewfXqVL+UHlInkJTHGWylb8k7Jx5qIMb4d+Fbe7tyU0kSb3qckSW3Rza1iw8OrGBxcu0Nr5ELo5lbQVglFsVN3NUGxcePG2beaJ/sbNM86a5511hzrq3nWWfPmUmerV2/v5wYwMNDX9bfnaoZ1Nr3cR2+qMQneGUOSpG4wPLyKgYE+Vq7sYWCgb8FaxbpZN7eCtsqCT68iSZKa19/fU8nWqHbq7V3O6OjWHZaXGlv0JEnSjMbGtrB69dUcc8waVq++mttu2zr7TouAraC26EmSOsSO8t1jcsQvwOjoVgYH13ZF66KtoLboSZI6pJunC1lq7OvWvUz0JEkdYfLQPZwHr3uZ6EmSOsLkoXvY16172UdPktQRnZpEV82zr1v3MtGTJHWEyYPUfl66lSRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIpywmRJkpaAsbEtDA2t2+FOJP39PZ0OS21mi54kSU0YG9vC6tVXc8wxa1i9+mpuu21rp0NqyNDQOkZGxhkd3crIyDiDg2s7HZIWgImeJElN6NaEaWJi24zLqiYTPUmSmtCtCVNv7/IZl1VNJnqSJDWhWxOm4eFVDAz0sXJlDwMDfQwPr+p0SFoADsaQJKkJw8OrGBxcu8Oghm7Q39/DmjXHdToMLTATPUmSmmDCpG7ipVtJkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKch49SVLXGRvbwtDQuh0mLe7v7+l0WNKiY4uepEVlbGwLq1dfzTHHrGH16qu57batnQ5Ji9DQ0DpGRsYZHd3KyMg4g4NrOx2StCiZ6Elqqfkman6BqxETE9tmXJZUMtGT1FLzTdT8AlcjenuXz7gsqWSiJ6ml5puo+QWuRgwPr2JgoI+VK3sYGOhjeHhVp0OSFiUHY0hqqd7e5YyObt1huRnDw6sYHFy7Qyd7qV5/fw9r1hzX6TCkRW9OiV6McXfgwZTSfS2OR1KXm2+i5he4JLVOQ4lejPFfgJRSujHG+ELgMqCIMZ6cUvp8WyOU1FVM1CRp8Wi0j96pwE35+duAPwNWA+9qR1CSJLWLU/hoKWk00dsjpfSbGOPewGNSSp9NKf0X8DttjE2StIhNJkyHH35RVyVMTuGjpaTRPno/iTGeChwCXAMQY+wD7mlXYJKkxW0yYZo0OLi2Ky7bO4WPlpJGE72/Aj4A3AeckcuOB65uR1CSpMWvWxOm+Y4Ml7pJo4ne7SmlHYbOpZQ+EWO8tg0xSZK6QLcmTE7ho6Wk4Uu3wJ5TlN8C9LYuHElSt5hMmDZvvp8VK3btmoTJkeFaShpN9EJ9QYxxT+Ch1oYjSeoWkwlTX18f4+Pjs+8gacHNmOjFGG8HCmD3GONtdav3Bj7VrsAkSZI0P7O16P0ZZWvelcDLa8oL4I6U0o/bFZgkSZLmZ8ZEL6X0FSinUkkp/WZhQpIkSVIrNNpH74EY45nAkUBP7YqU0mktj0qSJEnz1miidzHw+8DngTvaF44kSZJapdFE73hgZUrpf9oZjCRJklqn0Xvd3gbs1s5AJEmS1FrNXLq9Isb4Aeou3aaUrmt5VJI6amxsC0ND63a4c0B/f8/sO0qSFpVGE73B/PNddeUF8JjWhSNpMai9Wf3o6NauuVm9JGlHDSV6KaWV7Q5E0uLRrTerlyTtqNE+epKWkPqb03fLzeolSTtqqEUv39f2H4BnAn3U3Ps2pdTflsgkdczkzepr++hJkrpPo330PgQcCJwLfJzy1mh/A3y2TXFJ6qDJm9VLkrpbo5dujwNeklK6Angw/zyZHe9/K0mSpEWk0UTvYcDm/HxrjPFRwCbgkLZEJUmSpHlr9NLt9yj7510LfA34D2Ar8JM2xSVJkqR5ajTRexXbB2CcDfwT8CjgtEZ2jjEeRDnp8r7AQ8D5KaUPxBh7gU8DBwPrgZhSuivv82bgDOBB4OyU0lW5fAC4ENgduBJ4bUqpiDHull9jAPgVcHJKaX3e53TgrTmcd6SULmrwfUuSJHWtWS/dxhh3Af4c2ACQUvplSumVKaWTU0q3NPg6DwBvSCk9ATgaOCvGeBjwJuDalNKhlK2Fb8qveRhwCnA48HzgQzkOgPOAM4FD8+P5ufwM4K6U0iHA+4H35GP1AucATwWOAs6JMe7VYNySJElda9ZEL6X0IHAWcP9cXySltCml9J38fAvwQ+AA4ARgsnXtIuDF+fkJwKUppXtTSqPArcBRMcb9gD1TSutSSgVlC17tPpPHugx4bowxAMcD16SUJnJr4TVsTw4lqSXGxrawevXVHHPMGlavvprbbtva6ZAkqeHBGBcBr27FC8YYDwaeBHwTeHRKaROUySCwT97sAOD2mt025LID8vP68h32SSk9QDl4ZO8ZjiVJLTN527jR0a2MjIwzOLi20yFJUsN99I4ChmKM/4syaSomV6SUjm30xWKMPZRz770upfTrGON0m4YpyooZyue6T21sZ1JeEialRF9f33SxtcyyZcsW5HWqxDprnnXWnLnW1+bN9++0vFTq3XOsedZZ86yzuWk00ftwfsxZjHFXyiTvEymly3PxHTHG/VJKm/Jl2Ttz+QbgoJrdDwQ25vIDpyiv3WdDjHEZsAKYyOXPqtvn+vr4UkrnA+fnxWJ8fHwO77I5fX19LMTrVIl11jzrrDlzra8VK3bdaXmp1LvnWPOss+ZZZ9Pbf//9p13XUKI331Gqua/cR4EfppTeV7NqDXA68O7884qa8k/GGN8H7E856OLGlNKDMcYtMcajKS/9ngZ8sO5Y64ATgevyaNyrgHfVDMA4DnjzfN6PJNXztnGSFqNG73X7imlW3UvZYnZDSuneGQ7xdMq7aPwgxvjdXPYWygQvxRjPAG4DTgJIKd0cY0zALZQjds/Kg0IAXsP26VW+lB9QJpKXxBhvpWzJOyUfayLG+HbgW3m7c1NKE428b0lqlLeNk7QYhaLYqbvaTmKM1wNPA+5g++XTRwPfppwDD+CElNK32xLlwis2btw4+1bzZDN086yz5llnzbG+mmedNc86a551Nr186XaqMQkN99G7Gbg8pfTvkwUxxkHg8cAxwN9RXkJ92rwilSRJUss0Or3Ky4DhurLzgFPzfHbvBQ5rZWCSJEman0YTvTuAF9WVvZDto2SXM48JlSWpFZy0WJJ21Oil27OBz8QYb6KcR+8g4Ajy4AnK24t9cJp9JWlBTE5aDDA6upXBwbVdMUBibGwLQ0Prdhix29/f0+mwJFVAo9OrXB1j/F3gjyinO7kS+GJK6VeT64Gr2xalJDVgYmLbjMuLVbcmqJIWv0Zb9EgpjQOXtDEWSZqX3t7ljI5u3WG5G3Rrgipp8Zs20Ysxfjml9Pz8/GtMcdswaO4WaJLUTt06aXG3JqiSFr+ZWvQurnn+kXYHIknz1a2TFndrgipp8Zs20UspfbLm+bxugSZJml63JqiSFr+G++jFGJ8BPAnYYShYSuldrQ5KkiRJ89fovW4/CETga8A9Natmv3+apI6YnLJj8+b7WbFiV6fsmIX1JamKGm3ROxU4IqXU/hvASmqJ2ik7AKfsmIX1JamKGr0zxu3Ave0MRFJrOWVHc6wvSVXUaIveGcCHY4yforwd2m+llL7a8qgkzZtTdjTH+pJURY0megOUd8U4lp376PW3OihJ8zc5ZUdtnzNNz/qSVEWNJnrvAl6UUvqvdgYjqXUmp+zo6+tjfHx89h2WOOtLUhU12kfvbsBLtJIkSV2k0Ra9twH/FmM8F7izdkVK6aGWRyVJkqR5azTRuyD//MuaskDZR2+XlkYkSZKklmg00VvZ1igkSZLUcg0leimlsXYHIkmSpNZq9BZoK4Czmfpet04dL0mStAg1eun2M5R98T7HjvPoSZK62OQ9ficmttHbu9x7/EoV02iidzSwd0rp/nYGI0laWLX3+B0d3eo9fqWKaXQeva8DT2hnIJKkhec9fqVqa7RF78+BK2OM32Tne92e2+qgJEkLw3v8StXWaKL3TuAgYD2wZ0150eqAJEkLZ/Iev7V99CRVR6OJ3inAY1NKm9oZjCRpYU3e41dSNTXaR+9ngAMxJEmSukijLXqXAGtijB9k5z5617U8KkmSJM1bo4neWfnnu+rKC+AxrQtHkiRJrdLoLdC8160kSVKXabSPniRJkrrMjC16McavMcsUKimlY1sakSRJklpitku3H1mQKCRJktRyMyZ6KaWLFioQSZIktZZ99CRJkirKRE+SJKmiTPQkSZIqatpEL8Z4Q83zcxYmHEmSJLXKTC16j40xLs/P37AQwUiSJKl1Zhp1ewXwkxjjemD3GONXp9rIefQkSZIWp2kTvZTSX8QYjwEOBv4A+OhCBSVJkqT5m20eva8DX48xPtw59SRJkrrLbHfGACCldEGM8dnAy4EDgJ8DH08pXdfO4KSlbGxsC0ND65iY2EZv73KGh1fR39/T6bAkSV2koelVYoyvBD4N/LtJWYoAABT4SURBVAK4HNgEfDLG+Ko2xiYtaUND6xgZGWd0dCsjI+MMDq7tdEiSpC7TUIse8L+A56WUvjdZEGP8NPBZ4MPtCExa6iYmts24LEnSbBqdMHlv4Ja6sh8Dva0NR9Kk3t7lMy5LkjSbRhO9rwPvizHuARBjfATwXsBrSVKbDA+vYmCgj5UrexgY6GN4eFWnQ5IkdZlGL92+GrgU2BxjnKBsyVsLvLRdgUlLXX9/D2vWHNfpMNQFHLgjaTqNjrrdBDwzxnggsD+wMaW0oa2RSZIaMjlwB2B0dCuDg2v9J0ES0HiLHgA5uTPBk6RFxIE7kqbTaB89SdIi5cAdSdMx0ZOkLufAHUnTmfXSbYzxYcCzgK+nlO5re0SSpKY4cEfSdGZt0UspPQRcYZInSZLUXRq9dPvVGOPRbY1EkiRJLdXoqNsx4EsxxiuA24FickVK6W3tCEySJEnz02iitzvwf/LzA9sUiyRJklqo0QmT/6LdgUiSJKm1Gp4wOcb4BOBE4NEppcEY4+OA3VJK329bdJIkSZqzhgZjxBhPAr4KHACclosfCbyvTXFJkiRpnhoddXsu8LyU0quBB3PZ94AntiUqSZIkzVujl273oUzsYPuI26Lm+YxijBcAfwzcmVI6Ipf9A/Aq4Jd5s7eklK7M694MnEGZVJ6dUroqlw8AF1IODrkSeG1KqYgx7gZcDAwAvwJOTimtz/ucDrw1v8Y7UkoXNfieJUmSulqjLXojwMvryk4Bbmxw/wuB509R/v6U0pH5MZnkHZaPfXje50Mxxl3y9ucBZwKH5sfkMc8A7kopHQK8H3hPPlYvcA7wVOAo4JwY414NxixJktTVGk30zgbeEWP8CvCIGONVwNuB1zeyc0rpq8BEg691AnBpSunelNIocCtwVIxxP2DPlNK6lFJB2YL34pp9JlvqLgOeG2MMwPHANSmliZTSXcA1TJ1wSpIkVU6j06v8KMb4eMrLr1+gnDT5CymlrfN8/cEY42nAt4E35GTsAOCGmm025LL78/P6cvLP23OsD8QYNwN715ZPsc8OYoxnUrYWklKir69vfu+sAcuWLVuQ16kS66x51llzrK/mWWfNs86aZ53NTcPTq6SUfhNj/AYwCmxsQZJ3HmWrYJF//ivwCiBMsW0xQzlz3GcHKaXzgfMntxkfH5828Fbp6+tjIV6nSqyz5llnzbG+mmedNc86a551Nr39999/2nUNJXoxxn7gE8DRwF3AXjHGbwKnppTG5hJUSumOmuN/mLKlEMpWt4NqNj0Q2JjLD5yivHafDTHGZcAKykvFG4Bn1e1z/VzilSRJ6jaN9tG7iHJAxqNSSvsAewHfYnu/uKblPneT/gS4KT9fA5wSY9wtxriSctDFjSmlTcCWGOPRuf/dacAVNfucnp+fCFyX+/FdBRwXY9wrD8I4LpdJkiRVXqOXbgeA41JK9wOklLbGGP+WciqTWcUYP0XZstYXY9xAORL2WTHGIykvpa4H/jIf++YYYwJuAR4AzkopTc7d9xq2T6/ypfwA+ChwSYzxVsqWvFPysSZijG+nTEoBzk0pNTooRJIkqas1mujdQDk9yTdqyp4CrGtk55TSS6co/ugM278TeOcU5d8GjpiifBtw0jTHugC4oJE4JUmSqmTaRC/GeG7N4k+BK2OMX6QcxXoQ8ALgk+0NT5IkSXM1U4veQXXLl+ef+wD3Ap8DlrcjKEmSJM3ftIleSukvFjIQSZIktVbD8+jFGPcADgF6astTSmtbHZQkSZLmr9F59E4DhoH7gHtqVhVAfxvikiRJ0jw12qL3z8BLUkrXtDMYSZIktU6jEybfh3eUkCRJ6iqNJnp/D7wvxujdhCVJkrpEo5dufwKcC/xVjHGyLABFSmmXdgQmVcHY2BaGhtYxMbGN3t7lDA+vor+/Z/YdJUlqgUZb9C4BLgaeCDw2Pw7NPyVNY2hoHSMj44yObmVkZJzBQQepS5IWTqMtensDb0spFe0MRqqaiYltMy5LktROjbbofQx4eTsDkaqot3f5jMuSJLVToy16RwGDMca/A+6oXZFSOrblUUkVMTy8isHBtTv00ZMkaaE0muh9OD8kNaG/v4c1a47rdBiSpCWqoUQvpXRRuwORFiNHzUqSulmjt0B7xXTrUkoXtC4caXGZHDULMDq6lcHBtbbQSZK6RqOXbusHYuwL/C7wDcBET5XlqFlJUjdr9NLts+vLcivfE1oekbSI9PYuZ3R06w7LkiR1i0anV5nKhcAZLYpDWpSGh1cxMNDHypU9DAz0OWpWktRVGu2jV58Q7gH8GfA/LY9IWkQcNStJ6maN9tF7AKi/K8bPgVe1NhxJkiS1SqOJ3sq65btTSuOtDkaSJEmt0+hgjLF2ByJJkqTWmjHRizH+X3a+ZFurSCk9t7UhSZIkqRVma9H7+DTlBwBnUw7KkCRJ0iI0Y6KXUvpo7XKMcW/gzZSDMD4NnNu+0CRJkjQfjU6vsifwN8Ag8AXgySmln7YzMEmSJM3PbH30dgdeB7wBuB44JqV08wLEJUmSpHmarUVvFNgF+Gfg28CjY4yPrt0gpXRdm2KTJEnSPMyW6G2jHHX7mmnWF8BjWhqRJEmSWmK2wRgHL1AckiRJarH6e9hKkiSpIkz0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqykRPkiSpokz0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqykRPkiSpokz0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqalmnA1Bzxsa2MDS0jomJbfT2Lmd4eBX9/T2dDkuSJC1Ctuh1maGhdYyMjDM6upWRkXEGB9d2OiRJkrRImeh1mYmJbTMuS5IkTTLR6zK9vctnXJYkSZpkotdlhodXMTDQx8qVPQwM9DE8vKrTIUmSpEXKwRhdpr+/hzVrjut0GJIkqQvYoidJklRRJnqSJEkVZaInSZJUUfbRW2KccFmSpKXDFr0lxgmXJUlaOkz0lhgnXJYkaekw0VtinHBZkqSlw0RviXHCZUmSlo4FGYwRY7wA+GPgzpTSEbmsF/g0cDCwHogppbvyujcDZwAPAmenlK7K5QPAhcDuwJXAa1NKRYxxN+BiYAD4FXBySml93ud04K05lHeklC5q89td1JxwWZKkpWOhWvQuBJ5fV/Ym4NqU0qHAtXmZGONhwCnA4XmfD8UYd8n7nAecCRyaH5PHPAO4K6V0CPB+4D35WL3AOcBTgaOAc2KMe7Xh/UmSJC06C5LopZS+CkzUFZ8ATLauXQS8uKb80pTSvSmlUeBW4KgY437AnimldSmlgrIF78VTHOsy4LkxxgAcD1yTUprIrYXXsHPCueDGxrawevXVHH74RaxefTW33ba10yFJkqQK6uQ8eo9OKW0CSCltijHuk8sPAG6o2W5DLrs/P68vn9zn9nysB2KMm4G9a8un2GcHMcYzKVsLSSnR19c393c2i5e85DpGRsZ/u/z619/IV74S2/Z6VbJs2bK2fjZVZJ01x/pqnnXWPOusedbZ3CzGCZPDFGXFDOVz3WcHKaXzgfMntxkfH59qs5a4446tOy238/WqpK+vz7pqknXWHOuredZZ86yz5lln09t///2nXdfJUbd35Mux5J935vINwEE12x0IbMzlB05RvsM+McZlwArKS8XTHaujnOJEkiQthE4memuA0/Pz04EraspPiTHuFmNcSTno4sZ8mXdLjPHo3P/utLp9Jo91InBd7sd3FXBcjHGvPAjjuFzWUZNTnBxyyAqnOJEkSW2zUNOrfAp4FtAXY9xAORL23UCKMZ4B3AacBJBSujnGmIBbgAeAs1JKD+ZDvYbt06t8KT8APgpcEmO8lbIl75R8rIkY49uBb+Xtzk0p1Q8KWXCTU5zYDC1JktopFMWUXdaWumLjxvZf4TXRa5511jzrrDnWV/Oss+ZZZ82zzqaX++hNNS7BO2NIkiRVlYmeJElSRZnoSZIkVZSJniRJUkWZ6EmSJFWUiZ4kSVJFmehJkiRVlImeJElSRZnoSZIkVZSJniRJUkWZ6EmSJFWUiZ4kSVJFLet0AFK7jY1tYWhoHRMT2+jtXc7w8Cr6+3s6HZYkSW1ni54qb2hoHSMj44yObmVkZJzBwbWdDkmSpAVhoqfKm5jYNuOyJElVZaKnyuvtXT7jsiRJVWWip8obHl7FwEAfK1f2MDDQx/Dwqk6HJEnSgnAwhiqvv7+HNWuO63QYkiQtOFv0JEmSKspET5IkqaJM9CRJkirKRE+SJKmiTPQkSZIqykRPkiSpokz0JEmSKspET5IkqaJM9CRJkirKO2OoYWNjWxgaWsfExDZ6e5czPLyK/v6eToclSZKmYYueGjY0tI6RkXFGR7cyMjLO4ODaTockSZJmYKKnhk1MbJtxWZIkLS4mempYb+/yGZdnMja2hdWrr+aYY9awevXV3Hbb1laHJ0mS6pjoqWHDw6sYGOhj5coeBgb6GB5e1fC+XvaVJGnhORhDDevv72HNmuPmtK+XfSVJWni26GlBzOeyryRJmhsTPS2I+Vz2lSRJc+OlWy2I+Vz2he1z+G3efD8rVuzqHH6SJDXAFj11hcnBHLfeutnBHJIkNchET13BwRySJDXPRE9dwcEckiQ1z0RPXWFyMMchh6xwMIckSQ1yMIa6wuRgjr6+PsbHxzsdjiRJXcEWPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkijLRkyRJqigTPUmSpIoy0ZMkSaooEz1JkqSKMtGTJEmqKBM9SZKkigpFUXQ6hsXISpEkSd0kTFVoi97UwkI8YowjC/VaVXlYZ9aZ9bX4HtaZdWadLYrHlEz0JEmSKspET5IkqaJM9Drr/E4H0IWss+ZZZ82xvppnnTXPOmuedTYHDsaQJEmqKFv0JEmSKmpZpwOokhjjBcAfA3emlI7IZU8E/hPoAdYDp6aUfh1j3BX4CPBkys/h4pTSP+V9BoALgd2BK4HXppQq2fTawjq7HtgPuCcf+riU0p0L+FYWTJN19nDgfwNPAR6iPJeuz/t4njVfZ9ezBM6zGONBwMXAvpR1cH5K6QMxxl7g08DBlHUWU0p35X3eDJwBPAicnVK6KpcvifOsxXV2PZ5nO9VZjHFv4DLgD4ALU0qDNcdaEufZXNii11oXAs+vK/sI8KaU0u8BnwP+JpefBOyWyweAv4wxHpzXnQecCRyaH/XHrJILaU2dQflFfWR+VO6PYo0LabzOXgWQy58H/GuMcfL33vOs+TqDpXGePQC8IaX0BOBo4KwY42HAm4BrU0qHAtfmZfK6U4DDKev5QzHGXfKxlsp51so6A8+zneoM2Ab8PfDGKY61VM6zppnotVBK6avARF3x44Cv5ufXAC/JzwvgETHGZZT/gdwH/DrGuB+wZ0ppXf5v5GLgxW0PvkNaUWcLEedi0mSdHUb5h5L8ZfE/wFM8z4Am62wBwlw0UkqbUkrfyc+3AD8EDgBOAC7Km13E9nPmBODSlNK9KaVR4FbgqKV0nrWqzhY26s5qts5SSnenlL5OmfD91lI6z+bCRK/9bgJW5+cnAQfl55cBdwObgNuAf0kpTVCe5Btq9t+Qy5aSZuts0sdijN+NMf59jHHaySMraro6+x5wQoxxWYxxJWVL6EF4nkHzdTZpSZ1nudX8ScA3gUenlDZB+SUN7JM3OwC4vWa3yfNpSZ5n86yzSZ5n7FRn01mS51mjTPTa7xWUzdEjwCMpW6Gg/M/tQWB/YCXwhhjjY5h6duul1s+g2TqD8jLH7wHPyI+XL2zIHTddnV1A+Ufv28C/AWspL5d4njVfZ7DEzrMYYw/wWeB1KaWZWs+nO5+W3HnWgjoDz7NmLbnzrBkOxmizlNKPgOMAYoyPBV6YV70M+HJK6X7gzhjjNygvD30NOLDmEAcCGxcu4s6bQ539LKX087zvlhjjJymTwosXPPgOma7OUkoPAK+f3C7GuBb4b+AuPM+arTOW0nmWBz99FvhESunyXHxHjHG/lNKmfLlssu/YBnZs9Zw8nzawhM6zFtWZ59n0dTadJXWeNcsWvTaLMe6Tfz4MeCvlKD8oLz0+J8YYYoyPoOyI+qPcTL0lxnh0bq4/DbiiA6F3TLN1li+x9eV9dqUcXXnTwkfeOdPVWYxxj1xXxBifBzyQUrrF86z5OltK51k+Jz4K/DCl9L6aVWuA0/Pz09l+zqwBTokx7pYvdx8K3LiUzrNW1ZnnGTB9nU1pKZ1nc2GLXgvFGD8FPAvoizFuAM4BemKMZ+VNLgc+lp//R35+E2Wz88dSSt/P617D9mHiX8qPSmpFneUv5avyH8VdgP8CPrxw72JhNVln+1DWzUPAz9nxEpDnWXN1thtL5zx7OuX7/kGM8bu57C3Au4EUYzyD8h+vkwBSSjfHGBNwC+Vl7rNSSg/m/ZbKedaSOltif8+aqjOAGON6YE/g4THGF1NOPXMLS+c8a5p3xpAkSaooL91KkiRVlImeJElSRZnoSZIkVZSJniRJUkWZ6EmSJFWUiZ4kSVJFOY+eJM1BjPETwL0ppVfUlD2Tck6+Iybv1SlJnWSLniTNzdnAC/LdM4gxLqec2PYNrUzyYoy7tOpYkpYeJ0yWpDmKMZ4E/DNwBOVt1I5MKf1Rvq3am4AzgBWUdzd4TUrprrwuAccAy4Hv5nU/zMf8OLAZ+F3KG9q/MKV0/YK+MUmVYYueJM1RSukzwAjwKeBM4C/zqr8GXggcS3mD9buBf6/Z9QuU9zbdl/KWfpfUHfplwD8CjwTWtSl8SUuALXqSNA8xxkcDPwX+LqX0gVz238ArU0pfycsHAbcCu6eUHqrbvw/4JdCTUro7t+jdV9v3T5LmysEYkjQPKaU7YozjwM01xf3A52OMtUldAewTY/wl8E/AiUAfMLlNH2XLH8Dt7Y1a0lJhoidJrbcBeFlK6Zv1K2KMfwG8AHgOMAbsTdmiF2o281KLpJawj54ktd5/Au+KMfYDxBj3iTGuzuseCdwL/ArYA3hnZ0KUtBSY6ElS670P+DJwbYxxC7AW+IO87mPAxvy4Oa+TpLZwMIYkSVJF2aInSZJUUSZ6kiRJFWWiJ0mSVFEmepIkSRVloidJklRRJnqSJEkVZaInSZJUUSZ6kiRJFWWiJ0mSVFH/D44an85jppVhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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). "
   ]
  },
  {
   "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",
    "- `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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "button": 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": 124,
     "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`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "button": 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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 125,
     "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",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\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), 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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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?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Step 1: Get the data:\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
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    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  ...  \\\n",
      "Country                                                              ...   \n",
      "Denmark   272   293   299   106    93    73    93   109   129   129  ...   \n",
      "Norway    116    77   106    51    31    54    56    80    73    76  ...   \n",
      "Sweden    281   308   222   176   128   158   187   198   171   182  ...   \n",
      "\n",
      "         2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  \n",
      "Country                                                              \n",
      "Denmark    89    62   101    97   108    81    92    93    94    81  \n",
      "Norway     73    57    53    73    66    75    46    49    53    59  \n",
      "Sweden    129   205   139   193   165   167   159   134   140   140  \n",
      "\n",
      "[3 rows x 34 columns]\n"
     ]
    }
   ],
   "source": [
    "### type your answer here\n",
    "# Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it df_countries.\n",
    "df_countries=df_can.loc[['Denmark','Norway','Sweden'],years]\n",
    "\n",
    "#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",
    "df_total=pd.DataFrame(df_countries.sum(axis=0)) #sum along the columns\n",
    "#print(df_total)\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_total.index = map(int, df_total.index)\n",
    "# Reset the index in place.\n",
    "df_total.reset_index(inplace=True)\n",
    "# Rename columns\n",
    "df_total.columns = ['year', 'total']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # create df_countries dataframe\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "-->\n",
    "\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",
    "\n",
    "<!--\n",
    "\\\\ # reset index in place\n",
    "df_total.reset_index(inplace=True)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # rename columns\n",
    "df_total.columns = ['year', 'total']\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # change column year from string to int to create scatter plot\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # show resulting dataframe\n",
    "df_total.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10,6), color='darkblue')\n",
    "\n",
    "plt.title('Immigrants from Denmark, Norway, and Sweden from 1980-2013')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # generate scatter plot\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "-->\n",
    "\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",
    "\n",
    "<!--\n",
    "\\\\ # show plot\n",
    "plt.show()\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."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 142,
     "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) 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": 143,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.111039\n",
       "1     0.148483\n",
       "2     0.277598\n",
       "3     0.029051\n",
       "4     0.026469\n",
       "5     0.000000\n",
       "6     0.010975\n",
       "7     0.208522\n",
       "8     0.114913\n",
       "9     0.220788\n",
       "10    0.311168\n",
       "11    0.366043\n",
       "12    0.533893\n",
       "13    0.533247\n",
       "14    0.109748\n",
       "15    0.118786\n",
       "16    0.129116\n",
       "17    0.125888\n",
       "18    0.098773\n",
       "19    0.078760\n",
       "20    0.149128\n",
       "21    0.283409\n",
       "22    0.431892\n",
       "23    1.000000\n",
       "24    0.900581\n",
       "25    0.617818\n",
       "26    0.420271\n",
       "27    0.273725\n",
       "28    0.222079\n",
       "29    0.174952\n",
       "30    0.169787\n",
       "31    0.052937\n",
       "32    0.043254\n",
       "33    0.055520\n",
       "Name: Argentina, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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())\n",
    "norm_argentina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0d63b15160>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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."
   ]
  },
  {
   "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."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.087586\n",
       "1     0.125557\n",
       "2     0.043379\n",
       "3     0.008184\n",
       "4     0.000000\n",
       "5     0.007039\n",
       "6     0.010546\n",
       "7     0.027182\n",
       "8     0.029983\n",
       "9     0.068100\n",
       "10    0.159510\n",
       "11    0.310008\n",
       "12    0.226977\n",
       "13    0.201914\n",
       "14    0.282558\n",
       "15    0.313491\n",
       "16    0.435687\n",
       "17    0.461505\n",
       "18    0.475485\n",
       "19    0.695180\n",
       "20    0.828166\n",
       "21    0.850208\n",
       "22    0.741262\n",
       "23    0.850330\n",
       "24    0.854714\n",
       "25    1.000000\n",
       "26    0.779185\n",
       "27    0.636067\n",
       "28    0.694400\n",
       "29    0.684293\n",
       "30    0.703023\n",
       "31    0.657013\n",
       "32    0.767153\n",
       "33    0.794067\n",
       "Name: China, dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "norm_china\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\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",
    "-->\n",
    "\n",
    "<!--\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",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "button": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0d63ad9160>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\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",
    "\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",
    "\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",
    "-->"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}