02Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots.ipynb

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    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Pie Charts, Box Plots, Scatter Plots, and Bubble Plots\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Explore Matplotlib library further\n",
    "*   Create pie charts, box plots, scatter plots and bubble charts\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1.  [Exploring Datasets with *p*andas](#0)<br>\n",
    "2.  [Downloading and Prepping Data](#2)<br>\n",
    "3.  [Visualizing Data using Matplotlib](#4) <br>\n",
    "4.  [Pie Charts](#6) <br>\n",
    "5.  [Box Plots](#8) <br>\n",
    "6.  [Scatter Plots](#10) <br>\n",
    "7.  [Bubble Plots](#12) <br>\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "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/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and [**Numpy**](http://www.numpy.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data.\n"
   ]
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Import primary modules.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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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",
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   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas*'s `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires reading in Excel files. This module was **openpyxl** (formerlly **xlrd**). For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **openpyxl** module:\n",
    "\n",
    "```\n",
    "! pip3 install openpyxl\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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    }
   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Type        0\n",
       "Coverage    0\n",
       "OdName      0\n",
       "AREA        0\n",
       "AreaName    0\n",
       "REG         0\n",
       "RegName     0\n",
       "DEV         0\n",
       "DevName     0\n",
       "1980        0\n",
       "1981        0\n",
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       "1991        0\n",
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       "2010        0\n",
       "2011        0\n",
       "2012        0\n",
       "2013        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can = pd.read_excel(\n",
    "    'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
    "    sheet_name='Canada by Citizenship',\n",
    "    skiprows=range(20),\n",
    "    skipfooter=2,\n",
    "\n",
    "    engine=\"openpyxl\")\n",
    "print('Data downloaded and read into a dataframe!')\n",
    "\n",
    "df_can.drop(df_can.filter(regex=\"Unname\"),axis=1, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "df_can.dropna(inplace = True)\n",
    "df_can.isna().sum()"
   ]
  },
  {
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   },
   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501.0</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902.0</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2978.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>3009.0</td>\n",
       "      <td>2652.0</td>\n",
       "      <td>2111.0</td>\n",
       "      <td>1746.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925.0</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901.0</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912.0</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902.0</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3616.0</td>\n",
       "      <td>3626.0</td>\n",
       "      <td>4807.0</td>\n",
       "      <td>3623.0</td>\n",
       "      <td>4005.0</td>\n",
       "      <td>5393.0</td>\n",
       "      <td>4752.0</td>\n",
       "      <td>4325.0</td>\n",
       "      <td>3774.0</td>\n",
       "      <td>4331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957.0</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902.0</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925.0</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901.0</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
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      ],
      "text/plain": [
       "         Type    Coverage          OdName   AREA AreaName     REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan  935.0     Asia  5501.0   \n",
       "1  Immigrants  Foreigners         Albania  908.0   Europe   925.0   \n",
       "2  Immigrants  Foreigners         Algeria  903.0   Africa   912.0   \n",
       "3  Immigrants  Foreigners  American Samoa  909.0  Oceania   957.0   \n",
       "4  Immigrants  Foreigners         Andorra  908.0   Europe   925.0   \n",
       "\n",
       "           RegName    DEV             DevName  1980  ...    2004    2005  \\\n",
       "0    Southern Asia  902.0  Developing regions  16.0  ...  2978.0  3436.0   \n",
       "1  Southern Europe  901.0   Developed regions   1.0  ...  1450.0  1223.0   \n",
       "2  Northern Africa  902.0  Developing regions  80.0  ...  3616.0  3626.0   \n",
       "3        Polynesia  902.0  Developing regions   0.0  ...     0.0     0.0   \n",
       "4  Southern Europe  901.0   Developed regions   0.0  ...     0.0     0.0   \n",
       "\n",
       "     2006    2007    2008    2009    2010    2011    2012    2013  \n",
       "0  3009.0  2652.0  2111.0  1746.0  1758.0  2203.0  2635.0  2004.0  \n",
       "1   856.0   702.0   560.0   716.0   561.0   539.0   620.0   603.0  \n",
       "2  4807.0  3623.0  4005.0  5393.0  4752.0  4325.0  3774.0  4331.0  \n",
       "3     1.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "4     1.0     1.0     0.0     0.0     0.0     0.0     1.0     1.0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 6,
     "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(197, 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (197, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "collapsed": false,
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    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.3.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circular graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
    "\n",
    "Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "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.\n"
   ]
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    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">\n"
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      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951.0</td>\n",
       "      <td>4363.0</td>\n",
       "      <td>3819.0</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>2639.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3782.0</td>\n",
       "      <td>7494.0</td>\n",
       "      <td>7552.0</td>\n",
       "      <td>9894.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27523.0</td>\n",
       "      <td>29188.0</td>\n",
       "      <td>28284.0</td>\n",
       "      <td>29890.0</td>\n",
       "      <td>34534.0</td>\n",
       "      <td>40892.0</td>\n",
       "      <td>35441.0</td>\n",
       "      <td>38083.0</td>\n",
       "      <td>38543.0</td>\n",
       "      <td>618948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025.0</td>\n",
       "      <td>34314.0</td>\n",
       "      <td>30214.0</td>\n",
       "      <td>24696.0</td>\n",
       "      <td>27274.0</td>\n",
       "      <td>23850.0</td>\n",
       "      <td>28739.0</td>\n",
       "      <td>43203.0</td>\n",
       "      <td>47454.0</td>\n",
       "      <td>60256.0</td>\n",
       "      <td>...</td>\n",
       "      <td>159253.0</td>\n",
       "      <td>149054.0</td>\n",
       "      <td>133459.0</td>\n",
       "      <td>139894.0</td>\n",
       "      <td>141434.0</td>\n",
       "      <td>163845.0</td>\n",
       "      <td>146894.0</td>\n",
       "      <td>152218.0</td>\n",
       "      <td>155075.0</td>\n",
       "      <td>3317794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760.0</td>\n",
       "      <td>44802.0</td>\n",
       "      <td>42720.0</td>\n",
       "      <td>24638.0</td>\n",
       "      <td>22287.0</td>\n",
       "      <td>20844.0</td>\n",
       "      <td>24370.0</td>\n",
       "      <td>46698.0</td>\n",
       "      <td>54726.0</td>\n",
       "      <td>60893.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35955.0</td>\n",
       "      <td>33053.0</td>\n",
       "      <td>33495.0</td>\n",
       "      <td>34692.0</td>\n",
       "      <td>35078.0</td>\n",
       "      <td>33425.0</td>\n",
       "      <td>26778.0</td>\n",
       "      <td>29177.0</td>\n",
       "      <td>28691.0</td>\n",
       "      <td>1410947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081.0</td>\n",
       "      <td>15215.0</td>\n",
       "      <td>16769.0</td>\n",
       "      <td>15427.0</td>\n",
       "      <td>13678.0</td>\n",
       "      <td>15171.0</td>\n",
       "      <td>21179.0</td>\n",
       "      <td>28471.0</td>\n",
       "      <td>21924.0</td>\n",
       "      <td>25060.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24747.0</td>\n",
       "      <td>24676.0</td>\n",
       "      <td>26011.0</td>\n",
       "      <td>26547.0</td>\n",
       "      <td>26867.0</td>\n",
       "      <td>28818.0</td>\n",
       "      <td>27856.0</td>\n",
       "      <td>27173.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>765148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378.0</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>9074.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>6661.0</td>\n",
       "      <td>6543.0</td>\n",
       "      <td>7074.0</td>\n",
       "      <td>7705.0</td>\n",
       "      <td>6469.0</td>\n",
       "      <td>6790.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8394.0</td>\n",
       "      <td>9613.0</td>\n",
       "      <td>9463.0</td>\n",
       "      <td>10190.0</td>\n",
       "      <td>8995.0</td>\n",
       "      <td>8142.0</td>\n",
       "      <td>7677.0</td>\n",
       "      <td>7892.0</td>\n",
       "      <td>8503.0</td>\n",
       "      <td>241142.0</td>\n",
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       "<p>5 rows × 35 columns</p>\n",
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      "text/plain": [
       "                                    1980     1981     1982     1983     1984  \\\n",
       "Continent                                                                      \n",
       "Africa                            3951.0   4363.0   3819.0   2671.0   2639.0   \n",
       "Asia                             31025.0  34314.0  30214.0  24696.0  27274.0   \n",
       "Europe                           39760.0  44802.0  42720.0  24638.0  22287.0   \n",
       "Latin America and the Caribbean  13081.0  15215.0  16769.0  15427.0  13678.0   \n",
       "Northern America                  9378.0  10030.0   9074.0   7100.0   6661.0   \n",
       "\n",
       "                                    1985     1986     1987     1988     1989  \\\n",
       "Continent                                                                      \n",
       "Africa                            2650.0   3782.0   7494.0   7552.0   9894.0   \n",
       "Asia                             23850.0  28739.0  43203.0  47454.0  60256.0   \n",
       "Europe                           20844.0  24370.0  46698.0  54726.0  60893.0   \n",
       "Latin America and the Caribbean  15171.0  21179.0  28471.0  21924.0  25060.0   \n",
       "Northern America                  6543.0   7074.0   7705.0   6469.0   6790.0   \n",
       "\n",
       "                                 ...      2005      2006      2007      2008  \\\n",
       "Continent                        ...                                           \n",
       "Africa                           ...   27523.0   29188.0   28284.0   29890.0   \n",
       "Asia                             ...  159253.0  149054.0  133459.0  139894.0   \n",
       "Europe                           ...   35955.0   33053.0   33495.0   34692.0   \n",
       "Latin America and the Caribbean  ...   24747.0   24676.0   26011.0   26547.0   \n",
       "Northern America                 ...    8394.0    9613.0    9463.0   10190.0   \n",
       "\n",
       "                                     2009      2010      2011      2012  \\\n",
       "Continent                                                                 \n",
       "Africa                            34534.0   40892.0   35441.0   38083.0   \n",
       "Asia                             141434.0  163845.0  146894.0  152218.0   \n",
       "Europe                            35078.0   33425.0   26778.0   29177.0   \n",
       "Latin America and the Caribbean   26867.0   28818.0   27856.0   27173.0   \n",
       "Northern America                   8995.0    8142.0    7677.0    7892.0   \n",
       "\n",
       "                                     2013      Total  \n",
       "Continent                                             \n",
       "Africa                            38543.0   618948.0  \n",
       "Asia                             155075.0  3317794.0  \n",
       "Europe                            28691.0  1410947.0  \n",
       "Latin America and the Caribbean   24950.0   765148.0  \n",
       "Northern America                   8503.0   241142.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
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   ],
   "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()"
   ]
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    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "\n",
    "*   `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "*   `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "*   `shadow` - Draws a shadow beneath the pie (to give a 3D feel).\n"
   ]
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "*   Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "*   Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "*   Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "*   **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Caribbean) by passing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink', 'red']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1,0] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "    df_continents['2013'].plot(kind='pie',\n",
    "                                figsize=(15, 6),\n",
    "                                autopct='%1.1f%%', \n",
    "                                startangle=90,    \n",
    "                                shadow=True,       \n",
    "                                labels=None,                 # turn off labels on pie chart\n",
    "                                pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                                explode=explode_list         # 'explode' lowest 3 continents\n",
    "                                )\n",
    "\n",
    "    # scale the title up by 12% to match pctdistance\n",
    "    plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "    plt.axis('equal') \n",
    "\n",
    "    # add legend\n",
    "    plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions:\n",
    "\n",
    "*   **Minimum:** The smallest number in the dataset excluding the outliers.\n",
    "*   **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "*   **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "*   **Third quartile:** Middle number between `median` and `maximum`.\n",
    "*   **Maximum:** The largest number in the dataset excluding the outliers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/boxplot_complete.png\" width=440, align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `boxplot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the subset of the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980     701.0\n",
       "1981     756.0\n",
       "1982     598.0\n",
       "1983     309.0\n",
       "1984     246.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "\n",
    "1.  The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2.  25% of the years for period 1980 - 2013 had an annual immigrant count of \\~500 or fewer (First quartile).\n",
    "3.  75% of the years for period 1980 - 2013 had an annual immigrant count of \\~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyze these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123.0</td>\n",
       "      <td>8880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682.0</td>\n",
       "      <td>8670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308.0</td>\n",
       "      <td>8147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863.0</td>\n",
       "      <td>7338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527.0</td>\n",
       "      <td>5704.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country   China   India\n",
       "1980     5123.0  8880.0\n",
       "1981     6682.0  8670.0\n",
       "1982     3308.0  8147.0\n",
       "1983     1863.0  7338.0\n",
       "1984     1527.0  5704.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI = df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "    df_CI.head()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentiles associated with both countries using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "df_CI.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.plot(kind=\"box\", figsize=(10,10))\n",
    "\n",
    "plt.title(\"China and India data\")\n",
    "plt.ylabel(\"number of immigrants\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "    plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (\\~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**.\n",
    "\n",
    "Typical syntax is : <br>\n",
    "\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "\n",
    "Where\n",
    "\n",
    "*   `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,\n",
    "*   `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` \\* `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Tip regarding subplot convention**\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that a 3-digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced.\n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "df_top15 = df_can.sort_values([\"Total\"], ascending=False).head(15)\n",
    "# df_top15 = ...\n",
    "# df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "\n",
    "1.  Create a list of all years in decades 80's, 90's, and 00's.\n",
    "2.  Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "3.  Merge the three series into a new data frame. Call your dataframe **new_df**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>1259317.0</td>\n",
       "      <td>2204176.0</td>\n",
       "      <td>2414360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>82154.0</td>\n",
       "      <td>180395.0</td>\n",
       "      <td>303591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>32003.0</td>\n",
       "      <td>161528.0</td>\n",
       "      <td>340385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>179171.0</td>\n",
       "      <td>261966.0</td>\n",
       "      <td>83413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>201844.0</td>\n",
       "      <td>249985.0</td>\n",
       "      <td>53780.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        1980s      1990s  \\\n",
       "Country                                                                    \n",
       "Total                                               1259317.0  2204176.0   \n",
       "India                                                 82154.0   180395.0   \n",
       "China                                                 32003.0   161528.0   \n",
       "United Kingdom of Great Britain and Northern Ir...   179171.0   261966.0   \n",
       "Unknown                                              201844.0   249985.0   \n",
       "\n",
       "                                                        2000s  \n",
       "Country                                                        \n",
       "Total                                               2414360.0  \n",
       "India                                                303591.0  \n",
       "China                                                340385.0  \n",
       "United Kingdom of Great Britain and Northern Ir...    83413.0  \n",
       "Unknown                                               53780.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "#The correct answer is:\n",
    "\n",
    "# create a list of all years in decades 80's, 90's, and 00's\n",
    "years_80s = list(map(str, range(1980, 1990))) \n",
    "years_90s = list(map(str, range(1990, 2000))) \n",
    "years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "# slice the original dataframe df_can to create a series for each decade\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "# merge the three series into a new data frame\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "# display dataframe\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # create a list of all years in decades 80's, 90's, and 00's\n",
    "    years_80s = list(map(str, range(1980, 1990))) \n",
    "    years_90s = list(map(str, range(1990, 2000))) \n",
    "    years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "    # slice the original dataframe df_can to create a series for each decade\n",
    "    df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "    df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "    df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "    # merge the three series into a new data frame\n",
    "    new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "    # display dataframe\n",
    "    new_df.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.392790e+05</td>\n",
       "      <td>2.444464e+05</td>\n",
       "      <td>2.575616e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.152752e+05</td>\n",
       "      <td>5.475336e+05</td>\n",
       "      <td>6.046593e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.059100e+04</td>\n",
       "      <td>3.002800e+04</td>\n",
       "      <td>1.362900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.930700e+04</td>\n",
       "      <td>5.205800e+04</td>\n",
       "      <td>4.548750e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.432800e+04</td>\n",
       "      <td>6.530200e+04</td>\n",
       "      <td>6.818300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.948900e+04</td>\n",
       "      <td>1.709615e+05</td>\n",
       "      <td>1.502510e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.259317e+06</td>\n",
       "      <td>2.204176e+06</td>\n",
       "      <td>2.414360e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1980s         1990s         2000s\n",
       "count  1.500000e+01  1.500000e+01  1.500000e+01\n",
       "mean   1.392790e+05  2.444464e+05  2.575616e+05\n",
       "std    3.152752e+05  5.475336e+05  6.046593e+05\n",
       "min    1.059100e+04  3.002800e+04  1.362900e+04\n",
       "25%    1.930700e+04  5.205800e+04  4.548750e+04\n",
       "50%    3.432800e+04  6.530200e+04  6.818300e+04\n",
       "75%    7.948900e+04  1.709615e+05  1.502510e+05\n",
       "max    1.259317e+06  2.204176e+06  2.414360e+06"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the box plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Immigrants')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.plot(kind = \"box\")\n",
    "plt.title(\"Immigration by decade and country for T15 countries\")\n",
    "plt.ylabel(\"Immigrants\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "    plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "\n",
    "*   larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "*   smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "\n",
    "*   Q1 (25%) = 36,101.5 <br>\n",
    "*   Q3 (75%) = 105,505.5 <br>\n",
    "*   IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 \\* 69,404) <br>\n",
    "Outlier > 209,611.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Country</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>1259317.0</td>\n",
       "      <td>2204176.0</td>\n",
       "      <td>2414360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>India</td>\n",
       "      <td>82154.0</td>\n",
       "      <td>180395.0</td>\n",
       "      <td>303591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>China</td>\n",
       "      <td>32003.0</td>\n",
       "      <td>161528.0</td>\n",
       "      <td>340385.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Country      1980s      1990s      2000s\n",
       "0      0   Total  1259317.0  2204176.0  2414360.0\n",
       "1      1   India    82154.0   180395.0   303591.0\n",
       "2      2   China    32003.0   161528.0   340385.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df=new_df.reset_index()\n",
    "new_df[new_df['2000s']>209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df=new_df.reset_index()\n",
    "    new_df[new_df['2000s']> 209611.5]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The correct answer is:\n",
    "new_df[new_df['2000s']> 209611.5]\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5.\n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#matplotlib.pyplot.boxplot) on box plots for more information.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the data points are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>286274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>257282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>242350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>178370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>176544.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     total\n",
       "0  1980  286274.0\n",
       "1  1981  257282.0\n",
       "2  1982  242350.0\n",
       "3  1983  178370.0\n",
       "4  1984  176544.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use the sum() method to get the total population per year\n",
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "# change the years to type int (useful for regression later on)\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "# reset the index to put in back in as a column in the df_tot dataframe\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "# rename columns\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "# view the final dataframe\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the data  points together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "\n",
    "*   `x`: x-coordinates of the data.\n",
    "*   `y`: y-coordinates of the data.\n",
    "*   `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.52805011e+03, -1.86154455e+07])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']      # year on x-axis\n",
    "y = df_tot['total']     # total on y-axis\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a * x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1.\n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 9528 * Year + -18615445'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "# plot line of best fit\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# print out the line of best fit\n",
    "'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "\n",
    "When compared to the actual from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years.\n"
   ]
  },
  {
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   },
   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "**Step 1**: Get the data:\n",
    "\n",
    "1.  Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "2.  Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "3.  Reset the index in place.\n",
    "4.  Rename the columns to **year** and **total**.\n",
    "5.  Display the resulting dataframe.\n"
   ]
  },
  {
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   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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     "read_only": false
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # create df_countries dataframe\n",
    "    df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "    # create df_total by summing across three countries for each year\n",
    "    df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "    # reset index in place\n",
    "    df_total.reset_index(inplace=True)\n",
    "\n",
    "    # rename columns\n",
    "    df_total.columns = ['year', 'total']\n",
    "\n",
    "    # change column year from string to int to create scatter plot\n",
    "    df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "    # show resulting dataframe\n",
    "    df_total.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "**Step 2**: Generate the scatter plot by plotting the total versus year in **df_total**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # generate scatter plot\n",
    "    df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "    # add title and label to axes\n",
    "    plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The data points are replaced with bubbles, and the size of the bubble is determined by the third variable `z`, also known as the weight. In `maplotlib`, we can pass in an array or scalar to the parameter `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 to 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002.\n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "**Step 1**: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and include it in the dataframe.\n"
   ]
  },
  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>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>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",
       "      <th>Unknown</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>143137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>18078.0</td>\n",
       "      <td>128641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>16904.0</td>\n",
       "      <td>121175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>3404.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13635.0</td>\n",
       "      <td>89185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>7583.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14855.0</td>\n",
       "      <td>88272.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 198 columns</p>\n",
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      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980         16.0      1.0     80.0             0.0      0.0     1.0   \n",
       "1        1981         39.0      0.0     67.0             1.0      0.0     3.0   \n",
       "2        1982         39.0      0.0     71.0             0.0      0.0     6.0   \n",
       "3        1983         47.0      0.0     69.0             0.0      0.0     6.0   \n",
       "4        1984         71.0      0.0     63.0             0.0      0.0     4.0   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  Uzbekistan  Vanuatu  \\\n",
       "0                        0.0      368.0      0.0  ...         0.0      0.0   \n",
       "1                        0.0      426.0      0.0  ...         0.0      0.0   \n",
       "2                        0.0      626.0      0.0  ...         0.0      0.0   \n",
       "3                        0.0      241.0      0.0  ...         0.0      0.0   \n",
       "4                       42.0      237.0      0.0  ...         0.0      0.0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                     103.0    1191.0             0.0    1.0   \n",
       "1                                     117.0    1829.0             0.0    2.0   \n",
       "2                                     174.0    2162.0             0.0    1.0   \n",
       "3                                     124.0    3404.0             0.0    6.0   \n",
       "4                                     142.0    7583.0             0.0    0.0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  Unknown     Total  \n",
       "0          11.0      72.0  44000.0  143137.0  \n",
       "1          17.0     114.0  18078.0  128641.0  \n",
       "2          11.0     102.0  16904.0  121175.0  \n",
       "3           7.0      44.0  13635.0   89185.0  \n",
       "4          16.0      32.0  14855.0   88272.0  \n",
       "\n",
       "[5 rows x 198 columns]"
      ]
     },
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   ],
   "source": [
    "# transposed dataframe\n",
    "df_can_t = df_can[years].transpose()\n",
    "\n",
    "# cast the Years (the index) to type int\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "# let's label the index. This will automatically be the column name when we reset the index\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "# reset index to bring the Year in as a column\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "# view the changes\n",
    "df_can_t.head()"
   ]
  },
  {
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   "metadata": {
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   "source": [
    "**Step 2**: Create the normalized weights.\n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) to bring all values into the range \\[0, 1]. The general formula is:\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where $X$ is the original value, $X'$ is the corresponding normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the data points are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
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   "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())"
   ]
  },
  {
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   "source": [
    "**Step 3**: Plot the data.\n",
    "\n",
    "*   To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter.\n",
    "*   We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore, we will:\n",
    "    *   multiply weights by 2000 to scale it up on the graph, and,\n",
    "    *   add 10 to compensate for the min value (which has a 0 weight and therefore scale with $\\times 2000$).\n"
   ]
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    {
     "data": {
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       "<matplotlib.legend.Legend at 0x22bb35792e0>"
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                    )\n",
    "\n",
    "# Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax=ax0\n",
    "                    )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 to 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble is, the more immigrants are in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 to 1990, just before the onset of 1998 - 2002 great depression.\n",
    "\n",
    "On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "# normalized Chinese data\n",
    "# norm_china = ...\n",
    "# normalized Indian data\n",
    "# norm_india = ...\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # normalized Chinese data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalized Indian data\n",
    "    norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\n",
    "                       )\n",
    "\n",
    "    # India\n",
    "    ax1 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='India',\n",
    "                        alpha=0.5,\n",
    "                        color=\"blue\",\n",
    "                        s=norm_india * 2000 + 10,\n",
    "                        ax = ax0\n",
    "                       )\n",
    "\n",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01),\n",
    "[Weiqing Wang](https://www.linkedin.com/in/weiqing-wang-641640133?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By   | Change Description                 |\n",
    "| ----------------- | ------- | ------------ | ---------------------------------- |\n",
    "| 2021-05-29        | 2.6     | Weiqing Wang | Fixed typos and code smells.       |\n",
    "| 2021-01-20        | 2.5     | LakshmiHolla | Changed TOC markdown section       |\n",
    "| 2021-01-05        | 2.4     | LakshmiHolla | Changed markdown for outliers      |\n",
    "| 2020-11-12        | 2.3     | LakshmiHolla | Added example code for outliers    |\n",
    "| 2020-11-03        | 2.2     | LakshmiHolla | Changed URL of excel file          |\n",
    "| 2020-09-29        | 2.1     | LakshmiHolla | Made fix to a boxplot label        |\n",
    "| 2020-08-27        | 2.0     | Lavanya      | Moved lab to course repo in GitLab |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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