Robertboy18/Big Data Science 2020.ipynb

## Big Data Science 2020.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "import matplotlib.pyplot as plt\nimport numpy as np\n# ^^^ pyforest auto-imports - don't write above this line\n## NATIONAL UNDERGRADUATE BIG DATA CHALLENGE 2020\n\n### Team Members :  \n\n1 - Robert Joseph - University of Alberta\n\n2 - Hamza Quresh - Brock University\n\n3 - Shonnae Fraze - York University"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Problem Statement:\n\nThis year’s challenge prompts teams of students to probe and analyze public health and related information in order to understand urgent public health issues and develop novel solutions.  The challenge will be based on the analysis of open data from Health Canada, United Nations Office on Drugs and Crime, Drug Bank, the National Cancer Institute, Google Data Search,  and other open data sources identified by students."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Important sources to use   \n\n1 - https://www.kff.org/coronavirus-covid-19/report/kff-health-tracking-poll-early-april-2020/  \n    \n2 - https://dataunodc.un.org/  \n\n3 - https://www.kff.org/statedata/custom/"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Import the required modules\n\nimport pandas as pd\nimport seaborn as sns\n%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport numpy as np\nimport re ",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Data Importing "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now we will start importing the required date from various sources and start analysis them ."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# source : https://www.kaggle.com/imdevskp/corona-virus-report?select=covid_19_clean_complete.csv\n\nevery_Country = pd.read_csv('covid_19_clean_complete.csv') # Import the complete data of all the countries \nevery_Test = pd.read_csv('tests.csv') # tests per country",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Country \n\nLet us now just play around with our Data Frame regarding each and every country "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.head() # Check a few entries",
      "execution_count": 3,
      "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>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>Date</th>\n      <th>Confirmed</th>\n      <th>Deaths</th>\n      <th>Recovered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "  Province/State Country/Region      Lat     Long     Date  Confirmed  Deaths  \\\n0            NaN    Afghanistan  33.0000  65.0000  1/22/20          0       0   \n1            NaN        Albania  41.1533  20.1683  1/22/20          0       0   \n2            NaN        Algeria  28.0339   1.6596  1/22/20          0       0   \n3            NaN        Andorra  42.5063   1.5218  1/22/20          0       0   \n4            NaN         Angola -11.2027  17.8739  1/22/20          0       0   \n\n   Recovered  \n0          0  \n1          0  \n2          0  \n3          0  \n4          0  "
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.describe() # Display the statistics",
      "execution_count": 4,
      "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>Lat</th>\n      <th>Long</th>\n      <th>Confirmed</th>\n      <th>Deaths</th>\n      <th>Recovered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32065.000000</td>\n      <td>32065.000000</td>\n      <td>3.206500e+04</td>\n      <td>32065.000000</td>\n      <td>32065.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>21.181891</td>\n      <td>22.881195</td>\n      <td>5.044946e+03</td>\n      <td>335.569499</td>\n      <td>1525.388056</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>24.904260</td>\n      <td>70.245523</td>\n      <td>4.487834e+04</td>\n      <td>3095.690148</td>\n      <td>10978.558682</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-51.796300</td>\n      <td>-135.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>6.877000</td>\n      <td>-15.310100</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>23.634500</td>\n      <td>21.005900</td>\n      <td>2.100000e+01</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>41.153300</td>\n      <td>78.000000</td>\n      <td>4.600000e+02</td>\n      <td>6.000000</td>\n      <td>92.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>71.706900</td>\n      <td>178.065000</td>\n      <td>1.577147e+06</td>\n      <td>94702.000000</td>\n      <td>298418.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                Lat          Long     Confirmed        Deaths      Recovered\ncount  32065.000000  32065.000000  3.206500e+04  32065.000000   32065.000000\nmean      21.181891     22.881195  5.044946e+03    335.569499    1525.388056\nstd       24.904260     70.245523  4.487834e+04   3095.690148   10978.558682\nmin      -51.796300   -135.000000  0.000000e+00      0.000000       0.000000\n25%        6.877000    -15.310100  0.000000e+00      0.000000       0.000000\n50%       23.634500     21.005900  2.100000e+01      0.000000       0.000000\n75%       41.153300     78.000000  4.600000e+02      6.000000      92.000000\nmax       71.706900    178.065000  1.577147e+06  94702.000000  298418.000000"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.columns # Various Columns",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/plain": "Index(['Province/State', 'Country/Region', 'Lat', 'Long', 'Date', 'Confirmed',\n       'Deaths', 'Recovered'],\n      dtype='object')"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Clearly one observation can be made is that the Province/State is empty so we can just drop it\n\nevery_Country.drop('Province/State',inplace = True, axis = 1)\n\n# Another important observation is that we really do not need the lattitude and longitude columns as they serve no puprose to our analysis\nevery_Country.drop(['Lat','Long'],inplace = True, axis = 1)\n",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.head() # Check our statistics again",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country/Region</th>\n      <th>Date</th>\n      <th>Confirmed</th>\n      <th>Deaths</th>\n      <th>Recovered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Afghanistan</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Algeria</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Andorra</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Angola</td>\n      <td>1/22/20</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "  Country/Region     Date  Confirmed  Deaths  Recovered\n0    Afghanistan  1/22/20          0       0          0\n1        Albania  1/22/20          0       0          0\n2        Algeria  1/22/20          0       0          0\n3        Andorra  1/22/20          0       0          0\n4         Angola  1/22/20          0       0          0"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.shape # Therefore there are 7 columns and 32065 rows",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "(32065, 5)"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# convert some basic column names into arrays using np\n\nn = np.array(every_Country['Confirmed'])\nz = np.array(every_Country['Recovered'])\nk = np.array(every_Country['Deaths'])\nevery_Country.Date = pd.to_datetime(every_Country.Date)\nm = np.array(every_Country['Date'])",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Plot a simple line plot for various factors vs time \nplt.figure(figsize = (15,8))\n\n# 1\nplt.subplot(3, 1, 1)\nplt.plot(m,k)\nplt.ylabel('Number of Deaths')\nplt.xlabel('Time ')\nplt.title('Various Line plots')\n\n# 2\nplt.subplot(3,1,2)\nplt.plot(m,z,'r')\nplt.ylabel('Number of people Recovered')\nplt.xlabel('Time ')\n\n# 3\nplt.subplot(3,1,3)\nplt.plot(m,n,'g')\nplt.ylabel('Number of people Confirmed')\nplt.xlabel('Time ')",
      "execution_count": 10,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "C:\\Users\\robuj\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n\nTo register the converters:\n\t>>> from pandas.plotting import register_matplotlib_converters\n\t>>> register_matplotlib_converters()\n  warnings.warn(msg, FutureWarning)\n"
        },
        {
          "data": {
            "text/plain": "Text(0.5, 0, 'Time ')"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1080x576 with 3 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.dtypes # View the different data types",
      "execution_count": 11,
      "outputs": [
        {
          "data": {
            "text/plain": "Country/Region            object\nDate              datetime64[ns]\nConfirmed                  int64\nDeaths                     int64\nRecovered                  int64\ndtype: object"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Country.corr()",
      "execution_count": 12,
      "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>Confirmed</th>\n      <th>Deaths</th>\n      <th>Recovered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Confirmed</th>\n      <td>1.000000</td>\n      <td>0.928827</td>\n      <td>0.795360</td>\n    </tr>\n    <tr>\n      <th>Deaths</th>\n      <td>0.928827</td>\n      <td>1.000000</td>\n      <td>0.774348</td>\n    </tr>\n    <tr>\n      <th>Recovered</th>\n      <td>0.795360</td>\n      <td>0.774348</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "           Confirmed    Deaths  Recovered\nConfirmed   1.000000  0.928827   0.795360\nDeaths      0.928827  1.000000   0.774348\nRecovered   0.795360  0.774348   1.000000"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.heatmap(every_Country.corr())",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x24499245208>"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.jointplot(x = 'Deaths',y = 'Recovered',data = every_Country,kind = 'reg')",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "text/plain": "<seaborn.axisgrid.JointGrid at 0x24498fc3dd8>"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x432 with 3 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Test\n\nNow that those are the basic analysis we can move onto the the second dataset"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Test.head() # Check a few entries",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>Cases per 1M pop</th>\n      <th>Deaths per 1M pop</th>\n      <th>Total Tests</th>\n      <th>Tests per 1M pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>USA</td>\n      <td>4900.0</td>\n      <td>291.0</td>\n      <td>13472618.0</td>\n      <td>40729.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Russia</td>\n      <td>2176.0</td>\n      <td>21.0</td>\n      <td>7840880.0</td>\n      <td>53731.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Brazil</td>\n      <td>1464.0</td>\n      <td>95.0</td>\n      <td>735224.0</td>\n      <td>3462.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Spain</td>\n      <td>5991.0</td>\n      <td>598.0</td>\n      <td>3037840.0</td>\n      <td>64977.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UK</td>\n      <td>3698.0</td>\n      <td>531.0</td>\n      <td>3090566.0</td>\n      <td>45552.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "  Country  Cases per 1M pop  Deaths per 1M pop  Total Tests  Tests per 1M pop\n0     USA            4900.0              291.0   13472618.0           40729.0\n1  Russia            2176.0               21.0    7840880.0           53731.0\n2  Brazil            1464.0               95.0     735224.0            3462.0\n3   Spain            5991.0              598.0    3037840.0           64977.0\n4      UK            3698.0              531.0    3090566.0           45552.0"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "every_Test.describe() # Describe the statistics",
      "execution_count": 16,
      "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>Cases per 1M pop</th>\n      <th>Deaths per 1M pop</th>\n      <th>Total Tests</th>\n      <th>Tests per 1M pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>213.000000</td>\n      <td>179.000000</td>\n      <td>1.850000e+02</td>\n      <td>185.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1174.762441</td>\n      <td>60.254078</td>\n      <td>3.583131e+05</td>\n      <td>23700.627027</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2342.500570</td>\n      <td>150.781742</td>\n      <td>1.252668e+06</td>\n      <td>35790.049917</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.500000</td>\n      <td>0.040000</td>\n      <td>3.600000e+01</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>67.000000</td>\n      <td>2.000000</td>\n      <td>4.994000e+03</td>\n      <td>1888.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>247.000000</td>\n      <td>7.000000</td>\n      <td>4.582200e+04</td>\n      <td>9886.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1357.000000</td>\n      <td>35.000000</td>\n      <td>2.502460e+05</td>\n      <td>27801.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>19397.000000</td>\n      <td>1209.000000</td>\n      <td>1.347262e+07</td>\n      <td>185984.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Cases per 1M pop  Deaths per 1M pop   Total Tests  Tests per 1M pop\ncount        213.000000         179.000000  1.850000e+02        185.000000\nmean        1174.762441          60.254078  3.583131e+05      23700.627027\nstd         2342.500570         150.781742  1.252668e+06      35790.049917\nmin            0.500000           0.040000  3.600000e+01          4.000000\n25%           67.000000           2.000000  4.994000e+03       1888.000000\n50%          247.000000           7.000000  4.582200e+04       9886.000000\n75%         1357.000000          35.000000  2.502460e+05      27801.000000\nmax        19397.000000        1209.000000  1.347262e+07     185984.000000"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "canabis = pd.read_csv('2020canabis.csv')\ncanabis\ncanabis.describe()",
      "execution_count": 20,
      "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>Price In Millions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>16.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>93.406250</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>38.368213</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>41.470000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>56.725000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>88.635000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>126.707500</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>154.210000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Price In Millions\ncount          16.000000\nmean           93.406250\nstd            38.368213\nmin            41.470000\n25%            56.725000\n50%            88.635000\n75%           126.707500\nmax           154.210000"
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "canabis1 = np.array(canabis.iloc[0:,0])\ncanabis2 = np.array(canabis.iloc[0:,1])",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# source https://www.statista.com/statistics/1045766/cannabis-store-sales-canada/\nplt.figure(figsize = (15,7))\nplt.xlabel('Time')\nplt.ylabel('Price in Millions')\nplt.title('Monthly retail sales of legal cannabis stores in Canada')\nplt.scatter(canabis1,canabis2)",
      "execution_count": 27,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x2449ab60e48>"
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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nVK7uMdmTJEmS1DFLBwdmVK7uMdmTJEmS1DGrVw4zsHjRLmUDixexeuVwnyLafTlAiyRJkqSOaQzC4mic/WeyJ0mSJKmjVi0fMrmbB+zGKUmSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk11LVkLyI+GRE3RsQlLaadHBEZEQeV1xERH4qIKyPi4oh4UrfikiRJkqTdQTdb9j4NPH9iYUQcAjwPuK6p+AXA4eVxIvCRLsYlSZIkSbXXtWQvM88Fbmkx6f3AW4FsKjsG+ExWvgcMRsRDuxWbJEmSJNVdT+/Zi4iXAKOZedGESUPA9U2vt5SyVss4MSI2RsTGbdu2dSlSSZIkSVrYepbsRcT9gLcDf9FqcouybFFGZn4sM1dk5oolS5Z0MkRJkiRJqo09e7iuRwAPAy6KCICDgR9GxJFULXmHNM17MLC1h7FJkiRJUq30rGUvMzdn5oMyc1lmLqNK8J6UmT8DvgG8pozK+VTg1sy8oVexSZIkSVLddPOnF74IfBcYjogtEfG6KWb/FnAVcCXwL8AfdisuSZIkSdoddK0bZ2a+aprpy5qeJ/BH3YpFkiRJknY3PR2NU5IkSZLUGyZ7kiRJklRDJnuSJEmSVEMme5IkSZJUQyZ7kiRJklRDJnuSJEmSVENd++kFSZIkSTOzftMoazeMsHVsnKWDA6xeOcyq5UP9DksLlMmeJEmSNA+s3zTKmnWbGd++A4DRsXHWrNsMYMKnWbEbpyRJkjQPrN0wsjPRaxjfvoO1G0b6FJEWOpM9SZIkaR7YOjY+o3JpOiZ7kiRJ0jywdHBgRuXSdEz2JEmSpHlg9cphBhYv2qVsYPEiVq8c7lNEWugcoEWSJEmaBxqDsDgapzrFZE+SJEmaJ1YtHzK5U8fYjVOSJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqaM9+ByBJkiTNxfpNo6zdMMLWsXGWDg6weuUwq5YP9Tssqe9M9iRJkrRgrd80ypp1mxnfvgOA0bFx1qzbDGDCp92e3TglSZK0YK3dMLIz0WsY376DtRtG+hSRNH+Y7EmSJGnB2jo2PqNyaXdisidJkqQFa+ngwIzKpd2JyZ4kSZIWrNUrhxlYvGiXsoHFi1i9crhPEUnzhwO0SJIkacFqDMLiaJzSfZnsSZIkaUFbtXzI5E5qwW6ckiRJklRDtuxJkiS1yR/vlrSQmOxJkiS1wR/vlrTQ2I1TkiSpDf54t6SFxmRPkiSpDf54t6SFxmRPkiSpDf54t6SFxmRPkiSpDf54t6SFxgFaJEmS2uCPd0taaEz2JEmS2uSPd0taSOzGKUmSJEk1ZLInSZIkSTVkN05JkqTd0PpNo95/KNWcyZ4kSdJuZv2mUdas27zzR+JHx8ZZs24zgAmfVCN245QkSdrNrN0wsjPRaxjfvoO1G0b6FJGkbjDZkyRJ2s1sHRufUbmkhclkT5IkaTezdHBgRuWSFiaTPUmSpN3M6pXDDCxetEvZwOJFrF453KeIJHWDA7RIkiTtZhqDsDgap1RvJnuSJEm7oVXLh0zupJrrWjfOiPhkRNwYEZc0la2NiCsi4uKI+FpEDDZNWxMRV0bESESs7FZckiRJkrQ76OY9e58Gnj+h7EzgcZn5eODHwBqAiHgM8ErgseU9H46IRUiSJEmSZqVryV5mngvcMqHsjMy8u7z8HnBweX4M8KXMvCszrwauBI7sVmySJEmSVHf9HI3zd4F/L8+HgOubpm0pZZIkSZKkWehLshcRbwfuBj7fKGoxW07y3hMjYmNEbNy2bVu3QpQkSZKkBa3nyV5EHA+8CDguMxsJ3RbgkKbZDga2tnp/Zn4sM1dk5oolS5Z0N1hJkiRJWqB6muxFxPOBtwEvycw7myZ9A3hlROwdEQ8DDge+38vYJEmSJKlOuvY7exHxReAo4KCI2AK8g2r0zb2BMyMC4HuZ+YbMvDQiTgMuo+re+UeZuaNbsUmSJElS3cW9PSkXnhUrVuTGjRv7HYYkSZIk9UVEXJiZK1pN6+donJIkSZKkLjHZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGpo22YuIfSNij/L8URHxkohY3P3QJEmSJEmz1U7L3rnAPhExBJwFvBb4dDeDkiRJkiTNTTvJXmTmncCxwD9k5kuBx3Q3LEmSJEnSXLSV7EXE04DjgH8rZXt2LyRJkiRJ0ly1k+y9CVgDfC0zL42IhwNndzcsSZIkSdJcTNtCl5nnUt2313h9FfDGbgYlSZLqZf2mUdZuGGHr2DhLBwdYvXKYVcuH+h2WJNXatMleRDwKOBlY1jx/Zh7dvbAkSVJdrN80ypp1mxnfvgOA0bFx1qzbDGDCJ0ld1M69d18BPgp8HNjR3XAkSVLdrN0wsjPRaxjfvoO1G0ZM9iSpi9pJ9u7OzI90PRJJklRLW8fGZ1QuSeqMdgZoOT0i/jAiHhoRBzYeXY9MkiTVwtLBgRmVS5I6o51k73hgNXA+cGF5bOxmUJIkqT5WrxxmYPGiXcoGFi9i9crhPkUkSbuHdkbjfFgvApEkSfXUuC/P0TglqbfaGY1zMfAHwDNL0TnAP2fm9i7GJUmSamTV8iGTO0nqsXYGaPkIsBj4cHn96lL2e90KSpIkSZI0N+0ke0/JzCc0vf52RFzUrYAkSZIkSXPXzgAtOyLiEY0XEfFw/L09SZIkSZrX2mnZWw2cHRFXAQEcBry2q1FJkiRJkuakndE4z4qIw4FhqmTvisy8q+uRSZIkSZJmbdJkLyKOzsxvR8SxEyY9IiLIzHVdjk2SJEmSNEtTtew9C/g28OIW0xIw2ZMkSZKkeWrSZC8z31H+en+eJEmSJC0wU3XjfMtUb8zMv+98OJIkSZKkTpiqG+f9exaFJEmSJKmjpurG+a5eBiJJkiRJ6pypunF+aKo3ZuYbOx+OJEmSJKkTpurGeWHPopAkSZIkddRU3ThP7WUgkiRJkqTOmaob5wcy880RcTrV7+rtIjNf0tXIJEmSJEmzNlU3zs+Wv++dzYIj4pPAi4AbM/NxpexA4MvAMuAa4Lcy8xcREcAHgRcCdwInZOYPZ7NeSZIkSRLsMdmEzLyw/P1Oq0cby/408PwJZX8GnJWZhwNnldcALwAOL48TgY/MrBqSJEmSpGZTdeO8eKo3Zubjp5l+bkQsm1B8DHBUeX4qcA7wtlL+mcxM4HsRMRgRD83MG6ZahyRJkiSptam6cd5Dda/eF4DTgfEOrO/BjQQuM2+IiAeV8iHg+qb5tpSy+yR7EXEiVesfhx56aAdCkiRJkqT6maob5xOBVwH7USV87wYeC4xm5rUdjiNahTBJXB/LzBWZuWLJkiUdDkOSJEmS6mHSZA8gM6/IzHdk5pOoWvc+A/zJHNb384h4KED5e2Mp3wIc0jTfwcDWOaxHkiRJknZrUyZ7ETEUEX8aEecBv0OV6M1l8JRvAMeX58cDX28qf01Ungrc6v16kiRJkjR7Uw3Q8h3g/sBpwAnALWXSXhFxYGbeMtl7y/u/SDUYy0ERsQV4B/Ae4LSIeB1wHfDyMvu3qH524Uqqn1547SzrI0mSJEkCohoAs8WEiGu497655pkCyMx8eHdDm96KFSty48aN/Q5DkiRJkvoiIi7MzBWtpk3aspeZy7oWkSRJkiSpq6a8Z0+SJEmStDCZ7EmSJElSDZnsSZIkSVINTXrPXrOIWAQ8uHn+zLyuW0FJkiRJkuZm2mQvIk6i+tmEnwP3lOIEHt/FuCRJkiRJc9BOy96bgOHMvLnbwUiSJEmSOqOde/auB27tdiCSJEmSpM5pp2XvKuCciPg34K5GYWb+fdeikiRJkiTNSTvJ3nXlsVd5SJIkSZLmuWmTvcx8Vy8CkSRJkiR1zqTJXkR8IDPfHBGnU42+uYvMfElXI5MkSZIkzdpULXufLX/f24tAJEmSJEmdM2myl5kXlr/f6V04kiRJkqROaOenFyRJkiRJC4zJniRJkiTVUNvJXkTs281AJEmSJEmdM+1PL0TE04GPA/sBh0bEE4Dfz8w/7HZwkiTtztZvGmXthhG2jo2zdHCA1SuHWbV8qN9hSZIWiHZa9t4PrARuBsjMi4BndjMoSZJ2d+s3jbJm3WZGx8ZJYHRsnDXrNrN+02i/Q5MkLRBtdePMzOsnFO3oQiySJKlYu2GE8e27ft2Ob9/B2g0jfYpIkrTQTNuNE7i+dOXMiNgLeCNweXfDkiRp97Z1bHxG5ZIkTdROy94bgD8ChoAtwBPLa0mS1CVLBwdmVC5J0kTTJnuZeVNmHpeZD87MB2Xm72Tmzb0ITpKk3dXqlcMMLF60S9nA4kWsXjncp4gkSQvNtMleRJwaEYNNrx8QEZ/sbliSJO3eVi0f4pRjj2BocIAAhgYHOOXYIxyNU5LUtnbu2Xt8Zo41XmTmLyJieRdjkiRJVAmfyZ0kabbauWdvj4h4QONFRBxIe0miJEmSJKlP2kna3gecHxFfLa9fDry7eyFJkiRJkuZq2mQvMz8TERuBo4EAjs3My7oemSRJkiRp1iZN9iJi/8y8rXTb/BnwhaZpB2bmLb0IUJIkSZI0c1O17H0BeBFwIZBN5VFeP7yLcUmSJEmS5mDSZC8zXxQRATwrM6/rYUySJEmSpDmacjTOzEzgaz2KRZIkSZLUIe389ML3IuIpXY9EkiRJktQx7fz0wrOBN0TENcD/UO7Zy8zHdzMwSZIkSdLstZPsvaDrUUiS1CHrN42ydsMIW8fGWTo4wOqVw6xaPtTvsCRJ6rmpfnphH+ANwCOBzcAnMvPuXgUmSdJMrd80ypp1mxnfvgOA0bFx1qzbDGDCJ0na7Ux1z96pwAqqRO8FwPt6EpEkSbO0dsPIzkSvYXz7DtZuGOlTRJIk9c9U3Tgfk5lHAETEJ4Dv9yYkSZJmZ+vY+IzKJUmqs6la9rY3nth9U5K0ECwdHJhRuSRJdTZVsveEiLitPG4HHt94HhG39SpASZLatXrlMAOLF+1SNrB4EatXDvcpIkmS+mfSbpyZuWiyaZIkzUeNQVgcjVOSpPZ+ekGSpAVj1fIhkztJkpi6G6ckSZIkaYEy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBrqS7IXEX8SEZdGxCUR8cWI2CciHhYRF0TETyLiyxGxVz9ikyRJkqQ66HmyFxFDwBuBFZn5OGAR8Ergb4H3Z+bhwC+A1/U6NkmSJEmqi35149wTGIiIPYH7ATcARwNfLdNPBVb1KTZJkiRJWvB6nuxl5ijwXuA6qiTvVuBCYCwz7y6zbQGGWr0/Ik6MiI0RsXHbtm29CFmSJEmSFpx+dON8AHAM8DBgKbAv8IIWs2ar92fmxzJzRWauWLJkSfcClSRJkqQFrB/dOJ8LXJ2Z2zJzO7AOeDowWLp1AhwMbO1DbJIkSZJUC/1I9q4DnhoR94uIAJ4DXAacDbyszHM88PU+xCZJkiRJtdCPe/YuoBqI5YfA5hLDx4C3AW+JiCuBBwKf6HVskiRJklQXe04/S+dl5juAd0wovgo4sg/hSJIkSVLt9OunFyRJkiRJXWSyJ0mSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk1ZLInSZIkSTVksidJkiRJNWSyJ0mSJEk11JcfVZckzT/rN42ydsMIWxqfJHoAABgoSURBVMfGWTo4wOqVw6xaPtTvsCRJ0iyZ7EmSWL9plDXrNjO+fQcAo2PjrFm3GcCET5KkBcpunJIk1m4Y2ZnoNYxv38HaDSN9ikiSJM2VyZ4kia1j4zMqlyRJ85/JniSJpYMDMyqXJEnzn8meJInVK4cZWLxol7KBxYtYvXK4TxFJkqS5coAWSdLOQVgcjVOSpPow2ZMkAVXCZ3InSVJ92I1TkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmrIZE+SJEmSashkT5IkSZJqyGRPkiRJkmpoz34HIEkL2fpNo6zdMMLWsXGWDg6weuUwq5YP9TssSZIkkz1Jmq31m0ZZs24z49t3ADA6Ns6adZsBTPgkSVLf2Y1TkmZp7YaRnYlew/j2HazdMNKniCRJku7Vl2QvIgYj4qsRcUVEXB4RT4uIAyPizIj4Sfn7gH7EJknt2jo2PqNySZKkXupXy94Hgf/IzEcDTwAuB/4MOCszDwfOKq8lad5aOjgwo3JJkqRe6nmyFxH7A88EPgGQmb/MzDHgGODUMtupwKpexyZJM7F65TADixftUjaweBGrVw73KSJJkqR79aNl7+HANuBTEbEpIj4eEfsCD87MGwDK3we1enNEnBgRGyNi47Zt23oXtSRNsGr5EKccewRDgwMEMDQ4wCnHHuHgLJIkaV6IzOztCiNWAN8Dfi0zL4iIDwK3ASdl5mDTfL/IzCnv21uxYkVu3LixuwFLkiRJ0jwVERdm5opW0/rRsrcF2JKZF5TXXwWeBPw8Ih4KUP7e2IfYJEmSJKkWep7sZebPgOsjonFTy3OAy4BvAMeXsuOBr/c6NkmSJEmqi379qPpJwOcjYi/gKuC1VInnaRHxOuA64OV9ik2SJEmSFry+JHuZ+SOgVb/S5/Q6FtXf+k2jrN0wwtaxcZYODrB65bADaEiSJKn2+tWyJ/XE+k2jrFm3mfHtOwAYHRtnzbrNACZ8kiRJqrV+/ai61BNrN4zsTPQaxrfvYO2GkT5FJEmSJPWGyZ5qbevY+IzKJUmSpLow2VOtLR0cmFG5JEmSVBcme6q11SuHGVi8aJeygcWLWL1yeJJ3SJIkSfXgAC2qtcYgLI7GKUmSpN2NyZ5qb9Xyodokd/6MhCRJktplsictEP6MhCRJkmbCe/akBcKfkZAkSdJMmOxJC4Q/IyFJkqSZsBunWvLesPln6eAAoy0SO39GQpIkSa3Ysqf7aNwbNjo2TnLvvWHrN432O7Tdmj8jIUmSpJkw2dN9eG/Y/LRq+RCnHHsEQ4MDBDA0OMApxx5hi6skSZJashun7sN7w+avOv2MhCRJkrrLlj3dx2T3gHlvmCRJkrRwmOzpPrw3TJIkSVr47Map+2h0E3Q0TnWLo71KkiR1n8meWvLeMHVLY7TXxiBAjdFeAfc5SZKkDrIbp6SecrRXSZKk3jDZk9RTjvYqSZLUGyZ7knrK0V4lSZJ6w2RPUk852qskSVJvOECLpJ5ytFdJkqTeMNmT1HOO9ipJktR9duOUJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQaMtmTJEmSpBoy2ZMkSZKkGtqz3wHUyfpNo6zdMMLWsXGWDg6weuUwq5YP9TssSZIkSbshk70OWb9plDXrNjO+fQcAo2PjrFm3GcCET5IkSVLP2Y2zQ9ZuGNmZ6DWMb9/B2g0jfYpIkiRJ0u7MZK9Dto6Nz6hckiRJkrrJZK9Dlg4OzKhckiRJkrqpb8leRCyKiE0R8c3y+mERcUFE/CQivhwRe/UrttlYvXKYgcWLdikbWLyI1SuH+xSRJEmSpN1ZP1v23gRc3vT6b4H3Z+bhwC+A1/UlqllatXyIU449gqHBAQIYGhzglGOPcHAWSZIkSX3Rl9E4I+Jg4P8A7wbeEhEBHA38dpnlVOCdwEf6Ed9srVo+ZHInSZIkaV7oV8veB4C3AveU1w8ExjLz7vJ6C9Aya4qIEyNiY0Rs3LZtW/cjlSRJkqQFqOfJXkS8CLgxMy9sLm4xa7Z6f2Z+LDNXZOaKJUuWdCVGSZIkSVro+tGN89eAl0TEC4F9gP2pWvoGI2LP0rp3MLC1D7FJkiRJUi30vGUvM9dk5sGZuQx4JfDtzDwOOBt4WZnteODrvY5NkiRJkupiPv3O3tuoBmu5kuoevk/0OR5JkiRJWrD6MhpnQ2aeA5xTnl8FHNnPeCRJkiSpLuZTy54kSZIkqUNM9iRJkiSphkz2JEmSJKmGTPYkSZIkqYZM9iRJkiSphkz2JEmSJKmGTPYkSZIkqYYiM/sdw6xFxDbg2n7H0cJBwE39DqJDrMv8VJe61KUeYF3mo7rUA6zLfFSXeoB1ma/qUpe61APmb10Oy8wlrSYs6GRvvoqIjZm5ot9xdIJ1mZ/qUpe61AOsy3xUl3qAdZmP6lIPsC7zVV3qUpd6wMKsi904JUmSJKmGTPYkSZIkqYZM9rrjY/0OoIOsy/xUl7rUpR5gXeajutQDrMt8VJd6gHWZr+pSl7rUAxZgXbxnT5IkSZJqyJY9SZIkSaohkz1JkiRJqiGTvRYi4uCI+HpE/CQifhoRH4yIvaZ5z5sj4n6TTDsnIpaV5++OiOsj4o4J8xwaEWdHxKaIuDgiXjjHOmREvK/p9ckR8c65LLMs550RcUJ5/vKIuDQi7omIFU3zLI6IUyNic0RcHhFr5rjOHRHxo7KuiyLiLREx5323H3VpWu4d08/V8n2fjoijWpRPFv9eEfGpEv9Frd47W03bpfFYNsW8R0XEN9tYZjvHymERcVY5Ts6JiIPnWI+MiM82vd4zIra1E+80y72m6fl/RMTYxGVGxNER8cOIuKTsZ3vOZZ1Ny31pqdejO7CsnftcRPxxRFxZln1Q0zwPiIivlW3y/Yh43BzX2ZVtUpZ1VER8ujx/dER8NyLuioiTJ8z3prJdLo2IN3dgvbM65icsoy+xTxHPlHUqx+d9hijvx3HeIoaOHSNNy+z59omIt5flXFz+D//qXJZXltm3/Sz6c/7Vje+Ufp1/dfQ7P/p7/tWV85cO/S9ujn9tRFxR9p+vRcRg03xrovrOHImIlXNd72yZ7E0QEQGsA9Zn5uHAo4D9gHdP89Y3Ay3/2UxwOnBki/I/B07LzOXAK4EPtx10a3cBx0bTCVkXXAIcC5w7ofzlwN6ZeQTwZOD3Y4pEoA3jmfnEzHws8DzghcA75rC8VnpVl26ZLP7XA5T4nwe8rxP/qIvGdmk8runQchsmO1beC3wmMx8P/CVwyhzX8z/A4yJioLx+HjA6kwXE9EnaWuDVE96zB3Aq8MrMfBxwLXD8TNY7hVcB51H9L5mRiFg0xeT/Bp5LFWuz/wv8qGyT1wAfnOl6J5jzNmnTLcAbqfapnaJKVl9Ptf89AXhRRBzehfXPxUKOvVmvjvOJZn2MtKnr2ycinga8CHhS+ZyeC1w/l6Bb6Nl+1sfzr07va/08/+r0d34/z7+6ef7SSWcCjyv7z4+BNQAR8Riq/y+PBZ4PfHia79eumY8fWr8dDfxvZn4KIDN3AH8C/G5E3C8iFkXEe8uVhosj4qSIeCOwFDg7Is5uscxbgB1led/LzBtazJPA/uX5AcDWOdbjbqoRg/5k4oQJV7HOiqpV8YCIuKZxIJW6Xh8Riye8/Q5gvNTl8swcmaQu+5YT4AHgl8Btc6wPZZ03AicCfxyVReWqyg9KfX6/qZ5vbboi9J4Wi+trXSJiv/L5/7DEeUwpXxZVK+K/lKtdZzSd9N5aYtg1yMnjfwxwVpnnRmAM6NqPgU61PYD9y1WvyyLio5P8027nWNlZJ+Bs4JgOhP7vwP8pz18FfLExISKOjIjzo2p1Pz8ihkv5CRHxlYg4HTijxTK3NZ5k5lnA7ROmPxC4KzN/XF6fCfzmXCsSEfsBvwa8jqYT2aiu1p/bahtExB0R8ZcRcQHwtAmL3LnPZeamSZL65v3sCmBZRDx4jlWZzTb5r4h4YtN8/x0Rj5+w3F+WOpGZN2bmD4DtE+b5FeB7mXlnZt4NfAd46Rzrc58W7oj4x6arw9dExLua/h+0anHqW+yTmapOU+jXcd6IcapjZLLt88Kort6fFxEfitatzL3ePg8FbsrMu8o6b8rMrSXeJ0fEdyLiwojYEBEPLeXnRMQHynFzSUS0Sn76tZ/16/yr0/taP8+/uvad34fzr67VZZbnX5PFf0Y5BgC+BzRaho8BvpSZd2Xm1cCVtL7Y0HUme/f1WODC5oLMvA24Dngk1Y7+MGB5yeI/n5kfokrOnp2Zz564wMw8NjOnu9r2TuB3ImIL8C3gpLlWBPgn4LiIOGBC+T9y71WszwMfysxbgYuAZ5V5XgxsyMxd/sFn5nsz88vTrPerVFfmb6D63N6bmbfMrSq7xHAV1b77IKov61sz8ynAU4DXR8TDIuIFwCrgVzPzCcDftVhOv+vyv8BLM/NJwLOprlpFmXY48E/latoYJQnIzDdl5vkzWMdFwDFRdYN7GFXr5CEdin8g7u3C+bVS1nJ7lGlHAn8KHAE8gupK3i7aPFYu4t6k6KXA/SPigXOsy5eAV0bEPsDjgQuapl0BPLO0uv8F8DdN054GHJ+ZR09cYPkMpnITsDju7bbyMjqzbVYB/1GSyFsi4klN0ybbBvsCl2Tmr2bmec0La3Ofu6ixrHLyeBj3fuHN1my2yceBE0ocj6Jqlb+4eaGZeX5mvmmadV8CPDMiHhhV97AX0rnjZio3lf8HHwFOnjhxnsfetj4e5w1THSP3UfbBfwZekJnPAJa0mq8P2+cM4JCI+HFEfDginlXiXQz8A/CyzHwy8El2bR3bNzOfDvxhmdbvejT06/yrG/tav86/uvmd3+vzr27WZcbnX23G/7tUFyoBhti1pX1LKes5k737CqrWnMnKnwt8tJHFd/DE/1XApzPzYKp/mp+NOTZXl3+Sn6HqgtHsacAXyvPPAs8oz78MvKI8f2V5PRtHUl1JW0r1j/lPI+Lhs1zWZBoH5W8Ar4mIH1GdDD6Q6kB9LvCpzLwT5rSdulmXAP4mIi4G/pPqn0CjNeTqzPxReX4hsGyW6/gk1T+YjcAHgPOprjp2QnM3zsYV3cm2B8D3M/OqcrX2i9y7383UycCzImIT1ZfjKHOsU0kIllEdh9+aMPkA4CsRcQnwfqoTkoYzZ7tvZWZSHWfvj4jvU7X8dWLbvIoqUaL8fVXTtMm2wQ7gX+ewzvcADyjb/SRgE/3ZJl+h6k62mOpL99OzXPflwN9Stbb+B9VJR6eOm6msK39nfcz3MfZO6/hx3mSqY6SVRwNXlavz0NTKPFOd3D6ZeQfVCfCJVD0JvlxaIoeBxwFnlmPyz9n14ssXy/vPpepxMcgMdWk/69f5Vze+U/p1/tXN7/yGXp1/dbMuHT//ioi3l/g+37SOifrye3cdGQygZi5lQhYfEftTXU34KZP/M5qr11H16SUzv1uuJB4E3DjH5X4A+CHwqSnmadTnG8ApEXEg1RfIt2e5zt+mumq6HbgxIv6bqun9qlkubxcl2dpB9dkEcFJmbpgwz/PpzHbqZl2Oo7pC/OTM3B7VgB77lGl3Nc23g6oL6YyVL8WdXUki4nzgJ7OKtj2TbY+juO/2mNX2yaqbUqMVaT/gN8uV0bn6BtW9G0dRfWk1/BVwdma+NKr7Nc9pmvY/c1lhZn4X+HWAiPgNqntUZq1cjT6a6n63BBYBGRFvbaxyYgjl7/+WBHBWyonNa0sMAVxdHnM1o22SmXdGxJlU3Wd+izl0+cnMTwCfAIiIv6E66Ziru9n1Ius+E6Y3jvsdzOH7uUuxT2a6Os1Kt47zaY6RyerS6qRt1jq5fcpxew5wTkRsprrv90Lg0syc2CV759umed3uuju9n/Xl/KuL3yk9P//q9nd+L8+/ulyXjp5/RcTxVPfPPqdcyIXqeGhuiTyYud+iNSu27N3XWcD9IuI1sHOwgvdRtbrdSdVt4g1RBmQoByZUV+XvP4f1Xgc8pyzzV6h2um1TvqMN5YrKaVTJZMP53HufwnFUN6k3rhJ+n2pwhW/O4eTvOuDoqOwLPJWq29WcRcQS4KPAP5YDagPwB+VKPhHxqLLOMyj9/Ev5gZMtcxpdqwtV68SN5R/Ns6m6vnVUVH3/9y3PnwfcnZmXdXo9TSbbHgBHli4ee1BdwTxvsoVMJSIOamr1XkOLbkiz9EngLzNz84TyA7h3cJATOrQuACLiQeXv3sDbqPbtuXgZVRehwzJzWWYeQpV0Na4ed2QbTBQRg3HviHm/B5xbEsC5ms02+TjwIeAHc7ny37RtDqU6EZx1a06Ta4HHRMTepXvXczqwzPvoUuyT6UqdunicT3WMTFaXK4CHx72Dc72COejU9omI4dh1UJQnUtVhBFgS1QAuRDWqdHOPhFeU8mdQdcObVWLThf2sL+df3drX+nH+1c3v/F6ff3X5/KVj518luX0b8JJGa2bxDapbEfaOqhvq4VTbuPcy08eEB1UmfjrVFYSfUvV937tM2xP4e+Ayqm4Lf1zKT6L6Qjh7mmX/HVW2f0/5+85S/hiqke4uAn4E/MYc63BH0/MHA3c2rWsZ1VWji6n+uR7aNO/LqK7IPKuNdby01OEu4OdUfcyhGj3rK1RX6S4DVs+xLjvKZ3Jp+XxOBvYo0/agul9nM9U9BGcDB5Rpf1bW/yPgb+ZDXZr2oZupWm6/S9VF4ePA5WXbLKO6f6ox/8mNbTeL+JdRffFfTtVV4bAOHid3tChruT2oWma+TdU15TKqL4w9ZnmsvIzq2Pxx+dz27kI9jqL6woWq282PqY7PvwKuKeUnUH3ptbOO/6K6eDNe6rKylK8t22YEeHMHtsk5wPMnlL2R6h6wSbdBq89gkuW/scR/N9UVyo83fUY/ofofuA54QD+2SdO8V0z8HCZZz0NKfW6jujdjC7B/0zZr/J9/zhzrsydwc9N+PQJ8s3xWJ5Tya4CDyvMVwDnzIfY51ukcYMU0y+nJcd7OMTJNXV5c9qvzqM4BPt/v7UPVAnR+WdbFJd7GPvREqtENL6L6/np9U/1PKe+7BDiy3/WYsL5+nH917TuF3p9/LaOD3/n09/yro3Vp2oc6ff51JdW9eT8qj482TXt72Y9HqO75nfMxMptHlGAk9UhEPAH4l8zsy6hM2j2VrrQnZ+aL+h1LN0XEUqoT2kdn5j19Dgeo5zFfxzpNJSL2y8w7SjflfwJ+kpnv73dcMxUR51D9H9jY71ikXtvd/m812I1T6qGIeANVV5c/73csUt2U7l8XAG+fR4le7Y75OtapDa+PaiCKS6l6K/xzn+ORNAO76f8tAFv2JEmSJKmObNmTJEmSpBoy2ZMkSZKkGjLZkyRJkqQa8kfVJUkqyo9un1VePoRq6PHGb57emZlP70tgkiTNggO0SJLUQkS8k+o3s97b71gkSZoNu3FKktSGiLij/D0qIr4TEadFxI8j4j0RcVxEfD8iNkfEI8p8SyLiXyPiB+Xxa/2tgSRpd2OyJ0nSzD0BeBNwBPBq4FHlh3o/DpxU5vkg8P7MfArwm2WaJEk94z17kiTN3A8y8waAiPgpcEYp3ww8uzx/LvCYiGi8Z/+IuH9m3t7TSCVJuy2TPUmSZu6upuf3NL2+h3u/W/cAnpaZ470MTJKkBrtxSpLUHWcAf9x4ERFP7GMskqTdkMmeJEnd8UZgRURcHBGXAW/od0CSpN2LP70gSZIkSTVky54kSZIk1ZDJniRJkiTVkMmeJEmSJNWQyZ4kSZIk1ZDJniRJkiTVkMmeJEmSJNWQyZ4kSZIk1dD/B8UfjTMDpVQMAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 1080x504 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "canabis.corr()",
      "execution_count": 28,
      "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>Price In Millions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Price In Millions</th>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                   Price In Millions\nPrice In Millions                1.0"
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict = pd.read_csv('predictions.csv')",
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "predict",
      "execution_count": 30,
      "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>Unnamed: 0</th>\n      <th>Alcohol, total  (recorded + unrecorded) per capita (15+) consumption with 95%CI, projections to 2020 and 2025</th>\n      <th>Alcohol, total  (recorded + unrecorded) per capita (15+) consumption with 95%CI, projections to 2020 and 2025.1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Country</td>\n      <td>2025</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Afghanistan</td>\n      <td>0.2 [0-0.6]</td>\n      <td>0.2 [0-0.5]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Albania</td>\n      <td>9.2 [4.3-14.1]</td>\n      <td>8.3 [5-11.6]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Algeria</td>\n      <td>1.1 [0.9-1.3]</td>\n      <td>1 [0.8-1.2]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Andorra</td>\n      <td>10.5 [7.8-13.2]</td>\n      <td>11 [9.1-12.8]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Angola</td>\n      <td>6.6 [0.6-12.5]</td>\n      <td>6.1 [2.1-10]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Antigua and Barbuda</td>\n      <td>8.4 [7.1-9.7]</td>\n      <td>7.6 [6.4-8.8]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Argentina</td>\n      <td>10.3 [9.6-11.1]</td>\n      <td>10 [9.4-10.7]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Armenia</td>\n      <td>6.1 [4.3-8]</td>\n      <td>5.7 [4-7.5]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Australia</td>\n      <td>10.3 [8.5-12.2]</td>\n      <td>10.5 [9.3-11.8]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Austria</td>\n      <td>11.4 [10.7-12.1]</td>\n      <td>11.7 [11-12.3]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Azerbaijan</td>\n      <td>0.9 [0-4.3]</td>\n      <td>0.7 [0-3.5]</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Bahamas</td>\n      <td>4.3 [3.8-4.8]</td>\n      <td>4.4 [4-4.7]</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Bahrain</td>\n      <td>1.8 [0-6]</td>\n      <td>1.9 [0-4.7]</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Bangladesh</td>\n      <td>0 [0-0.1]</td>\n      <td>0 [0-0.1]</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Barbados</td>\n      <td>10.5 [8.7-12.3]</td>\n      <td>9.9 [8.3-11.6]</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Belarus</td>\n      <td>13.2 [5.8-20.7]</td>\n      <td>12.4 [7.5-17.3]</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Belgium</td>\n      <td>12.3 [11-13.6]</td>\n      <td>12.2 [11.3-13.1]</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Belize</td>\n      <td>7.2 [5.8-8.6]</td>\n      <td>7 [5.6-8.3]</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Benin</td>\n      <td>4.6 [3.8-5.3]</td>\n      <td>3.7 [3.1-4.4]</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Bhutan</td>\n      <td>0.4 [0-4.5]</td>\n      <td>0.5 [0-3.6]</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Bolivia (Plurinational State of)</td>\n      <td>5.2 [3.7-6.6]</td>\n      <td>4.9 [3.6-6.3]</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Bosnia and Herzegovina</td>\n      <td>7.8 [5.9-9.7]</td>\n      <td>7 [5.2-8.8]</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Botswana</td>\n      <td>11.8 [5.1-18.6]</td>\n      <td>10 [5.1-15]</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Brazil</td>\n      <td>8.3 [6.9-9.7]</td>\n      <td>7.8 [6.5-9.1]</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Brunei Darussalam</td>\n      <td>0.3 [0-1.2]</td>\n      <td>0.4 [0-1.2]</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Bulgaria</td>\n      <td>13.4 [11-15.9]</td>\n      <td>13 [10.7-15.3]</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Burkina Faso</td>\n      <td>8.3 [7.1-9.5]</td>\n      <td>8.2 [7.1-9.4]</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Burundi</td>\n      <td>6.6 [0.6-12.6]</td>\n      <td>7 [2.3-11.7]</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Cabo Verde</td>\n      <td>6.2 [4.4-8]</td>\n      <td>6 [4.3-7.6]</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>Sri Lanka</td>\n      <td>5.1 [4.1-6.1]</td>\n      <td>4.6 [3.7-5.6]</td>\n    </tr>\n    <tr>\n      <th>166</th>\n      <td>Sudan</td>\n      <td>0 [0-0]</td>\n      <td>0 [0-0]</td>\n    </tr>\n    <tr>\n      <th>167</th>\n      <td>Suriname</td>\n      <td>5.1 [2.8-7.5]</td>\n      <td>5.1 [2.8-7.3]</td>\n    </tr>\n    <tr>\n      <th>168</th>\n      <td>Sweden</td>\n      <td>10.1 [9.4-10.8]</td>\n      <td>9.8 [9.2-10.5]</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>Switzerland</td>\n      <td>10.3 [9.1-11.6]</td>\n      <td>10.9 [10.1-11.8]</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>Syrian Arab Republic</td>\n      <td>0.3 [0-1.5]</td>\n      <td>0.3 [0-1.1]</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>Tajikistan</td>\n      <td>4.9 [2.7-7]</td>\n      <td>4 [1.9-6.2]</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>Thailand</td>\n      <td>9.3 [8.3-10.3]</td>\n      <td>8.7 [7.8-9.7]</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>Timor-Leste</td>\n      <td>5.5 [3.3-7.6]</td>\n      <td>3.8 [2.2-5.3]</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>Togo</td>\n      <td>3.1 [2-4.2]</td>\n      <td>3.1 [2.1-4.1]</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>Tonga</td>\n      <td>1.7 [0.2-3.2]</td>\n      <td>1.6 [0.2-3.1]</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>Trinidad and Tobago</td>\n      <td>9.5 [6.8-12.2]</td>\n      <td>9 [6.4-11.5]</td>\n    </tr>\n    <tr>\n      <th>177</th>\n      <td>Tunisia</td>\n      <td>2.1 [1.8-2.5]</td>\n      <td>2 [1.7-2.3]</td>\n    </tr>\n    <tr>\n      <th>178</th>\n      <td>Turkey</td>\n      <td>1.9 [1.5-2.4]</td>\n      <td>1.9 [1.5-2.3]</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>Turkmenistan</td>\n      <td>6.8 [4.7-9]</td>\n      <td>6 [3.9-8]</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>Tuvalu</td>\n      <td>1.7 [0.1-3.3]</td>\n      <td>1.7 [0.2-3.2]</td>\n    </tr>\n    <tr>\n      <th>181</th>\n      <td>Uganda</td>\n      <td>10.3 [5.1-15.5]</td>\n      <td>9.8 [4.9-14.8]</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>Ukraine</td>\n      <td>10 [2-18]</td>\n      <td>9.5 [4.8-14.2]</td>\n    </tr>\n    <tr>\n      <th>183</th>\n      <td>United Arab Emirates</td>\n      <td>5.5 [3.7-7.2]</td>\n      <td>4.2 [3-5.4]</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>United Kingdom of Great Britain and Northern I...</td>\n      <td>11.8 [10.2-13.3]</td>\n      <td>11.5 [10.5-12.5]</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>United Republic of Tanzania</td>\n      <td>9.9 [7.5-12.4]</td>\n      <td>9.6 [7.3-12]</td>\n    </tr>\n    <tr>\n      <th>186</th>\n      <td>United States of America</td>\n      <td>10.3 [9.2-11.3]</td>\n      <td>10.1 [9.4-10.8]</td>\n    </tr>\n    <tr>\n      <th>187</th>\n      <td>Uruguay</td>\n      <td>13.3 [9.7-17]</td>\n      <td>12.1 [9.4-14.7]</td>\n    </tr>\n    <tr>\n      <th>188</th>\n      <td>Uzbekistan</td>\n      <td>2.6 [0.1-5]</td>\n      <td>2.6 [0.3-5]</td>\n    </tr>\n    <tr>\n      <th>189</th>\n      <td>Vanuatu</td>\n      <td>1.5 [1.2-1.8]</td>\n      <td>1.4 [1-1.7]</td>\n    </tr>\n    <tr>\n      <th>190</th>\n      <td>Venezuela (Bolivarian Republic of)</td>\n      <td>3.9 [1.3-6.6]</td>\n      <td>4.5 [2.8-6.1]</td>\n    </tr>\n    <tr>\n      <th>191</th>\n      <td>Viet Nam</td>\n      <td>11.4 [8.8-14]</td>\n      <td>9.9 [8-11.9]</td>\n    </tr>\n    <tr>\n      <th>192</th>\n      <td>Yemen</td>\n      <td>0.1 [0-0.2]</td>\n      <td>0.1 [0-0.2]</td>\n    </tr>\n    <tr>\n      <th>193</th>\n      <td>Zambia</td>\n      <td>4.7 [3.5-5.9]</td>\n      <td>4.8 [3.6-5.9]</td>\n    </tr>\n    <tr>\n      <th>194</th>\n      <td>Zimbabwe</td>\n      <td>4.2 [0.9-7.5]</td>\n      <td>4.5 [1.4-7.7]</td>\n    </tr>\n  </tbody>\n</table>\n<p>195 rows × 3 columns</p>\n</div>",
            "text/plain": "                                            Unnamed: 0  \\\n0                                              Country   \n1                                          Afghanistan   \n2                                              Albania   \n3                                              Algeria   \n4                                              Andorra   \n5                                               Angola   \n6                                  Antigua and Barbuda   \n7                                            Argentina   \n8                                              Armenia   \n9                                            Australia   \n10                                             Austria   \n11                                          Azerbaijan   \n12                                             Bahamas   \n13                                             Bahrain   \n14                                          Bangladesh   \n15                                            Barbados   \n16                                             Belarus   \n17                                             Belgium   \n18                                              Belize   \n19                                               Benin   \n20                                              Bhutan   \n21                    Bolivia (Plurinational State of)   \n22                              Bosnia and Herzegovina   \n23                                            Botswana   \n24                                              Brazil   \n25                                   Brunei Darussalam   \n26                                            Bulgaria   \n27                                        Burkina Faso   \n28                                             Burundi   \n29                                          Cabo Verde   \n..                                                 ...   \n165                                          Sri Lanka   \n166                                              Sudan   \n167                                           Suriname   \n168                                             Sweden   \n169                                        Switzerland   \n170                               Syrian Arab Republic   \n171                                         Tajikistan   \n172                                           Thailand   \n173                                        Timor-Leste   \n174                                               Togo   \n175                                              Tonga   \n176                                Trinidad and Tobago   \n177                                            Tunisia   \n178                                             Turkey   \n179                                       Turkmenistan   \n180                                             Tuvalu   \n181                                             Uganda   \n182                                            Ukraine   \n183                               United Arab Emirates   \n184  United Kingdom of Great Britain and Northern I...   \n185                        United Republic of Tanzania   \n186                           United States of America   \n187                                            Uruguay   \n188                                         Uzbekistan   \n189                                            Vanuatu   \n190                 Venezuela (Bolivarian Republic of)   \n191                                           Viet Nam   \n192                                              Yemen   \n193                                             Zambia   \n194                                           Zimbabwe   \n\n    Alcohol, total  (recorded + unrecorded) per capita (15+) consumption with 95%CI, projections to 2020 and 2025  \\\n0                                                 2025                                                              \n1                                          0.2 [0-0.6]                                                              \n2                                       9.2 [4.3-14.1]                                                              \n3                                        1.1 [0.9-1.3]                                                              \n4                                      10.5 [7.8-13.2]                                                              \n5                                       6.6 [0.6-12.5]                                                              \n6                                        8.4 [7.1-9.7]                                                              \n7                                      10.3 [9.6-11.1]                                                              \n8                                          6.1 [4.3-8]                                                              \n9                                      10.3 [8.5-12.2]                                                              \n10                                    11.4 [10.7-12.1]                                                              \n11                                         0.9 [0-4.3]                                                              \n12                                       4.3 [3.8-4.8]                                                              \n13                                           1.8 [0-6]                                                              \n14                                           0 [0-0.1]                                                              \n15                                     10.5 [8.7-12.3]                                                              \n16                                     13.2 [5.8-20.7]                                                              \n17                                      12.3 [11-13.6]                                                              \n18                                       7.2 [5.8-8.6]                                                              \n19                                       4.6 [3.8-5.3]                                                              \n20                                         0.4 [0-4.5]                                                              \n21                                       5.2 [3.7-6.6]                                                              \n22                                       7.8 [5.9-9.7]                                                              \n23                                     11.8 [5.1-18.6]                                                              \n24                                       8.3 [6.9-9.7]                                                              \n25                                         0.3 [0-1.2]                                                              \n26                                      13.4 [11-15.9]                                                              \n27                                       8.3 [7.1-9.5]                                                              \n28                                      6.6 [0.6-12.6]                                                              \n29                                         6.2 [4.4-8]                                                              \n..                                                 ...                                                              \n165                                      5.1 [4.1-6.1]                                                              \n166                                            0 [0-0]                                                              \n167                                      5.1 [2.8-7.5]                                                              \n168                                    10.1 [9.4-10.8]                                                              \n169                                    10.3 [9.1-11.6]                                                              \n170                                        0.3 [0-1.5]                                                              \n171                                        4.9 [2.7-7]                                                              \n172                                     9.3 [8.3-10.3]                                                              \n173                                      5.5 [3.3-7.6]                                                              \n174                                        3.1 [2-4.2]                                                              \n175                                      1.7 [0.2-3.2]                                                              \n176                                     9.5 [6.8-12.2]                                                              \n177                                      2.1 [1.8-2.5]                                                              \n178                                      1.9 [1.5-2.4]                                                              \n179                                        6.8 [4.7-9]                                                              \n180                                      1.7 [0.1-3.3]                                                              \n181                                    10.3 [5.1-15.5]                                                              \n182                                          10 [2-18]                                                              \n183                                      5.5 [3.7-7.2]                                                              \n184                                   11.8 [10.2-13.3]                                                              \n185                                     9.9 [7.5-12.4]                                                              \n186                                    10.3 [9.2-11.3]                                                              \n187                                      13.3 [9.7-17]                                                              \n188                                        2.6 [0.1-5]                                                              \n189                                      1.5 [1.2-1.8]                                                              \n190                                      3.9 [1.3-6.6]                                                              \n191                                      11.4 [8.8-14]                                                              \n192                                        0.1 [0-0.2]                                                              \n193                                      4.7 [3.5-5.9]                                                              \n194                                      4.2 [0.9-7.5]                                                              \n\n    Alcohol, total  (recorded + unrecorded) per capita (15+) consumption with 95%CI, projections to 2020 and 2025.1  \n0                                                 2020                                                               \n1                                          0.2 [0-0.5]                                                               \n2                                         8.3 [5-11.6]                                                               \n3                                          1 [0.8-1.2]                                                               \n4                                        11 [9.1-12.8]                                                               \n5                                         6.1 [2.1-10]                                                               \n6                                        7.6 [6.4-8.8]                                                               \n7                                        10 [9.4-10.7]                                                               \n8                                          5.7 [4-7.5]                                                               \n9                                      10.5 [9.3-11.8]                                                               \n10                                      11.7 [11-12.3]                                                               \n11                                         0.7 [0-3.5]                                                               \n12                                         4.4 [4-4.7]                                                               \n13                                         1.9 [0-4.7]                                                               \n14                                           0 [0-0.1]                                                               \n15                                      9.9 [8.3-11.6]                                                               \n16                                     12.4 [7.5-17.3]                                                               \n17                                    12.2 [11.3-13.1]                                                               \n18                                         7 [5.6-8.3]                                                               \n19                                       3.7 [3.1-4.4]                                                               \n20                                         0.5 [0-3.6]                                                               \n21                                       4.9 [3.6-6.3]                                                               \n22                                         7 [5.2-8.8]                                                               \n23                                         10 [5.1-15]                                                               \n24                                       7.8 [6.5-9.1]                                                               \n25                                         0.4 [0-1.2]                                                               \n26                                      13 [10.7-15.3]                                                               \n27                                       8.2 [7.1-9.4]                                                               \n28                                        7 [2.3-11.7]                                                               \n29                                         6 [4.3-7.6]                                                               \n..                                                 ...                                                               \n165                                      4.6 [3.7-5.6]                                                               \n166                                            0 [0-0]                                                               \n167                                      5.1 [2.8-7.3]                                                               \n168                                     9.8 [9.2-10.5]                                                               \n169                                   10.9 [10.1-11.8]                                                               \n170                                        0.3 [0-1.1]                                                               \n171                                        4 [1.9-6.2]                                                               \n172                                      8.7 [7.8-9.7]                                                               \n173                                      3.8 [2.2-5.3]                                                               \n174                                      3.1 [2.1-4.1]                                                               \n175                                      1.6 [0.2-3.1]                                                               \n176                                       9 [6.4-11.5]                                                               \n177                                        2 [1.7-2.3]                                                               \n178                                      1.9 [1.5-2.3]                                                               \n179                                          6 [3.9-8]                                                               \n180                                      1.7 [0.2-3.2]                                                               \n181                                     9.8 [4.9-14.8]                                                               \n182                                     9.5 [4.8-14.2]                                                               \n183                                        4.2 [3-5.4]                                                               \n184                                   11.5 [10.5-12.5]                                                               \n185                                       9.6 [7.3-12]                                                               \n186                                    10.1 [9.4-10.8]                                                               \n187                                    12.1 [9.4-14.7]                                                               \n188                                        2.6 [0.3-5]                                                               \n189                                        1.4 [1-1.7]                                                               \n190                                      4.5 [2.8-6.1]                                                               \n191                                       9.9 [8-11.9]                                                               \n192                                        0.1 [0-0.2]                                                               \n193                                      4.8 [3.6-5.9]                                                               \n194                                      4.5 [1.4-7.7]                                                               \n\n[195 rows x 3 columns]"
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "oldata = pd.read_csv('oldalcohol.csv',names = ['Country','Nan','Types','2016','2015','2014','2013','2012','2011','2010'])\noldata.columns\noldata.drop(oldata.index[[0,1]],inplace = True)\noldata.head()",
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>Nan</th>\n      <th>Types</th>\n      <th>2016</th>\n      <th>2015</th>\n      <th>2014</th>\n      <th>2013</th>\n      <th>2012</th>\n      <th>2011</th>\n      <th>2010</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Afghanistan</td>\n      <td>Data source</td>\n      <td>All types</td>\n      <td>NaN</td>\n      <td>0.02</td>\n      <td>0.03</td>\n      <td>0.03</td>\n      <td>0.04</td>\n      <td>0.04</td>\n      <td>0.03</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Afghanistan</td>\n      <td>Data source</td>\n      <td>Beer</td>\n      <td>NaN</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Afghanistan</td>\n      <td>Data source</td>\n      <td>Wine</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Afghanistan</td>\n      <td>Data source</td>\n      <td>Spirits</td>\n      <td>NaN</td>\n      <td>0.02</td>\n      <td>0.02</td>\n      <td>0.02</td>\n      <td>0.03</td>\n      <td>0.03</td>\n      <td>0.02</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Afghanistan</td>\n      <td>Data source</td>\n      <td>Other alcoholic beverages</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Country          Nan                      Types 2016  2015  2014  2013  \\\n2  Afghanistan  Data source                  All types  NaN  0.02  0.03  0.03   \n3  Afghanistan  Data source                       Beer  NaN  0.01  0.01  0.01   \n4  Afghanistan  Data source                       Wine  NaN     0     0     0   \n5  Afghanistan  Data source                    Spirits  NaN  0.02  0.02  0.02   \n6  Afghanistan  Data source  Other alcoholic beverages  NaN     0     0     0   \n\n   2012  2011  2010  \n2  0.04  0.04  0.03  \n3  0.01  0.01  0.01  \n4     0     0     0  \n5  0.03  0.03  0.02  \n6     0     0     0  "
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "oldata.drop(['Nan'],inplace = True,axis = 1)\noldata.shape",
      "execution_count": 32,
      "outputs": [
        {
          "data": {
            "text/plain": "(954, 9)"
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "oldata.head()\n#oldata.iloc[[-5,-4,-2,-2,-1]]",
      "execution_count": 33,
      "outputs": [
        {
          "data": {
            "text/plain": "\"z = [i for i in range(2,955,5)]\\noldata.drop(oldata.index[z],inplace = True)\\noldata.loc[z,'Types']\""
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#oldata.isnull().head()\nz = [i for i in range(2,5)]\nfinaldata = pd.DataFrame(oldata.loc[oldata['Types'] == 'All types'])",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(20,10))\n\nsns.heatmap(finaldata.isnull(), cbar=False)",
      "execution_count": 35,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2449adb2518>"
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1440x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "finaldata.head()",
      "execution_count": 36,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>Types</th>\n      <th>2016</th>\n      <th>2015</th>\n      <th>2014</th>\n      <th>2013</th>\n      <th>2012</th>\n      <th>2011</th>\n      <th>2010</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Afghanistan</td>\n      <td>All types</td>\n      <td>NaN</td>\n      <td>0.02</td>\n      <td>0.03</td>\n      <td>0.03</td>\n      <td>0.04</td>\n      <td>0.04</td>\n      <td>0.03</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Albania</td>\n      <td>All types</td>\n      <td>5.07</td>\n      <td>4.77</td>\n      <td>4.81</td>\n      <td>5.06</td>\n      <td>5.43</td>\n      <td>5.65</td>\n      <td>5.53</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Algeria</td>\n      <td>All types</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.54</td>\n      <td>0.49</td>\n      <td>0.44</td>\n      <td>0.39</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Andorra</td>\n      <td>All types</td>\n      <td>10.06</td>\n      <td>9.97</td>\n      <td>9.95</td>\n      <td>9.78</td>\n      <td>10.06</td>\n      <td>10.31</td>\n      <td>10.64</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Angola</td>\n      <td>All types</td>\n      <td>4.70</td>\n      <td>5.65</td>\n      <td>9.00</td>\n      <td>8.02</td>\n      <td>8.14</td>\n      <td>7.86</td>\n      <td>7.67</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        Country      Types   2016  2015  2014  2013   2012   2011   2010\n2   Afghanistan  All types    NaN  0.02  0.03  0.03   0.04   0.04   0.03\n7       Albania  All types   5.07  4.77  4.81  5.06   5.43   5.65   5.53\n12      Algeria  All types   0.56  0.56  0.56  0.54   0.49   0.44   0.39\n17      Andorra  All types  10.06  9.97  9.95  9.78  10.06  10.31  10.64\n22       Angola  All types   4.70  5.65  9.00  8.02   8.14   7.86   7.67"
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "finaldata.drop(['Types'],inplace = True,axis = 1)",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "finaldata.head()",
      "execution_count": 38,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>2016</th>\n      <th>2015</th>\n      <th>2014</th>\n      <th>2013</th>\n      <th>2012</th>\n      <th>2011</th>\n      <th>2010</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Afghanistan</td>\n      <td>NaN</td>\n      <td>0.02</td>\n      <td>0.03</td>\n      <td>0.03</td>\n      <td>0.04</td>\n      <td>0.04</td>\n      <td>0.03</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Albania</td>\n      <td>5.07</td>\n      <td>4.77</td>\n      <td>4.81</td>\n      <td>5.06</td>\n      <td>5.43</td>\n      <td>5.65</td>\n      <td>5.53</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Algeria</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.54</td>\n      <td>0.49</td>\n      <td>0.44</td>\n      <td>0.39</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Andorra</td>\n      <td>10.06</td>\n      <td>9.97</td>\n      <td>9.95</td>\n      <td>9.78</td>\n      <td>10.06</td>\n      <td>10.31</td>\n      <td>10.64</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Angola</td>\n      <td>4.70</td>\n      <td>5.65</td>\n      <td>9.00</td>\n      <td>8.02</td>\n      <td>8.14</td>\n      <td>7.86</td>\n      <td>7.67</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "        Country   2016  2015  2014  2013   2012   2011   2010\n2   Afghanistan    NaN  0.02  0.03  0.03   0.04   0.04   0.03\n7       Albania   5.07  4.77  4.81  5.06   5.43   5.65   5.53\n12      Algeria   0.56  0.56  0.56  0.54   0.49   0.44   0.39\n17      Andorra  10.06  9.97  9.95  9.78  10.06  10.31  10.64\n22       Angola   4.70  5.65  9.00  8.02   8.14   7.86   7.67"
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize = (10,10))\nsns.heatmap(finaldata.isnull())",
      "execution_count": 39,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x2449ac0f9e8>"
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x720 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "finaldata.reset_index(drop = True,inplace = True)",
      "execution_count": 40,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "finaldata.head()",
      "execution_count": 41,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>2016</th>\n      <th>2015</th>\n      <th>2014</th>\n      <th>2013</th>\n      <th>2012</th>\n      <th>2011</th>\n      <th>2010</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Afghanistan</td>\n      <td>NaN</td>\n      <td>0.02</td>\n      <td>0.03</td>\n      <td>0.03</td>\n      <td>0.04</td>\n      <td>0.04</td>\n      <td>0.03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>5.07</td>\n      <td>4.77</td>\n      <td>4.81</td>\n      <td>5.06</td>\n      <td>5.43</td>\n      <td>5.65</td>\n      <td>5.53</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Algeria</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.54</td>\n      <td>0.49</td>\n      <td>0.44</td>\n      <td>0.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Andorra</td>\n      <td>10.06</td>\n      <td>9.97</td>\n      <td>9.95</td>\n      <td>9.78</td>\n      <td>10.06</td>\n      <td>10.31</td>\n      <td>10.64</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Angola</td>\n      <td>4.70</td>\n      <td>5.65</td>\n      <td>9.00</td>\n      <td>8.02</td>\n      <td>8.14</td>\n      <td>7.86</td>\n      <td>7.67</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Country   2016  2015  2014  2013   2012   2011   2010\n0  Afghanistan    NaN  0.02  0.03  0.03   0.04   0.04   0.03\n1      Albania   5.07  4.77  4.81  5.06   5.43   5.65   5.53\n2      Algeria   0.56  0.56  0.56  0.54   0.49   0.44   0.39\n3      Andorra  10.06  9.97  9.95  9.78  10.06  10.31  10.64\n4       Angola   4.70  5.65  9.00  8.02   8.14   7.86   7.67"
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "finaldata.fillna(0.00)\nfinaldata.head()",
      "execution_count": 42,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Country</th>\n      <th>2016</th>\n      <th>2015</th>\n      <th>2014</th>\n      <th>2013</th>\n      <th>2012</th>\n      <th>2011</th>\n      <th>2010</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Afghanistan</td>\n      <td>NaN</td>\n      <td>0.02</td>\n      <td>0.03</td>\n      <td>0.03</td>\n      <td>0.04</td>\n      <td>0.04</td>\n      <td>0.03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Albania</td>\n      <td>5.07</td>\n      <td>4.77</td>\n      <td>4.81</td>\n      <td>5.06</td>\n      <td>5.43</td>\n      <td>5.65</td>\n      <td>5.53</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Algeria</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.56</td>\n      <td>0.54</td>\n      <td>0.49</td>\n      <td>0.44</td>\n      <td>0.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Andorra</td>\n      <td>10.06</td>\n      <td>9.97</td>\n      <td>9.95</td>\n      <td>9.78</td>\n      <td>10.06</td>\n      <td>10.31</td>\n      <td>10.64</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Angola</td>\n      <td>4.70</td>\n      <td>5.65</td>\n      <td>9.00</td>\n      <td>8.02</td>\n      <td>8.14</td>\n      <td>7.86</td>\n      <td>7.67</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       Country   2016  2015  2014  2013   2012   2011   2010\n0  Afghanistan    NaN  0.02  0.03  0.03   0.04   0.04   0.03\n1      Albania   5.07  4.77  4.81  5.06   5.43   5.65   5.53\n2      Algeria   0.56  0.56  0.56  0.54   0.49   0.44   0.39\n3      Andorra  10.06  9.97  9.95  9.78  10.06  10.31  10.64\n4       Angola   4.70  5.65  9.00  8.02   8.14   7.86   7.67"
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "n = np.array(finaldata.loc[finaldata['Country'] == 'Canada'])\nprint(n)\nn = n[0][1:][::-1]\nk = np.array(finaldata['Country'])\nprint(n)",
      "execution_count": 53,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[['Canada' '8.20' '8.00' '8.10' '8.20' '8.30' '8.20' '8.40']]\n['8.40' '8.20' '8.30' '8.20' '8.10' '8.00' '8.20']\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize = (5,5))\nplt.plot(n)",
      "execution_count": 54,
      "outputs": [
        {
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x2449b10e550>]"
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 360x360 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Big Data Scinece/Big Data Science 2020.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}