emission_scrub.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies \n",
    "import pandas as pd \n",
    "import csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Per capita CO₂ emissions (tonnes per capita)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1949</td>\n",
       "      <td>0.001912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1950</td>\n",
       "      <td>0.010871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1951</td>\n",
       "      <td>0.011684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.011544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1953</td>\n",
       "      <td>0.013218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year  Per capita CO₂ emissions (tonnes per capita)\n",
       "0  Afghanistan  AFG  1949                                      0.001912\n",
       "1  Afghanistan  AFG  1950                                      0.010871\n",
       "2  Afghanistan  AFG  1951                                      0.011684\n",
       "3  Afghanistan  AFG  1952                                      0.011544\n",
       "4  Afghanistan  AFG  1953                                      0.013218"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read from our original csv files and preview it\n",
    "data = pd.read_csv(\"data/co-emissions-per-capita.csv\")\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([1751, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check on how many years actually listed on the data \n",
    "data.groupby(['Year']).groups.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>CO2_emissions_tons_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1949</td>\n",
       "      <td>0.001912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1950</td>\n",
       "      <td>0.010871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1951</td>\n",
       "      <td>0.011684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.011544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1953</td>\n",
       "      <td>0.013218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Entity Code  Year  CO2_emissions_tons_per_capita\n",
       "0  Afghanistan  AFG  1949                       0.001912\n",
       "1  Afghanistan  AFG  1950                       0.010871\n",
       "2  Afghanistan  AFG  1951                       0.011684\n",
       "3  Afghanistan  AFG  1952                       0.011544\n",
       "4  Afghanistan  AFG  1953                       0.013218"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename our columns - eliminate space and updte it with under score\n",
    "# look at the data head \n",
    "data = data.rename(columns={\"Per capita CO₂ emissions (tonnes per capita)\" : \"CO2_emissions_tons_per_capita\"})\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>CO2_emissions_tons_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1751</td>\n",
       "      <td>0.011522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1800</td>\n",
       "      <td>2.309247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1801</td>\n",
       "      <td>2.253478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1802</td>\n",
       "      <td>4.337327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1803</td>\n",
       "      <td>2.475849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2013</td>\n",
       "      <td>893.012832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>2014</td>\n",
       "      <td>890.230156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2015</td>\n",
       "      <td>901.399532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>2016</td>\n",
       "      <td>899.315595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2017</td>\n",
       "      <td>908.860532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  CO2_emissions_tons_per_capita\n",
       "0    1751                       0.011522\n",
       "1    1800                       2.309247\n",
       "2    1801                       2.253478\n",
       "3    1802                       4.337327\n",
       "4    1803                       2.475849\n",
       "..    ...                            ...\n",
       "214  2013                     893.012832\n",
       "215  2014                     890.230156\n",
       "216  2015                     901.399532\n",
       "217  2016                     899.315595\n",
       "218  2017                     908.860532\n",
       "\n",
       "[219 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group our data by Year and sum total emmission based on the year. \n",
    "data = data.groupby([\"Year\"]).sum()\n",
    "data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2_emissions_tons_per_capita</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>869.636645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>863.100924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>858.225479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>859.062572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>867.379574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>866.162913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>854.147358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>878.764146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>888.096012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>884.272209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>907.525692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>915.658632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>921.000446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>933.068635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>944.312222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>943.559582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>898.608885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>916.898995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>907.572218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>907.679408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>893.012832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>890.230156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>901.399532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CO2_emissions_tons_per_capita\n",
       "Year                               \n",
       "1993                     869.636645\n",
       "1994                     863.100924\n",
       "1995                     858.225479\n",
       "1996                     859.062572\n",
       "1997                     867.379574\n",
       "1998                     866.162913\n",
       "1999                     854.147358\n",
       "2000                     878.764146\n",
       "2001                     888.096012\n",
       "2002                     884.272209\n",
       "2003                     907.525692\n",
       "2004                     915.658632\n",
       "2005                     921.000446\n",
       "2006                     933.068635\n",
       "2007                     944.312222\n",
       "2008                     943.559582\n",
       "2009                     898.608885\n",
       "2010                     916.898995\n",
       "2011                     907.572218\n",
       "2012                     907.679408\n",
       "2013                     893.012832\n",
       "2014                     890.230156\n",
       "2015                     901.399532"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use iloc to choose get a clean csv files to match our teammate's data \n",
    "# clean file has to be from 1993 - 2015\n",
    "clean_data = data.iloc[194:217, : ]\n",
    "clean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# safe our clean files into a new csv files to be use for our machine learning\n",
    "clean_data.to_csv(\"data/CO2_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import depedencies needed to test our predict linear regression\n",
    "import warnings \n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>CO2_emissions_tons_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993</td>\n",
       "      <td>869.636645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994</td>\n",
       "      <td>863.100924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>858.225479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996</td>\n",
       "      <td>859.062572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1997</td>\n",
       "      <td>867.379574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  CO2_emissions_tons_per_capita\n",
       "0  1993                     869.636645\n",
       "1  1994                     863.100924\n",
       "2  1995                     858.225479\n",
       "3  1996                     859.062572\n",
       "4  1997                     867.379574"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emission = pd.read_csv(\"data/CO2_data.csv\")\n",
    "emission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                               int64\n",
       "CO2_emissions_tons_per_capita    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emission.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23,) (23, 1)\n"
     ]
    }
   ],
   "source": [
    "X = emission[\"Year\"]\n",
    "y = emission[\"CO2_emissions_tons_per_capita\"].values.reshape(-1,1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/CO2_data.csv')  # load data set\n",
    "X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot \n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.xlim(1992, 2016)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Total CO2 Emission (Tons per capita)')\n",
    "plt.title('CO2 emission overtime')\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig(\"images/CO2_emission_overtime.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppm = pd.read_csv(\"data/co2-concentration-long-term.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only the range year for the data we want to use\n",
    "ppm = ppm.iloc[1845:1868, : ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>CO₂ concentration (parts per million)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>World</td>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>1993</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>World</td>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>1994</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>World</td>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>1995</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>World</td>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>1996</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>World</td>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>1997</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Entity      Code  Year  CO₂ concentration (parts per million)\n",
       "1845  World  OWID_WRL  1993                                 357.10\n",
       "1846  World  OWID_WRL  1994                                 358.83\n",
       "1847  World  OWID_WRL  1995                                 360.82\n",
       "1848  World  OWID_WRL  1996                                 362.61\n",
       "1849  World  OWID_WRL  1997                                 363.73"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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>CO₂ concentration (parts per million)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>1993</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>1994</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>1995</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>1996</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>1997</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  CO₂ concentration (parts per million)\n",
       "1845  1993                                 357.10\n",
       "1846  1994                                 358.83\n",
       "1847  1995                                 360.82\n",
       "1848  1996                                 362.61\n",
       "1849  1997                                 363.73"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop Entity and Code columns \n",
    "ppm = ppm.drop(columns=[\"Entity\", \"Code\"])\n",
    "ppm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppm = ppm.rename(columns={\"CO₂ concentration (parts per million)\" : \"CO2_concentration_per_million\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# safe our clean files into a new csv files to be use for our machine learning\n",
    "ppm.to_csv(\"data/co2_ppm.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Year</th>\n",
       "      <th>CO2_concentration_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1845</td>\n",
       "      <td>1993</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1846</td>\n",
       "      <td>1994</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1847</td>\n",
       "      <td>1995</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1848</td>\n",
       "      <td>1996</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1849</td>\n",
       "      <td>1997</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year  CO2_concentration_per_million\n",
       "0        1845  1993                         357.10\n",
       "1        1846  1994                         358.83\n",
       "2        1847  1995                         360.82\n",
       "3        1848  1996                         362.61\n",
       "4        1849  1997                         363.73"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppm_data = pd.read_csv(\"data/co2_ppm.csv\")\n",
    "ppm_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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>CO2_concentration_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993</td>\n",
       "      <td>357.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994</td>\n",
       "      <td>358.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>360.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996</td>\n",
       "      <td>362.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1997</td>\n",
       "      <td>363.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1998</td>\n",
       "      <td>366.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1999</td>\n",
       "      <td>368.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000</td>\n",
       "      <td>369.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2001</td>\n",
       "      <td>371.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2002</td>\n",
       "      <td>373.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2003</td>\n",
       "      <td>375.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2004</td>\n",
       "      <td>377.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2005</td>\n",
       "      <td>379.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2006</td>\n",
       "      <td>381.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>383.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008</td>\n",
       "      <td>385.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009</td>\n",
       "      <td>387.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>389.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011</td>\n",
       "      <td>391.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>393.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>396.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014</td>\n",
       "      <td>398.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015</td>\n",
       "      <td>400.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  CO2_concentration_per_million\n",
       "0   1993                         357.10\n",
       "1   1994                         358.83\n",
       "2   1995                         360.82\n",
       "3   1996                         362.61\n",
       "4   1997                         363.73\n",
       "5   1998                         366.70\n",
       "6   1999                         368.38\n",
       "7   2000                         369.55\n",
       "8   2001                         371.14\n",
       "9   2002                         373.28\n",
       "10  2003                         375.80\n",
       "11  2004                         377.52\n",
       "12  2005                         379.80\n",
       "13  2006                         381.90\n",
       "14  2007                         383.79\n",
       "15  2008                         385.60\n",
       "16  2009                         387.43\n",
       "17  2010                         389.90\n",
       "18  2011                         391.65\n",
       "19  2012                         393.85\n",
       "20  2013                         396.52\n",
       "21  2014                         398.65\n",
       "22  2015                         400.83"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppm_clean_data = ppm_data.drop([\"Unnamed: 0\"], axis = 1)\n",
    "ppm_clean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ppm_clean_data # load data set\n",
    "X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot \n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.xlim(1992, 2016)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Emission yearly average')\n",
    "plt.title('CO2 emission concentration parts per millions')\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig(\"images/CO2_emission_ppm.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit ('PythonData': conda)",
   "language": "python",
   "name": "python37564bitpythondatacondac4bad84f35734d9985b68da147cda670"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}