Plastics_Background_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88d5deef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a7fc3fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"./OECD_plastic_waste.csv\"\n",
    "plastic_csv = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "59538f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>Location</th>\n",
       "      <th>PLASTICS_EOLFATETYPES</th>\n",
       "      <th>Plastic end of life fates</th>\n",
       "      <th>TIME</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag Codes</th>\n",
       "      <th>Flags</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAM</td>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>LIT</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.021844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>OAM</td>\n",
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       "      <td>LIT</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2001</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.022556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OAM</td>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>LIT</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2002</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>OAM</td>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>LIT</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.024734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>LIT</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.025961</td>\n",
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       "      <td>India</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Total</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>13.799187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td>IND1</td>\n",
       "      <td>India</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Total</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>14.955459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>16.163692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>IND1</td>\n",
       "      <td>India</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Total</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>17.378130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>IND1</td>\n",
       "      <td>India</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>18.518411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1781 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LOCATION            Location PLASTICS_EOLFATETYPES  \\\n",
       "0         OAM  Other OECD America                   LIT   \n",
       "1         OAM  Other OECD America                   LIT   \n",
       "2         OAM  Other OECD America                   LIT   \n",
       "3         OAM  Other OECD America                   LIT   \n",
       "4         OAM  Other OECD America                   LIT   \n",
       "...       ...                 ...                   ...   \n",
       "1776     IND1               India                 TOTAL   \n",
       "1777     IND1               India                 TOTAL   \n",
       "1778     IND1               India                 TOTAL   \n",
       "1779     IND1               India                 TOTAL   \n",
       "1780     IND1               India                 TOTAL   \n",
       "\n",
       "     Plastic end of life fates  TIME  Time      Value  Flag Codes  Flags  \n",
       "0                     Littered  2000  2000   0.021844         NaN    NaN  \n",
       "1                     Littered  2001  2001   0.022556         NaN    NaN  \n",
       "2                     Littered  2002  2002   0.023735         NaN    NaN  \n",
       "3                     Littered  2003  2003   0.024734         NaN    NaN  \n",
       "4                     Littered  2004  2004   0.025961         NaN    NaN  \n",
       "...                        ...   ...   ...        ...         ...    ...  \n",
       "1776                     Total  2015  2015  13.799187         NaN    NaN  \n",
       "1777                     Total  2016  2016  14.955459         NaN    NaN  \n",
       "1778                     Total  2017  2017  16.163692         NaN    NaN  \n",
       "1779                     Total  2018  2018  17.378130         NaN    NaN  \n",
       "1780                     Total  2019  2019  18.518411         NaN    NaN  \n",
       "\n",
       "[1781 rows x 9 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3ac7fce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plastic_df = plastic_csv.filter([\"Location\", \"Plastic end of life fates\", \"Time\", \"Value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7ab89de8",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Plastic end of life fates</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
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       "      <td>Other OECD America</td>\n",
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       "      <td>Other OECD America</td>\n",
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       "      <td>Other OECD America</td>\n",
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       "      <td>2004</td>\n",
       "      <td>0.025961</td>\n",
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       "      <td>13.799187</td>\n",
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       "      <th>1777</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
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       "      <td>14.955459</td>\n",
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       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
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       "      <td>16.163692</td>\n",
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       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>India</td>\n",
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       "      <td>India</td>\n",
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       "<p>1781 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                Location Plastic end of life fates  Time      Value\n",
       "0     Other OECD America                  Littered  2000   0.021844\n",
       "1     Other OECD America                  Littered  2001   0.022556\n",
       "2     Other OECD America                  Littered  2002   0.023735\n",
       "3     Other OECD America                  Littered  2003   0.024734\n",
       "4     Other OECD America                  Littered  2004   0.025961\n",
       "...                  ...                       ...   ...        ...\n",
       "1776               India                     Total  2015  13.799187\n",
       "1777               India                     Total  2016  14.955459\n",
       "1778               India                     Total  2017  16.163692\n",
       "1779               India                     Total  2018  17.378130\n",
       "1780               India                     Total  2019  18.518411\n",
       "\n",
       "[1781 rows x 4 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c3cc02ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Other OECD America', 'Other non-OECD Asia', 'OECD Asia',\n",
       "       'OECD Oceania', 'Other Eurasia', 'Middle East & North Africa',\n",
       "       'Other EU', 'OECD Non-EU', 'OECD EU ', 'Other Africa',\n",
       "       'Latin America', 'United States', 'Canada', 'China', 'India'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df[\"Location\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0a4a4cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                      object\n",
       "Plastic end of life fates     object\n",
       "Time                           int64\n",
       "Value                        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2b56282b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/3022361127.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  plastic_df.groupby([\"Time\", \"Plastic end of life fates\"]).sum()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th>Plastic end of life fates</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2000</th>\n",
       "      <th>Incinerated</th>\n",
       "      <td>16.980004</td>\n",
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       "    <tr>\n",
       "      <th>Landfilled</th>\n",
       "      <td>92.773426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Littered</th>\n",
       "      <td>0.447137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mismanaged</th>\n",
       "      <td>40.188155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recycled</th>\n",
       "      <td>5.783667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th>Landfilled</th>\n",
       "      <td>173.837392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Littered</th>\n",
       "      <td>1.070475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mismanaged</th>\n",
       "      <td>78.259204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recycled</th>\n",
       "      <td>32.829025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>353.291097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Value\n",
       "Time Plastic end of life fates            \n",
       "2000 Incinerated                 16.980004\n",
       "     Landfilled                  92.773426\n",
       "     Littered                     0.447137\n",
       "     Mismanaged                  40.188155\n",
       "     Recycled                     5.783667\n",
       "...                                    ...\n",
       "2019 Landfilled                 173.837392\n",
       "     Littered                     1.070475\n",
       "     Mismanaged                  78.259204\n",
       "     Recycled                    32.829025\n",
       "     Total                      353.291097\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df.groupby([\"Time\", \"Plastic end of life fates\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b44fece8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/521832330.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  plastic_df.groupby(\"Location\").sum()\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
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       "      <th>Location</th>\n",
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       "      <th>Canada</th>\n",
       "      <td>241140</td>\n",
       "      <td>220.085534</td>\n",
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       "      <th>China</th>\n",
       "      <td>241140</td>\n",
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       "      <th>India</th>\n",
       "      <td>241140</td>\n",
       "      <td>383.218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America</th>\n",
       "      <td>241140</td>\n",
       "      <td>510.085546</td>\n",
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       "    <tr>\n",
       "      <th>Middle East &amp; North Africa</th>\n",
       "      <td>241140</td>\n",
       "      <td>459.079861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD Asia</th>\n",
       "      <td>241140</td>\n",
       "      <td>348.394220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD EU</th>\n",
       "      <td>241140</td>\n",
       "      <td>1693.391120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD Non-EU</th>\n",
       "      <td>241140</td>\n",
       "      <td>489.907781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OECD Oceania</th>\n",
       "      <td>241140</td>\n",
       "      <td>42.206248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Africa</th>\n",
       "      <td>202969</td>\n",
       "      <td>425.866179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other EU</th>\n",
       "      <td>241140</td>\n",
       "      <td>71.685575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Eurasia</th>\n",
       "      <td>241140</td>\n",
       "      <td>448.355718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other OECD America</th>\n",
       "      <td>241140</td>\n",
       "      <td>349.084825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other non-OECD Asia</th>\n",
       "      <td>241140</td>\n",
       "      <td>561.544169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>241140</td>\n",
       "      <td>2438.663501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Time        Value\n",
       "Location                                       \n",
       "Canada                      241140   220.085534\n",
       "China                       241140  1605.409396\n",
       "India                       241140   383.218800\n",
       "Latin America               241140   510.085546\n",
       "Middle East & North Africa  241140   459.079861\n",
       "OECD Asia                   241140   348.394220\n",
       "OECD EU                     241140  1693.391120\n",
       "OECD Non-EU                 241140   489.907781\n",
       "OECD Oceania                241140    42.206248\n",
       "Other Africa                202969   425.866179\n",
       "Other EU                    241140    71.685575\n",
       "Other Eurasia               241140   448.355718\n",
       "Other OECD America          241140   349.084825\n",
       "Other non-OECD Asia         241140   561.544169\n",
       "United States               241140  2438.663501"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df.groupby(\"Location\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "720849fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "plastic_df_cleaning_test = plastic_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6b5bb0a0",
   "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>Location</th>\n",
       "      <th>Plastic end of life fates</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.021844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.022556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.023735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.024734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other OECD America</td>\n",
       "      <td>Littered</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.025961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
       "      <td>2015</td>\n",
       "      <td>13.799187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1777</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
       "      <td>2016</td>\n",
       "      <td>14.955459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
       "      <td>2017</td>\n",
       "      <td>16.163692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
       "      <td>2018</td>\n",
       "      <td>17.378130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>India</td>\n",
       "      <td>Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>18.518411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1781 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Location Plastic end of life fates  Time      Value\n",
       "0     Other OECD America                  Littered  2000   0.021844\n",
       "1     Other OECD America                  Littered  2001   0.022556\n",
       "2     Other OECD America                  Littered  2002   0.023735\n",
       "3     Other OECD America                  Littered  2003   0.024734\n",
       "4     Other OECD America                  Littered  2004   0.025961\n",
       "...                  ...                       ...   ...        ...\n",
       "1776               India                     Total  2015  13.799187\n",
       "1777               India                     Total  2016  14.955459\n",
       "1778               India                     Total  2017  16.163692\n",
       "1779               India                     Total  2018  17.378130\n",
       "1780               India                     Total  2019  18.518411\n",
       "\n",
       "[1781 rows x 4 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df_cleaning_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "20749ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/2658796172.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD Asia\"] = \"Asia\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD Asia\"] = \"Asia\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1e61c172",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/3744698723.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"Other non-OECD Asia\"] = \"Asia\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"Other non-OECD Asia\"] = \"Asia\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4343bb28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/2386286960.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD Oceania\"] = \"Oceania\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD Oceania\"] = \"Oceania\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3c803e85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/3188339076.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD EU \"] = \"EU\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD EU \"] = \"EU\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a0f7bfdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/3291418121.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"Other EU\"] = \"EU\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"Other EU\"] = \"EU\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "3f5e849e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/1112351468.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD Non-EU\"] = \"Non-EU\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"OECD Non-EU\"] = \"Non-EU\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4b96c3ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nf/mdxt63n15r5_l8m95ry9byp40000gn/T/ipykernel_23976/2012813786.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"Other OECD America\"] = \"Other America\"\n"
     ]
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].loc[plastic_df_cleaning_test[\"Location\"] == \"Other OECD America\"] = \"Other America\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fc58ce7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Other America', 'Asia', 'Oceania', 'Other Eurasia',\n",
       "       'Middle East & North Africa', 'EU', 'Non-EU', 'Other Africa',\n",
       "       'Latin America', 'United States', 'Canada', 'China', 'India'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df_cleaning_test[\"Location\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f8fda53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Location</th>\n",
       "      <th>Plastic end of life fates</th>\n",
       "      <th>Time</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.559237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2001</td>\n",
       "      <td>6.124187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2002</td>\n",
       "      <td>6.818249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2003</td>\n",
       "      <td>7.522466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Total</td>\n",
       "      <td>2004</td>\n",
       "      <td>8.322068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Mismanaged</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.389710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Mismanaged</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.343975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Mismanaged</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.297112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Mismanaged</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.252924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Mismanaged</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.254107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Location Plastic end of life fates  Time     Value\n",
       "20       Asia                     Total  2000  5.559237\n",
       "21       Asia                     Total  2001  6.124187\n",
       "22       Asia                     Total  2002  6.818249\n",
       "23       Asia                     Total  2003  7.522466\n",
       "24       Asia                     Total  2004  8.322068\n",
       "...       ...                       ...   ...       ...\n",
       "1176     Asia                Mismanaged  2015  0.389710\n",
       "1177     Asia                Mismanaged  2016  0.343975\n",
       "1178     Asia                Mismanaged  2017  0.297112\n",
       "1179     Asia                Mismanaged  2018  0.252924\n",
       "1180     Asia                Mismanaged  2019  0.254107\n",
       "\n",
       "[240 rows x 4 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plastic_df_cleaning_test[plastic_df_cleaning_test[\"Location\"] == \"Asia\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5b460b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "plastic_df_cleaning_test.to_csv(\"./clean_plastic_data.csv\")"
   ]
  }
 ],
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