dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(\"co2emisson.csv\")\n",
    "populationdf=pd.read_csv(\"Population.csv\")\n",
    "gdpdf=pd.read_csv(\"GDP.csv\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=df1.pivot(index='country_name',columns='year',values='value')\n",
    "df5=pd.DataFrame({   \n",
    "    'country_name':df1['country_name'],   \n",
    "    'country_code':df1['country_code']   \n",
    "    })\n",
    "df5=df5.drop_duplicates()\n",
    "df5.head()\n",
    "co2df=df5.merge(df4,on='country_name',how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "populationdf=populationdf.rename(columns={'Country Name':'country_name','Country Code':'country_code'})\n",
    "for columns in co2df.columns:\n",
    "    co2df=co2df.rename(columns={columns:str(columns)})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year=co2df.columns[3:-1]\n",
    "\n",
    "len(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "co2df.head()\n",
    "sum=[]\n",
    "for i in year:\n",
    "    sum.append(co2df[i].sum())\n",
    "len(sum)\n",
    "plt.figure(figsize=(15,10));\n",
    "plt.scatter(year,sum)\n",
    "plt.xticks(rotation=90);\n",
    "plt.title('World c02 emission per year');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.merge(populationdf,co2df,on=['country_code','country_name'],suffixes=('_pop','_co2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>1960_pop</th>\n",
       "      <th>1961_pop</th>\n",
       "      <th>1962_pop</th>\n",
       "      <th>1963_pop</th>\n",
       "      <th>1964_pop</th>\n",
       "      <th>1965_pop</th>\n",
       "      <th>1966_pop</th>\n",
       "      <th>1967_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2010_co2</th>\n",
       "      <th>2011_co2</th>\n",
       "      <th>2012_co2</th>\n",
       "      <th>2013_co2</th>\n",
       "      <th>2014_co2</th>\n",
       "      <th>2015_co2</th>\n",
       "      <th>2016_co2</th>\n",
       "      <th>2017_co2</th>\n",
       "      <th>2018_co2</th>\n",
       "      <th>2019_co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>54208.0</td>\n",
       "      <td>55434.0</td>\n",
       "      <td>56234.0</td>\n",
       "      <td>56699.0</td>\n",
       "      <td>57029.0</td>\n",
       "      <td>57357.0</td>\n",
       "      <td>57702.0</td>\n",
       "      <td>58044.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2508.228000</td>\n",
       "      <td>2500.894000</td>\n",
       "      <td>1349.456000</td>\n",
       "      <td>861.745000</td>\n",
       "      <td>872.746000</td>\n",
       "      <td>898.415000</td>\n",
       "      <td>883.747000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>130836765.0</td>\n",
       "      <td>134159786.0</td>\n",
       "      <td>137614644.0</td>\n",
       "      <td>141202036.0</td>\n",
       "      <td>144920186.0</td>\n",
       "      <td>148769974.0</td>\n",
       "      <td>152752671.0</td>\n",
       "      <td>156876454.0</td>\n",
       "      <td>...</td>\n",
       "      <td>529639.997445</td>\n",
       "      <td>521760.009862</td>\n",
       "      <td>543439.999305</td>\n",
       "      <td>565430.012964</td>\n",
       "      <td>587699.992426</td>\n",
       "      <td>574029.994987</td>\n",
       "      <td>578510.015696</td>\n",
       "      <td>587509.992346</td>\n",
       "      <td>590040.005282</td>\n",
       "      <td>603030.016154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>8996967.0</td>\n",
       "      <td>9169406.0</td>\n",
       "      <td>9351442.0</td>\n",
       "      <td>9543200.0</td>\n",
       "      <td>9744772.0</td>\n",
       "      <td>9956318.0</td>\n",
       "      <td>10174840.0</td>\n",
       "      <td>10399936.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7110.000134</td>\n",
       "      <td>8930.000305</td>\n",
       "      <td>8079.999924</td>\n",
       "      <td>5989.999771</td>\n",
       "      <td>4880.000114</td>\n",
       "      <td>5949.999809</td>\n",
       "      <td>5300.000191</td>\n",
       "      <td>4780.000210</td>\n",
       "      <td>6070.000172</td>\n",
       "      <td>6079.999924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>AFW</td>\n",
       "      <td>96396419.0</td>\n",
       "      <td>98407221.0</td>\n",
       "      <td>100506960.0</td>\n",
       "      <td>102691339.0</td>\n",
       "      <td>104953470.0</td>\n",
       "      <td>107289875.0</td>\n",
       "      <td>109701811.0</td>\n",
       "      <td>112195950.0</td>\n",
       "      <td>...</td>\n",
       "      <td>165489.997715</td>\n",
       "      <td>171780.000016</td>\n",
       "      <td>177939.995751</td>\n",
       "      <td>193299.997561</td>\n",
       "      <td>201680.001885</td>\n",
       "      <td>198410.002500</td>\n",
       "      <td>206030.001298</td>\n",
       "      <td>204740.000457</td>\n",
       "      <td>211719.999298</td>\n",
       "      <td>220740.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>5454938.0</td>\n",
       "      <td>5531451.0</td>\n",
       "      <td>5608499.0</td>\n",
       "      <td>5679409.0</td>\n",
       "      <td>5734995.0</td>\n",
       "      <td>5770573.0</td>\n",
       "      <td>5781305.0</td>\n",
       "      <td>5774440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22799.999237</td>\n",
       "      <td>23870.000839</td>\n",
       "      <td>23870.000839</td>\n",
       "      <td>26959.999084</td>\n",
       "      <td>29629.999161</td>\n",
       "      <td>31649.999619</td>\n",
       "      <td>29760.000229</td>\n",
       "      <td>24250.000000</td>\n",
       "      <td>23959.999084</td>\n",
       "      <td>25209.999084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country_name country_code     1960_pop     1961_pop  \\\n",
       "0                        Aruba          ABW      54208.0      55434.0   \n",
       "1  Africa Eastern and Southern          AFE  130836765.0  134159786.0   \n",
       "2                  Afghanistan          AFG    8996967.0    9169406.0   \n",
       "3   Africa Western and Central          AFW   96396419.0   98407221.0   \n",
       "4                       Angola          AGO    5454938.0    5531451.0   \n",
       "\n",
       "      1962_pop     1963_pop     1964_pop     1965_pop     1966_pop  \\\n",
       "0      56234.0      56699.0      57029.0      57357.0      57702.0   \n",
       "1  137614644.0  141202036.0  144920186.0  148769974.0  152752671.0   \n",
       "2    9351442.0    9543200.0    9744772.0    9956318.0   10174840.0   \n",
       "3  100506960.0  102691339.0  104953470.0  107289875.0  109701811.0   \n",
       "4    5608499.0    5679409.0    5734995.0    5770573.0    5781305.0   \n",
       "\n",
       "      1967_pop  ...       2010_co2       2011_co2       2012_co2  \\\n",
       "0      58044.0  ...    2508.228000    2500.894000    1349.456000   \n",
       "1  156876454.0  ...  529639.997445  521760.009862  543439.999305   \n",
       "2   10399936.0  ...    7110.000134    8930.000305    8079.999924   \n",
       "3  112195950.0  ...  165489.997715  171780.000016  177939.995751   \n",
       "4    5774440.0  ...   22799.999237   23870.000839   23870.000839   \n",
       "\n",
       "        2013_co2       2014_co2       2015_co2       2016_co2       2017_co2  \\\n",
       "0     861.745000     872.746000     898.415000     883.747000            NaN   \n",
       "1  565430.012964  587699.992426  574029.994987  578510.015696  587509.992346   \n",
       "2    5989.999771    4880.000114    5949.999809    5300.000191    4780.000210   \n",
       "3  193299.997561  201680.001885  198410.002500  206030.001298  204740.000457   \n",
       "4   26959.999084   29629.999161   31649.999619   29760.000229   24250.000000   \n",
       "\n",
       "        2018_co2       2019_co2  \n",
       "0            NaN            NaN  \n",
       "1  590040.005282  603030.016154  \n",
       "2    6070.000172    6079.999924  \n",
       "3  211719.999298  220740.000024  \n",
       "4   23959.999084   25209.999084  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1960, 2019):\n",
    "    df[f'{i}'] = df[f'{i}_co2']/df[f'{i}_pop']        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>1960_pop</th>\n",
       "      <th>1961_pop</th>\n",
       "      <th>1962_pop</th>\n",
       "      <th>1963_pop</th>\n",
       "      <th>1964_pop</th>\n",
       "      <th>1965_pop</th>\n",
       "      <th>1966_pop</th>\n",
       "      <th>1967_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>130836765.0</td>\n",
       "      <td>134159786.0</td>\n",
       "      <td>137614644.0</td>\n",
       "      <td>141202036.0</td>\n",
       "      <td>144920186.0</td>\n",
       "      <td>148769974.0</td>\n",
       "      <td>152752671.0</td>\n",
       "      <td>156876454.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.001022</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.000918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>8996967.0</td>\n",
       "      <td>9169406.0</td>\n",
       "      <td>9351442.0</td>\n",
       "      <td>9543200.0</td>\n",
       "      <td>9744772.0</td>\n",
       "      <td>9956318.0</td>\n",
       "      <td>10174840.0</td>\n",
       "      <td>10399936.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>AFW</td>\n",
       "      <td>96396419.0</td>\n",
       "      <td>98407221.0</td>\n",
       "      <td>100506960.0</td>\n",
       "      <td>102691339.0</td>\n",
       "      <td>104953470.0</td>\n",
       "      <td>107289875.0</td>\n",
       "      <td>109701811.0</td>\n",
       "      <td>112195950.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.000477</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>0.000516</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.000486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>5454938.0</td>\n",
       "      <td>5531451.0</td>\n",
       "      <td>5608499.0</td>\n",
       "      <td>5679409.0</td>\n",
       "      <td>5734995.0</td>\n",
       "      <td>5770573.0</td>\n",
       "      <td>5781305.0</td>\n",
       "      <td>5774440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.000976</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.000951</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.001135</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.000778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1608800.0</td>\n",
       "      <td>1659800.0</td>\n",
       "      <td>1711319.0</td>\n",
       "      <td>1762621.0</td>\n",
       "      <td>1814135.0</td>\n",
       "      <td>1864791.0</td>\n",
       "      <td>1914573.0</td>\n",
       "      <td>1965598.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.001669</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0.001534</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.001558</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>0.001783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 183 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country_name country_code     1960_pop     1961_pop  \\\n",
       "1  Africa Eastern and Southern          AFE  130836765.0  134159786.0   \n",
       "2                  Afghanistan          AFG    8996967.0    9169406.0   \n",
       "3   Africa Western and Central          AFW   96396419.0   98407221.0   \n",
       "4                       Angola          AGO    5454938.0    5531451.0   \n",
       "5                      Albania          ALB    1608800.0    1659800.0   \n",
       "\n",
       "      1962_pop     1963_pop     1964_pop     1965_pop     1966_pop  \\\n",
       "1  137614644.0  141202036.0  144920186.0  148769974.0  152752671.0   \n",
       "2    9351442.0    9543200.0    9744772.0    9956318.0   10174840.0   \n",
       "3  100506960.0  102691339.0  104953470.0  107289875.0  109701811.0   \n",
       "4    5608499.0    5679409.0    5734995.0    5770573.0    5781305.0   \n",
       "5    1711319.0    1762621.0    1814135.0    1864791.0    1914573.0   \n",
       "\n",
       "      1967_pop  ...      2009      2010      2011      2012      2013  \\\n",
       "1  156876454.0  ...  0.000995  0.001022  0.000979  0.000993  0.001005   \n",
       "2   10399936.0  ...  0.000172  0.000244  0.000297  0.000259  0.000186   \n",
       "3  112195950.0  ...  0.000430  0.000472  0.000477  0.000481  0.000508   \n",
       "4    5774440.0  ...  0.000939  0.000976  0.000986  0.000951  0.001036   \n",
       "5    1965598.0  ...  0.001441  0.001528  0.001669  0.001503  0.001534   \n",
       "\n",
       "       2014      2015      2016      2017      2018  \n",
       "1  0.001017  0.000967  0.000948  0.000938  0.000918  \n",
       "2  0.000146  0.000173  0.000150  0.000132  0.000163  \n",
       "3  0.000516  0.000494  0.000499  0.000483  0.000486  \n",
       "4  0.001100  0.001135  0.001032  0.000813  0.000778  \n",
       "5  0.001668  0.001604  0.001558  0.001789  0.001783  \n",
       "\n",
       "[5 rows x 183 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean=df.dropna()\n",
    "df_clean.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop']\n",
      "/tmp/ipykernel_3552/442622669.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n",
      "/tmp/ipykernel_3552/442622669.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std\n"
     ]
    }
   ],
   "source": [
    "for i in range(1960, 2019):\n",
    "    df_clean[f'{i}'] = df_clean[f'{i}_co2']/df_clean[f'{i}_pop'] \n",
    "    mean=df_clean[f'{i}'].mean()\n",
    "    std=df_clean[f'{i}'].std()\n",
    "    df_clean[f'{i}_norm']=(df_clean[f'{i}']-mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>1960_pop</th>\n",
       "      <th>1961_pop</th>\n",
       "      <th>1962_pop</th>\n",
       "      <th>1963_pop</th>\n",
       "      <th>1964_pop</th>\n",
       "      <th>1965_pop</th>\n",
       "      <th>1966_pop</th>\n",
       "      <th>1967_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2009_norm</th>\n",
       "      <th>2010_norm</th>\n",
       "      <th>2011_norm</th>\n",
       "      <th>2012_norm</th>\n",
       "      <th>2013_norm</th>\n",
       "      <th>2014_norm</th>\n",
       "      <th>2015_norm</th>\n",
       "      <th>2016_norm</th>\n",
       "      <th>2017_norm</th>\n",
       "      <th>2018_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>130836765.0</td>\n",
       "      <td>134159786.0</td>\n",
       "      <td>137614644.0</td>\n",
       "      <td>141202036.0</td>\n",
       "      <td>144920186.0</td>\n",
       "      <td>148769974.0</td>\n",
       "      <td>152752671.0</td>\n",
       "      <td>156876454.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.673716</td>\n",
       "      <td>-0.651994</td>\n",
       "      <td>-0.665753</td>\n",
       "      <td>-0.674941</td>\n",
       "      <td>-0.659362</td>\n",
       "      <td>-0.651130</td>\n",
       "      <td>-0.662813</td>\n",
       "      <td>-0.671216</td>\n",
       "      <td>-0.681192</td>\n",
       "      <td>-0.696148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>8996967.0</td>\n",
       "      <td>9169406.0</td>\n",
       "      <td>9351442.0</td>\n",
       "      <td>9543200.0</td>\n",
       "      <td>9744772.0</td>\n",
       "      <td>9956318.0</td>\n",
       "      <td>10174840.0</td>\n",
       "      <td>10399936.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.830875</td>\n",
       "      <td>-0.807807</td>\n",
       "      <td>-0.804086</td>\n",
       "      <td>-0.823438</td>\n",
       "      <td>-0.828031</td>\n",
       "      <td>-0.832058</td>\n",
       "      <td>-0.830822</td>\n",
       "      <td>-0.842438</td>\n",
       "      <td>-0.856288</td>\n",
       "      <td>-0.863600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>AFW</td>\n",
       "      <td>96396419.0</td>\n",
       "      <td>98407221.0</td>\n",
       "      <td>100506960.0</td>\n",
       "      <td>102691339.0</td>\n",
       "      <td>104953470.0</td>\n",
       "      <td>107289875.0</td>\n",
       "      <td>109701811.0</td>\n",
       "      <td>112195950.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.781535</td>\n",
       "      <td>-0.762048</td>\n",
       "      <td>-0.767564</td>\n",
       "      <td>-0.778623</td>\n",
       "      <td>-0.761652</td>\n",
       "      <td>-0.755205</td>\n",
       "      <td>-0.762837</td>\n",
       "      <td>-0.767481</td>\n",
       "      <td>-0.779962</td>\n",
       "      <td>-0.791851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>5454938.0</td>\n",
       "      <td>5531451.0</td>\n",
       "      <td>5608499.0</td>\n",
       "      <td>5679409.0</td>\n",
       "      <td>5734995.0</td>\n",
       "      <td>5770573.0</td>\n",
       "      <td>5781305.0</td>\n",
       "      <td>5774440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.684281</td>\n",
       "      <td>-0.661080</td>\n",
       "      <td>-0.664503</td>\n",
       "      <td>-0.683430</td>\n",
       "      <td>-0.652926</td>\n",
       "      <td>-0.633850</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>-0.653334</td>\n",
       "      <td>-0.708258</td>\n",
       "      <td>-0.727194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1608800.0</td>\n",
       "      <td>1659800.0</td>\n",
       "      <td>1711319.0</td>\n",
       "      <td>1762621.0</td>\n",
       "      <td>1814135.0</td>\n",
       "      <td>1864791.0</td>\n",
       "      <td>1914573.0</td>\n",
       "      <td>1965598.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.588386</td>\n",
       "      <td>-0.550632</td>\n",
       "      <td>-0.525956</td>\n",
       "      <td>-0.571540</td>\n",
       "      <td>-0.550553</td>\n",
       "      <td>-0.515667</td>\n",
       "      <td>-0.527933</td>\n",
       "      <td>-0.540593</td>\n",
       "      <td>-0.496404</td>\n",
       "      <td>-0.504047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country_name country_code     1960_pop     1961_pop  \\\n",
       "1  Africa Eastern and Southern          AFE  130836765.0  134159786.0   \n",
       "2                  Afghanistan          AFG    8996967.0    9169406.0   \n",
       "3   Africa Western and Central          AFW   96396419.0   98407221.0   \n",
       "4                       Angola          AGO    5454938.0    5531451.0   \n",
       "5                      Albania          ALB    1608800.0    1659800.0   \n",
       "\n",
       "      1962_pop     1963_pop     1964_pop     1965_pop     1966_pop  \\\n",
       "1  137614644.0  141202036.0  144920186.0  148769974.0  152752671.0   \n",
       "2    9351442.0    9543200.0    9744772.0    9956318.0   10174840.0   \n",
       "3  100506960.0  102691339.0  104953470.0  107289875.0  109701811.0   \n",
       "4    5608499.0    5679409.0    5734995.0    5770573.0    5781305.0   \n",
       "5    1711319.0    1762621.0    1814135.0    1864791.0    1914573.0   \n",
       "\n",
       "      1967_pop  ...  2009_norm  2010_norm  2011_norm  2012_norm  2013_norm  \\\n",
       "1  156876454.0  ...  -0.673716  -0.651994  -0.665753  -0.674941  -0.659362   \n",
       "2   10399936.0  ...  -0.830875  -0.807807  -0.804086  -0.823438  -0.828031   \n",
       "3  112195950.0  ...  -0.781535  -0.762048  -0.767564  -0.778623  -0.761652   \n",
       "4    5774440.0  ...  -0.684281  -0.661080  -0.664503  -0.683430  -0.652926   \n",
       "5    1965598.0  ...  -0.588386  -0.550632  -0.525956  -0.571540  -0.550553   \n",
       "\n",
       "   2014_norm  2015_norm  2016_norm  2017_norm  2018_norm  \n",
       "1  -0.651130  -0.662813  -0.671216  -0.681192  -0.696148  \n",
       "2  -0.832058  -0.830822  -0.842438  -0.856288  -0.863600  \n",
       "3  -0.755205  -0.762837  -0.767481  -0.779962  -0.791851  \n",
       "4  -0.633850  -0.627154  -0.653334  -0.708258  -0.727194  \n",
       "5  -0.515667  -0.527933  -0.540593  -0.496404  -0.504047  \n",
       "\n",
       "[5 rows x 242 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>1960_co2</th>\n",
       "      <th>1961_co2</th>\n",
       "      <th>1962_co2</th>\n",
       "      <th>1963_co2</th>\n",
       "      <th>1964_co2</th>\n",
       "      <th>1965_co2</th>\n",
       "      <th>...</th>\n",
       "      <th>2009_norm</th>\n",
       "      <th>2010_norm</th>\n",
       "      <th>2011_norm</th>\n",
       "      <th>2012_norm</th>\n",
       "      <th>2013_norm</th>\n",
       "      <th>2014_norm</th>\n",
       "      <th>2015_norm</th>\n",
       "      <th>2016_norm</th>\n",
       "      <th>2017_norm</th>\n",
       "      <th>2018_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>677243299.0</td>\n",
       "      <td>694665117.0</td>\n",
       "      <td>118545.901306</td>\n",
       "      <td>123758.90333</td>\n",
       "      <td>128093.897815</td>\n",
       "      <td>132810.33253</td>\n",
       "      <td>144345.352398</td>\n",
       "      <td>155803.780096</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.673716</td>\n",
       "      <td>-0.651994</td>\n",
       "      <td>-0.665753</td>\n",
       "      <td>-0.674941</td>\n",
       "      <td>-0.659362</td>\n",
       "      <td>-0.651130</td>\n",
       "      <td>-0.662813</td>\n",
       "      <td>-0.671216</td>\n",
       "      <td>-0.681192</td>\n",
       "      <td>-0.696148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>39835428.0</td>\n",
       "      <td>414.371000</td>\n",
       "      <td>491.37800</td>\n",
       "      <td>689.396000</td>\n",
       "      <td>707.73100</td>\n",
       "      <td>839.743000</td>\n",
       "      <td>1008.425000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.830875</td>\n",
       "      <td>-0.807807</td>\n",
       "      <td>-0.804086</td>\n",
       "      <td>-0.823438</td>\n",
       "      <td>-0.828031</td>\n",
       "      <td>-0.832058</td>\n",
       "      <td>-0.830822</td>\n",
       "      <td>-0.842438</td>\n",
       "      <td>-0.856288</td>\n",
       "      <td>-0.863600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>AFW</td>\n",
       "      <td>458803476.0</td>\n",
       "      <td>470898870.0</td>\n",
       "      <td>8760.463000</td>\n",
       "      <td>9376.51900</td>\n",
       "      <td>9710.216000</td>\n",
       "      <td>11540.04900</td>\n",
       "      <td>13985.938000</td>\n",
       "      <td>19827.469000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.781535</td>\n",
       "      <td>-0.762048</td>\n",
       "      <td>-0.767564</td>\n",
       "      <td>-0.778623</td>\n",
       "      <td>-0.761652</td>\n",
       "      <td>-0.755205</td>\n",
       "      <td>-0.762837</td>\n",
       "      <td>-0.767481</td>\n",
       "      <td>-0.779962</td>\n",
       "      <td>-0.791851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>32866268.0</td>\n",
       "      <td>33933611.0</td>\n",
       "      <td>550.050000</td>\n",
       "      <td>454.70800</td>\n",
       "      <td>1180.774000</td>\n",
       "      <td>1151.43800</td>\n",
       "      <td>1224.778000</td>\n",
       "      <td>1188.108000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.684281</td>\n",
       "      <td>-0.661080</td>\n",
       "      <td>-0.664503</td>\n",
       "      <td>-0.683430</td>\n",
       "      <td>-0.652926</td>\n",
       "      <td>-0.633850</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>-0.653334</td>\n",
       "      <td>-0.708258</td>\n",
       "      <td>-0.727194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2837849.0</td>\n",
       "      <td>2811666.0</td>\n",
       "      <td>2024.184000</td>\n",
       "      <td>2280.87400</td>\n",
       "      <td>2464.224000</td>\n",
       "      <td>2082.85600</td>\n",
       "      <td>2016.850000</td>\n",
       "      <td>2174.531000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.588386</td>\n",
       "      <td>-0.550632</td>\n",
       "      <td>-0.525956</td>\n",
       "      <td>-0.571540</td>\n",
       "      <td>-0.550553</td>\n",
       "      <td>-0.515667</td>\n",
       "      <td>-0.527933</td>\n",
       "      <td>-0.540593</td>\n",
       "      <td>-0.496404</td>\n",
       "      <td>-0.504047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country_name country_code         2020         2021  \\\n",
       "1  Africa Eastern and Southern          AFE  677243299.0  694665117.0   \n",
       "2                  Afghanistan          AFG   38928341.0   39835428.0   \n",
       "3   Africa Western and Central          AFW  458803476.0  470898870.0   \n",
       "4                       Angola          AGO   32866268.0   33933611.0   \n",
       "5                      Albania          ALB    2837849.0    2811666.0   \n",
       "\n",
       "        1960_co2      1961_co2       1962_co2      1963_co2       1964_co2  \\\n",
       "1  118545.901306  123758.90333  128093.897815  132810.33253  144345.352398   \n",
       "2     414.371000     491.37800     689.396000     707.73100     839.743000   \n",
       "3    8760.463000    9376.51900    9710.216000   11540.04900   13985.938000   \n",
       "4     550.050000     454.70800    1180.774000    1151.43800    1224.778000   \n",
       "5    2024.184000    2280.87400    2464.224000    2082.85600    2016.850000   \n",
       "\n",
       "        1965_co2  ...  2009_norm  2010_norm  2011_norm  2012_norm  2013_norm  \\\n",
       "1  155803.780096  ...  -0.673716  -0.651994  -0.665753  -0.674941  -0.659362   \n",
       "2    1008.425000  ...  -0.830875  -0.807807  -0.804086  -0.823438  -0.828031   \n",
       "3   19827.469000  ...  -0.781535  -0.762048  -0.767564  -0.778623  -0.761652   \n",
       "4    1188.108000  ...  -0.684281  -0.661080  -0.664503  -0.683430  -0.652926   \n",
       "5    2174.531000  ...  -0.588386  -0.550632  -0.525956  -0.571540  -0.550553   \n",
       "\n",
       "   2014_norm  2015_norm  2016_norm  2017_norm  2018_norm  \n",
       "1  -0.651130  -0.662813  -0.671216  -0.681192  -0.696148  \n",
       "2  -0.832058  -0.830822  -0.842438  -0.856288  -0.863600  \n",
       "3  -0.755205  -0.762837  -0.767481  -0.779962  -0.791851  \n",
       "4  -0.633850  -0.627154  -0.653334  -0.708258  -0.727194  \n",
       "5  -0.515667  -0.527933  -0.540593  -0.496404  -0.504047  \n",
       "\n",
       "[5 rows x 182 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1960, 2022):\n",
    "    # if f'{i}'in df_clean.columns :\n",
    "    #     df_clean = df_clean.drop([ f'{i}'],axis=1) \n",
    "    if f'{i}_pop' in df_clean.columns :\n",
    "        df_clean = df_clean.drop([ f'{i}_pop'],axis=1) \n",
    "    # if f'{i}_co2' in df_clean.columns :\n",
    "    #     df_clean = df_clean.drop([ f'{i}_co2'],axis=1) \n",
    "df_clean.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3552/332365329.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean['difference']=df_clean['2018_norm']-df_clean['1960_norm']\n",
      "/tmp/ipykernel_3552/332365329.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean['difference_norm']=(df_clean['difference']-mean)/std\n",
      "/tmp/ipykernel_3552/332365329.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean['sum']=df_clean.loc[:,'1960_norm':'2018_norm'].sum(axis=1)\n",
      "/tmp/ipykernel_3552/332365329.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_clean['sum_norm']=(df_clean['sum']-mean)/std\n"
     ]
    }
   ],
   "source": [
    "df_clean['difference']=df_clean['2018_norm']-df_clean['1960_norm']\n",
    "mean=df_clean['difference'].mean()\n",
    "std=df_clean['difference'].std()\n",
    "df_clean['difference_norm']=(df_clean['difference']-mean)/std\n",
    "df_clean['sum']=df_clean.loc[:,'1960_norm':'2018_norm'].sum(axis=1)\n",
    "mean=df_clean['sum'].mean()\n",
    "std=df_clean['sum'].std()\n",
    "df_clean['sum_norm']=(df_clean['sum']-mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Country with co2 per population change from 1960-2018')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_change=df_clean[['country_name','difference_norm','2018_norm','1960_norm','sum','sum_norm']]\n",
    "df_change=df_change.drop(df_change[(df_change['difference_norm'] > -3) & (df_change['difference_norm'] < 3)].index)\n",
    "plt.bar(df_change['country_name'],df_change['difference_norm'])\n",
    "plt.title(\"Country with co2 per population change from 1960-2018\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_alldata=df_clean.loc[[i for i in df_change.index],['country_name']+[f'{i}_norm' for i in range(1960,2019)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year=np.arange(1960,2019)\n",
    "fig,ax=plt.subplots()\n",
    "colors = ['red', 'green', 'blue', 'yellow', 'black']\n",
    "for i in range(1,5):\n",
    "    ax.scatter(year, df_alldata.iloc[i,1:], c=colors[i], label=df_alldata.iloc[i,0])\n",
    "ax.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>country_name</th>\n",
       "      <th>difference_norm</th>\n",
       "      <th>2018_norm</th>\n",
       "      <th>1960_norm</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>3.902243</td>\n",
       "      <td>3.183284</td>\n",
       "      <td>-0.492621</td>\n",
       "      <td>228.920728</td>\n",
       "      <td>4.201671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>-7.002041</td>\n",
       "      <td>2.503791</td>\n",
       "      <td>9.099700</td>\n",
       "      <td>219.687051</td>\n",
       "      <td>4.032194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>5.888751</td>\n",
       "      <td>5.997827</td>\n",
       "      <td>0.450635</td>\n",
       "      <td>382.594581</td>\n",
       "      <td>7.022242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country_name  difference_norm  2018_norm  1960_norm         sum  \\\n",
       "8    United Arab Emirates         3.902243   3.183284  -0.492621  228.920728   \n",
       "138            Luxembourg        -7.002041   2.503791   9.099700  219.687051   \n",
       "190                 Qatar         5.888751   5.997827   0.450635  382.594581   \n",
       "\n",
       "     sum_norm  \n",
       "8    4.201671  \n",
       "138  4.032194  \n",
       "190  7.022242  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_change=df_clean[['country_name','difference_norm','2018_norm','1960_norm','sum','sum_norm']]\n",
    "df_change=df_change.drop(df_change[(df_change['sum_norm'] > -3) & (df_change['sum_norm'] <3)].index)\n",
    "df_change\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>country_name</th>\n",
       "      <th>difference_norm</th>\n",
       "      <th>2018_norm</th>\n",
       "      <th>1960_norm</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>3.902243</td>\n",
       "      <td>3.183284</td>\n",
       "      <td>-0.492621</td>\n",
       "      <td>228.920728</td>\n",
       "      <td>4.201671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>-7.002041</td>\n",
       "      <td>2.503791</td>\n",
       "      <td>9.099700</td>\n",
       "      <td>219.687051</td>\n",
       "      <td>4.032194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>5.888751</td>\n",
       "      <td>5.997827</td>\n",
       "      <td>0.450635</td>\n",
       "      <td>382.594581</td>\n",
       "      <td>7.022242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country_name  difference_norm  2018_norm  1960_norm         sum  \\\n",
       "8    United Arab Emirates         3.902243   3.183284  -0.492621  228.920728   \n",
       "138            Luxembourg        -7.002041   2.503791   9.099700  219.687051   \n",
       "190                 Qatar         5.888751   5.997827   0.450635  382.594581   \n",
       "\n",
       "     sum_norm  \n",
       "8    4.201671  \n",
       "138  4.032194  \n",
       "190  7.022242  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_change=df_clean[['country_name','difference_norm','2018_norm','1960_norm','sum','sum_norm']]\n",
    "df_change=df_change.drop(df_change[(df_change['sum_norm'] > -3) & (df_change['sum_norm'] <3)].index)\n",
    "df_change\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_alldata=df_clean.loc[[i for i in df_change.index],['country_name']+[f'{i}_norm' for i in range(1960,2019)]]\n",
    "year=np.arange(1960,2019)\n",
    "fig,ax=plt.subplots()\n",
    "colors = ['red', 'green', 'blue', 'yellow', 'black']\n",
    "for i in range(len(df_alldata.index)):\n",
    "    ax.scatter(year, df_alldata.iloc[i,1:], c=colors[i], label=df_alldata.iloc[i,0])\n",
    "ax.legend()\n",
    "plt.title(\"Country with relativey high co2 per population from 1960-2018\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_alldata=df_clean.loc[[i for i in df_change.index],['country_name']+[f'{i}_co2' for i in range(1960,2019)]]\n",
    "year=np.arange(1960,2019)\n",
    "fig,ax=plt.subplots()\n",
    "colors = ['red', 'green', 'blue', 'yellow', 'black']\n",
    "for i in range(len(df_alldata.index)):\n",
    "    ax.scatter(year, df_alldata.iloc[i,1:], c=colors[i], label=df_alldata.iloc[i,0])\n",
    "ax.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "5b3e0efed2c7fde64c5d5e6d6f3e56f65ae90f43e872a33dc08664ba45b317db"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}