253 lines
78 KiB
Text
253 lines
78 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "c5fc2bff-487f-456d-972e-b54c3b6b8dab",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"\n",
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"gdp_data = pd.read_csv('imf_gdp_capita.csv').rename(columns={'GDP per capita, current prices\\n (U.S. dollars per capita)':'Country'})\n",
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"gdp_data = gdp_data[gdp_data[\"Country\"] == \"Kenya\"]\n",
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"\n",
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"gdp_data = pd.melt(gdp_data, id_vars=['Country'], var_name='Year', value_name='GDP_per_capita')\n",
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"gdp_data['Year'] = pd.to_numeric(gdp_data['Year'])\n",
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"gdp_data['GDP_per_capita'] = pd.to_numeric(gdp_data['GDP_per_capita'])\n",
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"\n",
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"el_access_data = pd.read_csv('share-of-the-population-with-access-to-electricity.csv').rename(columns={'Entity':'Country', 'Access to electricity (% of population)':'electricity_access'})\n",
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"el_access_data = el_access_data[el_access_data[\"Country\"] == \"Kenya\"]\n",
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"\n",
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"\n",
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"electricity_data = pd.read_csv('share-elec-by-source.csv').rename(columns={'Entity':'Country'})\n",
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"electricity_data = electricity_data[electricity_data[\"Country\"] == \"Kenya\"]\n",
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"\n",
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"renewable_columns = ['Hydro - % electricity', 'Solar - % electricity', \n",
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" 'Wind - % electricity', 'Other renewables excluding bioenergy - % electricity']\n",
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"non_renewable_columns = ['Coal - % electricity', 'Gas - % electricity', \n",
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" 'Oil - % electricity', 'Nuclear - % electricity']\n",
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"\n",
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"electricity_data['Renewable'] = electricity_data[renewable_columns].sum(axis=1)\n",
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"electricity_data['Non-renewable'] = electricity_data[non_renewable_columns].sum(axis=1)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "9cf6077e-536d-43e7-8663-c9087b8af7f2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Country Year GDP_per_capita electricity_access Renewable GDP_growth\n",
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"20 Kenya 2000 617.139 15.175694 40.371230 -4.745007\n",
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"21 Kenya 2001 617.047 17.048136 59.459465 -0.014908\n",
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"22 Kenya 2002 611.893 18.912030 67.572815 -0.835269\n",
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"23 Kenya 2003 668.475 16.000000 73.357664 9.247042\n",
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"24 Kenya 2004 692.709 22.642206 62.824677 3.625266\n",
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"25 Kenya 2005 778.323 24.522501 59.701494 12.359302\n",
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"26 Kenya 2006 854.981 26.422052 56.591209 9.849124\n",
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"27 Kenya 2007 1028.226 28.342442 67.016490 20.263023\n",
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"28 Kenya 2008 1118.755 30.280056 65.040647 8.804387\n",
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"29 Kenya 2009 1123.268 23.000000 53.435117 0.403395\n",
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"30 Kenya 2010 1176.311 19.200000 67.877096 4.722203\n",
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"31 Kenya 2011 1178.599 36.157864 63.218392 0.194506\n",
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"32 Kenya 2012 1396.220 38.125990 71.725830 18.464380\n",
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"33 Kenya 2013 1490.422 40.092150 72.524755 6.746931\n",
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"34 Kenya 2014 1613.101 36.000000 71.304350 8.231159\n",
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"35 Kenya 2015 1625.176 41.600000 85.015288 0.748558\n",
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"36 Kenya 2016 1688.852 53.100000 83.884296 3.918099\n",
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"37 Kenya 2017 1805.398 55.831993 74.422903 6.900901\n",
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"38 Kenya 2018 1987.302 61.180614 85.093162 10.075562\n",
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"39 Kenya 2019 2107.735 69.700000 86.919106 6.060126\n",
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"40 Kenya 2020 2067.987 71.492714 92.327584 -1.885816\n",
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"41 Kenya 2021 2208.691 76.542450 90.057995 6.803911\n",
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"Pearson Correlation with GDP Growth:\n",
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" electricity_access Renewable GDP_growth\n",
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"electricity_access 1.000000 0.834904 0.048234\n",
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"Renewable 0.834904 1.000000 0.124918\n",
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"GDP_growth 0.048234 0.124918 1.000000\n",
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"\n",
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"Regression Results with GDP Growth as Control:\n",
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" OLS Regression Results \n",
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"==============================================================================\n",
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"Dep. Variable: Renewable R-squared: 0.704\n",
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"Model: OLS Adj. R-squared: 0.673\n",
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"Method: Least Squares F-statistic: 22.62\n",
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"Date: Sun, 17 Nov 2024 Prob (F-statistic): 9.42e-06\n",
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"Time: 19:14:23 Log-Likelihood: -73.771\n",
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"No. Observations: 22 AIC: 153.5\n",
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"Df Residuals: 19 BIC: 156.8\n",
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"Df Model: 2 \n",
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"Covariance Type: nonrobust \n",
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"======================================================================================\n",
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" coef std err t P>|t| [0.025 0.975]\n",
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"--------------------------------------------------------------------------------------\n",
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"const 48.4419 3.787 12.791 0.000 40.515 56.368\n",
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"electricity_access 0.5613 0.084 6.651 0.000 0.385 0.738\n",
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"GDP_growth 0.1767 0.260 0.679 0.505 -0.368 0.721\n",
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"==============================================================================\n",
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"Omnibus: 0.546 Durbin-Watson: 1.538\n",
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"Prob(Omnibus): 0.761 Jarque-Bera (JB): 0.060\n",
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"Skew: 0.121 Prob(JB): 0.970\n",
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"Kurtosis: 3.083 Cond. No. 101.\n",
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"==============================================================================\n",
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"\n",
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"Notes:\n",
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"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
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]
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}
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],
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"source": [
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"import statsmodels.api as sm\n",
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"from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
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"\n",
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"# Merge data on the Year column\n",
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"merged_data = pd.merge(gdp_data, el_access_data[['Year', 'electricity_access']], on='Year', how='left')\n",
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"merged_data = pd.merge(merged_data, electricity_data[['Year', 'Renewable']], on='Year', how='left')\n",
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"\n",
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"# Calculate year-on-year GDP growth\n",
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"merged_data['GDP_growth'] = merged_data['GDP_per_capita'].pct_change() * 100 # Percentage change\n",
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"\n",
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"# Drop NaN values that might have been generated by pct_change\n",
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"merged_data.dropna(subset=['electricity_access', 'Renewable', 'GDP_growth'], inplace=True)\n",
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"\n",
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"\n",
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"# Step 1: Correlation analysis (now with GDP growth included)\n",
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"correlation = merged_data[['electricity_access', 'Renewable', 'GDP_growth']].corr()\n",
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"print(\"Pearson Correlation with GDP Growth:\")\n",
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"print(correlation)\n",
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"\n",
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"# Step 2: Regression analysis (with GDP growth as a control)\n",
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"X = merged_data[['electricity_access', 'GDP_growth']] # Include GDP growth as a predictor\n",
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"y = merged_data['Renewable']\n",
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"\n",
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"# Add constant for intercept\n",
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"X = sm.add_constant(X)\n",
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"\n",
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"# Run the regression model\n",
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"model = sm.OLS(y, X).fit()\n",
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"\n",
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"# Print regression results\n",
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"print(\"\\nRegression Results with GDP Growth as Control:\")\n",
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"print(model.summary())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"id": "76beea43-efc1-4184-92dc-2c8e7285bf8a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import seaborn as sns\n",
|
|
"\n",
|
|
"# Plot scatter plot with regression line\n",
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"\n",
|
|
"sns.regplot(\n",
|
|
" x='electricity_access', y='Renewable', data=merged_data, \n",
|
|
" scatter_kws={'color': 'blue', 'label': 'Data points'}, \n",
|
|
" line_kws={'color': 'red', 'linewidth': 2, 'label': 'Regression line'}\n",
|
|
")\n",
|
|
"\n",
|
|
"# Add titles and labels\n",
|
|
"plt.title('Access to Electricity vs. Share of Renewable Electricity in Kenya', fontsize=16)\n",
|
|
"plt.xlabel('Access to Electricity (% of Population)', fontsize=12)\n",
|
|
"plt.ylabel('Share of Renewable Electricity (%)', fontsize=12)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Add the legend\n",
|
|
"plt.legend()\n",
|
|
"\n",
|
|
"plt.savefig('regression.png', dpi=300, bbox_inches='tight')\n",
|
|
"\n",
|
|
"# Show plot\n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "0aa2149a-7bbc-4454-890a-5bcb801fb4bb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\\begin{tabular}{rrrrr}\n",
|
|
"\\toprule\n",
|
|
"Year & GDP per capita & Population percentage with access to electricity & Share of electricity produced from renewable sources & GDP growth rate \\\\\n",
|
|
"\\midrule\n",
|
|
"2000 & 617.139000 & 15.175694 & 40.371230 & -4.745007 \\\\\n",
|
|
"2001 & 617.047000 & 17.048136 & 59.459465 & -0.014908 \\\\\n",
|
|
"2002 & 611.893000 & 18.912030 & 67.572815 & -0.835269 \\\\\n",
|
|
"2003 & 668.475000 & 16.000000 & 73.357664 & 9.247042 \\\\\n",
|
|
"2004 & 692.709000 & 22.642206 & 62.824677 & 3.625266 \\\\\n",
|
|
"2005 & 778.323000 & 24.522501 & 59.701494 & 12.359302 \\\\\n",
|
|
"2006 & 854.981000 & 26.422052 & 56.591209 & 9.849124 \\\\\n",
|
|
"2007 & 1028.226000 & 28.342442 & 67.016490 & 20.263023 \\\\\n",
|
|
"2008 & 1118.755000 & 30.280056 & 65.040647 & 8.804387 \\\\\n",
|
|
"2009 & 1123.268000 & 23.000000 & 53.435117 & 0.403395 \\\\\n",
|
|
"2010 & 1176.311000 & 19.200000 & 67.877096 & 4.722203 \\\\\n",
|
|
"2011 & 1178.599000 & 36.157864 & 63.218392 & 0.194506 \\\\\n",
|
|
"2012 & 1396.220000 & 38.125990 & 71.725830 & 18.464380 \\\\\n",
|
|
"2013 & 1490.422000 & 40.092150 & 72.524755 & 6.746931 \\\\\n",
|
|
"2014 & 1613.101000 & 36.000000 & 71.304350 & 8.231159 \\\\\n",
|
|
"2015 & 1625.176000 & 41.600000 & 85.015288 & 0.748558 \\\\\n",
|
|
"2016 & 1688.852000 & 53.100000 & 83.884296 & 3.918099 \\\\\n",
|
|
"2017 & 1805.398000 & 55.831993 & 74.422903 & 6.900901 \\\\\n",
|
|
"2018 & 1987.302000 & 61.180614 & 85.093162 & 10.075562 \\\\\n",
|
|
"2019 & 2107.735000 & 69.700000 & 86.919106 & 6.060126 \\\\\n",
|
|
"2020 & 2067.987000 & 71.492714 & 92.327584 & -1.885816 \\\\\n",
|
|
"2021 & 2208.691000 & 76.542450 & 90.057995 & 6.803911 \\\\\n",
|
|
"\\bottomrule\n",
|
|
"\\end{tabular}\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(merged_data.drop(['Country'], axis=1).rename(columns={'GDP_per_capita':'GDP per capita', 'electricity_access':'Population percentage with access to electricity', 'GDP_growth':'GDP growth rate', 'Renewable':'Share of electricity produced from renewable sources'}).to_latex(index=False))\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.12.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|