254 lines
78 KiB
Text
254 lines
78 KiB
Text
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{
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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": [
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"<Figure size 1000x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"# Plot scatter plot with regression line\n",
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"plt.figure(figsize=(10, 6))\n",
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"\n",
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"sns.regplot(\n",
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" x='electricity_access', y='Renewable', data=merged_data, \n",
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" scatter_kws={'color': 'blue', 'label': 'Data points'}, \n",
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" line_kws={'color': 'red', 'linewidth': 2, 'label': 'Regression line'}\n",
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")\n",
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"\n",
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"# Add titles and labels\n",
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"plt.title('Access to Electricity vs. Share of Renewable Electricity in Kenya', fontsize=16)\n",
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"plt.xlabel('Access to Electricity (% of Population)', fontsize=12)\n",
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"plt.ylabel('Share of Renewable Electricity (%)', fontsize=12)\n",
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"\n",
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"\n",
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"# Add the legend\n",
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"plt.legend()\n",
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"\n",
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"plt.savefig('regression.png', dpi=300, bbox_inches='tight')\n",
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"\n",
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"# Show plot\n",
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"plt.show()\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": 47,
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"id": "0aa2149a-7bbc-4454-890a-5bcb801fb4bb",
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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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"\\begin{tabular}{rrrrr}\n",
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"\\toprule\n",
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"Year & GDP per capita & Population percentage with access to electricity & Share of electricity produced from renewable sources & GDP growth rate \\\\\n",
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"\\midrule\n",
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"2000 & 617.139000 & 15.175694 & 40.371230 & -4.745007 \\\\\n",
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"2001 & 617.047000 & 17.048136 & 59.459465 & -0.014908 \\\\\n",
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"2002 & 611.893000 & 18.912030 & 67.572815 & -0.835269 \\\\\n",
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"2003 & 668.475000 & 16.000000 & 73.357664 & 9.247042 \\\\\n",
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"2004 & 692.709000 & 22.642206 & 62.824677 & 3.625266 \\\\\n",
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"2005 & 778.323000 & 24.522501 & 59.701494 & 12.359302 \\\\\n",
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"2006 & 854.981000 & 26.422052 & 56.591209 & 9.849124 \\\\\n",
|
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|
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"2007 & 1028.226000 & 28.342442 & 67.016490 & 20.263023 \\\\\n",
|
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"2008 & 1118.755000 & 30.280056 & 65.040647 & 8.804387 \\\\\n",
|
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"2009 & 1123.268000 & 23.000000 & 53.435117 & 0.403395 \\\\\n",
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"2010 & 1176.311000 & 19.200000 & 67.877096 & 4.722203 \\\\\n",
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"2011 & 1178.599000 & 36.157864 & 63.218392 & 0.194506 \\\\\n",
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"2012 & 1396.220000 & 38.125990 & 71.725830 & 18.464380 \\\\\n",
|
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"2013 & 1490.422000 & 40.092150 & 72.524755 & 6.746931 \\\\\n",
|
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|
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"2014 & 1613.101000 & 36.000000 & 71.304350 & 8.231159 \\\\\n",
|
||
|
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"2015 & 1625.176000 & 41.600000 & 85.015288 & 0.748558 \\\\\n",
|
||
|
|
"2016 & 1688.852000 & 53.100000 & 83.884296 & 3.918099 \\\\\n",
|
||
|
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"2017 & 1805.398000 & 55.831993 & 74.422903 & 6.900901 \\\\\n",
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"2018 & 1987.302000 & 61.180614 & 85.093162 & 10.075562 \\\\\n",
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||
|
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"2019 & 2107.735000 & 69.700000 & 86.919106 & 6.060126 \\\\\n",
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"2020 & 2067.987000 & 71.492714 & 92.327584 & -1.885816 \\\\\n",
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"2021 & 2208.691000 & 76.542450 & 90.057995 & 6.803911 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n"
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]
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}
|
||
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],
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"source": [
|
||
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|
"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"
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||
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]
|
||
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|
}
|
||
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|
],
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"metadata": {
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||
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"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
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"language": "python",
|
||
|
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"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"
|
||
|
|
}
|
||
|
|
},
|
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|
|
"nbformat": 4,
|
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"nbformat_minor": 5
|
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}
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