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lab 8 examples and slides
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‎lab_07/duckdb_example.ipynb

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"cell_type": "code",
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"execution_count": 2,
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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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" Series Name Series Code \\\n",
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"0 Access to electricity (% of population) EG.ELC.ACCS.ZS \n",
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"1 Access to electricity (% of population) EG.ELC.ACCS.ZS \n",
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"2 Access to electricity (% of population) EG.ELC.ACCS.ZS \n",
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"3 Access to electricity (% of population) EG.ELC.ACCS.ZS \n",
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"4 Access to electricity (% of population) EG.ELC.ACCS.ZS \n",
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".. ... ... \n",
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"266 None None \n",
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"267 None None \n",
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"268 None None \n",
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"269 Data from database: World Development Indicators None \n",
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"270 Last Updated: 01/28/2025 None \n",
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"\n",
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" Country Name Country Code 1990 [YR1990] 2000 [YR2000] 2014 [YR2014] \\\n",
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"0 Afghanistan AFG .. 4.4 89.5 \n",
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"1 Albania ALB 100 99.4 100 \n",
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"2 Algeria DZA .. 98.6 99.3 \n",
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"3 American Samoa ASM .. .. .. \n",
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"4 Andorra AND 100 100 100 \n",
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".. ... ... ... ... ... \n",
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"266 None None None None None \n",
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"267 None None None None None \n",
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"268 None None None None None \n",
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"269 None None None None None \n",
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"270 None None None None None \n",
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"\n",
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" 2015 [YR2015] 2016 [YR2016] 2017 [YR2017] 2018 [YR2018] 2019 [YR2019] \\\n",
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"0 71.5 97.7 97.7 93.4 97.7 \n",
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"1 100 99.9 99.9 100 100 \n",
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"2 99.4 99.4 99.5 99.6 99.5 \n",
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"3 .. .. .. .. .. \n",
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"4 100 100 100 100 100 \n",
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".. ... ... ... ... ... \n",
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"266 None None None None None \n",
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"267 None None None None None \n",
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"268 None None None None None \n",
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"269 None None None None None \n",
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"270 None None None None None \n",
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"\n",
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" 2020 [YR2020] 2021 [YR2021] 2022 [YR2022] 2023 [YR2023] \n",
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"0 97.7 97.7 85.3 .. \n",
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"1 100 100 100 .. \n",
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"2 99.7 99.8 100 .. \n",
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"3 .. .. .. .. \n",
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"4 100 100 100 .. \n",
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".. ... ... ... ... \n",
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"266 None None None None \n",
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"267 None None None None \n",
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"268 None None None None \n",
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"269 None None None None \n",
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"270 None None None None \n",
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"\n",
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"[271 rows x 16 columns]\n"
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]
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}
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],
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"source": [
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"with duckdb.connect('world_bank.db') as con:\n",
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" df = con.table('electricity').to_df()\n",
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" print(df)"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Series Name</th>\n",
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" <th>Series Code</th>\n",
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" <th>Country Name</th>\n",
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" <th>Country Code</th>\n",
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" <th>1990 [YR1990]</th>\n",
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" <th>2000 [YR2000]</th>\n",
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" <th>2014 [YR2014]</th>\n",
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" <th>2015 [YR2015]</th>\n",
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" <th>2016 [YR2016]</th>\n",
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" <th>2017 [YR2017]</th>\n",
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" <th>2018 [YR2018]</th>\n",
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" <th>2019 [YR2019]</th>\n",
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" <th>2020 [YR2020]</th>\n",
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" <th>2021 [YR2021]</th>\n",
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" <th>2022 [YR2022]</th>\n",
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" <th>2023 [YR2023]</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Access to electricity (% of population)</td>\n",
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" <td>EG.ELC.ACCS.ZS</td>\n",
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" <td>Afghanistan</td>\n",
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" <td>AFG</td>\n",
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" <td>..</td>\n",
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" <td>4.4</td>\n",
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" <td>89.5</td>\n",
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" <td>71.5</td>\n",
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" <td>97.7</td>\n",
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" <td>97.7</td>\n",
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" <td>93.4</td>\n",
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" <td>97.7</td>\n",
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" <td>97.7</td>\n",
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" <td>97.7</td>\n",
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" <td>85.3</td>\n",
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" <td>..</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Access to electricity (% of population)</td>\n",
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" <td>EG.ELC.ACCS.ZS</td>\n",
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" <td>Albania</td>\n",
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" <td>ALB</td>\n",
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" <td>100</td>\n",
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" <td>99.4</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>99.9</td>\n",
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" <td>99.9</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>..</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Access to electricity (% of population)</td>\n",
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" <td>EG.ELC.ACCS.ZS</td>\n",
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" <td>Algeria</td>\n",
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" <td>DZA</td>\n",
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" <td>..</td>\n",
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" <td>98.6</td>\n",
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" <td>99.3</td>\n",
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" <td>99.4</td>\n",
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" <td>99.4</td>\n",
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" <td>99.5</td>\n",
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" <td>99.6</td>\n",
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" <td>99.5</td>\n",
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" <td>99.7</td>\n",
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" <td>99.8</td>\n",
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" <td>100</td>\n",
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" <td>..</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Access to electricity (% of population)</td>\n",
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" <td>EG.ELC.ACCS.ZS</td>\n",
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" <td>American Samoa</td>\n",
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" <td>ASM</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" <td>..</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Access to electricity (% of population)</td>\n",
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" <td>EG.ELC.ACCS.ZS</td>\n",
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" <td>Andorra</td>\n",
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" <td>AND</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>100</td>\n",
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" <td>..</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Series Name Series Code Country Name \\\n",
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"0 Access to electricity (% of population) EG.ELC.ACCS.ZS Afghanistan \n",
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"1 Access to electricity (% of population) EG.ELC.ACCS.ZS Albania \n",
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"2 Access to electricity (% of population) EG.ELC.ACCS.ZS Algeria \n",
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"3 Access to electricity (% of population) EG.ELC.ACCS.ZS American Samoa \n",
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"4 Access to electricity (% of population) EG.ELC.ACCS.ZS Andorra \n",
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"\n",
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" Country Code 1990 [YR1990] 2000 [YR2000] 2014 [YR2014] 2015 [YR2015] \\\n",
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"0 AFG .. 4.4 89.5 71.5 \n",
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"1 ALB 100 99.4 100 100 \n",
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"2 DZA .. 98.6 99.3 99.4 \n",
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"3 ASM .. .. .. .. \n",
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"4 AND 100 100 100 100 \n",
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"\n",
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" 2016 [YR2016] 2017 [YR2017] 2018 [YR2018] 2019 [YR2019] 2020 [YR2020] \\\n",
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"0 97.7 97.7 93.4 97.7 97.7 \n",
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"1 99.9 99.9 100 100 100 \n",
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"2 99.4 99.5 99.6 99.5 99.7 \n",
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"3 .. .. .. .. .. \n",
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"4 100 100 100 100 100 \n",
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"\n",
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" 2021 [YR2021] 2022 [YR2022] 2023 [YR2023] \n",
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"0 97.7 85.3 .. \n",
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"1 100 100 .. \n",
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"2 99.8 100 .. \n",
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"3 .. .. .. \n",
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"4 100 100 .. "
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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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": 4,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"print(con.execute(\"SHOW TABLES\").fetchall())"
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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": 5,
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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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"[('electricity',)]\n"
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]
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}
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],
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"source": [
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"# create a connection to the world_bank.db\n",
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"con2 = duckdb.connect('world_bank.db')\n",
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"\n",
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"# show all tables in the database\n",
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"print(con2.execute(\"SHOW TABLES\").fetchall())"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [

‎lab_07/world_bank.db

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