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cmkl-mat-assignments/mat-203/00010/Whr2024Descriptive.ipynb

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2024-10-03 09:51:17 +07:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Gamma function\n",
"from scipy.special import gamma\n",
"\n",
"# To calculate statistics\n",
"from scipy.stats import norm\n",
"from scipy.stats import hmean, trim_mean, iqr, median_abs_deviation, skew, kurtosis\n",
"from scipy.stats.mstats import gmean, winsorize"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"DataWhr2024 = pd.read_csv(\"../DataWhr2024.csv\")\n",
"UnM49 = pd.read_csv(\"./UnM49.csv\", sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"UnM49 = UnM49[['Country or Area', 'Sub-region Name', 'Region Name']]\n",
"UnM49 = UnM49.rename({'Country or Area':'Country name', 'Sub-region Name':'Subregion', 'Region Name':'Continent'}, axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Data\n",
"Dat = pd.merge(DataWhr2024, UnM49)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Data of 2023\n",
"Dat2023 = Dat[Dat['year'] == 2023]\n",
"Dat2023 = Dat2023.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Iceland', 'India', 'Indonesia', 'Iraq', 'Ireland', 'Israel', 'Italy']"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Countries that starts with the same letter that your name\n",
"StartsWith = 'I' # The first letter of your name\n",
"list(Dat[Dat['Country name'].str.startswith(StartsWith)]['Country name'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# Data of 2023 from the region selected\n",
"CountrySelected = 'Iraq' # Change to the country that you selected\n",
"SubregionSelected = Dat[Dat['Country name']==CountrySelected]['Subregion'].unique()[0]\n",
"\n",
"DatSelected = Dat2023[Dat2023['Subregion']==SubregionSelected]\n",
"DatSelected = DatSelected.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>Life Ladder</th>\n",
" <th>Log GDP per capita</th>\n",
" <th>Social support</th>\n",
" <th>Healthy life expectancy at birth</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
" <th>Negative affect</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>121.0</td>\n",
" <td>121.000000</td>\n",
" <td>114.000000</td>\n",
" <td>121.000000</td>\n",
" <td>120.000000</td>\n",
" <td>119.000000</td>\n",
" <td>114.000000</td>\n",
" <td>114.000000</td>\n",
" <td>121.000000</td>\n",
" <td>121.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2023.0</td>\n",
" <td>5.601455</td>\n",
" <td>9.527377</td>\n",
" <td>0.792537</td>\n",
" <td>65.283333</td>\n",
" <td>0.789227</td>\n",
" <td>0.027851</td>\n",
" <td>0.722447</td>\n",
" <td>0.650488</td>\n",
" <td>0.295818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>1.159381</td>\n",
" <td>1.151462</td>\n",
" <td>0.131982</td>\n",
" <td>5.580870</td>\n",
" <td>0.124732</td>\n",
" <td>0.162959</td>\n",
" <td>0.182586</td>\n",
" <td>0.113365</td>\n",
" <td>0.085531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2023.0</td>\n",
" <td>1.446000</td>\n",
" <td>7.147000</td>\n",
" <td>0.368000</td>\n",
" <td>52.200000</td>\n",
" <td>0.228000</td>\n",
" <td>-0.268000</td>\n",
" <td>0.153000</td>\n",
" <td>0.261000</td>\n",
" <td>0.114000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2023.0</td>\n",
" <td>4.609000</td>\n",
" <td>8.612500</td>\n",
" <td>0.703000</td>\n",
" <td>60.700000</td>\n",
" <td>0.734500</td>\n",
" <td>-0.088750</td>\n",
" <td>0.663250</td>\n",
" <td>0.579000</td>\n",
" <td>0.231000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2023.0</td>\n",
" <td>5.868000</td>\n",
" <td>9.670000</td>\n",
" <td>0.831000</td>\n",
" <td>66.300000</td>\n",
" <td>0.807000</td>\n",
" <td>0.022000</td>\n",
" <td>0.781000</td>\n",
" <td>0.661000</td>\n",
" <td>0.289000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2023.0</td>\n",
" <td>6.482000</td>\n",
" <td>10.496250</td>\n",
" <td>0.895000</td>\n",
" <td>69.775000</td>\n",
" <td>0.877000</td>\n",
" <td>0.122500</td>\n",
" <td>0.844500</td>\n",
" <td>0.738000</td>\n",
" <td>0.358000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2023.0</td>\n",
" <td>7.699000</td>\n",
" <td>11.676000</td>\n",
" <td>0.979000</td>\n",
" <td>74.600000</td>\n",
" <td>0.965000</td>\n",
" <td>0.590000</td>\n",
" <td>0.948000</td>\n",
" <td>0.843000</td>\n",
" <td>0.516000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year Life Ladder Log GDP per capita Social support \\\n",
"count 121.0 121.000000 114.000000 121.000000 \n",
"mean 2023.0 5.601455 9.527377 0.792537 \n",
"std 0.0 1.159381 1.151462 0.131982 \n",
"min 2023.0 1.446000 7.147000 0.368000 \n",
"25% 2023.0 4.609000 8.612500 0.703000 \n",
"50% 2023.0 5.868000 9.670000 0.831000 \n",
"75% 2023.0 6.482000 10.496250 0.895000 \n",
"max 2023.0 7.699000 11.676000 0.979000 \n",
"\n",
" Healthy life expectancy at birth Freedom to make life choices \\\n",
"count 120.000000 119.000000 \n",
"mean 65.283333 0.789227 \n",
"std 5.580870 0.124732 \n",
"min 52.200000 0.228000 \n",
"25% 60.700000 0.734500 \n",
"50% 66.300000 0.807000 \n",
"75% 69.775000 0.877000 \n",
"max 74.600000 0.965000 \n",
"\n",
" Generosity Perceptions of corruption Positive affect Negative affect \n",
"count 114.000000 114.000000 121.000000 121.000000 \n",
"mean 0.027851 0.722447 0.650488 0.295818 \n",
"std 0.162959 0.182586 0.113365 0.085531 \n",
"min -0.268000 0.153000 0.261000 0.114000 \n",
"25% -0.088750 0.663250 0.579000 0.231000 \n",
"50% 0.022000 0.781000 0.661000 0.289000 \n",
"75% 0.122500 0.844500 0.738000 0.358000 \n",
"max 0.590000 0.948000 0.843000 0.516000 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dat2023.describe()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country name</th>\n",
" <th>year</th>\n",
" <th>Life Ladder</th>\n",
" <th>Log GDP per capita</th>\n",
" <th>Social support</th>\n",
" <th>Healthy life expectancy at birth</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
" <th>Negative affect</th>\n",
" <th>Subregion</th>\n",
" <th>Continent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>2023</td>\n",
" <td>1.446</td>\n",
" <td>NaN</td>\n",
" <td>0.368</td>\n",
" <td>55.2</td>\n",
" <td>0.228</td>\n",
" <td>NaN</td>\n",
" <td>0.738</td>\n",
" <td>0.261</td>\n",
" <td>0.460</td>\n",
" <td>Southern Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>2023</td>\n",
" <td>5.445</td>\n",
" <td>9.689</td>\n",
" <td>0.691</td>\n",
" <td>69.2</td>\n",
" <td>0.872</td>\n",
" <td>0.068</td>\n",
" <td>0.855</td>\n",
" <td>0.597</td>\n",
" <td>0.314</td>\n",
" <td>Southern Europe</td>\n",
" <td>Europe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Argentina</td>\n",
" <td>2023</td>\n",
" <td>6.393</td>\n",
" <td>9.994</td>\n",
" <td>0.892</td>\n",
" <td>67.3</td>\n",
" <td>0.832</td>\n",
" <td>-0.129</td>\n",
" <td>0.846</td>\n",
" <td>0.720</td>\n",
" <td>0.301</td>\n",
" <td>Latin America and the Caribbean</td>\n",
" <td>Americas</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Armenia</td>\n",
" <td>2023</td>\n",
" <td>5.679</td>\n",
" <td>9.730</td>\n",
" <td>0.819</td>\n",
" <td>68.2</td>\n",
" <td>0.819</td>\n",
" <td>-0.179</td>\n",
" <td>0.681</td>\n",
" <td>0.575</td>\n",
" <td>0.423</td>\n",
" <td>Western Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Australia</td>\n",
" <td>2023</td>\n",
" <td>7.025</td>\n",
" <td>10.846</td>\n",
" <td>0.896</td>\n",
" <td>71.2</td>\n",
" <td>0.876</td>\n",
" <td>0.187</td>\n",
" <td>0.482</td>\n",
" <td>0.731</td>\n",
" <td>0.248</td>\n",
" <td>Australia and New Zealand</td>\n",
" <td>Oceania</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>Uruguay</td>\n",
" <td>2023</td>\n",
" <td>6.662</td>\n",
" <td>10.122</td>\n",
" <td>0.908</td>\n",
" <td>67.5</td>\n",
" <td>0.904</td>\n",
" <td>-0.050</td>\n",
" <td>0.662</td>\n",
" <td>0.753</td>\n",
" <td>0.265</td>\n",
" <td>Latin America and the Caribbean</td>\n",
" <td>Americas</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>Uzbekistan</td>\n",
" <td>2023</td>\n",
" <td>6.385</td>\n",
" <td>9.026</td>\n",
" <td>0.909</td>\n",
" <td>65.9</td>\n",
" <td>0.927</td>\n",
" <td>0.247</td>\n",
" <td>0.650</td>\n",
" <td>0.752</td>\n",
" <td>0.202</td>\n",
" <td>Central Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>Yemen</td>\n",
" <td>2023</td>\n",
" <td>3.532</td>\n",
" <td>NaN</td>\n",
" <td>0.825</td>\n",
" <td>56.6</td>\n",
" <td>0.583</td>\n",
" <td>NaN</td>\n",
" <td>0.771</td>\n",
" <td>0.447</td>\n",
" <td>0.341</td>\n",
" <td>Western Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>Zambia</td>\n",
" <td>2023</td>\n",
" <td>3.686</td>\n",
" <td>8.115</td>\n",
" <td>0.664</td>\n",
" <td>56.1</td>\n",
" <td>0.854</td>\n",
" <td>0.092</td>\n",
" <td>0.814</td>\n",
" <td>0.653</td>\n",
" <td>0.359</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>Zimbabwe</td>\n",
" <td>2023</td>\n",
" <td>3.572</td>\n",
" <td>7.679</td>\n",
" <td>0.694</td>\n",
" <td>55.0</td>\n",
" <td>0.735</td>\n",
" <td>-0.069</td>\n",
" <td>0.757</td>\n",
" <td>0.610</td>\n",
" <td>0.179</td>\n",
" <td>Sub-Saharan Africa</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>121 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" Country name year Life Ladder Log GDP per capita Social support \\\n",
"0 Afghanistan 2023 1.446 NaN 0.368 \n",
"1 Albania 2023 5.445 9.689 0.691 \n",
"2 Argentina 2023 6.393 9.994 0.892 \n",
"3 Armenia 2023 5.679 9.730 0.819 \n",
"4 Australia 2023 7.025 10.846 0.896 \n",
".. ... ... ... ... ... \n",
"116 Uruguay 2023 6.662 10.122 0.908 \n",
"117 Uzbekistan 2023 6.385 9.026 0.909 \n",
"118 Yemen 2023 3.532 NaN 0.825 \n",
"119 Zambia 2023 3.686 8.115 0.664 \n",
"120 Zimbabwe 2023 3.572 7.679 0.694 \n",
"\n",
" Healthy life expectancy at birth Freedom to make life choices \\\n",
"0 55.2 0.228 \n",
"1 69.2 0.872 \n",
"2 67.3 0.832 \n",
"3 68.2 0.819 \n",
"4 71.2 0.876 \n",
".. ... ... \n",
"116 67.5 0.904 \n",
"117 65.9 0.927 \n",
"118 56.6 0.583 \n",
"119 56.1 0.854 \n",
"120 55.0 0.735 \n",
"\n",
" Generosity Perceptions of corruption Positive affect Negative affect \\\n",
"0 NaN 0.738 0.261 0.460 \n",
"1 0.068 0.855 0.597 0.314 \n",
"2 -0.129 0.846 0.720 0.301 \n",
"3 -0.179 0.681 0.575 0.423 \n",
"4 0.187 0.482 0.731 0.248 \n",
".. ... ... ... ... \n",
"116 -0.050 0.662 0.753 0.265 \n",
"117 0.247 0.650 0.752 0.202 \n",
"118 NaN 0.771 0.447 0.341 \n",
"119 0.092 0.814 0.653 0.359 \n",
"120 -0.069 0.757 0.610 0.179 \n",
"\n",
" Subregion Continent \n",
"0 Southern Asia Asia \n",
"1 Southern Europe Europe \n",
"2 Latin America and the Caribbean Americas \n",
"3 Western Asia Asia \n",
"4 Australia and New Zealand Oceania \n",
".. ... ... \n",
"116 Latin America and the Caribbean Americas \n",
"117 Central Asia Asia \n",
"118 Western Asia Asia \n",
"119 Sub-Saharan Africa Africa \n",
"120 Sub-Saharan Africa Africa \n",
"\n",
"[121 rows x 13 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dat2023"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>year</th>\n",
" <th>Life Ladder</th>\n",
" <th>Log GDP per capita</th>\n",
" <th>Social support</th>\n",
" <th>Healthy life expectancy at birth</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
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" <th>Subregion</th>\n",
" <th>Continent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Afghanistan</td>\n",
" <td>2022</td>\n",
" <td>1.281</td>\n",
" <td>NaN</td>\n",
" <td>0.228</td>\n",
" <td>54.875</td>\n",
" <td>0.368</td>\n",
" <td>NaN</td>\n",
" <td>0.733</td>\n",
" <td>0.206</td>\n",
" <td>0.576</td>\n",
" <td>Southern Asia</td>\n",
" <td>Asia</td>\n",
" </tr>\n",
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],
"text/plain": [
" Country name year Life Ladder Log GDP per capita Social support \\\n",
"13 Afghanistan 2022 1.281 NaN 0.228 \n",
"\n",
" Healthy life expectancy at birth Freedom to make life choices \\\n",
"13 54.875 0.368 \n",
"\n",
" Generosity Perceptions of corruption Positive affect Negative affect \\\n",
"13 NaN 0.733 0.206 0.576 \n",
"\n",
" Subregion Continent \n",
"13 Southern Asia Asia "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dat.loc[Dat[\"Life Ladder\"] == Dat[\"Life Ladder\"].min()]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
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" 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>year</th>\n",
" <th>Life Ladder</th>\n",
" <th>Log GDP per capita</th>\n",
" <th>Social support</th>\n",
" <th>Healthy life expectancy at birth</th>\n",
" <th>Freedom to make life choices</th>\n",
" <th>Generosity</th>\n",
" <th>Perceptions of corruption</th>\n",
" <th>Positive affect</th>\n",
" <th>Negative affect</th>\n",
" <th>Subregion</th>\n",
" <th>Continent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>Denmark</td>\n",
" <td>2005</td>\n",
" <td>8.019</td>\n",
" <td>10.849</td>\n",
" <td>0.972</td>\n",
" <td>68.3</td>\n",
" <td>0.971</td>\n",
" <td>NaN</td>\n",
" <td>0.237</td>\n",
" <td>0.777</td>\n",
" <td>0.154</td>\n",
" <td>Northern Europe</td>\n",
" <td>Europe</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country name year Life Ladder Log GDP per capita Social support \\\n",
"497 Denmark 2005 8.019 10.849 0.972 \n",
"\n",
" Healthy life expectancy at birth Freedom to make life choices \\\n",
"497 68.3 0.971 \n",
"\n",
" Generosity Perceptions of corruption Positive affect Negative affect \\\n",
"497 NaN 0.237 0.777 0.154 \n",
"\n",
" Subregion Continent \n",
"497 Northern Europe Europe "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dat.loc[Dat[\"Life Ladder\"] == Dat[\"Life Ladder\"].max()]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"avg_health = Dat[\"Healthy life expectancy at birth\"].median()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65.22\n"
]
}
],
"source": [
"print(avg_health)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.231)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#p = [0.25, 0.5, 0.75]\n",
"np.quantile(Dat2023[\"Negative affect\"], 0.25)\n",
"\n",
"#Dat[\"Negative affect\"]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4.6125, 5.42 , 6.3215])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = [0.25, 0.5, 0.75]\n",
"np.quantile(Dat[\"Life Ladder\"], p)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.42\n",
"5.4660082404265635\n",
"5.3416056329049155\n",
"5.207292342092034\n",
"5.446561471442401\n",
"1.7089999999999996\n",
"5.4660082404265635\n"
]
}
],
"source": [
"ll = Dat[\"Life Ladder\"]\n",
"\n",
"print(ll.median())\n",
"print(ll.mean())\n",
"print(gmean(ll))\n",
"print(hmean(ll))\n",
"print(trim_mean(ll, 0.25))\n",
"print(iqr(ll).mean())\n",
"print(winsorize(ll).mean())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": 2
}