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Add regression example for lec4
2024-10-30 12:46:07 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Загрузка данных"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
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"text/plain": [
" T Al2O3 TiO2 Density\n",
"0 20 0.0 0.0 1.06250\n",
"1 25 0.0 0.0 1.05979\n",
"2 35 0.0 0.0 1.05404"
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
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" <td>0.0</td>\n",
" <td>1.04158</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.08438</td>\n",
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"text/plain": [
" T Al2O3 TiO2 Density\n",
"0 30 0.00 0.0 1.05696\n",
"1 55 0.00 0.0 1.04158\n",
"2 25 0.05 0.0 1.08438"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"density_train = pd.read_csv(\"data/density/density_train.csv\", sep=\";\", decimal=\",\")\n",
"density_test = pd.read_csv(\"data/density/density_test.csv\", sep=\";\", decimal=\",\")\n",
"\n",
"display(density_train.head(3))\n",
"display(density_test.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Формирование выборок"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
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"<div>\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20</td>\n",
" <td>0.0</td>\n",
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" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" T Al2O3 TiO2\n",
"0 20 0.0 0.0\n",
"1 25 0.0 0.0\n",
"2 35 0.0 0.0"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/plain": [
"0 1.06250\n",
"1 1.05979\n",
"2 1.05404\n",
"Name: Density, dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
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"\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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>55</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
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" <td>0.0</td>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" T Al2O3 TiO2\n",
"0 30 0.00 0.0\n",
"1 55 0.00 0.0\n",
"2 25 0.05 0.0"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0 1.05696\n",
"1 1.04158\n",
"2 1.08438\n",
"Name: Density, dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"density_y_train = density_train[\"Density\"]\n",
"density_train = density_train.drop([\"Density\"], axis=1)\n",
"\n",
"display(density_train.head(3))\n",
"display(density_y_train.head(3))\n",
"\n",
"density_y_test = density_test[\"Density\"]\n",
"density_test = density_test.drop([\"Density\"], axis=1)\n",
"\n",
"display(density_test.head(3))\n",
"display(density_y_test.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Определение перечня алгоритмов решения задачи аппроксимации (регрессии)"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n",
"\n",
"random_state = 9\n",
"\n",
"models = {\n",
" \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
" \"linear_poly\": {\n",
" \"model\": make_pipeline(\n",
" PolynomialFeatures(degree=2),\n",
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
" )\n",
" },\n",
" \"linear_interact\": {\n",
" \"model\": make_pipeline(\n",
" PolynomialFeatures(interaction_only=True),\n",
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
" )\n",
" },\n",
" \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
" \"decision_tree\": {\n",
" \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=random_state)\n",
" },\n",
" \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
" \"random_forest\": {\n",
" \"model\": ensemble.RandomForestRegressor(\n",
" max_depth=7, random_state=random_state, n_jobs=-1\n",
" )\n",
" },\n",
" \"mlp\": {\n",
" \"model\": neural_network.MLPRegressor(\n",
" activation=\"tanh\",\n",
" hidden_layer_sizes=(3,),\n",
" max_iter=500,\n",
" early_stopping=True,\n",
" random_state=random_state,\n",
" )\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Определение функции для стандартизации значений в столбце \"Температура\" для MLP"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [],
"source": [
"from pandas import DataFrame\n",
"from sklearn import preprocessing\n",
"\n",
"stndart_scaler = preprocessing.StandardScaler()\n",
"\n",
"def std_temp(df: DataFrame) -> DataFrame:\n",
" df[\"T\"] = stndart_scaler.fit_transform(\n",
" df[\"T\"].to_numpy().reshape(-1, 1)\n",
" ).reshape(df[\"T\"].shape)\n",
" return df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Обучение и оценка моделей с помощью различных алгоритмов"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: linear\n",
"Model: linear_poly\n",
"Model: linear_interact\n",
"Model: ridge\n",
"Model: decision_tree\n",
"Model: knn\n",
"Model: random_forest\n",
"Model: mlp\n"
]
}
],
"source": [
"import math\n",
"from pandas import DataFrame\n",
"from sklearn import metrics\n",
"\n",
"for model_name in models.keys():\n",
" print(f\"Model: {model_name}\")\n",
" X_train: DataFrame = density_train.copy()\n",
" X_test: DataFrame = density_test.copy()\n",
"\n",
" if model_name == \"mlp\":\n",
" X_train = std_temp(X_train)\n",
" X_test = std_temp(X_test)\n",
"\n",
" fitted_model = models[model_name][\"model\"].fit(\n",
" X_train.values, density_y_train.values.ravel()\n",
" )\n",
" y_train_pred = fitted_model.predict(X_train.values)\n",
" y_test_pred = fitted_model.predict(X_test.values)\n",
" models[model_name][\"fitted\"] = fitted_model\n",
" models[model_name][\"train_preds\"] = y_train_pred\n",
" models[model_name][\"preds\"] = y_test_pred\n",
" models[model_name][\"RMSE_train\"] = math.sqrt(\n",
" metrics.mean_squared_error(density_y_train, y_train_pred)\n",
" )\n",
" models[model_name][\"RMSE_test\"] = math.sqrt(\n",
" metrics.mean_squared_error(density_y_test, y_test_pred)\n",
" )\n",
" models[model_name][\"RMAE_test\"] = math.sqrt(\n",
" metrics.mean_absolute_error(density_y_test, y_test_pred)\n",
" )\n",
" models[model_name][\"R2_test\"] = metrics.r2_score(density_y_test, y_test_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Вывод результатов оценки"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table id=\"T_b1edf\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_b1edf_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
" <th id=\"T_b1edf_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
" <th id=\"T_b1edf_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
" <th id=\"T_b1edf_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row0\" class=\"row_heading level0 row0\" >linear_poly</th>\n",
" <td id=\"T_b1edf_row0_col0\" class=\"data row0 col0\" >0.000319</td>\n",
" <td id=\"T_b1edf_row0_col1\" class=\"data row0 col1\" >0.000362</td>\n",
" <td id=\"T_b1edf_row0_col2\" class=\"data row0 col2\" >0.016643</td>\n",
" <td id=\"T_b1edf_row0_col3\" class=\"data row0 col3\" >0.999965</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
" <td id=\"T_b1edf_row1_col0\" class=\"data row1 col0\" >0.001131</td>\n",
" <td id=\"T_b1edf_row1_col1\" class=\"data row1 col1\" >0.001491</td>\n",
" <td id=\"T_b1edf_row1_col2\" class=\"data row1 col2\" >0.033198</td>\n",
" <td id=\"T_b1edf_row1_col3\" class=\"data row1 col3\" >0.999413</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row2\" class=\"row_heading level0 row2\" >linear</th>\n",
" <td id=\"T_b1edf_row2_col0\" class=\"data row2 col0\" >0.002464</td>\n",
" <td id=\"T_b1edf_row2_col1\" class=\"data row2 col1\" >0.003261</td>\n",
" <td id=\"T_b1edf_row2_col2\" class=\"data row2 col2\" >0.049891</td>\n",
" <td id=\"T_b1edf_row2_col3\" class=\"data row2 col3\" >0.997191</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row3\" class=\"row_heading level0 row3\" >random_forest</th>\n",
" <td id=\"T_b1edf_row3_col0\" class=\"data row3 col0\" >0.002716</td>\n",
" <td id=\"T_b1edf_row3_col1\" class=\"data row3 col1\" >0.005575</td>\n",
" <td id=\"T_b1edf_row3_col2\" class=\"data row3 col2\" >0.067298</td>\n",
" <td id=\"T_b1edf_row3_col3\" class=\"data row3 col3\" >0.991788</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row4\" class=\"row_heading level0 row4\" >decision_tree</th>\n",
" <td id=\"T_b1edf_row4_col0\" class=\"data row4 col0\" >0.000346</td>\n",
" <td id=\"T_b1edf_row4_col1\" class=\"data row4 col1\" >0.006433</td>\n",
" <td id=\"T_b1edf_row4_col2\" class=\"data row4 col2\" >0.076138</td>\n",
" <td id=\"T_b1edf_row4_col3\" class=\"data row4 col3\" >0.989067</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row5\" class=\"row_heading level0 row5\" >ridge</th>\n",
" <td id=\"T_b1edf_row5_col0\" class=\"data row5 col0\" >0.013989</td>\n",
" <td id=\"T_b1edf_row5_col1\" class=\"data row5 col1\" >0.015356</td>\n",
" <td id=\"T_b1edf_row5_col2\" class=\"data row5 col2\" >0.116380</td>\n",
" <td id=\"T_b1edf_row5_col3\" class=\"data row5 col3\" >0.937703</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
" <td id=\"T_b1edf_row6_col0\" class=\"data row6 col0\" >0.053108</td>\n",
" <td id=\"T_b1edf_row6_col1\" class=\"data row6 col1\" >0.056776</td>\n",
" <td id=\"T_b1edf_row6_col2\" class=\"data row6 col2\" >0.217611</td>\n",
" <td id=\"T_b1edf_row6_col3\" class=\"data row6 col3\" >0.148414</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1edf_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
" <td id=\"T_b1edf_row7_col0\" class=\"data row7 col0\" >0.079478</td>\n",
" <td id=\"T_b1edf_row7_col1\" class=\"data row7 col1\" >0.067910</td>\n",
" <td id=\"T_b1edf_row7_col2\" class=\"data row7 col2\" >0.247692</td>\n",
" <td id=\"T_b1edf_row7_col3\" class=\"data row7 col3\" >-0.218339</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x26429205520>"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n",
" [\"RMSE_train\", \"RMSE_test\", \"RMAE_test\", \"R2_test\"]\n",
"]\n",
"reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n",
" cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n",
").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"RMAE_test\", \"R2_test\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Вывод реального и \"спрогнозированного\" результата для обучающей и тестовой выборок"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение лучшей модели"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'linear_poly'"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"best_model = str(reg_metrics.sort_values(by=\"RMSE_test\").iloc[0].name)\n",
"\n",
"display(best_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вывод для обучающей выборки"
]
},
{
"cell_type": "code",
"execution_count": 153,
"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
" <th>DensityPred</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.06250</td>\n",
" <td>1.063174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.05979</td>\n",
" <td>1.060117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>35</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.05404</td>\n",
" <td>1.053941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.05103</td>\n",
" <td>1.050822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>45</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.04794</td>\n",
" <td>1.047683</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density DensityPred\n",
"0 20 0.0 0.0 1.06250 1.063174\n",
"1 25 0.0 0.0 1.05979 1.060117\n",
"2 35 0.0 0.0 1.05404 1.053941\n",
"3 40 0.0 0.0 1.05103 1.050822\n",
"4 45 0.0 0.0 1.04794 1.047683"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat(\n",
" [\n",
" density_train,\n",
" density_y_train,\n",
" pd.Series(\n",
" models[best_model][\"train_preds\"],\n",
" index=density_y_train.index,\n",
" name=\"DensityPred\",\n",
" ),\n",
" ],\n",
" axis=1,\n",
").head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вывод для тестовой выборки"
]
},
{
"cell_type": "code",
"execution_count": 154,
"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>T</th>\n",
" <th>Al2O3</th>\n",
" <th>TiO2</th>\n",
" <th>Density</th>\n",
" <th>DensityPred</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>30</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.05696</td>\n",
" <td>1.057040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>55</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>1.04158</td>\n",
" <td>1.041341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>25</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.08438</td>\n",
" <td>1.084063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>30</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.08112</td>\n",
" <td>1.080764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>1.07781</td>\n",
" <td>1.077444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" T Al2O3 TiO2 Density DensityPred\n",
"0 30 0.00 0.0 1.05696 1.057040\n",
"1 55 0.00 0.0 1.04158 1.041341\n",
"2 25 0.05 0.0 1.08438 1.084063\n",
"3 30 0.05 0.0 1.08112 1.080764\n",
"4 35 0.05 0.0 1.07781 1.077444"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat(\n",
" [\n",
" density_test,\n",
" density_y_test,\n",
" pd.Series(\n",
" models[best_model][\"preds\"],\n",
" index=density_y_test.index,\n",
" name=\"DensityPred\",\n",
" ),\n",
" ],\n",
" axis=1,\n",
").head(5)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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