{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "45bdce35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature data: (8124, 22)\n", "Target data: (8124,)\n" ] }, { "data": { "text/html": [ "
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cap_shapecap_surfacecap_colorbruisesodorgill_attachmentgill_spacinggill_sizegill_colorstalk_shape...stalk_surface_below_ringstalk_color_above_ringstalk_color_below_ringveil_typeveil_colorring_numberring_typespore-print-colorpopulationhabitat
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" ], "text/plain": [ " cap_shape cap_surface cap_color bruises odor gill_attachment gill_spacing \\\n", "0 x s n t p f c \n", "1 x s y t a f c \n", "2 b s w t l f c \n", "3 x y w t p f c \n", "4 x s g f n f w \n", "\n", " gill_size gill_color stalk_shape ... stalk_surface_below_ring \\\n", "0 n k e ... s \n", "1 b k e ... s \n", "2 b n e ... s \n", "3 n n e ... s \n", "4 b k t ... s \n", "\n", " stalk_color_above_ring stalk_color_below_ring veil_type veil_color \\\n", "0 w w p w \n", "1 w w p w \n", "2 w w p w \n", "3 w w p w \n", "4 w w p w \n", "\n", " ring_number ring_type spore-print-color population habitat \n", "0 o p k s u \n", "1 o p n n g \n", "2 o p n n m \n", "3 o p k s u \n", "4 o e n a g \n", "\n", "[5 rows x 22 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0 p\n", "1 e\n", "2 e\n", "3 p\n", "4 e\n", "Name: class, dtype: category\n", "Categories (2, str): ['e', 'p']" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "class\n", "e 4208\n", "p 3916\n", "Name: count, dtype: int64\n" ] } ], "source": [ "from sklearn.datasets import fetch_openml\n", "\n", "mushroom = fetch_openml(data_id=43922, as_frame=True)\n", "\n", "X = mushroom.data\n", "y = mushroom.target\n", "\n", "print(\"Feature data:\", X.shape)\n", "print(\"Target data:\", y.shape)\n", "display(X.head())\n", "display(y.head())\n", "print(y.value_counts())" ] }, { "cell_type": "code", "execution_count": 2, "id": "69e1a254", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing cells: 0\n", "Rows with missing cells: 0\n", "stalk_root\n", "b 3776\n", "? 2480\n", "e 1120\n", "c 556\n", "r 192\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(\"Missing cells:\", X.isna().sum().sum())\n", "print(\"Rows with missing cells:\", X.isna().any(axis=1).sum())\n", "print(X[\"stalk_root\"].value_counts(dropna=False))" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.12.10)", "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": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 5 }