{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45bdce35",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sklearn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m fetch_openml\n\u001b[1;32m      3\u001b[0m mushroom \u001b[38;5;241m=\u001b[39m fetch_openml(data_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m43922\u001b[39m, as_frame\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      5\u001b[0m X \u001b[38;5;241m=\u001b[39m mushroom\u001b[38;5;241m.\u001b[39mdata\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
     ]
    }
   ],
   "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": 3,
   "id": "69e1a254",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing cells:\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[43mX\u001b[49m\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39msum())\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRows with missing cells:\u001b[39m\u001b[38;5;124m\"\u001b[39m, X\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39many(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39msum())\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(X[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstalk_root\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts(dropna\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3459b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "a = \"hello\"\n",
    "print(a)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.9.6)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}