AIM-PIbd-32-Katysheva-N-E/lab_1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораборной\n",
    "\n",
    "Выгрузка данных из csv файла в датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
      "       'test preparation course', 'math score', 'reading score',\n",
      "       'writing score'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n",
    "print (df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df.plot.hist(column=[\"math score\"], bins=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "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
}