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

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   "source": [
    "## Начало лабораборной\n",
    "\n",
    "Выгрузка данных из csv файла в датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "\n",
    "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n",
    "print (df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race/ethnicity               1000 non-null   object\n",
      " 2   parental level of education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test preparation course      1000 non-null   object\n",
      " 5   math score                   1000 non-null   int64 \n",
      " 6   reading score                1000 non-null   int64 \n",
      " 7   writing score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                count    mean        std   min    25%   50%   75%    max\n",
      "math score     1000.0  66.089  15.163080   0.0  57.00  66.0  77.0  100.0\n",
      "reading score  1000.0  69.169  14.600192  17.0  59.00  70.0  79.0  100.0\n",
      "writing score  1000.0  68.054  15.195657  10.0  57.75  69.0  79.0  100.0\n"
     ]
    }
   ],
   "source": [
    "print(df.describe().transpose())"
   ]
  }
 ],
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