lab_1/lab1.ipynb

@@ -31,6 +31,57 @@
     "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())"
+   ]
   }
  ],
  "metadata": {