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lab 1 #1

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Arutunyan-Dmitry merged 9 commits from lab_1 into main 2024-09-14 12:20:22 +04:00
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1 changed files with 28 additions and 30 deletions
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@ -11,7 +11,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -27,6 +27,8 @@
], ],
"source": [ "source": [
"import pandas as pd\n", "import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.dates as md\n",
"\n", "\n",
"df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n", "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n",
"print (df.columns)" "print (df.columns)"
@ -34,53 +36,49 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "ename": "TypeError",
"output_type": "stream", "evalue": "'RangeIndex' object is not callable",
"text": [ "output_type": "error",
"<class 'pandas.core.frame.DataFrame'>\n", "traceback": [
"RangeIndex: 1000 entries, 0 to 999\n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"Data columns (total 8 columns):\n", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
" # Column Non-Null Count Dtype \n", "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
"--- ------ -------------- ----- \n", "\u001b[1;31mTypeError\u001b[0m: 'RangeIndex' object is not callable"
" 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": [ "source": [
"df.info()" "df.index(2)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "ename": "ImportError",
"output_type": "stream", "evalue": "matplotlib is required for plotting when the default backend \"matplotlib\" is selected.",
"text": [ "output_type": "error",
" count mean std min 25% 50% 75% max\n", "traceback": [
"math score 1000.0 66.089 15.163080 0.0 57.00 66.0 77.0 100.0\n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"reading score 1000.0 69.169 14.600192 17.0 59.00 70.0 79.0 100.0\n", "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"writing score 1000.0 68.054 15.195657 10.0 57.75 69.0 79.0 100.0\n" "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmath score\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m80\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1409\u001b[0m, in \u001b[0;36mPlotAccessor.hist\u001b[1;34m(self, by, bins, **kwargs)\u001b[0m\n\u001b[0;32m 1349\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhist\u001b[39m(\n\u001b[0;32m 1350\u001b[0m \u001b[38;5;28mself\u001b[39m, by: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, bins: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 1351\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PlotAccessor:\n\u001b[0;32m 1352\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1353\u001b[0m \u001b[38;5;124;03m Draw one histogram of the DataFrame's columns.\u001b[39;00m\n\u001b[0;32m 1354\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1407\u001b[0m \u001b[38;5;124;03m >>> ax = df.plot.hist(column=[\"age\"], by=\"gender\", figsize=(10, 8))\u001b[39;00m\n\u001b[0;32m 1408\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1409\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhist\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:947\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 946\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 947\u001b[0m plot_backend \u001b[38;5;241m=\u001b[39m \u001b[43m_get_plot_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbackend\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 949\u001b[0m x, y, kind, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_call_args(\n\u001b[0;32m 950\u001b[0m plot_backend\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent, args, kwargs\n\u001b[0;32m 951\u001b[0m )\n\u001b[0;32m 953\u001b[0m kind \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kind_aliases\u001b[38;5;241m.\u001b[39mget(kind, kind)\n",
"File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1944\u001b[0m, in \u001b[0;36m_get_plot_backend\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m 1941\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend_str \u001b[38;5;129;01min\u001b[39;00m _backends:\n\u001b[0;32m 1942\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _backends[backend_str]\n\u001b[1;32m-> 1944\u001b[0m module \u001b[38;5;241m=\u001b[39m \u001b[43m_load_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend_str\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1945\u001b[0m _backends[backend_str] \u001b[38;5;241m=\u001b[39m module\n\u001b[0;32m 1946\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n",
"File \u001b[1;32mc:\\Users\\Наталья\\Desktop\\5semestr\\AIM\\aimenv\\Lib\\site-packages\\pandas\\plotting\\_core.py:1874\u001b[0m, in \u001b[0;36m_load_backend\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m 1872\u001b[0m module \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpandas.plotting._matplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1873\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m-> 1874\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m 1875\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib is required for plotting when the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1876\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdefault backend \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is selected.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 1877\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\n\u001b[0;32m 1880\u001b[0m found_backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
"\u001b[1;31mImportError\u001b[0m: matplotlib is required for plotting when the default backend \"matplotlib\" is selected."
] ]
} }
], ],
"source": [ "source": [
"print(df.describe().transpose())" "\n",
"df.plot.hist(column=[\"math score\"], bins=80)"
] ]
} }
], ],