лаба 1 готова!
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a.puchkina
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29498513a5
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107
lab_1/lab1.ipynb
107
lab_1/lab1.ipynb
@ -11,7 +11,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 63,
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"metadata": {},
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"outputs": [
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{
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@ -31,6 +31,111 @@
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"df = pd.read_csv(\"..//static//csv//mobile phone price prediction.csv\")\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001BFC924FE60>\n"
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]
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},
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{
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"ename": "TypeError",
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"evalue": "unsupported operand type(s) for +=: 'int' and 'str'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[68], line 12\u001b[0m\n\u001b[0;32m 10\u001b[0m price \u001b[38;5;241m=\u001b[39m df[df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompany\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m c_value][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrice\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39msum()\n\u001b[0;32m 11\u001b[0m c_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m count\n\u001b[1;32m---> 12\u001b[0m \u001b[43mp_total\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mprice\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(c_value, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount =\u001b[39m\u001b[38;5;124m\"\u001b[39m, count, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m price =\u001b[39m\u001b[38;5;124m\"\u001b[39m, price)\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTotal count = \u001b[39m\u001b[38;5;124m\"\u001b[39m, c_total)\n",
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"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +=: 'int' and 'str'"
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]
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}
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],
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"source": [
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"average_prices = df.groupby('company')['Price']\n",
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"print(average_prices)\n",
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"\n",
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"c_values = df[\"company\"].unique()\n",
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"\n",
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"c_total = 0\n",
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"p_total = 0\n",
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"for c_value in c_values:\n",
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" count = df[df[\"company\"] == c_value].shape[0]\n",
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" price = df[df[\"company\"] == c_value][\"Price\"].sum()\n",
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" countrys = df1.groupby(\"Country\").size().reset_index(name=\"Count\")\n",
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" c_total += count\n",
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" p_total += price\n",
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" print(c_value, \"count =\", count, \" price =\", price)\n",
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"print(\"Total count = \", c_total)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" price = 89 6,990\n",
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"90 6,999\n",
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"91 7,499\n",
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"92 7,999\n",
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"93 8,033\n",
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" ... \n",
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"854 36,990\n",
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"855 45,215\n",
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"856 69,999\n",
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"857 68,899\n",
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"858 63,490\n",
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"Name: Price, Length: 186, dtype: object\n"
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]
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}
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],
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"source": [
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"count = df[df[\"company\"] == \"Vivo\"].shape[0]\n",
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"price = df[df[\"company\"] == \"Vivo\"][\"Price\"].replace(\",\", \"\")\n",
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"print(\" price =\", price)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'matplotlib'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[61], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[0;32m 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompany\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompany\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m; \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m df1 \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mexplode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompany\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
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"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"df['company'] = df['company'].str.split('; ')\n",
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"df1 = df.explode('company')\n",
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"companys = df1.groupby(\"company\").size().reset_index(name=\"Count\") # type: ignore\n",
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"company_counts_sorted = companys.sort_values(by='Count', ascending=False)\n",
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"top_countries = company_counts_sorted.head(50)\n",
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"\n",
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"top_countries.plot.bar(x='company', y='Count', color=['green'])\n",
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"plt.title('Top Countries by count of people')\n",
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"plt.xlabel('Country')\n",
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"plt.ylabel('Number of People')\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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