2024-09-13 23:36:05 +04:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Выгрузка в датафрейм\n"
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]
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},
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{
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"cell_type": "code",
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2024-09-14 09:51:51 +04:00
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"execution_count": 1,
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2024-09-13 23:36:05 +04:00
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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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"Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n",
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" 'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n",
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" 'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n",
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" 'Airbags'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pn\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"import matplotlib.ticker as ticker\n",
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"df = pn.read_csv(\".//static//csv//car_price_prediction.csv\").head(15000)\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": "markdown",
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"metadata": {},
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"source": [
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"По диаграмме видно, что большинство авто были выпущены последние 20 лет"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1000x600 with 0 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAHHCAYAAACWQK1nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB5ZUlEQVR4nO3deXgURfoH8G/PPTkmB7kIBIii3IiAYEAOl0hUPFBWFFEOWWEFXJHfirIqIrqLyqqg4r2C54rsrqiASAQUhXAFkPu+jyTkmJxzT/3+mEybIZNkEjqZJHw/z5MH0l3TXe90T/KmqrpKEkIIEBEREdElUQW7AkRERETNAZMqIiIiIgUwqSIiIiJSAJMqIiIiIgUwqSIiIiJSAJMqIiIiIgUwqSIiIiJSAJMqIiIiIgVogl0BIiKqHavVivz8fGg0GsTFxQW7OkRUji1VRERNwI8//og77rgDkZGRMBqNaNWqFR577LFgV4uIKmBLFQEA3n33XXz99dfYuXMn8vPz0aJFC3To0AETJkzAAw88AJWK+TdRsLz99tt49NFHccMNN2DBggVo1aoVAKBt27ZBrhkRVSRx7T8CgJSUFLRs2RJ/+MMfYDKZYDabsWnTJnz55Ze499578e9//zvYVSS6LB0+fBjdunXD+PHj8fbbb0OSpGBXiYiqwKSKAAAOhwNarbbS9kcffRRvvfUWjh8/jnbt2jV8xYguc48++ii+++47HD582O9nlIgaD/bpEABU+cPam0hV7P775ptvMGzYMCQmJkKv1+PKK6/ECy+8AJfL5fPawYMHQ5Ik+SsmJgbDhg3Dnj17fMpJkoTZs2f7bJs3bx4kScLgwYN9tlutVsyePRtXX301DAYDWrZsibvvvhtHjx4FAJw4cQKSJGHx4sU+r5syZQokScK4cePkbYsXL4YkSdDpdLhw4YJP+YyMDLne27Zt89m3dOlS9OrVC0ajETExMXjggQdw9uzZSu/dgQMHMHLkSMTGxsJoNKJDhw54+umnAQCzZ8/2eW/8ff3000/y+9i1a9dKxw9EVa/95z//CUmScOLECZ/tZrMZ06ZNQ1JSEvR6Pdq3b4+XX34ZbrdbLuN9j//5z39WOm7Xrl19rtlPP/0ESZLwn//8p8o6jhs3LqCEXcn7rqb3vmIMOTk5mDBhAuLj42EwGHDNNdfg448/9jmn9z2RJAnLli3z2We1WhEVFeX3PduxYwduueUWmEwmhIWFYciQIdi0aZNPmU2bNqFXr16YPHky4uPjodfr0bVrV3zwwQd+6xDIvV/Ve+X9uvgY77zzDrp27YqQkBCfctVd10Bj9H4Oq/u6uD4VVff6M2fOyOXWrl2LAQMGIDQ0FJGRkbjzzjuxf/9+v8es6n25+OfUokWL8Ic//AFxcXHQ6/Xo3Lkz3nnnnRrfE8Bz31c8dlRUFAYPHoxffvnFp1y7du1w2223VXkc72fM+/Oiuvp7v7yf+7FjxyImJgYOh6PScYcOHYoOHTr4bPvss8/Qp08fhISEICoqCgMHDsTq1at96nrxfTZx4kQYDAaf+gX6WW6KOKaKfJjNZjidThQXFyMzMxP//Oc/cd9996FNmzZymcWLFyMsLAzTp09HWFgY1q5di1mzZqGoqAjz5s3zOV7Hjh3x9NNPQwiBo0eP4rXXXsOtt96KU6dOVVuHuXPnVtrucrlw2223Yc2aNbjvvvvw2GOPobi4GOnp6dizZw+uvPJKv8c7cuRIpV9AFanVanz22Wd4/PHH5W2LFi2CwWCA1Wr1Kbt48WKMHz8e1113HebOnYvs7GwsWLAAGzZswI4dOxAZGQkA2LVrFwYMGACtVouJEyeiXbt2OHr0KL777jv8/e9/x91334327dvLx3388cfRqVMnTJw4Ud7WqVOnKutcH8rKyjBo0CCcPXsWkyZNQps2bbBx40bMnDkT58+fx/z58xu0PhdT8r779NNP5bK//PIL3n//fbz++uuIiYkBAMTHxwMALBYLBg8ejCNHjmDq1KlITk7G0qVLMW7cOJjN5koDxQ0GAxYtWoThw4fL2/73v/9Vuo8AYO/evRgwYABMJhNmzJgBrVaL9957D4MHD8bPP/+Mvn37AgDy8vKwbds2aDQaTJkyBVdeeSWWLVuGiRMnIi8vD0899VSV71lN937F9woAcnNzfT4HALBkyRJMnjwZgwcPxqOPPorQ0FDs378f//jHP6o9bqAxDhw40Od6/P3vfwcAuU4A0K9fvxrPNWfOHCQnJ/tsi46OBuAZ5H/LLbfgiiuuwOzZs2GxWPDmm2+if//+2L59u9+kvnXr1vLPoZKSEjzyyCOVyrzzzjvo0qUL7rjjDmg0Gnz33XeYPHky3G43pkyZUmOdY2Ji8PrrrwMAzpw5gwULFuDWW2/F6dOn5Z8ldVWx/l4rV670Gcrx4IMP4pNPPsEPP/zgk7hlZWVh7dq1eO655+Rtzz//PGbPno1+/fphzpw50Ol02Lx5M9auXYuhQ4f6rcNzzz2Hf/3rX1iyZInPHyq1+Sw3OYKogg4dOggA8teYMWOEw+HwKVNWVlbpdZMmTRIhISHCarXK2wYNGiQGDRrkU+5vf/ubACBycnLkbQDEc889J38/Y8YMERcXJ3r16uXz+o8++kgAEK+99lql87vdbiGEEMePHxcAxKJFi+R9I0eOFF27dhVJSUli7Nix8vZFixYJAGLUqFGiW7du8vbS0lJhMpnE/fffLwCIrVu3CiGEsNvtIi4uTnTt2lVYLBa5/PLlywUAMWvWLHnbwIEDRXh4uDh58qTfel6sbdu2PnWraNCgQaJLly5+99WkqtfOmzdPABDHjx+Xt73wwgsiNDRUHDp0yKfsU089JdRqtTh16pQQ4vf3eN68eZWO26VLF59rtm7dOgFALF26tMo6jh07VrRt27bGWJS+77y890HF98Jr/vz5AoD47LPP5G12u12kpKSIsLAwUVRUJIT4/T0ZNWqU0Gg0IisrSy4/ZMgQ+V6q+J4NHz5c6HQ6cfToUXnbuXPnRHh4uBg4cKC8rW3btgKAWLx4sbzN6XSKIUOGCL1eL3Jzc33qEMi979W/f39x4403yt/7O8aoUaNEZGSkzz0fyHWtTYwV+bt+1fFeP+/n1J8ePXqIuLg4kZeXJ2/77bffhEqlEmPGjKlUvl+/fqJr167y9xcuXKj0c0oI//dkWlqauOKKK2qst7/7/v333xcAxJYtW+Rtbdu2FcOGDavyON5rsW7dOnlboJ97l8slWrduLe69916fcq+99pqQJEkcO3ZMCCHE4cOHhUqlEnfddZdwuVw+ZSv+TKv4c+y9994TAMSbb75ZqR6BfpabInb/kY9FixYhPT0dn3/+OSZMmIDPP//cp/UEAIxGo/z/4uJi5ObmYsCAASgrK8OBAwd8yjocDuTm5uLChQvIyMjA119/je7du8stAhc7e/Ys3nzzTTz77LMICwvz2fff//4XMTExePTRRyu9rqrBu5mZmVi6dCnmzp1b5ROMDz74IA4cOCB38/33v/9FREQEhgwZ4lNu27ZtyMnJweTJk2EwGOTtw4YNQ8eOHbFixQoAwIULF7B+/Xo89NBDPi181dWzJi6XC7m5ucjNzYXdbq/TMWqydOlSDBgwAFFRUfK5cnNzkZqaCpfLhfXr1/uULysr8ymXm5tbZfO99z4xm811rl993ndVWblyJRISEjBq1Ch5m1arxV/+8heUlJTg559/9infs2dPdOnSRW55OXnyJNatW1epS8TlcmH16tUYPnw4rrjiCnl7y5Ytcf/99+PXX39FUVGRvD0+Ph4PPvig/L1arca0adNgs9nw448/+q17IPe+3W6HXq+v9j0oLi5GSEiIzz0fiNrGWF/Onz+PnTt3Yty4cXLLFQB0794dN910E1auXFnpNVarNaB4K96ThYWFyM3NxaBBg3Ds2DEUFhbW+Hq32y1/dnbu3IlPPvkELVu2rNRK7b2f8/Ly4HQ6azxuoFQqFUaPHo1vv/0WxcXF8vbPP/8c/fr1k1v+li1bBrfbjVmzZlW6l/z9TPvmm28wefJkPPHEE5g6dWql/bX5LDc
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Диаграмма рассеяния: Цена vs Год выпуска\n",
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"plt.figure(figsize=(10, 6))\n",
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"df.plot.scatter(x='Prod. year', y='Price', alpha=0.5)\n",
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"plt.title('Зависимость цены автомобиля от года выпуска')\n",
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"plt.xlabel('Год выпуска')\n",
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"plt.ylabel('Цена')\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Большая чать машин дешевле 50000"
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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": 30,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 1000x600 with 1 Axes>"
|
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|
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|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
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|
|
"output_type": "display_data"
|
|
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|
|
}
|
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|
|
],
|
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|
|
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|
"source": [
|
|
|
|
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
|
|
|
|
"df['Price'].plot.hist(bins=100, edgecolor='black')\n",
|
|
|
|
|
|
"plt.title('Распределение цен на автомобили')\n",
|
|
|
|
|
|
"plt.xlabel('Цена')\n",
|
|
|
|
|
|
"plt.ylabel('Количество автомобилей')\n",
|
|
|
|
|
|
"plt.grid(True)\n",
|
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|
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|
|
"\n",
|
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|
"\n",
|
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|
|
|
|
"# Увеличение количества делений на оси X\n",
|
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|
|
"plt.xticks(rotation=20) \n",
|
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"\n",
|
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|
|
|
|
"plt.show()"
|
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|
|
]
|
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|
|
},
|
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|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"На данном срезе авто марки \"Хёндай\" в среднем самые дорогие."
|
|
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|
|
|
]
|
|
|
|
|
|
},
|
|
|
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|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 31,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
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|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Срез данных: с 1-й по 30-ю строку\n",
|
|
|
|
|
|
"df_slice = df.iloc[:30]\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Группировка по производителю и расчет средней цены\n",
|
|
|
|
|
|
"avg_price_by_manufacturer = df_slice.groupby('Manufacturer')['Price'].mean().sort_values(ascending=False)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Столбчатая диаграмма: Средняя цена по производителям\n",
|
|
|
|
|
|
"plt.figure(figsize=(12, 8))\n",
|
|
|
|
|
|
"avg_price_by_manufacturer.plot(kind='bar', color='salmon')\n",
|
|
|
|
|
|
"plt.title('Средняя цена автомобилей по производителям (с 1-й по 30-ю строку)')\n",
|
|
|
|
|
|
"plt.xlabel('Производитель')\n",
|
|
|
|
|
|
"plt.ylabel('Средняя цена')\n",
|
|
|
|
|
|
"plt.grid(True)\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"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": "3.12.5"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
|
"nbformat_minor": 2
|
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|
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|
|
}
|
