pred_analytics/lab1.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Был взят 17 вариант, а именно .csv с Ценами на автомобили.![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Загрузка (чтение) файла\n",
    "df = pd.read_csv('car_price_prediction.csv')\n",
    "\n",
    "# Сохранение данных в файл\n",
    "# df.to_сsv('car_price_prediction.csv', index=False, engine='openpyxl')\n",
    "\n",
    "# Получение сведений о df\n",
    "df.info()\n",
    "\n",
    "# Получение информации о колонках df\n",
    "print(df.columns)\n",
    "\n",
    "# Вывод отдельных строк/столбцов df\n",
    "print(df[0:3])\n",
    "print(df.loc[50,\"Price\"])\n",
    "\n",
    "# Группировка и агрегация\n",
    "grouped = df.groupby([\"Price\", \"Model\"])\n",
    "print(grouped)\n",
    "df[\"Price\"].agg(['sum', 'mean'])\n",
    "\n",
    "# Сортировка данных в df\n",
    "df.sort_values(by=\"Price\", ascending=False)\n",
    "\n",
    "# Удаление строк/стобцов из df\n",
    "df.drop(labels=[2,3], axis=0)\n",
    "df.drop(\"Price\", axis=1)\n",
    "\n",
    "# Cоздание новых столбцов на основе данных из существующих столбцов датафрейма\n",
    "df = df.assign(Test = df[\"Fuel type\"] + df[\"Engine volume\"])\n",
    "print(df)\n",
    "\n",
    "# Удаление строк с пустыми значениями\n",
    "df_cleaned = df.dropna()\n",
    "\n",
    "# Удаление только строк, в которых все значения NaN\n",
    "df_cleaned = df.dropna(how=\"all\")\n",
    "\n",
    "# Заполнение пустых значений на основе существующих данных\n",
    "median_value = df[\"Price\"].median()\n",
    "df[\"Price\"].fillna(median_value, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'test')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация линейчатая\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('car_price_prediction.csv', nrows=50)\n",
    "\n",
    "\n",
    "x = df[\"Cylinders\"]\n",
    "y = df[\"Category\"]\n",
    "\n",
    "plt.plot(x, y, marker='o')\n",
    "\n",
    "plt.xlabel('Количество цилиндров')\n",
    "plt.ylabel('Категория авто')\n",
    "plt.title('test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 100 artists>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Столбчатая \n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"car_price_prediction.csv\", nrows=100)\n",
    "\n",
    "x = df[\"Airbags\"]\n",
    "y = df[\"Category\"]\n",
    "\n",
    "plt.bar(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Гистограмма\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"car_price_prediction.csv\", nrows=100)\n",
    "\n",
    "df.plot.hist(column=[\"Price\"], bins=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApIAAAIoCAYAAADa2Qt1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/GU6VOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAoh0lEQVR4nO3dd5RV5b344e+ZGRgYaSIORUBQjBggRiEqgr+gMXKNeiWgRoMGTaIIaMQaSwgQo9i4dpFYIMWyLOj12mJXzMUCChEFy0VsNEFhIKAis39/ZHGSEYThlZlDeZ61WIs5+z17v3v2u4YPp00uy7IsAABgAxUVegIAAGyehCQAAEmEJAAASYQkAABJhCQAAEmEJAAASYQkAABJhCQAAEmEJAAASYQkAABJhCSQ5K9//Wscdthh0aJFiygpKYlcLlflz/HHH1/oKQJQw0oKPQFg83PDDTfEkCFDIpfLxY9+9KPo3Llz1KtXLyIiPvzww7jlllsKPEMAaoOQBDbIihUr4te//nVERNx+++1x9NFHV9n+wgsvCEmArYSntoENMn369Fi2bFk0aNAgfvKTn1T7fr169YpcLrfG7SNGjIhcLhfPPPNMldufeeaZyOVyMWLEiCq3jx8/PnK5XIwfP77K7ZWVldGtW7fI5XLRq1evah37nHPOiVwuFyeeeGJkWVat81i6dGmMHDkyvvOd70RZWVk0btw49thjjxg2bFisXLlynfft2bNnlJSUxNy5c9e6/Wc/+1nkcrmYNGlSldufe+656NOnTzRv3jxKS0ujTZs20bdv33j++efzY+bMmRPDhw+PffbZJ8rLy6O0tDTatWsXgwcPjgULFqxxrOOPPz5yuVwUFRXFu+++u8b2iRMn5l+m8NVrsLbvcUTENddck7/P7Nmz1/m9ALYMQhLYIowbNy6mTJlS7fEjR46Myy+/PPr37x9jx45da2h+1YIFC2KvvfaKESNGRHFxcQwaNCh+/vOfR4sWLeLSSy+Nf/zjH+u8/8CBA2PVqlUxbty4NbYtXrw47rnnnujUqVN07949f/vVV18dvXr1iscffzx++MMfxplnnhkHHHBATJs2Le655578uOeeey5Gjx4dzZs3j2OOOSZOPfXU2HnnnWPMmDHRvXv3WLJkyVrnVFRUFGPHjl3j9jFjxkRxcfF6vyerLVy4cI3gBLZ8ntoGNkjnzp2jYcOGsXTp0rjzzjvjmGOOKfSUoqKiIi644ILo2rVrtWJy9OjRMWLEiOjbt2/88Y9/jKKi6v2fevDgwTFz5sw4//zz46KLLqqybf78+dGgQYN13v/II4+MoUOHxi233BLnnXdelXi97bbbYsWKFXHiiSfmb5s2bVqcccYZ0bJly/jb3/4W7dq1y2/LsqzKI5sHHHBAzJs3b405/OlPf4oBAwbEddddFxdccMEaczr00EPj1ltvjd/97ndRt27diIj4+OOP4957743DDjss7r///vV+XyIihg0bFkuWLInvfve7MXXq1GrdB9j8eUQS2CD169ePm2++OcrKyqJ///5x2GGHxbnnnhsjRoyIESNGxM0331zrc/rd734X8+fPj6uvvnq9Y2+88cY466yz4uCDD4477rij2o+6zZs3LyZMmBA777zzWh95a968eZSUrPv/5vXq1YsBAwbErFmz4qmnnqqy7ZZbbonS0tI47rjj8reNHTs2Kisr4/e//32ViIz459PLrVq1yn9dXl6+1pA97rjjolGjRvHEE0+sdU4nn3xyfPzxx1Ue3bzllluipKQkBgwYsM7zWW3atGlx0003xS9+8YvYfffdq3UfYMsgJIENdtRRR8Xbb78dZ5xxRjz22GNx6aWXxsiRI2PkyJG1/kabt956K6655pr46U9/Gj169Fjn2D//+c8xePDgiIg4//zz84/AVcfkyZMjy7LYf//9o06dOsnzPemkkyIi4qabbsrfNmXKlHj11VejX79+0bRp0/ztL730UkREHHTQQdXa94QJE6J3796x/fbb5z+SqaioKCoqKmLOnDlrvU/Hjh3jgAMOiBtvvDEi/vla07Fjx8YxxxwTTZo0qdZxhw4dGg0aNFjjUVpgyyckgSStWrWKgw46KL744ovo0qVLfPHFF5Fl2RpvFKlpp59+etSpUycuvfTS9Y494YQTYs8994xtttkmBg4cGJ9//nm1j7P6NYY77LBD8lwj/hlu3//+9+P++++PRYsWRUTkH8X996e1Vx8zl8tFy5Yt17vf0aNHR79+/eLVV1+Ngw46KM4888wYPnx4DB8+PBo3brzOcx00aFBMnDgxXn/99XjkkUdi9uzZMWjQoGqdz9133x3PPPNMDB8+PLbffvtq3QfYcniNJJBk+fLlMWjQoCgqKoqbbrrpGz1Kl+rRRx+Nhx9+OC688MJo3br1esd36dIlnnjiibj99ttjyJAhccEFF8QVV1xRrWOtfnTuo48++iZTjoh/Pp387LPPxp/+9KcYOHBg3HHHHbHLLrus8U7oJk2a5F8Lua6A/fLLL+PCCy+Mli1bxtSpU6O8vDy/LcuyuOyyy9Y5nz59+kTLli1jzJgxMXv27Pje974XXbt2XeOd9F/12Wefxdlnnx0dO3aMU045Zb3nDWx5PCIJJBkxYkTMmjUrBg8eHHvvvXetH3/lypVx+umnR7t27eKss86q1n0ee+yxaNKkSQwaNCh++MMfxpVXXhkTJ06s1n27desWRUVF8fTTT6/3Y37Wp2/fvrH99tvHzTffHHfffXcsWbIkfvnLX64xbq+99srPe10WLlwYS5Ysie7du1eJyIh/PiW/YsWKdd6/pKQkfvnLX8Yf//jHeOSRR6r9aOTll18e7733Xlx11VUF+Y8EUHhCEthgU6dOjSuvvDJat24dF198cUHmcMMNN8TMmTPjiiuuyP9WnfVZ/dRrLpeLW2+9NRo2bBjHH398LFu2bL33bd68efTr1y/+7//+L0aOHLnG9gULFsSXX35ZrXnUrVs3jj/++HjjjTfi/PPPjzp16qz1V0qefPLJUVxcHL/5zW/ivffeq7Ity7L86x7Ly8ujfv368corr8Ty5cvzYz799NM49dRTqzWnk046Kbbddtvo0KHDGh8yvzZz586NSy65JA499NDo3bt3tY4BbHk8tQ1skMrKyjjppJPiyy+/jOuuuy4aNmy4Qfdf/aaO1SZPnhwREQ888EDMnDkzf/tbb72V337jjTfGySefXOV+U6dOjf333z/69euXchrRunXruOaaa2LAgAFx5plnrvWzFL/qhhtuiOnTp8dFF10UDz/8cBxwwAGRZVm89dZb8dhjj8X8+fOr/QaVgQMHxhVXXBFz5syJfv36rfFIYsQ/n4q/6qqr4le/+lV06tQp+vTpEzvuuGPMmzcvnnvuuTjkkEPiqquuiqKiohg8eHCMHj06dt999zjssMOioqIiHnnkkdhxxx2rvLt7Xd+P999/v1pzj/jn9albt27813/9V7XvA2x5hCSwQa699tp4+eWXo2/fvnH44Ydv8P2/7mnTK6+8cq23P/TQQ/HQQw+tEZLFxcXV+rifdfnZz34W9913X/zhD3+IPn36xMEHH7zO8c2aNYsXXnghrrjiirj77rvjuuuui3r16kX79u3j3HPPjW222abax955552jR48e8fzzz6/xJpt/d8opp0Tnzp1j9OjR8cgjj8SyZcuivLw89t577zjqqKPy40aNGhVNmzaN8ePHxw033JD/YPIRI0ZE586dqz2vDXHaaafFLrvsUiP7BjYPuay6vxcMoADGjx8fJ5xwQrV/heHm4rPPPovWrVtHgwYNYtasWdX+UHSATYmfXAAFMG7cuFi0aFEMHDhQRAKbLU9tA5u0XXfdNQYOHFjoaWw0l1xySXz88ccxduzYKC8vz39AOsDmyFPbALUol8tFnTp1Yvfdd49rr7029tlnn0JPCSCZRyQBapH/uwNbEi/MAQAgiZAEACBJrT+1XVlZGXPmzImGDRtGLper7cMDALAeWZbF0qVLo1WrVuv8ZIlaD8k5c+ZEmzZtavuwAABsoA8++CBat279tdtrPSRX/zq1Dz7
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"car_price_prediction.csv\", nrows=100)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.boxplot(df[\"Price\"], vert=False, patch_artist=True, boxprops=dict(facecolor='lightblue'))\n",
    "plt.title('Ящик с усами', fontsize=14)\n",
    "plt.xlabel('Цена', fontsize=12)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Диаграмма с областями\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"car_price_prediction.csv\", nrows=100)\n",
    "\n",
    "average_price_by_year = df.groupby(\"Prod. year\")[\"Price\"].mean()\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.fill_between(\n",
    "    average_price_by_year.index, average_price_by_year, color=\"skyblue\", alpha=0.5\n",
    ")\n",
    "plt.plot(\n",
    "    average_price_by_year.index, average_price_by_year, color=\"Slateblue\", linewidth=2\n",
    ")\n",
    "plt.title(\"Диаграмма с областями: средняя цена по годам выпуска\", fontsize=14)\n",
    "plt.xlabel(\"Год выпуска\", fontsize=12)\n",
    "plt.ylabel(\"Средняя цена\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Диаграмма рассеяния\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"car_price_prediction.csv\", nrows=100)\n",
    "\n",
    "# Построение диаграммы рассеяния (scatter) для объема двигателя и цены\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df[\"Engine volume\"], df[\"Price\"], alpha=0.5, color=\"coral\")\n",
    "plt.title(\"Диаграмма рассеяния: объем двигателя и цена\", fontsize=14)\n",
    "plt.xlabel(\"Объем двигателя (л)\", fontsize=12)\n",
    "plt.ylabel(\"Цена\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Круговая диаграмма\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"car_price_prediction.csv\", nrows=100)\n",
    "\n",
    "category_counts = df[\"Category\"].value_counts()\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(\n",
    "    category_counts,\n",
    "    autopct=\"%1.1f%%\",\n",
    "    startangle=140,\n",
    ")\n",
    "plt.title(\"Распределение автомобилей по категориям\", fontsize=14)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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
}