201 lines
116 KiB
Plaintext
201 lines
116 KiB
Plaintext
|
{
|
|||
|
|
"cells": [
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 1,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
|
|||
|
|
" 'price', 'x', 'y', 'z'],\n",
|
|||
|
|
" dtype='object')\n",
|
|||
|
|
"Unnamed: 0 6\n",
|
|||
|
|
"carat 0.24\n",
|
|||
|
|
"cut Very Good\n",
|
|||
|
|
"color J\n",
|
|||
|
|
"clarity VVS2\n",
|
|||
|
|
"depth 62.8\n",
|
|||
|
|
"table 57.0\n",
|
|||
|
|
"price 336\n",
|
|||
|
|
"x 3.94\n",
|
|||
|
|
"y 3.96\n",
|
|||
|
|
"z 2.48\n",
|
|||
|
|
"Name: 5, dtype: object\n",
|
|||
|
|
"Группировка по огранке (cut):\n",
|
|||
|
|
"cut\n",
|
|||
|
|
"Fair 1610\n",
|
|||
|
|
"Good 4906\n",
|
|||
|
|
"Ideal 21551\n",
|
|||
|
|
"Premium 13793\n",
|
|||
|
|
"Very Good 12083\n",
|
|||
|
|
"dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
|
|||
|
|
"print(df.columns)\n",
|
|||
|
|
"print(df.iloc[5])\n",
|
|||
|
|
"\n",
|
|||
|
|
"grouped_by_cut = df.groupby('cut').size()\n",
|
|||
|
|
"print(\"Группировка по огранке (cut):\")\n",
|
|||
|
|
"print(grouped_by_cut)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"линейная диаграмма\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 3,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAHWCAYAAACVPVriAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACWAElEQVR4nO3deXhTZdoG8DtN9w1KC4VCoSwKokAVBFGgoJRVBPkYFUZBcEBFNjuKICOLMrKICG7g4IC7qFAFBSoFKaAgSFll2IQiUGhpWbpCm6bn++P1JE2TtkmznJPk/l1XryQnJydv3p7lyZN30UiSJIGIiIiIyEP5KF0AIiIiIiJnYsBLRERERB6NAS8REREReTQGvERERETk0RjwEhEREZFHY8BLRERERB6NAS8REREReTQGvERERETk0XyVLgAREdmvtLQUV69eRXl5OWJiYpQuDhGRqjDDS0Tkpvbt24cRI0YgKioKAQEBaNSoEf7v//5P6WJRDcaPH4/ExESli0EeJiUlBaGhocjJyVG6KKrEgNfLLV++HH379kV0dDT8/PzQsGFDJCQk4JNPPkF5ebnSxSOiKqxbtw7dunXD//73P/z73/9GamoqUlNT8cEHHyhdNKpGRkYGPvzwQ7z88stKF0X1bty4gaeeegp33HEH6tSpg9DQUHTo0AFLly6FTqczW//69esYN24c6tevj5CQEPTq1Qv79+9XoOTK6NevH1q1aoV58+YpXRRV0kiSJCldCFJO165d0ahRI9x///0IDw/H9evX8euvv2L16tV49NFH8eWXXypdRCKq5OrVq2jdujXuvfdefPPNN/D391e6SGSlKVOmYNOmTThx4oTSRVG9q1evYsCAAejRowfi4uLg4+ODXbt24bPPPsNjjz2GL774wrBueXk5unfvjkOHDuHFF19EVFQU3n//fZw/fx7p6em45ZZbFPwkrrNs2TK88MILyMrKQlhYmNLFUReJvFppaanF5RMmTJAASBkZGa4tEBHVaNGiRVJoaKh09epVpYtCNigtLZWioqKkf/3rX0oXxa3J16dLly4Zln311VcSAOmbb74xLLt8+bJUt25dafjw4UoUUxHZ2dmSVquV/vvf/ypdFNVhkwYv5+fnZ3F5XFwcAMDHx7iLrFu3DgMHDkRMTAwCAgLQsmVLvPbaa9Dr9Sav7dmzJzQajeEvKioKAwcOxO+//26ynkajwezZs02WvfHGG9BoNOjZs6fJ8ps3b2L27Nm49dZbERgYiEaNGmHo0KE4ffo0AODs2bPQaDT46KOPTF733HPPQaPR4MknnzQs++ijj6DRaODv72/W1mn37t2Gcu/bt8/kuW+++QYdO3ZEUFAQoqKi8PjjjyMzM9Os7o4fP45HHnkE9evXR1BQEFq3bo0ZM2YAAGbPnm1SN5b+0tLSDPV4xx13mG3fGlW9dtGiRdBoNDh79qzJ8uvXr2PKlCmIjY1FQEAAWrVqhQULFpg0a5HreNGiRWbbveOOO0z+Z2lpadBoNFizZk2VZXzyyScN+1lN3n//fdx+++0ICAhATEwMnnvuOVy/ft3k89ZUr9Wxdp8FgM8++8ywH9SrVw+PPfYYzp8/b7benj17MGDAAERERCAkJATt27fH0qVLDc8fPnwYTz75JFq0aIHAwEA0bNgQY8aMwZUrV2qsj19//RXx8fF4/fXXDf+zW265BfPnzzdriqTRaDBhwgR8/vnnaN26NQIDA9GxY0fs2LHDZD1536yosLAQDRs2NNkvZfL+YE1dV7XfVzwubXn/nTt34m9/+xuaNm2KgIAAxMbG4vnnn8eNGzdMXv/kk08iNDTUrDxr1qwxO9as3X9WrVqF+++/Hw0aNEBAQADatm2LZcuWmb2HJT///DNyc3PRu3dvk+Xy8VLVX8V6Amw/Xt966y00a9YMQUFBSEhIMNuvn3zySZP3i4iIQM+ePbFz506T9eLi4vDggw9W+fnkz1F5X9mzZw/69euHOnXqIDg4GAkJCfjll1+sqjNL5PNGxXPAmjVrEB0djaFDhxqW1a9fH4888gjWrVuHkpKSGre7adMmJCQkICwsDOHh4bj77rsNWWRb9hFrjzkAOHDgAPr374/w8HCEhobigQcewK+//mqyjnzNqnjePnr0KCIiIvDggw+irKzMsLxBgwZo37491q1bV+Pn9TYcpYEAiBNHWVkZCgoKkJ6ejkWLFuGxxx5D06ZNDet89NFHCA0NRVJSEkJDQ/HTTz9h5syZyM/PxxtvvGGyvTZt2mDGjBmQJAmnT5/G4sWLMWDAAJw7d67aMlhqe6TX6/Hggw9i69ateOyxxzB58mQUFBQgNTUVv//+O1q2bGlxe3/88QdWrFhR5ftptVp89tlneP755w3LVq1ahcDAQNy8edNk3Y8++gijR4/G3XffjXnz5iE7OxtLly7FL7/8ggMHDqBu3boARBDTvXt3+Pn5Ydy4cYiLi8Pp06fx/fff49///jeGDh2KVq1aGbb7/PPP47bbbsO4ceMMy2677bYqy+wMxcXFSEhIQGZmJp5++mk0bdoUu3btwvTp03Hp0iUsWbLEpeWpbPbs2ZgzZw569+6NZ599FidOnMCyZcvw22+/4ZdffoGfnx9mzJiBf/zjHwCA3NxcPP/88xg3bhy6d+9u9ftYs8/++9//xiuvvIJHHnkE//jHP5CTk4N33nkHPXr0MNkPUlNT8eCDD6JRo0aYPHkyGjZsiGPHjuGHH37A5MmTDeucOXMGo0ePRsOGDXH06FH85z//wdGjR/Hrr79WG6RfuXIFP//8M37++WeMGTMGHTt2xNatWzF9+nScPXsWy5cvN1l/+/bt+OqrrzBp0iQEBATg/fffR79+/bB3795qv1S9+eabyM7OrrbeKtZzcnIyvv322yrX/fTTTw33Kx53tr7/N998g+LiYjz77LOIjIzE3r178c477+DChQv45ptvatxuZbbsP8uWLcPtt9+Ohx56CL6+vvj+++8xfvx4lJeX47nnnqv2fXbt2gWNRoM777zT4vOTJk3C3XffbbJMLpfM1uP1k08+QUFBAZ577jncvHkTS5cuxf33348jR44gOjrasF5UVBTeeustAMCFCxewdOlSDBgwAOfPnzfs17Xx008/oX///ujYsSNmzZoFHx8fw5eGnTt3onPnzjVuo7S0FPn5+bhx4wb27duHRYsWoVmzZibn0gMHDuCuu+4ySdIAQOfOnfGf//wHJ0+eRLt27ap8j48++ghjxozB7bffjunTp6Nu3bo4cOAAUlJSMGLECJvPMdYcc0ePHkX37t0RHh6OqVOnws/PDx988AF69uyJ7du3o0uXLha3ff78efTr1w9t2rTB119/DV9f01CuY8eO+O6772qsV6+jdIqZ1KF169YSAMPfyJEjJZ1OZ7JOcXGx2euefvppKTg4WLp586ZhWUJCgpSQkGCy3ssvvywBkC5fvmxYBkCaNWuW4fHUqVOlBg0aSB07djR5/cqVKyUA0uLFi83ev7y8XJIkScrIyJAASKtWrTI898gjj0h33HGHFBsbK40aNcqwfNWqVRIAafjw4VK7du0My4uKiqTw8HBpxIgREgDpt99+kyRJ/AzZoEED6Y477pBu3LhhWP+HH36QAEgzZ840LOvRo4cUFhYm/fnnnxbLWVmzZs1MylZRQkKCdPvtt1t8riZVvfaNN94wa6ry2muvSSEhIdLJkydN1p02bZqk1Wqlc+fOSZJkrOM33njDbLu33367yf9s27ZtZj8vVjZq1CipWbNm1X6Oy5cvS/7+/lKfPn0kvV5vWP7uu+9KAKSVK1eavcbSvlATa/bZs2fPSlqtVvr3v/9tst6RI0ckX19fw/KysjKpefPmUrNmzaRr166ZrFtxP7B0PH355ZcSAGnHjh01lheANHv2bJPlTz75pARAOnLkiGGZfEzv27fPsOzPP/+UAgMDpYcfftiwbNasWVLFS8Lly5elsLAwqX///hIAadu2bSbvderUKQmA9PHHH1e5DdmMGTMkjUZjsqzyvm/L+1uqu3nz5kkajcbk2Bs1apQUEhJitu4333xj8TNJUs3
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x500 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"df_subset = df.head(30)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(8, 5))\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.plot(df_subset['price'], df_subset['carat'], marker='o', linestyle='-', color='blue')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('Зависимость цены от веса бриллианта (первые 30 строк)')\n",
|
|||
|
|
"plt.xlabel('Цена')\n",
|
|||
|
|
"plt.ylabel('Вес')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Вывод: На данной диаграмме отображается зависимость цены бриллиантов от их веса для первых 30 записей из набора данных. Судя по диаграмме, можно увидеть, что с цена не зависит от веса."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
" круговая диаграмма"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 500x500 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"color_counts = df['color'].value_counts() \n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(5, 5))\n",
|
|||
|
|
"plt.pie(color_counts, labels=color_counts.index, autopct='%1.1f%%', colors=plt.cm.Paired(range(len(color_counts))))\n",
|
|||
|
|
"plt.title('Распределение бриллиантов по цветам')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Вывод: Из данной диаграммы можем сделать вывод о том, что бриллиантов цвета G самое большое колличество, а с цветом J - самое маленькое."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"стобчатая диаграмма"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 700x300 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"grouped_by_cut = df.groupby('cut').size()\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(7, 3))\n",
|
|||
|
|
"plt.bar(grouped_by_cut.index, grouped_by_cut.values, color='blue')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('Количество бриллиантов по cut')\n",
|
|||
|
|
"plt.xlabel('cut')\n",
|
|||
|
|
"plt.ylabel('Количество бриллиантов')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Вывод: На данной диаграмме видно, что бриллиантов с огранкой \"Ideal cut\" больше всего, а бриллиантов с огранкой \"Fair\" - меньше всего."
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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": "3.12.6"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|