181 lines
131 KiB
Plaintext
181 lines
131 KiB
Plaintext
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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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"Начало лабораторной работы"
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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": 12,
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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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" Name Networth Age Country \\\n",
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"Rank \n",
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"1 Elon Musk 219.0 50 United States \n",
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"2 Jeff Bezos 171.0 58 United States \n",
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"3 Bernard Arnault & family 158.0 73 France \n",
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"4 Bill Gates 129.0 66 United States \n",
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"5 Warren Buffett 118.0 91 United States \n",
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"... ... ... ... ... \n",
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"2578 Jorge Gallardo Ballart 1.0 80 Spain \n",
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"2578 Nari Genomal 1.0 82 Philippines \n",
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"2578 Ramesh Genomal 1.0 71 Philippines \n",
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"2578 Sunder Genomal 1.0 68 Philippines \n",
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"2578 Horst-Otto Gerberding 1.0 69 Germany \n",
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"\n",
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" Source Industry \n",
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"Rank \n",
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"1 Tesla, SpaceX Automotive \n",
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"2 Amazon Technology \n",
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"3 LVMH Fashion & Retail \n",
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"4 Microsoft Technology \n",
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"5 Berkshire Hathaway Finance & Investments \n",
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"... ... ... \n",
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"2578 pharmaceuticals Healthcare \n",
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"2578 apparel Fashion & Retail \n",
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"2578 apparel Fashion & Retail \n",
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"2578 garments Fashion & Retail \n",
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"2578 flavors and fragrances Food & Beverage \n",
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"\n",
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"[2600 rows x 6 columns]\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 pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"df = pd.read_csv(\"..\\\\static\\\\csv\\\\Forbes Billionaires.csv\", index_col=\"Rank \")\n",
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"\n",
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"print(df)\n",
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"\n"
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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": 16,
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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": [
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"<Figure size 1000x600 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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"plt.figure(figsize=(10, 6))\n",
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"plt.hist(df['Age'], bins=10, edgecolor='black')\n",
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"plt.title('Распределение возраста')\n",
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"plt.xlabel('Возраст')\n",
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"plt.ylabel('Количество')\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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"Данная диаграмма отображает распределение возраста среди участников датасета, что позволяет сделать вывод о том, что большинство участников находятся в возрастной группе 30-50 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": 19,
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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": [
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|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"subset_df = df.iloc[0:30]\n",
|
|||
|
|
"country_counts = subset_df['Country'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"plt.pie(country_counts, labels=country_counts.index, autopct='%1.1f%%', startangle=140)\n",
|
|||
|
|
"plt.title('Распределение по странам')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает распределение участников по странам (срез данных от 1 до 30 строки), что позволяет сделать вывод о том, что большинство участников происходят из США"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"industry_counts = df['Industry'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"plt.bar(industry_counts.index, industry_counts.values, color='skyblue')\n",
|
|||
|
|
"plt.title('Распределение по отраслям')\n",
|
|||
|
|
"plt.xlabel('Отрасль')\n",
|
|||
|
|
"plt.ylabel('Количество')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает распределение участников по отраслям"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"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
|
|||
|
|
}
|