174 lines
219 KiB
Plaintext
174 lines
219 KiB
Plaintext
|
{
|
|||
|
|
"cells": [
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Начало лабораторной\n",
|
|||
|
|
"\n",
|
|||
|
|
"Выгрузка данных из cvs файла в датафрейм"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 1,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Index(['no', 'Country (or dependency)', 'Population 2020', 'Yearly Change',\n",
|
|||
|
|
" 'Net Change', 'Density (P/Km²)', 'Land Area (Km²)', 'Migrants (net)',\n",
|
|||
|
|
" 'Fert. Rate', 'Med. Age', 'Urban Pop %', 'World Share'],\n",
|
|||
|
|
" dtype='object')\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"import seaborn as sns\n",
|
|||
|
|
"\n",
|
|||
|
|
"df = pd.read_csv(\".//static//csv//world-population-by-country-2020.csv\")\n",
|
|||
|
|
"print(df.columns)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает топ 10 стран с самым молодым населением, что позволяет сделать вывод о том, что в Африке плохо с медициной"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 2,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"srez = df.sort_values(\"Med. Age\").head(10)\n",
|
|||
|
|
"# Диаграмма цен:\n",
|
|||
|
|
"plt.figure(figsize=(8, 8))\n",
|
|||
|
|
"plt.pie(\n",
|
|||
|
|
" srez[\"Med. Age\"],\n",
|
|||
|
|
" labels=srez[\"Country (or dependency)\"],\n",
|
|||
|
|
" autopct=\"%1.1f%%\",\n",
|
|||
|
|
"\n",
|
|||
|
|
")\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает среднее количество детей, которое женщина рожает за свою жизнь, что позволяет сделать вывод о том, что в среднем человечетво умножает себя"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 3,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1800x400 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(18, 4))\n",
|
|||
|
|
"srez = df.sort_values(\"Fert. Rate\")\n",
|
|||
|
|
"sns.boxplot(x = srez[\"Fert. Rate\"])\n",
|
|||
|
|
"plt.title('Box Plot для Fert. Rate')\n",
|
|||
|
|
"plt.xlabel('Fert. Rate')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данная диаграмма отображает население по странам, что позволяет сделать вывод о том, что среднестатистический человек - азиат"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 4,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"srez = df\n",
|
|||
|
|
"srez['Population 2020'] = srez['Population 2020'].replace({'$': '', ',': ''}, regex=True)\n",
|
|||
|
|
"srez['Population 2020']=srez['Population 2020'].astype(float)\n",
|
|||
|
|
"avg_price_by_manufacturer = srez.groupby('Country (or dependency)')['Population 2020'].mean().sort_values(ascending=False).head(100)\n",
|
|||
|
|
"plt.figure(figsize=(16, 6))\n",
|
|||
|
|
"avg_price_by_manufacturer.plot(kind='bar', color='salmon')\n",
|
|||
|
|
"plt.xlabel('Страна')\n",
|
|||
|
|
"plt.ylabel('Население')\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": "3.12.7"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|