180 lines
113 KiB
Plaintext
180 lines
113 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\n",
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"\n",
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"Выгрузка данных из csv файла в датафрейм"
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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": 7,
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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(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], 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 pd\n",
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"df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\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": "code",
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"execution_count": 2,
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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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"2\n"
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]
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},
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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 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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"import matplotlib.pyplot as plt\n",
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"import pandas as pd \n",
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"df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n",
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"\n",
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"labels = 'Мужчины', 'Женщины'\n",
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"sizes = [df[df[\"sex\"] == \"male\"].shape[0],df[df[\"sex\"] == \"female\"].shape[0]]\n",
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"\n",
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"print(len([df[df[\"sex\"] == \"male\"].count(),df[df[\"sex\"] == \"female\"].count()]))\n",
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"\n",
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"plt.pie(sizes, labels=labels)\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 круговая диаграмма показывает соотношение мужчин и женщин. Из диаграмм мы видим, что мужчин больше."
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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": 6,
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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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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n",
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"\n",
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"df_first_30 = df.head(30)\n",
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"\n",
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"region_charges = df_first_30.groupby('region')['charges'].mean().reset_index()\n",
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"\n",
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"plt.figure(figsize=(10, 6))\n",
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"plt.bar(region_charges['region'], region_charges['charges'], color=['blue', 'green', 'red', 'purple'], alpha=0.7)\n",
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"plt.title('Средняя стоимость страховки по регионам (первые 30 строк)')\n",
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"plt.xlabel('Регион')\n",
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"plt.ylabel('Средняя стоимость страховки')\n",
|
||
|
|
"plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Данная столбчатая диаграмма показывает распределение стоимости страховки (charges) по регионам (region) для первых 30 строк данных. Это поможет нам увидеть, как стоимость страховки распределяется по регионам в выбранной выборке."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1200x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"\n",
|
||
|
|
"df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n",
|
||
|
|
"\n",
|
||
|
|
"age_groups = df.groupby('age')['charges'].mean().reset_index()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(12, 6))\n",
|
||
|
|
"plt.bar(age_groups['age'], age_groups['charges'], color='green', alpha=0.7)\n",
|
||
|
|
"plt.title('Средняя стоимость страховки по возрастным группам')\n",
|
||
|
|
"plt.xlabel('Возраст')\n",
|
||
|
|
"plt.ylabel('Средняя стоимость страховки')\n",
|
||
|
|
"plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Данная гистограмма показывает распределение стоимости страховки (charges) по возрастным группам. Это поможет нам увидеть, как стоимость страховки меняется в зависимости от возраста."
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|