156 lines
150 KiB
Plaintext
156 lines
150 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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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(['Store ID ', 'Store_Area', 'Items_Available', 'Daily_Customer_Count',\n",
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" 'Store_Sales'],\n",
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" 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//Stores.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": 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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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"df['Items_per_Area'] = df['Items_Available'] / df['Store_Area']\n",
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"\n",
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"plt.figure(figsize=(10, 6))\n",
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"plt.bar(df['Store ID'], df['Items_per_Area'], color='skyblue')\n",
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"\n",
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"plt.xlabel('Store ID')\n",
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"plt.ylabel('Items per Area (Items per square meter)')\n",
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"plt.title('Коэффициент заполненности магазина')\n",
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"plt.xticks(df['Store ID']) \n",
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"\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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"Благодаря этой диаграмме я узнал коэффициент заполненности магазина. Мы можем увидеть сколько товаров приходиться на квадратный метр площади магазина."
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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": 17,
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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 1000x700 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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"\n",
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"df_filtered = df[df['Store ID'].between(1, 15)].copy()\n",
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"\n",
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"df_filtered.loc[:, 'Avg_Sales_Per_Customer'] = df_filtered['Store_Sales'] / df_filtered['Daily_Customer_Count']\n",
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"\n",
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"plt.figure(figsize=(10, 7))\n",
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"plt.pie(df_filtered['Avg_Sales_Per_Customer'], labels=df_filtered['Store ID'], autopct='%1.1f%%', startangle=140)\n",
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"plt.title('Средние продажи на одного клиента по магазинам (ID 1-15)')\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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"Я построил круговую диаграмму которая высчитывает средние продажи на одного клиента в магазине и по диагрмме можно сказать что во втором магазине этот показатель самый большой (выборка делалась с 1 по 15 магазин)."
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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": 18,
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"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(8,6))\n",
|
|||
|
|
"plt.plot(df['Store_Area'], df['Items_Available'], marker='o', color='b')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('Items Available vs Store Area')\n",
|
|||
|
|
"plt.xlabel('Store Area')\n",
|
|||
|
|
"plt.ylabel('Items Available')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Я построил линейчатую диограмму чтобы узнать как зависит площадь магазина от количества товаров и по графику видно что чем больше площадь тем больше товаров."
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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
|
|||
|
|
}
|