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MII/mai/lab1.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"# Лабораторная работа 1\n",
"\n",
"Вариант - 9\n",
"\n",
"Датасет - магазины\n",
"\n",
"Поля \n",
"1. ID магазина\n",
"2. площадь магазина \n",
"3. количество разных товаров \n",
"4. количество покупателей\n",
"5. объем продаж"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка и сохранение данных"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 1,
done
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"data/Stores.csv\")\n",
"df.to_csv(\"data/Stores_updated.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о датафрейме с данными"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Общая информация о датафрейме"
]
},
{
"cell_type": "code",
commit
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"execution_count": 2,
add
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
done
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"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 896 entries, 0 to 895\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 Store ID 896 non-null int64\n",
" 1 Store_Area 896 non-null int64\n",
" 2 Items_Available 896 non-null int64\n",
" 3 Daily_Customer_Count 896 non-null int64\n",
" 4 Store_Sales 896 non-null int64\n",
"dtypes: int64(5)\n",
"memory usage: 35.1 KB\n"
add
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]
}
],
"source": [
done
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"df.info()"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"2. Статистическая информация"
]
},
{
"cell_type": "code",
commit
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"execution_count": 3,
done
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Store ID</th>\n",
" <th>Store_Area</th>\n",
" <th>Items_Available</th>\n",
" <th>Daily_Customer_Count</th>\n",
" <th>Store_Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>896.000000</td>\n",
" <td>896.000000</td>\n",
" <td>896.000000</td>\n",
" <td>896.000000</td>\n",
" <td>896.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>448.500000</td>\n",
" <td>1485.409598</td>\n",
" <td>1782.035714</td>\n",
" <td>786.350446</td>\n",
" <td>59351.305804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>258.797218</td>\n",
" <td>250.237011</td>\n",
" <td>299.872053</td>\n",
" <td>265.389281</td>\n",
" <td>17190.741895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>775.000000</td>\n",
" <td>932.000000</td>\n",
" <td>10.000000</td>\n",
" <td>14920.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>224.750000</td>\n",
" <td>1316.750000</td>\n",
" <td>1575.500000</td>\n",
" <td>600.000000</td>\n",
" <td>46530.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>448.500000</td>\n",
" <td>1477.000000</td>\n",
" <td>1773.500000</td>\n",
" <td>780.000000</td>\n",
" <td>58605.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>672.250000</td>\n",
" <td>1653.500000</td>\n",
" <td>1982.750000</td>\n",
" <td>970.000000</td>\n",
" <td>71872.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>896.000000</td>\n",
" <td>2229.000000</td>\n",
" <td>2667.000000</td>\n",
" <td>1560.000000</td>\n",
" <td>116320.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Store ID Store_Area Items_Available Daily_Customer_Count \\\n",
"count 896.000000 896.000000 896.000000 896.000000 \n",
"mean 448.500000 1485.409598 1782.035714 786.350446 \n",
"std 258.797218 250.237011 299.872053 265.389281 \n",
"min 1.000000 775.000000 932.000000 10.000000 \n",
"25% 224.750000 1316.750000 1575.500000 600.000000 \n",
"50% 448.500000 1477.000000 1773.500000 780.000000 \n",
"75% 672.250000 1653.500000 1982.750000 970.000000 \n",
"max 896.000000 2229.000000 2667.000000 1560.000000 \n",
"\n",
" Store_Sales \n",
"count 896.000000 \n",
"mean 59351.305804 \n",
"std 17190.741895 \n",
"min 14920.000000 \n",
"25% 46530.000000 \n",
"50% 58605.000000 \n",
"75% 71872.500000 \n",
"max 116320.000000 "
]
},
commit
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"execution_count": 3,
done
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о колонках датафрейма"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"1. Названия колонок"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 4,
add
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"metadata": {},
done
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"outputs": [
{
"data": {
"text/plain": [
"Index(['Store ID ', 'Store_Area', 'Items_Available', 'Daily_Customer_Count',\n",
" 'Store_Sales'],\n",
" dtype='object')"
]
},
commit
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"execution_count": 4,
done
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"metadata": {},
"output_type": "execute_result"
}
],
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"source": [
done
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"df.columns"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"Вывод отдельных строк и столбцов"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Столбец \"Store_Area\""
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
done
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"data": {
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"text/plain": [
" Store_Area\n",
"0 1659\n",
"1 1461\n",
"2 1340\n",
"3 1451\n",
"4 1770\n",
".. ...\n",
"891 1582\n",
"892 1387\n",
"893 1200\n",
"894 1299\n",
"895 1174\n",
"\n",
"[896 rows x 1 columns]"
]
},
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"execution_count": 5,
done
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"metadata": {},
"output_type": "execute_result"
add
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}
],
"source": [
done
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"df[[\"Store_Area\"]]"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"2. Несколько столбцокв"
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]
},
{
"cell_type": "code",
commit
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
done
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"data": {
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" <td>76440</td>\n",
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" <td>895</td>\n",
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" <tr>\n",
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" Store ID Store_Sales\n",
"0 1 66490\n",
"1 2 39820\n",
"2 3 54010\n",
"3 4 53730\n",
"4 5 46620\n",
".. ... ...\n",
"891 892 66390\n",
"892 893 82080\n",
"893 894 76440\n",
"894 895 96610\n",
"895 896 54340\n",
"\n",
"[896 rows x 2 columns]"
]
},
commit
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"execution_count": 6,
done
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"metadata": {},
"output_type": "execute_result"
add
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}
],
"source": [
done
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"df[[\"Store ID \", \"Store_Sales\"]]"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"3. Первая строка"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 7,
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"metadata": {},
"outputs": [
{
done
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"data": {
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" Store ID Store_Area Items_Available Daily_Customer_Count Store_Sales\n",
"0 1 1659 1961 530 66490"
]
},
commit
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"execution_count": 7,
done
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"metadata": {},
"output_type": "execute_result"
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}
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"source": [
done
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"df.iloc[[0]]"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"4. Вывод по условию"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
done
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"data": {
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"</table>\n",
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"</div>"
],
"text/plain": [
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"\n",
"[896 rows x 5 columns]"
]
},
commit
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"execution_count": 8,
done
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"metadata": {},
"output_type": "execute_result"
add
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}
],
"source": [
done
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"df[df[\"Store_Sales\"] > 2000]"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"Группировка и агрегация данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Средняя продажа по каждому магазину"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 9,
add
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"metadata": {},
"outputs": [
{
done
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" Store_Sales\n",
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commit
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"execution_count": 9,
done
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby([\"Store ID \"])[[\"Store_Sales\"]].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Для каждого магазина общая сумма продаж и среднее количество посетителей"
]
},
{
"cell_type": "code",
commit
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"execution_count": 10,
done
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"metadata": {},
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add
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"data": {
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add
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"text/plain": [
done
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" Store_Sales Daily_Customer_Count\n",
"Store ID \n",
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"2 39820 210.0\n",
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"\n",
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add
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]
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commit
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"execution_count": 10,
add
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"metadata": {},
"output_type": "execute_result"
done
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}
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"source": [
"df.groupby(\"Store ID \").agg({\"Store_Sales\": \"sum\", \"Daily_Customer_Count\": \"mean\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сортировка данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Сортировка по продажам по убыванию"
]
},
{
"cell_type": "code",
commit
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"execution_count": 11,
done
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"metadata": {},
"outputs": [
add
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{
"data": {
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"text/plain": [
done
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add
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]
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commit
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"execution_count": 11,
add
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"metadata": {},
done
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"output_type": "execute_result"
add
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}
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done
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"df.sort_values(\"Store_Sales\", ascending=False)"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"2. Сортировка по нескольким столбцам"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 12,
add
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"metadata": {},
"outputs": [
{
"data": {
done
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add
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"text/plain": [
done
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" Store ID Store_Area Items_Available Daily_Customer_Count Store_Sales\n",
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add
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]
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commit
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"execution_count": 12,
add
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"metadata": {},
"output_type": "execute_result"
done
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}
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"source": [
"df.sort_values([\"Store_Area\", \"Store_Sales\"], ascending=[True, False])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление строк/столбцов"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Удаление столбца"
]
},
{
"cell_type": "code",
commit
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"execution_count": 13,
done
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"metadata": {},
"outputs": [
add
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{
"data": {
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" <th>892</th>\n",
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" <th>893</th>\n",
" <td>894</td>\n",
" <td>1436</td>\n",
" <td>1060</td>\n",
" <td>76440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>894</th>\n",
" <td>895</td>\n",
" <td>1560</td>\n",
" <td>770</td>\n",
" <td>96610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>895</th>\n",
" <td>896</td>\n",
" <td>1429</td>\n",
" <td>1110</td>\n",
" <td>54340</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>896 rows × 4 columns</p>\n",
"</div>"
],
add
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"text/plain": [
done
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" Store ID Items_Available Daily_Customer_Count Store_Sales\n",
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".. ... ... ... ...\n",
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"893 894 1436 1060 76440\n",
"894 895 1560 770 96610\n",
"895 896 1429 1110 54340\n",
"\n",
"[896 rows x 4 columns]"
add
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]
},
commit
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"execution_count": 13,
add
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"metadata": {},
done
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"output_type": "execute_result"
add
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}
],
"source": [
done
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"df.drop(\"Store_Area\", axis=1)"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"Удаление строки"
add
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]
},
{
"cell_type": "code",
commit
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"execution_count": 14,
add
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"metadata": {},
"outputs": [
{
"data": {
done
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],
add
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"text/plain": [
done
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" Store ID Store_Area Items_Available Daily_Customer_Count Store_Sales\n",
"1 2 1461 1752 210 39820\n",
"2 3 1340 1609 720 54010\n",
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".. ... ... ... ... ...\n",
"891 892 1582 1910 1080 66390\n",
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"893 894 1200 1436 1060 76440\n",
"894 895 1299 1560 770 96610\n",
"895 896 1174 1429 1110 54340\n",
"\n",
"[895 rows x 5 columns]"
add
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]
},
commit
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"execution_count": 14,
add
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"metadata": {},
"output_type": "execute_result"
done
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}
],
"source": [
"df.drop(0, axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создание новых столбцов"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Создание нового столбца с доходом на одного клиента"
]
},
{
"cell_type": "code",
commit
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"execution_count": 15,
done
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"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Revenue_per_Customer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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"</div>"
],
"text/plain": [
" Revenue_per_Customer\n",
"0 125.452830\n",
"1 189.619048\n",
"2 75.013889\n",
"3 86.661290\n",
"4 103.600000\n",
".. ...\n",
"891 61.472222\n",
"892 96.564706\n",
"893 72.113208\n",
"894 125.467532\n",
"895 48.954955\n",
"\n",
"[896 rows x 1 columns]"
]
},
commit
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"execution_count": 15,
done
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Revenue_per_Customer\"] = df[\"Store_Sales\"] / df[\"Daily_Customer_Count\"]\n",
"df[[\"Revenue_per_Customer\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление строк с пустыми значениями"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Удаление строк с NaN"
]
},
{
"cell_type": "code",
commit
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"execution_count": 16,
done
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"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>Store ID</th>\n",
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" <th>Items_Available</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>1659</td>\n",
" <td>1961</td>\n",
" <td>530</td>\n",
" <td>66490</td>\n",
" <td>125.452830</td>\n",
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" <td>82080</td>\n",
" <td>96.564706</td>\n",
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" <tr>\n",
" <th>893</th>\n",
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" <td>96610</td>\n",
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" <tr>\n",
" <th>895</th>\n",
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" <td>1174</td>\n",
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" <td>1110</td>\n",
" <td>54340</td>\n",
" <td>48.954955</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>896 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Store ID Store_Area Items_Available Daily_Customer_Count \\\n",
"0 1 1659 1961 530 \n",
"1 2 1461 1752 210 \n",
"2 3 1340 1609 720 \n",
"3 4 1451 1748 620 \n",
"4 5 1770 2111 450 \n",
".. ... ... ... ... \n",
"891 892 1582 1910 1080 \n",
"892 893 1387 1663 850 \n",
"893 894 1200 1436 1060 \n",
"894 895 1299 1560 770 \n",
"895 896 1174 1429 1110 \n",
"\n",
" Store_Sales Revenue_per_Customer \n",
"0 66490 125.452830 \n",
"1 39820 189.619048 \n",
"2 54010 75.013889 \n",
"3 53730 86.661290 \n",
"4 46620 103.600000 \n",
".. ... ... \n",
"891 66390 61.472222 \n",
"892 82080 96.564706 \n",
"893 76440 72.113208 \n",
"894 96610 125.467532 \n",
"895 54340 48.954955 \n",
"\n",
"[896 rows x 6 columns]"
]
},
commit
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"execution_count": 16,
done
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Заполнить пустые значения для определённого столбца"
]
},
{
"cell_type": "code",
commit
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"execution_count": 17,
done
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"metadata": {},
"outputs": [],
"source": [
"df.fillna({\"Store_Sales\": df[\"Store_Sales\"].mean()}, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Заполнение пустых значений"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Заполнение средним значением"
]
},
{
"cell_type": "code",
commit
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"execution_count": 18,
done
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"metadata": {},
"outputs": [],
"source": [
"df.fillna(df.mean(), inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Визуализация данных с Pandas и Matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Линейная диаграмма (plot). Продажи по магазинам"
]
},
{
"cell_type": "code",
commit
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"execution_count": 19,
done
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"metadata": {},
"outputs": [
add
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{
"data": {
done
2024-09-20 23:45:27 +04:00
"image/png": "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
add
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
done
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}
],
"source": [
"import matplotlib.pyplot as plt\n",
"df.plot(x=\"Store ID \", y=\"Store_Sales\", kind=\"line\")\n",
"\n",
"plt.xlabel(\"Store ID\") \n",
"plt.ylabel(\"Store Sales\")\n",
"plt.title(\"Store Sales by Store ID\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Столбчатая диаграмма (bar). Средние продажи"
]
},
{
"cell_type": "code",
commit
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"execution_count": 20,
done
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"metadata": {},
"outputs": [
add
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{
"data": {
done
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"image/png": "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
add
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
done
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"df.plot(x=\"Store ID \", y=\"Store_Sales\", kind=\"bar\")\n",
add
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"\n",
done
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"plt.xlabel(\"Store ID\") \n",
"plt.ylabel(\"Store Sales\")\n",
"plt.title(\"Store Sales by Store ID\")\n",
"\n",
"plt.show()"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"3. Гистограмма (hist). Распределение количества товаров"
add
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]
},
{
"cell_type": "code",
commit
2024-09-28 10:43:58 +04:00
"execution_count": 21,
add
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"metadata": {},
"outputs": [
{
"data": {
done
2024-09-20 23:45:27 +04:00
"image/png": "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
add
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"text/plain": [
done
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"<Figure size 640x480 with 1 Axes>"
add
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]
},
"metadata": {},
done
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"output_type": "display_data"
}
],
"source": [
"df[\"Items_Available\"].plot(kind=\"hist\")\n",
"\n",
"plt.xlabel(\"Number of Items Available\") \n",
"plt.ylabel(\"Frequency\") \n",
"plt.title(\"Distribution of Items Available in Stores\") \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Ящик с усами (box). Продажи"
]
},
{
"cell_type": "code",
commit
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"execution_count": 22,
done
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"metadata": {},
"outputs": [
add
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{
"data": {
done
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"image/png": "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
add
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
done
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"df[\"Store_Sales\"].plot(kind=\"box\")\n",
"\n",
"plt.ylabel(\"Store Sales (US $)\") \n",
"plt.title(\"Box Plot of Store Sales\") \n",
"\n",
"plt.show()"
add
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
done
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"5. Диаграмма с областями (area). "
add
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]
},
{
"cell_type": "code",
commit
2024-09-28 10:43:58 +04:00
"execution_count": 23,
add
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"metadata": {},
"outputs": [
{
done
2024-09-20 23:45:27 +04:00
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(x=\"Store ID \", y=\"Store_Sales\", kind=\"area\")\n",
"\n",
"plt.xlabel(\"Store ID\") \n",
"plt.ylabel(\"Store Sales\")\n",
"plt.title(\"Store Sales by Store ID\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. Диаграмма рассеяния (scatter). Зависимость продаж от количества посетителей"
]
},
{
"cell_type": "code",
commit
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"execution_count": 24,
done
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"metadata": {},
"outputs": [
add
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{
"data": {
done
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"image/png": "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
add
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
done
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"df.plot(kind=\"scatter\", x=\"Daily_Customer_Count\", y=\"Store_Sales\")\n",
"\n",
"plt.xlabel(\"Daily Customer Count\") \n",
"plt.ylabel(\"Store Sales (US $)\")\n",
"plt.title(\"Store Sales vs. Daily Customer Count\") \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"7. Круговая диаграмма (pie). Количество товаров"
]
},
{
"cell_type": "code",
commit
2024-09-28 10:43:58 +04:00
"execution_count": 25,
done
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"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Определение порога для объединения редких значений\n",
"threshold = 0.02 # Порог 2%\n",
"\n",
"# Подсчёт количества уникальных значений и расчёт частот\n",
"value_counts = df[\"Daily_Customer_Count\"].value_counts()\n",
"total_count = value_counts.sum()\n",
"\n",
"# Условие для агрегации значений ниже порога\n",
"other_values = value_counts[value_counts / total_count < threshold].sum()\n",
"main_values = value_counts[value_counts / total_count >= threshold]\n",
add
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"\n",
done
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"# Добавление категории \"Other\"\n",
"main_values[\"Other\"] = other_values\n",
add
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"\n",
done
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"# Построение диаграммы\n",
"main_values.plot(kind=\"pie\", \n",
" autopct='%1.1f%%', # Проценты\n",
" startangle=90, # Начальный угол\n",
" counterclock=False, # По часовой стрелке\n",
" cmap=\"Set3\", # Цветовая схема\n",
" wedgeprops={'edgecolor': 'black'}) # Границы сегментов\n",
add
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"\n",
done
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"plt.title(\"Distribution of Daily Customer Count in Stores (Aggregated)\")\n",
"plt.subplots_adjust(left=0.3, right=0.7, top=0.9, bottom=0.1)\n",
"plt.show()\n"
add
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]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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
}
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