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lab3
PIbd-33_Dyakonov_R_R_MAI/lab1.ipynb

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1 lab
2024-09-27 20:16:13 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Тестим Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Считываем csv:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"\n",
"# Считаем csv\n",
"data_frame = pandas.read_csv(\"data/kc_house_data.csv\", index_col=\"id\")\n",
"# Сохраняем data_frame в новый csv\n",
"data_frame.to_csv(\"data/new_kc_house_data.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о data_frame:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Общая информация о data_frame:\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 21613 entries, 7129300520 to 1523300157\n",
"Data columns (total 20 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 21613 non-null object \n",
" 1 price 21613 non-null float64\n",
" 2 bedrooms 21613 non-null int64 \n",
" 3 bathrooms 21613 non-null float64\n",
" 4 sqft_living 21613 non-null int64 \n",
" 5 sqft_lot 21613 non-null int64 \n",
" 6 floors 21613 non-null float64\n",
" 7 waterfront 21613 non-null int64 \n",
" 8 view 21613 non-null int64 \n",
" 9 condition 21613 non-null int64 \n",
" 10 grade 21613 non-null int64 \n",
" 11 sqft_above 21613 non-null int64 \n",
" 12 sqft_basement 21613 non-null int64 \n",
" 13 yr_built 21613 non-null int64 \n",
" 14 yr_renovated 21613 non-null int64 \n",
" 15 zipcode 21613 non-null int64 \n",
" 16 lat 21613 non-null float64\n",
" 17 long 21613 non-null float64\n",
" 18 sqft_living15 21613 non-null int64 \n",
" 19 sqft_lot15 21613 non-null int64 \n",
"dtypes: float64(5), int64(14), object(1)\n",
"memory usage: 3.5+ MB\n",
"None \n",
"\n",
"Первые строки data_frame:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"7129300520 5650 1.0 0 0 3 7 1180 \n",
"6414100192 7242 2.0 0 0 3 7 2170 \n",
"5631500400 10000 1.0 0 0 3 6 770 \n",
"2487200875 5000 1.0 0 0 5 7 1050 \n",
"1954400510 8080 1.0 0 0 3 8 1680 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"7129300520 0 1955 0 98178 47.5112 -122.257 \n",
"6414100192 400 1951 1991 98125 47.7210 -122.319 \n",
"5631500400 0 1933 0 98028 47.7379 -122.233 \n",
"2487200875 910 1965 0 98136 47.5208 -122.393 \n",
"1954400510 0 1987 0 98074 47.6168 -122.045 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"7129300520 1340 5650 \n",
"6414100192 1690 7639 \n",
"5631500400 2720 8062 \n",
"2487200875 1360 5000 \n",
"1954400510 1800 7503 \n",
"\n",
"Описание данных data_frame:\n",
" price bedrooms bathrooms sqft_living sqft_lot \\\n",
"count 2.161300e+04 21613.000000 21613.000000 21613.000000 2.161300e+04 \n",
"mean 5.400881e+05 3.370842 2.114757 2079.899736 1.510697e+04 \n",
"std 3.671272e+05 0.930062 0.770163 918.440897 4.142051e+04 \n",
"min 7.500000e+04 0.000000 0.000000 290.000000 5.200000e+02 \n",
"25% 3.219500e+05 3.000000 1.750000 1427.000000 5.040000e+03 \n",
"50% 4.500000e+05 3.000000 2.250000 1910.000000 7.618000e+03 \n",
"75% 6.450000e+05 4.000000 2.500000 2550.000000 1.068800e+04 \n",
"max 7.700000e+06 33.000000 8.000000 13540.000000 1.651359e+06 \n",
"\n",
" floors waterfront view condition grade \\\n",
"count 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"mean 1.494309 0.007542 0.234303 3.409430 7.656873 \n",
"std 0.539989 0.086517 0.766318 0.650743 1.175459 \n",
"min 1.000000 0.000000 0.000000 1.000000 1.000000 \n",
"25% 1.000000 0.000000 0.000000 3.000000 7.000000 \n",
"50% 1.500000 0.000000 0.000000 3.000000 7.000000 \n",
"75% 2.000000 0.000000 0.000000 4.000000 8.000000 \n",
"max 3.500000 1.000000 4.000000 5.000000 13.000000 \n",
"\n",
" sqft_above sqft_basement yr_built yr_renovated zipcode \\\n",
"count 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"mean 1788.390691 291.509045 1971.005136 84.402258 98077.939805 \n",
"std 828.090978 442.575043 29.373411 401.679240 53.505026 \n",
"min 290.000000 0.000000 1900.000000 0.000000 98001.000000 \n",
"25% 1190.000000 0.000000 1951.000000 0.000000 98033.000000 \n",
"50% 1560.000000 0.000000 1975.000000 0.000000 98065.000000 \n",
"75% 2210.000000 560.000000 1997.000000 0.000000 98118.000000 \n",
"max 9410.000000 4820.000000 2015.000000 2015.000000 98199.000000 \n",
"\n",
" lat long sqft_living15 sqft_lot15 \n",
"count 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"mean 47.560053 -122.213896 1986.552492 12768.455652 \n",
"std 0.138564 0.140828 685.391304 27304.179631 \n",
"min 47.155900 -122.519000 399.000000 651.000000 \n",
"25% 47.471000 -122.328000 1490.000000 5100.000000 \n",
"50% 47.571800 -122.230000 1840.000000 7620.000000 \n",
"75% 47.678000 -122.125000 2360.000000 10083.000000 \n",
"max 47.777600 -121.315000 6210.000000 871200.000000 \n",
"\n",
"Количество строк и столбцов data_frame: (21613, 20)\n",
"\n",
"Названия столбцов: Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
" 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
" 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
" 'sqft_living15', 'sqft_lot15'],\n",
" dtype='object')\n",
"\n",
"Типы данных каждого столбца:\n",
"date object\n",
"price float64\n",
"bedrooms int64\n",
"bathrooms float64\n",
"sqft_living int64\n",
"sqft_lot int64\n",
"floors float64\n",
"waterfront int64\n",
"view int64\n",
"condition int64\n",
"grade int64\n",
"sqft_above int64\n",
"sqft_basement int64\n",
"yr_built int64\n",
"yr_renovated int64\n",
"zipcode int64\n",
"lat float64\n",
"long float64\n",
"sqft_living15 int64\n",
"sqft_lot15 int64\n",
"dtype: object \n",
"\n",
"Количество пропущенных значений в каждом столбце:\n",
"date 0\n",
"price 0\n",
"bedrooms 0\n",
"bathrooms 0\n",
"sqft_living 0\n",
"sqft_lot 0\n",
"floors 0\n",
"waterfront 0\n",
"view 0\n",
"condition 0\n",
"grade 0\n",
"sqft_above 0\n",
"sqft_basement 0\n",
"yr_built 0\n",
"yr_renovated 0\n",
"zipcode 0\n",
"lat 0\n",
"long 0\n",
"sqft_living15 0\n",
"sqft_lot15 0\n",
"dtype: int64 \n",
"\n"
]
}
],
"source": [
"# Общая информация о data_frame\n",
"print(\"Общая информация о data_frame:\")\n",
"print(data_frame.info(), \"\\n\")\n",
"\n",
"# Первые строки\n",
"print(\"Первые строки data_frame:\")\n",
"print(data_frame.head(), \"\\n\")\n",
"\n",
"# Описание данных\n",
"print(\"Описание данных data_frame:\")\n",
"print(data_frame.describe(), \"\\n\")\n",
"\n",
"# Количество строк и столбцов \n",
"print(f\"Количество строк и столбцов data_frame: {data_frame.shape}\\n\")\n",
"\n",
"# Названия столбцов\n",
"print(f\"Названия столбцов: {data_frame.columns}\\n\")\n",
"\n",
"# Типы данных каждого столбца\n",
"print(\"Типы данных каждого столбца:\")\n",
"print(data_frame.dtypes, \"\\n\")\n",
"\n",
"# Количество пропущенных значений\n",
"print(\"Количество пропущенных значений в каждом столбце:\")\n",
"print(data_frame.isnull().sum(), \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о колонках"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Список всех столбцов: Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
" 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
" 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
" 'sqft_living15', 'sqft_lot15'],\n",
" dtype='object')\n",
"\n",
"Типы данных каждого столбца:\n",
"date object\n",
"price float64\n",
"bedrooms int64\n",
"bathrooms float64\n",
"sqft_living int64\n",
"sqft_lot int64\n",
"floors float64\n",
"waterfront int64\n",
"view int64\n",
"condition int64\n",
"grade int64\n",
"sqft_above int64\n",
"sqft_basement int64\n",
"yr_built int64\n",
"yr_renovated int64\n",
"zipcode int64\n",
"lat float64\n",
"long float64\n",
"sqft_living15 int64\n",
"sqft_lot15 int64\n",
"dtype: object \n",
"\n",
"Описание всех столбцов DataFrame:\n",
" date price bedrooms bathrooms \\\n",
"count 21613 2.161300e+04 21613.000000 21613.000000 \n",
"unique 372 NaN NaN NaN \n",
"top 20140623T000000 NaN NaN NaN \n",
"freq 142 NaN NaN NaN \n",
"mean NaN 5.400881e+05 3.370842 2.114757 \n",
"std NaN 3.671272e+05 0.930062 0.770163 \n",
"min NaN 7.500000e+04 0.000000 0.000000 \n",
"25% NaN 3.219500e+05 3.000000 1.750000 \n",
"50% NaN 4.500000e+05 3.000000 2.250000 \n",
"75% NaN 6.450000e+05 4.000000 2.500000 \n",
"max NaN 7.700000e+06 33.000000 8.000000 \n",
"\n",
" sqft_living sqft_lot floors waterfront view \\\n",
"count 21613.000000 2.161300e+04 21613.000000 21613.000000 21613.000000 \n",
"unique NaN NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN NaN \n",
"mean 2079.899736 1.510697e+04 1.494309 0.007542 0.234303 \n",
"std 918.440897 4.142051e+04 0.539989 0.086517 0.766318 \n",
"min 290.000000 5.200000e+02 1.000000 0.000000 0.000000 \n",
"25% 1427.000000 5.040000e+03 1.000000 0.000000 0.000000 \n",
"50% 1910.000000 7.618000e+03 1.500000 0.000000 0.000000 \n",
"75% 2550.000000 1.068800e+04 2.000000 0.000000 0.000000 \n",
"max 13540.000000 1.651359e+06 3.500000 1.000000 4.000000 \n",
"\n",
" condition grade sqft_above sqft_basement yr_built \\\n",
"count 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"unique NaN NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN NaN \n",
"mean 3.409430 7.656873 1788.390691 291.509045 1971.005136 \n",
"std 0.650743 1.175459 828.090978 442.575043 29.373411 \n",
"min 1.000000 1.000000 290.000000 0.000000 1900.000000 \n",
"25% 3.000000 7.000000 1190.000000 0.000000 1951.000000 \n",
"50% 3.000000 7.000000 1560.000000 0.000000 1975.000000 \n",
"75% 4.000000 8.000000 2210.000000 560.000000 1997.000000 \n",
"max 5.000000 13.000000 9410.000000 4820.000000 2015.000000 \n",
"\n",
" yr_renovated zipcode lat long sqft_living15 \\\n",
"count 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 \n",
"unique NaN NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN NaN \n",
"mean 84.402258 98077.939805 47.560053 -122.213896 1986.552492 \n",
"std 401.679240 53.505026 0.138564 0.140828 685.391304 \n",
"min 0.000000 98001.000000 47.155900 -122.519000 399.000000 \n",
"25% 0.000000 98033.000000 47.471000 -122.328000 1490.000000 \n",
"50% 0.000000 98065.000000 47.571800 -122.230000 1840.000000 \n",
"75% 0.000000 98118.000000 47.678000 -122.125000 2360.000000 \n",
"max 2015.000000 98199.000000 47.777600 -121.315000 6210.000000 \n",
"\n",
" sqft_lot15 \n",
"count 21613.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 12768.455652 \n",
"std 27304.179631 \n",
"min 651.000000 \n",
"25% 5100.000000 \n",
"50% 7620.000000 \n",
"75% 10083.000000 \n",
"max 871200.000000 \n",
"\n",
"Количество пропущенных значений в каждом столбце:\n",
"date 0\n",
"price 0\n",
"bedrooms 0\n",
"bathrooms 0\n",
"sqft_living 0\n",
"sqft_lot 0\n",
"floors 0\n",
"waterfront 0\n",
"view 0\n",
"condition 0\n",
"grade 0\n",
"sqft_above 0\n",
"sqft_basement 0\n",
"yr_built 0\n",
"yr_renovated 0\n",
"zipcode 0\n",
"lat 0\n",
"long 0\n",
"sqft_living15 0\n",
"sqft_lot15 0\n",
"dtype: int64 \n",
"\n",
"Количество уникальных значений в столбце 'date':\n",
"date\n",
"20140623T000000 142\n",
"20140626T000000 131\n",
"20140625T000000 131\n",
"20140708T000000 127\n",
"20150427T000000 126\n",
" ... \n",
"20150131T000000 1\n",
"20150117T000000 1\n",
"20150308T000000 1\n",
"20150515T000000 1\n",
"20140803T000000 1\n",
"Name: count, Length: 372, dtype: int64 \n",
"\n"
]
}
],
"source": [
"# Список всех столбцов\n",
"print(f\"Список всех столбцов: {data_frame.columns}\\n\")\n",
"\n",
"# Типы данных каждого столбца\n",
"print(\"Типы данных каждого столбца:\")\n",
"print(data_frame.dtypes, \"\\n\")\n",
"\n",
"# Описание всех столбцов\n",
"print(\"Описание всех столбцов DataFrame:\")\n",
"print(data_frame.describe(include=\"all\"), \"\\n\")\n",
"\n",
"# Количество пропущенных значений в каждом столбце\n",
"print(\"Количество пропущенных значений в каждом столбце:\")\n",
"print(data_frame.isnull().sum(), \"\\n\")\n",
"\n",
"# Количество уникальных значений в столбце 'date'\n",
"print(\"Количество уникальных значений в столбце 'date':\")\n",
"print(data_frame[\"date\"].value_counts(), \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вывод строки и стобца"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Столбец 'date':\n",
"id\n",
"7129300520 20141013T000000\n",
"6414100192 20141209T000000\n",
"5631500400 20150225T000000\n",
"2487200875 20141209T000000\n",
"1954400510 20150218T000000\n",
" ... \n",
"263000018 20140521T000000\n",
"6600060120 20150223T000000\n",
"1523300141 20140623T000000\n",
"291310100 20150116T000000\n",
"1523300157 20141015T000000\n",
"Name: date, Length: 21613, dtype: object \n",
"\n",
"Строка с индексом 2:\n",
"date 20150225T000000\n",
"price 180000.0\n",
"bedrooms 2\n",
"bathrooms 1.0\n",
"sqft_living 770\n",
"sqft_lot 10000\n",
"floors 1.0\n",
"waterfront 0\n",
"view 0\n",
"condition 3\n",
"grade 6\n",
"sqft_above 770\n",
"sqft_basement 0\n",
"yr_built 1933\n",
"yr_renovated 0\n",
"zipcode 98028\n",
"lat 47.7379\n",
"long -122.233\n",
"sqft_living15 2720\n",
"sqft_lot15 8062\n",
"Name: 5631500400, dtype: object \n",
"\n",
"Значение в первой строке и столбце 'date':\n",
"3 \n",
"\n",
"Значение в строке с индексом 1 и столбце 'date':\n",
"20141013T000000 \n",
"\n",
"Столбцы 'date' и 'price':\n",
" date price\n",
"id \n",
"7129300520 20141013T000000 221900.0\n",
"6414100192 20141209T000000 538000.0\n",
"5631500400 20150225T000000 180000.0\n",
"2487200875 20141209T000000 604000.0\n",
"1954400510 20150218T000000 510000.0\n",
"... ... ...\n",
"263000018 20140521T000000 360000.0\n",
"6600060120 20150223T000000 400000.0\n",
"1523300141 20140623T000000 402101.0\n",
"291310100 20150116T000000 400000.0\n",
"1523300157 20141015T000000 325000.0\n",
"\n",
"[21613 rows x 2 columns] \n",
"\n",
"Первые две строки DataFrame:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"7129300520 5650 1.0 0 0 3 7 1180 \n",
"6414100192 7242 2.0 0 0 3 7 2170 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"7129300520 0 1955 0 98178 47.5112 -122.257 \n",
"6414100192 400 1951 1991 98125 47.7210 -122.319 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"7129300520 1340 5650 \n",
"6414100192 1690 7639 \n",
"\n"
]
}
],
"source": [
"# Вывод столбца 'date'\n",
"print(\"Столбец 'date':\")\n",
"print(data_frame[\"date\"], \"\\n\")\n",
"\n",
"# Вывод строки с индексом 2\n",
"print(\"Строка с индексом 2:\")\n",
"print(data_frame.iloc[2], \"\\n\")\n",
"\n",
"# Вывод значения в первой строке и столбце 'date'\n",
"print(\"Значение в первой строке и столбце 'date':\")\n",
"print(data_frame.iloc[0, 2], \"\\n\")\n",
"\n",
"# Вывод конкретного значения по метке строки и имени столбца\n",
"print(\"Значение в строке с индексом 1 и столбце 'date':\")\n",
"print(data_frame.loc[7129300520, \"date\"], \"\\n\")\n",
"\n",
"# Вывод нескольких столбцов 'date' и 'price'\n",
"print(\"Столбцы 'date' и 'price':\")\n",
"print(data_frame[[\"date\", \"price\"]], \"\\n\")\n",
"\n",
"# Вывод первых двух строк\n",
"print(\"Первые две строки DataFrame:\")\n",
"print(data_frame.iloc[:2], \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Группировка и агрегация данных"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Средняя цена по количеству спален:\n",
"bedrooms\n",
"0 4.095038e+05\n",
"1 3.176429e+05\n",
"2 4.013727e+05\n",
"3 4.662321e+05\n",
"4 6.354195e+05\n",
"5 7.865998e+05\n",
"6 8.255206e+05\n",
"7 9.511847e+05\n",
"8 1.105077e+06\n",
"9 8.939998e+05\n",
"10 8.193333e+05\n",
"11 5.200000e+05\n",
"33 6.400000e+05\n",
"Name: price, dtype: float64\n",
"\n",
"Количество продаж по почтовому индексу:\n",
"zipcode\n",
"98001 362\n",
"98002 199\n",
"98003 280\n",
"98004 317\n",
"98005 168\n",
" ... \n",
"98177 255\n",
"98178 262\n",
"98188 136\n",
"98198 280\n",
"98199 317\n",
"Name: price, Length: 70, dtype: int64\n",
"\n",
"Максимальная цена по количеству ванных комнат:\n",
"bathrooms\n",
"0.00 1295650.0\n",
"0.50 312500.0\n",
"0.75 785000.0\n",
"1.00 1300000.0\n",
"1.25 1388000.0\n",
"1.50 1500000.0\n",
"1.75 3278000.0\n",
"2.00 2200000.0\n",
"2.25 2400000.0\n",
"2.50 3070000.0\n",
"2.75 2700000.0\n",
"3.00 4489000.0\n",
"3.25 3640900.0\n",
"3.50 3710000.0\n",
"3.75 3650000.0\n",
"4.00 3400000.0\n",
"4.25 3850000.0\n",
"4.50 7062500.0\n",
"4.75 3650000.0\n",
"5.00 5350000.0\n",
"5.25 5110800.0\n",
"5.50 4500000.0\n",
"5.75 5570000.0\n",
"6.00 5300000.0\n",
"6.25 3300000.0\n",
"6.50 2238890.0\n",
"6.75 4668000.0\n",
"7.50 450000.0\n",
"7.75 6885000.0\n",
"8.00 7700000.0\n",
"Name: price, dtype: float64\n",
"\n",
"Общая цена по количеству этажей:\n",
"floors\n",
"1.0 4.722489e+09\n",
"1.5 1.067653e+09\n",
"2.0 5.347512e+09\n",
"2.5 1.707158e+08\n",
"3.0 3.570885e+08\n",
"3.5 7.466500e+06\n",
"Name: price, dtype: float64\n"
]
}
],
"source": [
"# 1. Средняя цена по количеству спален\n",
"print(\"Средняя цена по количеству спален:\")\n",
"print(data_frame.groupby(\"bedrooms\")[\"price\"].mean())\n",
"\n",
"# 2. Количество продаж по почтовому индексу\n",
"print(\"\\nКоличество продаж по почтовому индексу:\")\n",
"print(data_frame.groupby(\"zipcode\")[\"price\"].count())\n",
"\n",
"# 3. Максимальная цена по количеству ванных комнат\n",
"print(\"\\nМаксимальная цена по количеству ванных комнат:\")\n",
"print(data_frame.groupby(\"bathrooms\")[\"price\"].max())\n",
"\n",
"# 4. Общая цена по количеству этажей\n",
"print(\"\\nОбщая цена по количеству этажей:\")\n",
"print(data_frame.groupby(\"floors\")[\"price\"].sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Сортировка данных"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Сортировка по цене (возрастание):\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"3421079032 20150217T000000 75000.0 1 0.00 670 \n",
"40000362 20140506T000000 78000.0 2 1.00 780 \n",
"8658300340 20140523T000000 80000.0 1 0.75 430 \n",
"3028200080 20150324T000000 81000.0 2 1.00 730 \n",
"3883800011 20141105T000000 82000.0 3 1.00 860 \n",
"... ... ... ... ... ... \n",
"8907500070 20150413T000000 5350000.0 5 5.00 8000 \n",
"2470100110 20140804T000000 5570000.0 5 5.75 9200 \n",
"9208900037 20140919T000000 6885000.0 6 7.75 9890 \n",
"9808700762 20140611T000000 7062500.0 5 4.50 10040 \n",
"6762700020 20141013T000000 7700000.0 6 8.00 12050 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"3421079032 43377 1.0 0 0 3 3 670 \n",
"40000362 16344 1.0 0 0 1 5 780 \n",
"8658300340 5050 1.0 0 0 2 4 430 \n",
"3028200080 9975 1.0 0 0 1 5 730 \n",
"3883800011 10426 1.0 0 0 3 6 860 \n",
"... ... ... ... ... ... ... ... \n",
"8907500070 23985 2.0 0 4 3 12 6720 \n",
"2470100110 35069 2.0 0 0 3 13 6200 \n",
"9208900037 31374 2.0 0 4 3 13 8860 \n",
"9808700762 37325 2.0 1 2 3 11 7680 \n",
"6762700020 27600 2.5 0 3 4 13 8570 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"3421079032 0 1966 0 98022 47.2638 -121.906 \n",
"40000362 0 1942 0 98168 47.4739 -122.280 \n",
"8658300340 0 1912 0 98014 47.6499 -121.909 \n",
"3028200080 0 1943 0 98168 47.4808 -122.315 \n",
"3883800011 0 1954 0 98146 47.4987 -122.341 \n",
"... ... ... ... ... ... ... \n",
"8907500070 1280 2009 0 98004 47.6232 -122.220 \n",
"2470100110 3000 2001 0 98039 47.6289 -122.233 \n",
"9208900037 1030 2001 0 98039 47.6305 -122.240 \n",
"9808700762 2360 1940 2001 98004 47.6500 -122.214 \n",
"6762700020 3480 1910 1987 98102 47.6298 -122.323 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"3421079032 1160 42882 \n",
"40000362 1700 10387 \n",
"8658300340 1200 7500 \n",
"3028200080 860 9000 \n",
"3883800011 1140 11250 \n",
"... ... ... \n",
"8907500070 4600 21750 \n",
"2470100110 3560 24345 \n",
"9208900037 4540 42730 \n",
"9808700762 3930 25449 \n",
"6762700020 3940 8800 \n",
"\n",
"[21613 rows x 20 columns]\n",
"\n",
"Сортировка по цене (убывание):\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"6762700020 20141013T000000 7700000.0 6 8.00 12050 \n",
"9808700762 20140611T000000 7062500.0 5 4.50 10040 \n",
"9208900037 20140919T000000 6885000.0 6 7.75 9890 \n",
"2470100110 20140804T000000 5570000.0 5 5.75 9200 \n",
"8907500070 20150413T000000 5350000.0 5 5.00 8000 \n",
"... ... ... ... ... ... \n",
"3883800011 20141105T000000 82000.0 3 1.00 860 \n",
"3028200080 20150324T000000 81000.0 2 1.00 730 \n",
"8658300340 20140523T000000 80000.0 1 0.75 430 \n",
"40000362 20140506T000000 78000.0 2 1.00 780 \n",
"3421079032 20150217T000000 75000.0 1 0.00 670 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"6762700020 27600 2.5 0 3 4 13 8570 \n",
"9808700762 37325 2.0 1 2 3 11 7680 \n",
"9208900037 31374 2.0 0 4 3 13 8860 \n",
"2470100110 35069 2.0 0 0 3 13 6200 \n",
"8907500070 23985 2.0 0 4 3 12 6720 \n",
"... ... ... ... ... ... ... ... \n",
"3883800011 10426 1.0 0 0 3 6 860 \n",
"3028200080 9975 1.0 0 0 1 5 730 \n",
"8658300340 5050 1.0 0 0 2 4 430 \n",
"40000362 16344 1.0 0 0 1 5 780 \n",
"3421079032 43377 1.0 0 0 3 3 670 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"6762700020 3480 1910 1987 98102 47.6298 -122.323 \n",
"9808700762 2360 1940 2001 98004 47.6500 -122.214 \n",
"9208900037 1030 2001 0 98039 47.6305 -122.240 \n",
"2470100110 3000 2001 0 98039 47.6289 -122.233 \n",
"8907500070 1280 2009 0 98004 47.6232 -122.220 \n",
"... ... ... ... ... ... ... \n",
"3883800011 0 1954 0 98146 47.4987 -122.341 \n",
"3028200080 0 1943 0 98168 47.4808 -122.315 \n",
"8658300340 0 1912 0 98014 47.6499 -121.909 \n",
"40000362 0 1942 0 98168 47.4739 -122.280 \n",
"3421079032 0 1966 0 98022 47.2638 -121.906 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"6762700020 3940 8800 \n",
"9808700762 3930 25449 \n",
"9208900037 4540 42730 \n",
"2470100110 3560 24345 \n",
"8907500070 4600 21750 \n",
"... ... ... \n",
"3883800011 1140 11250 \n",
"3028200080 860 9000 \n",
"8658300340 1200 7500 \n",
"40000362 1700 10387 \n",
"3421079032 1160 42882 \n",
"\n",
"[21613 rows x 20 columns]\n",
"\n",
"Сортировка по количеству спален и затем по цене (возрастание):\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"9543000205 20150413T000000 139950.0 0 0.00 844 \n",
"3980300371 20140926T000000 142000.0 0 0.00 290 \n",
"6896300380 20141002T000000 228000.0 0 1.00 390 \n",
"7849202190 20141223T000000 235000.0 0 0.00 1470 \n",
"2310060040 20140925T000000 240000.0 0 2.50 1810 \n",
"... ... ... ... ... ... \n",
"5566100170 20141029T000000 650000.0 10 2.00 3610 \n",
"8812401450 20141229T000000 660000.0 10 3.00 2920 \n",
"627300145 20140814T000000 1148000.0 10 5.25 4590 \n",
"1773100755 20140821T000000 520000.0 11 3.00 3000 \n",
"2402100895 20140625T000000 640000.0 33 1.75 1620 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"9543000205 4269 1.0 0 0 4 7 844 \n",
"3980300371 20875 1.0 0 0 1 1 290 \n",
"6896300380 5900 1.0 0 0 2 4 390 \n",
"7849202190 4800 2.0 0 0 3 7 1470 \n",
"2310060040 5669 2.0 0 0 3 7 1810 \n",
"... ... ... ... ... ... ... ... \n",
"5566100170 11914 2.0 0 0 4 7 3010 \n",
"8812401450 3745 2.0 0 0 4 7 1860 \n",
"627300145 10920 1.0 0 2 3 9 2500 \n",
"1773100755 4960 2.0 0 0 3 7 2400 \n",
"2402100895 6000 1.0 0 0 5 7 1040 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"9543000205 0 1913 0 98001 47.2781 -122.250 \n",
"3980300371 0 1963 0 98024 47.5308 -121.888 \n",
"6896300380 0 1953 0 98118 47.5260 -122.261 \n",
"7849202190 0 1996 0 98065 47.5265 -121.828 \n",
"2310060040 0 2003 0 98038 47.3493 -122.053 \n",
"... ... ... ... ... ... ... \n",
"5566100170 600 1958 0 98006 47.5705 -122.175 \n",
"8812401450 1060 1913 0 98105 47.6635 -122.320 \n",
"627300145 2090 2008 0 98004 47.5861 -122.113 \n",
"1773100755 600 1918 1999 98106 47.5560 -122.363 \n",
"2402100895 580 1947 0 98103 47.6878 -122.331 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"9543000205 1380 9600 \n",
"3980300371 1620 22850 \n",
"6896300380 2170 6000 \n",
"7849202190 1060 7200 \n",
"2310060040 1810 5685 \n",
"... ... ... \n",
"5566100170 2040 11914 \n",
"8812401450 1810 3745 \n",
"627300145 2730 10400 \n",
"1773100755 1420 4960 \n",
"2402100895 1330 4700 \n",
"\n",
"[21613 rows x 20 columns]\n",
"\n",
"Сортировка по почтовому индексу и количеству ванных комнат (убывание):\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"1068000375 20140923T000000 3200000.0 6 5.00 7100 \n",
"3271800295 20150203T000000 1569500.0 5 4.50 5620 \n",
"1370802115 20141205T000000 1925000.0 3 4.50 3950 \n",
"1370802455 20140813T000000 1050000.0 4 4.50 3180 \n",
"2771604190 20140617T000000 824000.0 7 4.25 3670 \n",
"... ... ... ... ... ... \n",
"1312900180 20150325T000000 225000.0 3 1.00 1250 \n",
"3356403400 20140724T000000 159000.0 3 1.00 1360 \n",
"1278000210 20150311T000000 110000.0 2 1.00 828 \n",
"4045700455 20150316T000000 363000.0 3 0.75 2510 \n",
"9543000205 20150413T000000 139950.0 0 0.00 844 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"1068000375 18200 2.5 0 0 3 13 5240 \n",
"3271800295 5800 3.0 0 3 3 11 4700 \n",
"1370802115 6134 2.0 0 3 3 11 2880 \n",
"1370802455 4606 2.0 0 3 4 9 1990 \n",
"2771604190 4000 2.0 0 1 3 8 2800 \n",
"... ... ... ... ... ... ... ... \n",
"1312900180 7820 1.0 0 0 3 7 1250 \n",
"3356403400 20000 1.0 0 0 4 7 1360 \n",
"1278000210 4524 1.0 0 0 3 6 828 \n",
"4045700455 20000 2.0 0 0 4 7 2510 \n",
"9543000205 4269 1.0 0 0 4 7 844 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"1068000375 1860 1933 2002 98199 47.6427 -122.408 \n",
"3271800295 920 1999 0 98199 47.6482 -122.412 \n",
"1370802115 1070 1998 0 98199 47.6413 -122.405 \n",
"1370802455 1190 1929 0 98199 47.6402 -122.405 \n",
"2771604190 870 1964 0 98199 47.6375 -122.388 \n",
"... ... ... ... ... ... ... \n",
"1312900180 0 1967 0 98001 47.3397 -122.291 \n",
"3356403400 0 1953 0 98001 47.2861 -122.253 \n",
"1278000210 0 1968 2007 98001 47.2655 -122.244 \n",
"4045700455 0 1961 0 98001 47.2871 -122.287 \n",
"9543000205 0 1913 0 98001 47.2781 -122.250 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"1068000375 3130 6477 \n",
"3271800295 2360 5800 \n",
"1370802115 3050 5281 \n",
"1370802455 2110 5323 \n",
"2771604190 2010 4000 \n",
"... ... ... \n",
"1312900180 1300 7920 \n",
"3356403400 1530 9997 \n",
"1278000210 828 5402 \n",
"4045700455 2130 20000 \n",
"9543000205 1380 9600 \n",
"\n",
"[21613 rows x 20 columns]\n"
]
}
],
"source": [
"print(\"Сортировка по цене (возрастание):\")\n",
"print(data_frame.sort_values(by=\"price\"))\n",
"\n",
"# 2. Сортировка по цене (убывание)\n",
"print(\"\\nСортировка по цене (убывание):\")\n",
"print(data_frame.sort_values(by=\"price\", ascending=False))\n",
"\n",
"# 3. Сортировка по количеству спален и затем по цене (возрастание)\n",
"print(\"\\nСортировка по количеству спален и затем по цене (возрастание):\")\n",
"print(data_frame.sort_values(by=[\"bedrooms\", \"price\"]))\n",
"\n",
"# 4. Сортировка по почтовому индексу и количеству ванных комнат (убывание)\n",
"print(\"\\nСортировка по почтовому индексу и количеству ванных комнат (убывание):\")\n",
"print(data_frame.sort_values(by=[\"zipcode\", \"bathrooms\"], ascending=[False, False]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление строк и столбцов"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Удаление строки с индексом 1736800520:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"7129300520 5650 1.0 0 0 3 7 1180 \n",
"6414100192 7242 2.0 0 0 3 7 2170 \n",
"5631500400 10000 1.0 0 0 3 6 770 \n",
"2487200875 5000 1.0 0 0 5 7 1050 \n",
"1954400510 8080 1.0 0 0 3 8 1680 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 8 1530 \n",
"6600060120 5813 2.0 0 0 3 8 2310 \n",
"1523300141 1350 2.0 0 0 3 7 1020 \n",
"291310100 2388 2.0 0 0 3 8 1600 \n",
"1523300157 1076 2.0 0 0 3 7 1020 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"7129300520 0 1955 0 98178 47.5112 -122.257 \n",
"6414100192 400 1951 1991 98125 47.7210 -122.319 \n",
"5631500400 0 1933 0 98028 47.7379 -122.233 \n",
"2487200875 910 1965 0 98136 47.5208 -122.393 \n",
"1954400510 0 1987 0 98074 47.6168 -122.045 \n",
"... ... ... ... ... ... ... \n",
"263000018 0 2009 0 98103 47.6993 -122.346 \n",
"6600060120 0 2014 0 98146 47.5107 -122.362 \n",
"1523300141 0 2009 0 98144 47.5944 -122.299 \n",
"291310100 0 2004 0 98027 47.5345 -122.069 \n",
"1523300157 0 2008 0 98144 47.5941 -122.299 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"7129300520 1340 5650 \n",
"6414100192 1690 7639 \n",
"5631500400 2720 8062 \n",
"2487200875 1360 5000 \n",
"1954400510 1800 7503 \n",
"... ... ... \n",
"263000018 1530 1509 \n",
"6600060120 1830 7200 \n",
"1523300141 1020 2007 \n",
"291310100 1410 1287 \n",
"1523300157 1020 1357 \n",
"\n",
"[21612 rows x 20 columns]\n",
"\n",
"Удаление строк с индексами 1736800520 и 6300500875:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"7129300520 5650 1.0 0 0 3 7 1180 \n",
"6414100192 7242 2.0 0 0 3 7 2170 \n",
"5631500400 10000 1.0 0 0 3 6 770 \n",
"2487200875 5000 1.0 0 0 5 7 1050 \n",
"1954400510 8080 1.0 0 0 3 8 1680 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 8 1530 \n",
"6600060120 5813 2.0 0 0 3 8 2310 \n",
"1523300141 1350 2.0 0 0 3 7 1020 \n",
"291310100 2388 2.0 0 0 3 8 1600 \n",
"1523300157 1076 2.0 0 0 3 7 1020 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"7129300520 0 1955 0 98178 47.5112 -122.257 \n",
"6414100192 400 1951 1991 98125 47.7210 -122.319 \n",
"5631500400 0 1933 0 98028 47.7379 -122.233 \n",
"2487200875 910 1965 0 98136 47.5208 -122.393 \n",
"1954400510 0 1987 0 98074 47.6168 -122.045 \n",
"... ... ... ... ... ... ... \n",
"263000018 0 2009 0 98103 47.6993 -122.346 \n",
"6600060120 0 2014 0 98146 47.5107 -122.362 \n",
"1523300141 0 2009 0 98144 47.5944 -122.299 \n",
"291310100 0 2004 0 98027 47.5345 -122.069 \n",
"1523300157 0 2008 0 98144 47.5941 -122.299 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"7129300520 1340 5650 \n",
"6414100192 1690 7639 \n",
"5631500400 2720 8062 \n",
"2487200875 1360 5000 \n",
"1954400510 1800 7503 \n",
"... ... ... \n",
"263000018 1530 1509 \n",
"6600060120 1830 7200 \n",
"1523300141 1020 2007 \n",
"291310100 1410 1287 \n",
"1523300157 1020 1357 \n",
"\n",
"[21611 rows x 20 columns]\n",
"\n",
"Удаление столбца 'zipcode':\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition grade sqft_above \\\n",
"id \n",
"7129300520 5650 1.0 0 0 3 7 1180 \n",
"6414100192 7242 2.0 0 0 3 7 2170 \n",
"5631500400 10000 1.0 0 0 3 6 770 \n",
"2487200875 5000 1.0 0 0 5 7 1050 \n",
"1954400510 8080 1.0 0 0 3 8 1680 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 8 1530 \n",
"6600060120 5813 2.0 0 0 3 8 2310 \n",
"1523300141 1350 2.0 0 0 3 7 1020 \n",
"291310100 2388 2.0 0 0 3 8 1600 \n",
"1523300157 1076 2.0 0 0 3 7 1020 \n",
"\n",
" sqft_basement yr_built yr_renovated lat long \\\n",
"id \n",
"7129300520 0 1955 0 47.5112 -122.257 \n",
"6414100192 400 1951 1991 47.7210 -122.319 \n",
"5631500400 0 1933 0 47.7379 -122.233 \n",
"2487200875 910 1965 0 47.5208 -122.393 \n",
"1954400510 0 1987 0 47.6168 -122.045 \n",
"... ... ... ... ... ... \n",
"263000018 0 2009 0 47.6993 -122.346 \n",
"6600060120 0 2014 0 47.5107 -122.362 \n",
"1523300141 0 2009 0 47.5944 -122.299 \n",
"291310100 0 2004 0 47.5345 -122.069 \n",
"1523300157 0 2008 0 47.5941 -122.299 \n",
"\n",
" sqft_living15 sqft_lot15 \n",
"id \n",
"7129300520 1340 5650 \n",
"6414100192 1690 7639 \n",
"5631500400 2720 8062 \n",
"2487200875 1360 5000 \n",
"1954400510 1800 7503 \n",
"... ... ... \n",
"263000018 1530 1509 \n",
"6600060120 1830 7200 \n",
"1523300141 1020 2007 \n",
"291310100 1410 1287 \n",
"1523300157 1020 1357 \n",
"\n",
"[21613 rows x 19 columns]\n",
"\n",
"Удаление столбцов 'bathrooms' и 'floors':\n",
" date price bedrooms sqft_living sqft_lot \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1180 5650 \n",
"6414100192 20141209T000000 538000.0 3 2570 7242 \n",
"5631500400 20150225T000000 180000.0 2 770 10000 \n",
"2487200875 20141209T000000 604000.0 4 1960 5000 \n",
"1954400510 20150218T000000 510000.0 3 1680 8080 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 1530 1131 \n",
"6600060120 20150223T000000 400000.0 4 2310 5813 \n",
"1523300141 20140623T000000 402101.0 2 1020 1350 \n",
"291310100 20150116T000000 400000.0 3 1600 2388 \n",
"1523300157 20141015T000000 325000.0 2 1020 1076 \n",
"\n",
" waterfront view condition grade sqft_above sqft_basement \\\n",
"id \n",
"7129300520 0 0 3 7 1180 0 \n",
"6414100192 0 0 3 7 2170 400 \n",
"5631500400 0 0 3 6 770 0 \n",
"2487200875 0 0 5 7 1050 910 \n",
"1954400510 0 0 3 8 1680 0 \n",
"... ... ... ... ... ... ... \n",
"263000018 0 0 3 8 1530 0 \n",
"6600060120 0 0 3 8 2310 0 \n",
"1523300141 0 0 3 7 1020 0 \n",
"291310100 0 0 3 8 1600 0 \n",
"1523300157 0 0 3 7 1020 0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"id \n",
"7129300520 1955 0 98178 47.5112 -122.257 1340 \n",
"6414100192 1951 1991 98125 47.7210 -122.319 1690 \n",
"5631500400 1933 0 98028 47.7379 -122.233 2720 \n",
"2487200875 1965 0 98136 47.5208 -122.393 1360 \n",
"1954400510 1987 0 98074 47.6168 -122.045 1800 \n",
"... ... ... ... ... ... ... \n",
"263000018 2009 0 98103 47.6993 -122.346 1530 \n",
"6600060120 2014 0 98146 47.5107 -122.362 1830 \n",
"1523300141 2009 0 98144 47.5944 -122.299 1020 \n",
"291310100 2004 0 98027 47.5345 -122.069 1410 \n",
"1523300157 2008 0 98144 47.5941 -122.299 1020 \n",
"\n",
" sqft_lot15 \n",
"id \n",
"7129300520 5650 \n",
"6414100192 7639 \n",
"5631500400 8062 \n",
"2487200875 5000 \n",
"1954400510 7503 \n",
"... ... \n",
"263000018 1509 \n",
"6600060120 7200 \n",
"1523300141 2007 \n",
"291310100 1287 \n",
"1523300157 1357 \n",
"\n",
"[21613 rows x 18 columns]\n"
]
}
],
"source": [
"print(\"Удаление строки с индексом 1736800520:\")\n",
"print(data_frame.drop(index=1736800520))\n",
"\n",
"# 2. Удаление нескольких строк по индексам (например, удаляем строки с индексами 0 и 2)\n",
"print(\"\\nУдаление строк с индексами 1736800520 и 6300500875:\")\n",
"print(data_frame.drop(index=[1736800520, 6300500875]))\n",
"\n",
"# 3. Удаление столбца по имени (например, удаляем столбец 'zipcode')\n",
"print(\"\\nУдаление столбца 'zipcode':\")\n",
"print(data_frame.drop(columns=\"zipcode\"))\n",
"\n",
"# 4. Удаление нескольких столбцов (например, 'bathrooms' и 'floors')\n",
"print(\"\\nУдаление столбцов 'bathrooms' и 'floors':\")\n",
"print(data_frame.drop(columns=[\"bathrooms\", \"floors\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создание новых столбцов на основе данных из существующих столбцов"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Создание нового столбца 'price_per_bedroom':\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... sqft_above \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 1180 \n",
"6414100192 7242 2.0 0 0 3 ... 2170 \n",
"5631500400 10000 1.0 0 0 3 ... 770 \n",
"2487200875 5000 1.0 0 0 5 ... 1050 \n",
"1954400510 8080 1.0 0 0 3 ... 1680 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 1530 \n",
"6600060120 5813 2.0 0 0 3 ... 2310 \n",
"1523300141 1350 2.0 0 0 3 ... 1020 \n",
"291310100 2388 2.0 0 0 3 ... 1600 \n",
"1523300157 1076 2.0 0 0 3 ... 1020 \n",
"\n",
" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
"id \n",
"7129300520 0 1955 0 98178 47.5112 -122.257 \n",
"6414100192 400 1951 1991 98125 47.7210 -122.319 \n",
"5631500400 0 1933 0 98028 47.7379 -122.233 \n",
"2487200875 910 1965 0 98136 47.5208 -122.393 \n",
"1954400510 0 1987 0 98074 47.6168 -122.045 \n",
"... ... ... ... ... ... ... \n",
"263000018 0 2009 0 98103 47.6993 -122.346 \n",
"6600060120 0 2014 0 98146 47.5107 -122.362 \n",
"1523300141 0 2009 0 98144 47.5944 -122.299 \n",
"291310100 0 2004 0 98027 47.5345 -122.069 \n",
"1523300157 0 2008 0 98144 47.5941 -122.299 \n",
"\n",
" sqft_living15 sqft_lot15 price_per_bedroom \n",
"id \n",
"7129300520 1340 5650 73966.666667 \n",
"6414100192 1690 7639 179333.333333 \n",
"5631500400 2720 8062 90000.000000 \n",
"2487200875 1360 5000 151000.000000 \n",
"1954400510 1800 7503 170000.000000 \n",
"... ... ... ... \n",
"263000018 1530 1509 120000.000000 \n",
"6600060120 1830 7200 100000.000000 \n",
"1523300141 1020 2007 201050.500000 \n",
"291310100 1410 1287 133333.333333 \n",
"1523300157 1020 1357 162500.000000 \n",
"\n",
"[21613 rows x 21 columns]\n",
"\n",
"Создание нового столбца 'total_rooms':\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... sqft_basement \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 0 \n",
"6414100192 7242 2.0 0 0 3 ... 400 \n",
"5631500400 10000 1.0 0 0 3 ... 0 \n",
"2487200875 5000 1.0 0 0 5 ... 910 \n",
"1954400510 8080 1.0 0 0 3 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 0 \n",
"6600060120 5813 2.0 0 0 3 ... 0 \n",
"1523300141 1350 2.0 0 0 3 ... 0 \n",
"291310100 2388 2.0 0 0 3 ... 0 \n",
"1523300157 1076 2.0 0 0 3 ... 0 \n",
"\n",
" yr_built yr_renovated zipcode lat long sqft_living15 \\\n",
"id \n",
"7129300520 1955 0 98178 47.5112 -122.257 1340 \n",
"6414100192 1951 1991 98125 47.7210 -122.319 1690 \n",
"5631500400 1933 0 98028 47.7379 -122.233 2720 \n",
"2487200875 1965 0 98136 47.5208 -122.393 1360 \n",
"1954400510 1987 0 98074 47.6168 -122.045 1800 \n",
"... ... ... ... ... ... ... \n",
"263000018 2009 0 98103 47.6993 -122.346 1530 \n",
"6600060120 2014 0 98146 47.5107 -122.362 1830 \n",
"1523300141 2009 0 98144 47.5944 -122.299 1020 \n",
"291310100 2004 0 98027 47.5345 -122.069 1410 \n",
"1523300157 2008 0 98144 47.5941 -122.299 1020 \n",
"\n",
" sqft_lot15 price_per_bedroom total_rooms \n",
"id \n",
"7129300520 5650 73966.666667 4.00 \n",
"6414100192 7639 179333.333333 5.25 \n",
"5631500400 8062 90000.000000 3.00 \n",
"2487200875 5000 151000.000000 7.00 \n",
"1954400510 7503 170000.000000 5.00 \n",
"... ... ... ... \n",
"263000018 1509 120000.000000 5.50 \n",
"6600060120 7200 100000.000000 6.50 \n",
"1523300141 2007 201050.500000 2.75 \n",
"291310100 1287 133333.333333 5.50 \n",
"1523300157 1357 162500.000000 2.75 \n",
"\n",
"[21613 rows x 22 columns]\n",
"\n",
"Создание нового столбца 'is_expensive':\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... yr_built \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 1955 \n",
"6414100192 7242 2.0 0 0 3 ... 1951 \n",
"5631500400 10000 1.0 0 0 3 ... 1933 \n",
"2487200875 5000 1.0 0 0 5 ... 1965 \n",
"1954400510 8080 1.0 0 0 3 ... 1987 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 2009 \n",
"6600060120 5813 2.0 0 0 3 ... 2014 \n",
"1523300141 1350 2.0 0 0 3 ... 2009 \n",
"291310100 2388 2.0 0 0 3 ... 2004 \n",
"1523300157 1076 2.0 0 0 3 ... 2008 \n",
"\n",
" yr_renovated zipcode lat long sqft_living15 \\\n",
"id \n",
"7129300520 0 98178 47.5112 -122.257 1340 \n",
"6414100192 1991 98125 47.7210 -122.319 1690 \n",
"5631500400 0 98028 47.7379 -122.233 2720 \n",
"2487200875 0 98136 47.5208 -122.393 1360 \n",
"1954400510 0 98074 47.6168 -122.045 1800 \n",
"... ... ... ... ... ... \n",
"263000018 0 98103 47.6993 -122.346 1530 \n",
"6600060120 0 98146 47.5107 -122.362 1830 \n",
"1523300141 0 98144 47.5944 -122.299 1020 \n",
"291310100 0 98027 47.5345 -122.069 1410 \n",
"1523300157 0 98144 47.5941 -122.299 1020 \n",
"\n",
" sqft_lot15 price_per_bedroom total_rooms is_expensive \n",
"id \n",
"7129300520 5650 73966.666667 4.00 False \n",
"6414100192 7639 179333.333333 5.25 True \n",
"5631500400 8062 90000.000000 3.00 False \n",
"2487200875 5000 151000.000000 7.00 True \n",
"1954400510 7503 170000.000000 5.00 True \n",
"... ... ... ... ... \n",
"263000018 1509 120000.000000 5.50 True \n",
"6600060120 7200 100000.000000 6.50 True \n",
"1523300141 2007 201050.500000 2.75 True \n",
"291310100 1287 133333.333333 5.50 True \n",
"1523300157 1357 162500.000000 2.75 True \n",
"\n",
"[21613 rows x 23 columns]\n",
"\n",
"Создание нового столбца 'floor_area_ratio':\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... yr_renovated \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 0 \n",
"6414100192 7242 2.0 0 0 3 ... 1991 \n",
"5631500400 10000 1.0 0 0 3 ... 0 \n",
"2487200875 5000 1.0 0 0 5 ... 0 \n",
"1954400510 8080 1.0 0 0 3 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 0 \n",
"6600060120 5813 2.0 0 0 3 ... 0 \n",
"1523300141 1350 2.0 0 0 3 ... 0 \n",
"291310100 2388 2.0 0 0 3 ... 0 \n",
"1523300157 1076 2.0 0 0 3 ... 0 \n",
"\n",
" zipcode lat long sqft_living15 sqft_lot15 \\\n",
"id \n",
"7129300520 98178 47.5112 -122.257 1340 5650 \n",
"6414100192 98125 47.7210 -122.319 1690 7639 \n",
"5631500400 98028 47.7379 -122.233 2720 8062 \n",
"2487200875 98136 47.5208 -122.393 1360 5000 \n",
"1954400510 98074 47.6168 -122.045 1800 7503 \n",
"... ... ... ... ... ... \n",
"263000018 98103 47.6993 -122.346 1530 1509 \n",
"6600060120 98146 47.5107 -122.362 1830 7200 \n",
"1523300141 98144 47.5944 -122.299 1020 2007 \n",
"291310100 98027 47.5345 -122.069 1410 1287 \n",
"1523300157 98144 47.5941 -122.299 1020 1357 \n",
"\n",
" price_per_bedroom total_rooms is_expensive floor_area_ratio \n",
"id \n",
"7129300520 73966.666667 4.00 False 0.333333 \n",
"6414100192 179333.333333 5.25 True 0.666667 \n",
"5631500400 90000.000000 3.00 False 0.500000 \n",
"2487200875 151000.000000 7.00 True 0.250000 \n",
"1954400510 170000.000000 5.00 True 0.333333 \n",
"... ... ... ... ... \n",
"263000018 120000.000000 5.50 True 1.000000 \n",
"6600060120 100000.000000 6.50 True 0.500000 \n",
"1523300141 201050.500000 2.75 True 1.000000 \n",
"291310100 133333.333333 5.50 True 0.666667 \n",
"1523300157 162500.000000 2.75 True 1.000000 \n",
"\n",
"[21613 rows x 24 columns]\n"
]
}
],
"source": [
"# 1. Создание нового столбца 'price_per_bedroom'\n",
"print(\"Создание нового столбца 'price_per_bedroom':\")\n",
"data_frame[\"price_per_bedroom\"] = data_frame[\"price\"] / data_frame[\"bedrooms\"]\n",
"print(data_frame)\n",
"\n",
"# 2. Создание нового столбца 'total_rooms' (сумма спален и ванных комнат)\n",
"print(\"\\nСоздание нового столбца 'total_rooms':\")\n",
"data_frame[\"total_rooms\"] = data_frame[\"bedrooms\"] + data_frame[\"bathrooms\"]\n",
"print(data_frame)\n",
"\n",
"# 3. Создание нового столбца 'is_expensive' (определяем, дорогой ли дом)\n",
"print(\"\\nСоздание нового столбца 'is_expensive':\")\n",
"data_frame[\"is_expensive\"] = data_frame[\"price\"] > 300000\n",
"print(data_frame)\n",
"\n",
"# 4. Создание нового столбца 'floor_area_ratio' (соотношение этажей к количеству спален)\n",
"print(\"\\nСоздание нового столбца 'floor_area_ratio':\")\n",
"data_frame[\"floor_area_ratio\"] = data_frame[\"floors\"] / data_frame[\"bedrooms\"]\n",
"print(data_frame)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление строк с пустыми значениями\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Исходный DataFrame с пустыми значениями:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... yr_renovated \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 0 \n",
"6414100192 7242 2.0 0 0 3 ... 1991 \n",
"5631500400 10000 1.0 0 0 3 ... 0 \n",
"2487200875 5000 1.0 0 0 5 ... 0 \n",
"1954400510 8080 1.0 0 0 3 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 0 \n",
"6600060120 5813 2.0 0 0 3 ... 0 \n",
"1523300141 1350 2.0 0 0 3 ... 0 \n",
"291310100 2388 2.0 0 0 3 ... 0 \n",
"1523300157 1076 2.0 0 0 3 ... 0 \n",
"\n",
" zipcode lat long sqft_living15 sqft_lot15 \\\n",
"id \n",
"7129300520 98178 47.5112 -122.257 1340 5650 \n",
"6414100192 98125 47.7210 -122.319 1690 7639 \n",
"5631500400 98028 47.7379 -122.233 2720 8062 \n",
"2487200875 98136 47.5208 -122.393 1360 5000 \n",
"1954400510 98074 47.6168 -122.045 1800 7503 \n",
"... ... ... ... ... ... \n",
"263000018 98103 47.6993 -122.346 1530 1509 \n",
"6600060120 98146 47.5107 -122.362 1830 7200 \n",
"1523300141 98144 47.5944 -122.299 1020 2007 \n",
"291310100 98027 47.5345 -122.069 1410 1287 \n",
"1523300157 98144 47.5941 -122.299 1020 1357 \n",
"\n",
" price_per_bedroom total_rooms is_expensive floor_area_ratio \n",
"id \n",
"7129300520 73966.666667 4.00 False 0.333333 \n",
"6414100192 179333.333333 5.25 True 0.666667 \n",
"5631500400 90000.000000 3.00 False 0.500000 \n",
"2487200875 151000.000000 7.00 True 0.250000 \n",
"1954400510 170000.000000 5.00 True 0.333333 \n",
"... ... ... ... ... \n",
"263000018 120000.000000 5.50 True 1.000000 \n",
"6600060120 100000.000000 6.50 True 0.500000 \n",
"1523300141 201050.500000 2.75 True 1.000000 \n",
"291310100 133333.333333 5.50 True 0.666667 \n",
"1523300157 162500.000000 2.75 True 1.000000 \n",
"\n",
"[21613 rows x 24 columns]\n",
"\n",
"Удаление строк с любыми пустыми значениями:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... yr_renovated \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 0 \n",
"6414100192 7242 2.0 0 0 3 ... 1991 \n",
"5631500400 10000 1.0 0 0 3 ... 0 \n",
"2487200875 5000 1.0 0 0 5 ... 0 \n",
"1954400510 8080 1.0 0 0 3 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 0 \n",
"6600060120 5813 2.0 0 0 3 ... 0 \n",
"1523300141 1350 2.0 0 0 3 ... 0 \n",
"291310100 2388 2.0 0 0 3 ... 0 \n",
"1523300157 1076 2.0 0 0 3 ... 0 \n",
"\n",
" zipcode lat long sqft_living15 sqft_lot15 \\\n",
"id \n",
"7129300520 98178 47.5112 -122.257 1340 5650 \n",
"6414100192 98125 47.7210 -122.319 1690 7639 \n",
"5631500400 98028 47.7379 -122.233 2720 8062 \n",
"2487200875 98136 47.5208 -122.393 1360 5000 \n",
"1954400510 98074 47.6168 -122.045 1800 7503 \n",
"... ... ... ... ... ... \n",
"263000018 98103 47.6993 -122.346 1530 1509 \n",
"6600060120 98146 47.5107 -122.362 1830 7200 \n",
"1523300141 98144 47.5944 -122.299 1020 2007 \n",
"291310100 98027 47.5345 -122.069 1410 1287 \n",
"1523300157 98144 47.5941 -122.299 1020 1357 \n",
"\n",
" price_per_bedroom total_rooms is_expensive floor_area_ratio \n",
"id \n",
"7129300520 73966.666667 4.00 False 0.333333 \n",
"6414100192 179333.333333 5.25 True 0.666667 \n",
"5631500400 90000.000000 3.00 False 0.500000 \n",
"2487200875 151000.000000 7.00 True 0.250000 \n",
"1954400510 170000.000000 5.00 True 0.333333 \n",
"... ... ... ... ... \n",
"263000018 120000.000000 5.50 True 1.000000 \n",
"6600060120 100000.000000 6.50 True 0.500000 \n",
"1523300141 201050.500000 2.75 True 1.000000 \n",
"291310100 133333.333333 5.50 True 0.666667 \n",
"1523300157 162500.000000 2.75 True 1.000000 \n",
"\n",
"[21613 rows x 24 columns]\n",
"\n",
"Удаление строк с пустыми значениями в столбце 'price':\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... yr_renovated \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 0 \n",
"6414100192 7242 2.0 0 0 3 ... 1991 \n",
"5631500400 10000 1.0 0 0 3 ... 0 \n",
"2487200875 5000 1.0 0 0 5 ... 0 \n",
"1954400510 8080 1.0 0 0 3 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 0 \n",
"6600060120 5813 2.0 0 0 3 ... 0 \n",
"1523300141 1350 2.0 0 0 3 ... 0 \n",
"291310100 2388 2.0 0 0 3 ... 0 \n",
"1523300157 1076 2.0 0 0 3 ... 0 \n",
"\n",
" zipcode lat long sqft_living15 sqft_lot15 \\\n",
"id \n",
"7129300520 98178 47.5112 -122.257 1340 5650 \n",
"6414100192 98125 47.7210 -122.319 1690 7639 \n",
"5631500400 98028 47.7379 -122.233 2720 8062 \n",
"2487200875 98136 47.5208 -122.393 1360 5000 \n",
"1954400510 98074 47.6168 -122.045 1800 7503 \n",
"... ... ... ... ... ... \n",
"263000018 98103 47.6993 -122.346 1530 1509 \n",
"6600060120 98146 47.5107 -122.362 1830 7200 \n",
"1523300141 98144 47.5944 -122.299 1020 2007 \n",
"291310100 98027 47.5345 -122.069 1410 1287 \n",
"1523300157 98144 47.5941 -122.299 1020 1357 \n",
"\n",
" price_per_bedroom total_rooms is_expensive floor_area_ratio \n",
"id \n",
"7129300520 73966.666667 4.00 False 0.333333 \n",
"6414100192 179333.333333 5.25 True 0.666667 \n",
"5631500400 90000.000000 3.00 False 0.500000 \n",
"2487200875 151000.000000 7.00 True 0.250000 \n",
"1954400510 170000.000000 5.00 True 0.333333 \n",
"... ... ... ... ... \n",
"263000018 120000.000000 5.50 True 1.000000 \n",
"6600060120 100000.000000 6.50 True 0.500000 \n",
"1523300141 201050.500000 2.75 True 1.000000 \n",
"291310100 133333.333333 5.50 True 0.666667 \n",
"1523300157 162500.000000 2.75 True 1.000000 \n",
"\n",
"[21613 rows x 24 columns]\n",
"\n",
"Удаление строк, где все значения пустые:\n",
" date price bedrooms bathrooms sqft_living \\\n",
"id \n",
"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
"... ... ... ... ... ... \n",
"263000018 20140521T000000 360000.0 3 2.50 1530 \n",
"6600060120 20150223T000000 400000.0 4 2.50 2310 \n",
"1523300141 20140623T000000 402101.0 2 0.75 1020 \n",
"291310100 20150116T000000 400000.0 3 2.50 1600 \n",
"1523300157 20141015T000000 325000.0 2 0.75 1020 \n",
"\n",
" sqft_lot floors waterfront view condition ... yr_renovated \\\n",
"id ... \n",
"7129300520 5650 1.0 0 0 3 ... 0 \n",
"6414100192 7242 2.0 0 0 3 ... 1991 \n",
"5631500400 10000 1.0 0 0 3 ... 0 \n",
"2487200875 5000 1.0 0 0 5 ... 0 \n",
"1954400510 8080 1.0 0 0 3 ... 0 \n",
"... ... ... ... ... ... ... ... \n",
"263000018 1131 3.0 0 0 3 ... 0 \n",
"6600060120 5813 2.0 0 0 3 ... 0 \n",
"1523300141 1350 2.0 0 0 3 ... 0 \n",
"291310100 2388 2.0 0 0 3 ... 0 \n",
"1523300157 1076 2.0 0 0 3 ... 0 \n",
"\n",
" zipcode lat long sqft_living15 sqft_lot15 \\\n",
"id \n",
"7129300520 98178 47.5112 -122.257 1340 5650 \n",
"6414100192 98125 47.7210 -122.319 1690 7639 \n",
"5631500400 98028 47.7379 -122.233 2720 8062 \n",
"2487200875 98136 47.5208 -122.393 1360 5000 \n",
"1954400510 98074 47.6168 -122.045 1800 7503 \n",
"... ... ... ... ... ... \n",
"263000018 98103 47.6993 -122.346 1530 1509 \n",
"6600060120 98146 47.5107 -122.362 1830 7200 \n",
"1523300141 98144 47.5944 -122.299 1020 2007 \n",
"291310100 98027 47.5345 -122.069 1410 1287 \n",
"1523300157 98144 47.5941 -122.299 1020 1357 \n",
"\n",
" price_per_bedroom total_rooms is_expensive floor_area_ratio \n",
"id \n",
"7129300520 73966.666667 4.00 False 0.333333 \n",
"6414100192 179333.333333 5.25 True 0.666667 \n",
"5631500400 90000.000000 3.00 False 0.500000 \n",
"2487200875 151000.000000 7.00 True 0.250000 \n",
"1954400510 170000.000000 5.00 True 0.333333 \n",
"... ... ... ... ... \n",
"263000018 120000.000000 5.50 True 1.000000 \n",
"6600060120 100000.000000 6.50 True 0.500000 \n",
"1523300141 201050.500000 2.75 True 1.000000 \n",
"291310100 133333.333333 5.50 True 0.666667 \n",
"1523300157 162500.000000 2.75 True 1.000000 \n",
"\n",
"[21613 rows x 24 columns]\n"
]
}
],
"source": [
"# 1. Исходный DataFrame с пустыми значениями\n",
"print(\"Исходный DataFrame с пустыми значениями:\")\n",
"print(data_frame)\n",
"\n",
"# 2. Удаление строк с любыми пустыми значениями\n",
"print(\"\\nУдаление строк с любыми пустыми значениями:\")\n",
"print(data_frame.dropna())\n",
"\n",
"# 3. Удаление строк только с пустыми значениями в определенном столбце (например, 'price')\n",
"print(\"\\nУдаление строк с пустыми значениями в столбце 'price':\")\n",
"print(data_frame.dropna(subset=[\"price\"]))\n",
"\n",
"# 4. Удаление строк, где все значения пустые\n",
"print(\"\\nУдаление строк, где все значения пустые:\")\n",
"print(data_frame.dropna(how=\"all\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHKCAYAAAD/zGr0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJaUlEQVR4nO3dd1yV9f//8ecBWcpwoOAg0ajEbViGIxsoWuZoaKmhpmalWfkti0rRsrChYeVITe1Tmqvlp+EiyYW5R7kVRyq4t0LC+/eHP8/HI4gcRQ5ePu6323W7eb2v9/W+Xmd4zpNrHZsxxggAAMAi3FxdAAAAQH4i3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3OC62rZtm3r06KHKlSvL29tb/v7+atCggYYNG6YzZ864ujwAgAUVcXUBsK5ffvlFTzzxhLy8vBQTE6Pq1asrIyNDCxcu1Guvvaa///5bo0ePdnWZAACLsfHDmbgeUlJSVLNmTVWoUEG///67ypYt67B869at+uWXX/TSSy+5qEIAgFVxWArXxYcffqiTJ0/qyy+/zBZsJCksLMwh2NhsNvXq1UsTJ07UHXfcIW9vb0VERGj+/PnZ1t2zZ4+eeeYZBQUFycvLS9WqVdO4ceNyrGPAgAGy2WzZpvvuu8+h33333afq1atnW//jjz+WzWbTjh077G3nzp3ToEGDdPvtt8vLy8th3OXLlzvdLyedO3eWr69vtvbp06fLZrMpKSnJoT09PV1xcXEKCwuTl5eXQkJC1LdvX6Wnpzv0u/A8X6pFixYKDQ3N9tjr16+vUqVKycfHRxEREZo+fXqudV9w33335fi8X5gufj4lacSIEapWrZq8vLxUrlw59ezZU0ePHr3idi68vhebN2+evLy89Nxzzzm0r1q1Ss2bN5e/v798fX314IMPasmSJU7VP2HCBIc+l76Pli1bZu97QVJSUo6vWefOnbM951lZWUpISFC1atXk7e2toKAg9ejRQ0eOHMlW42+//abGjRvLz89P/v7+uuuuuzRp0qRc6794uuDiNnd3d5UvX17PPvus/fk/efKkihUrluMfIv/884/c3d0VHx+f4/N48eMaNmyYatSoIW9vb5UuXVrNmjVz+H+QW63X8t6cMGFCnj4Djh49qpdfflkhISHy8vJSWFiYPvjgA2VlZdn77NixQzabTR9//HG27VSvXj3bmHAdDkvhuvjvf/+rypUrq379+nle548//tCUKVPUu3dveXl5acSIEWrWrJmWLl1qDx5paWm655577F/SpUuX1m+//aauXbvq+PHjevnll3Mce+TIkfawEBsbe02PbciQIerXr5/atGmj119/XV5eXlqwYEG2Q2x57XetsrKy1LJlSy1cuFDPPvuswsPDtW7dOn3yySfavHmzfvzxx6sad9iwYWrZsqU6dOigjIwMTZ48WU888YR+/vlnPfzww1dcv0KFCtm+9H799Vd9++23Dm0DBgzQwIEDFRUVpeeff16bNm3SyJEjtWzZMi1atEgeHh55rnnNmjVq3bq1HnroIQ0fPtze/vfff6tRo0by9/dX37595eHhoS+++EL33Xef/vjjD9WrVy/bWFWqVNFbb70lSTp48KBeeeWVK27/9ddfz3OtOenRo4cmTJigLl26qHfv3kpJSdHnn3+uVatWOTwXEyZM0DPPPKNq1aopNjZWxYsX16pVqzRz5ky1b99eb731lrp16+ZQ+7PPPqtGjRrluN02bdro0Ucf1blz55ScnKzRo0frzJkz+vrrr+Xr66s2bdpoypQpGjp0qNzd3e3rffvttzLGqEOHDrk+rq5du2rChAlq3ry5unXrpnPnzmnBggVasmSJ6tata+/XpEkTxcTEOKw7ZMiQbOHuat6bn3zyiQIDAyVJ7733nsOy06dPq3HjxtqzZ4969OihW265RYsXL1ZsbKz27dunhISEXB8fCiED5LNjx44ZSaZVq1Z5XkeSkWSWL19ub9u5c6fx9vY2bdq0sbd17drVlC1b1hw8eNBh/SeffNIEBASY06dPO7S/+eabRpJD/2rVqpnGjRs79GvcuLGpVq1atro++ugjI8mkpKTY2yIjI014eLjJysqyt40fP95IMsuWLXO6X046depkihUrlq192rRpRpKZN2+eve3rr782bm5uZsGCBQ59R40aZSSZRYsW2dskmZ49e2Yb9+GHHzYVK1Z0aLv0uczIyDDVq1c3DzzwQK61G5P353P//v3G09PTNG3a1GRmZtr7ff7550aSGTduXK7biYuLMxc+xnbs2GHKli1rGjZsaM6cOePQr3Xr1sbT09Ns27bN3rZ3717j5+dn7r333mzjNmjQwNx///32+ZSUFCPJjB8/3uExXvw++vXXX40k06xZM3PxR+sff/xhJJnff//dYRudOnVyeM4XLFhgJJmJEyc69Js5c6ZD+9GjR42fn5+pV69etsd58Xstt9ovJsnExcU5tNWvX99UrVrVPj9r1iwjyfz2228O/WrWrJnt/9Klfv/9dyPJ9O7dO9uyi+u9Xu/NMWPGGElm586d9rZLX7t3333XFCtWzGzevNlh3TfeeMO4u7ubXbt2GWP+91x+9NFH2baT0+cKXIfDUsh3x48flyT5+fk5tV5kZKQiIiLs87fccotatWqlWbNmKTMzU8YYfffdd3rkkUdkjNHBgwftU3R0tI4dO6aVK1c6jHn27FlJkre39xW3n5mZ6TDmwYMHdfr06Wz9Tpw4oRIlSmQ7HHK1/a7VtGnTFB4eripVqjjU/sADD0g6f5jmYmfPns32OP/9999s4/r4+Nj/feTIER07dkyNGjXK9hxfi7lz5yojI0Mvv/yy3Nz+93HUvXt3+fv765dffsnTOIcOHVJ0dLT8/Pw0Y8YMh9c7MzNTs2fPVuvWrVW5cmV7e9myZdW+fXstXLjQ/p69ICMjQ15eXnl+HMYYxcbG6rHHHsu2F6hMmTKSzh/Cyc20adMUEBCgJk2aOLw2ERER8vX1tb+Oc+bM0YkTJ/TGG29ke19f7Xvt9OnTOnjwoFJTU/Xdd99pzZo1evDBB+3Lo6KiVK5cOU2cONHe9tdff2nt2rXq2LFjrmN/9913stlsiouLy7bsaut15r2ZkZEhSbm+ntOmTVOjRo1UokQJh+c+KipKmZmZ2Q6PX3i+Lp4yMzOv6rHg+ripD0vNnz9fH330kVasWKF9+/bphx9+UOvWrZ0awxijIUOGaPTo0dq5c6cCAwP1wgsv2Hdn34z8/f0lnf9yd8Ztt92Wre3222/X6dOndeDAAbm5ueno0aMaPXr0ZQ/t7N+/32H+4MGD8vDwUNGiRa+4/Y0bN6p06dJX7BcZGamxY8fqiy++UIsWLeTl5aWTJ09edb9rtWXLFm3YsOGytV/6nHz55Zf68ssvs/WrWLGiw/zPP/+sQYMGafXq1Q7n7uRnWNu5c6ck6Y477nBo9/T0VOXKle3Lr6RFixbatGmTypQpI3PJNRIHDhzQ6dOns21DksLDw5WVlaXdu3erWrVq9vajR49mez5yM3HiRP3999+aOnWq/byXCypXrqzg4GB9/PHHqlWrlsqVKydJ2c6H2rJli44dO2YPQ5e68Dpu27ZNknI8R+xqffTRR/roo4/s882aNdMHH3xgn3dzc1OHDh00cuRInT59WkWLFtXEiRPl7e2tJ554Itext23bpnLlyqlkyZL5Vq8z780L5w7ldA7bBVu2bNHatWvz/H8oLi4ux7AWFBSUl/JRAG7qcHPq1CnVqlVLzzzzjB599NGrGuOll17S7Nmz9fHHH6tGjRo6fPiwDh8+nM+V3lj8/f1Vrlw5/fXXX/k67oUT+zp27KhOnTrl2KdmzZoO8zt27NAtt9ySpy/k0NBQjRkzxqFt2rRp2YJUfHy89uzZk+2E1Uvltd+1ysrKUo0aNTR06NAcl4eEhDjMt2rVKttJxW+//bZSU1Pt8wsWLFDLli117733asSIESpbtqw8PDw0fvz4bF/ehcHGjRv122+/qW3btvq///s/jR8//pr
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# 1. Столбчатая диаграмма: средняя цена по количеству спален\n",
"data_frame.groupby(\"bedrooms\")[\"price\"].mean().plot.bar(color=\"skyblue\")\n",
"plt.title(\"Средняя цена по количеству спален\")\n",
"plt.xlabel(\"Количество спален\")\n",
"plt.ylabel(\"Средняя цена\")\n",
"plt.show()\n",
"\n",
"# 2. Гистограмма: распределение цен\n",
"data_frame[\"price\"].plot.hist(bins=30, color=\"orange\", alpha=0.7)\n",
"plt.title(\"Гистограмма цен\")\n",
"plt.xlabel(\"Цена\")\n",
"plt.ylabel(\"Частота\")\n",
"plt.show()\n",
"\n",
"# 3. Ящик с усами: цена по количеству ванных комнат\n",
"data_frame.boxplot(column=\"price\", by=\"bathrooms\")\n",
"plt.title(\"Ящик с усами цен по количеству ванных комнат\")\n",
"plt.suptitle(\"\")\n",
"plt.xlabel(\"Количество ванных комнат\")\n",
"plt.ylabel(\"Цена\")\n",
"plt.show()\n",
"\n",
"# 4. Диаграмма с областями: суммарная цена по количеству этажей\n",
"data_frame.groupby(\"floors\")[\"price\"].sum().plot.area(color=\"lightgreen\", alpha=0.5)\n",
"plt.title(\"Суммарная цена по количеству этажей\")\n",
"plt.xlabel(\"Количество этажей\")\n",
"plt.ylabel(\"Суммарная цена\")\n",
"plt.show()\n",
"\n",
"# 5. Диаграмма рассеяния: цена vs. площадь\n",
"data_frame.plot.scatter(x=\"sqft_living\", y=\"price\", color=\"purple\", alpha=0.5)\n",
"plt.title(\"Диаграмма рассеяния: Цена vs Площадь\")\n",
"plt.xlabel(\"Площадь (sqft)\")\n",
"plt.ylabel(\"Цена\")\n",
"plt.show()\n"
]
}
],
"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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