1864 lines
296 KiB
Plaintext
1864 lines
296 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Считываем csv:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas\n",
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"\n",
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"# Считаем csv\n",
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"data_frame = pandas.read_csv(\"data/kc_house_data.csv\", index_col=\"id\")\n",
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"# Сохраняем data_frame в новый csv\n",
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"data_frame.to_csv(\"data/new_kc_house_data.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Получение сведений о data_frame:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Общая информация о data_frame:\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 21613 entries, 7129300520 to 1523300157\n",
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"Data columns (total 20 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 date 21613 non-null object \n",
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" 1 price 21613 non-null float64\n",
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" 2 bedrooms 21613 non-null int64 \n",
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" 3 bathrooms 21613 non-null float64\n",
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" 4 sqft_living 21613 non-null int64 \n",
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" 5 sqft_lot 21613 non-null int64 \n",
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" 6 floors 21613 non-null float64\n",
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" 7 waterfront 21613 non-null int64 \n",
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" 8 view 21613 non-null int64 \n",
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" 9 condition 21613 non-null int64 \n",
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" 10 grade 21613 non-null int64 \n",
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" 11 sqft_above 21613 non-null int64 \n",
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" 12 sqft_basement 21613 non-null int64 \n",
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" 13 yr_built 21613 non-null int64 \n",
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" 14 yr_renovated 21613 non-null int64 \n",
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" 15 zipcode 21613 non-null int64 \n",
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" 16 lat 21613 non-null float64\n",
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" 17 long 21613 non-null float64\n",
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" 18 sqft_living15 21613 non-null int64 \n",
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" 19 sqft_lot15 21613 non-null int64 \n",
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"dtypes: float64(5), int64(14), object(1)\n",
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"memory usage: 3.5+ MB\n",
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"None \n",
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"\n",
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"Первые строки data_frame:\n",
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" date price bedrooms bathrooms sqft_living \\\n",
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"id \n",
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"7129300520 20141013T000000 221900.0 3 1.00 1180 \n",
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"6414100192 20141209T000000 538000.0 3 2.25 2570 \n",
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"5631500400 20150225T000000 180000.0 2 1.00 770 \n",
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"2487200875 20141209T000000 604000.0 4 3.00 1960 \n",
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"1954400510 20150218T000000 510000.0 3 2.00 1680 \n",
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"\n",
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" sqft_lot floors waterfront view condition grade sqft_above \\\n",
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"id \n",
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"7129300520 5650 1.0 0 0 3 7 1180 \n",
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"6414100192 7242 2.0 0 0 3 7 2170 \n",
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"5631500400 10000 1.0 0 0 3 6 770 \n",
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"2487200875 5000 1.0 0 0 5 7 1050 \n",
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"1954400510 8080 1.0 0 0 3 8 1680 \n",
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"\n",
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" sqft_basement yr_built yr_renovated zipcode lat long \\\n",
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"id \n",
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"7129300520 0 1955 0 98178 47.5112 -122.257 \n",
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"6414100192 400 1951 1991 98125 47.7210 -122.319 \n",
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"5631500400 0 1933 0 98028 47.7379 -122.233 \n",
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"2487200875 910 1965 0 98136 47.5208 -122.393 \n",
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"1954400510 0 1987 0 98074 47.6168 -122.045 \n",
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"\n",
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" sqft_living15 sqft_lot15 \n",
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"id \n",
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"7129300520 1340 5650 \n",
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"6414100192 1690 7639 \n",
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"5631500400 2720 8062 \n",
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"2487200875 1360 5000 \n",
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"1954400510 1800 7503 \n",
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"\n",
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"Описание данных data_frame:\n",
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" price bedrooms bathrooms sqft_living sqft_lot \\\n",
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"count 2.161300e+04 21613.000000 21613.000000 21613.000000 2.161300e+04 \n",
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"mean 5.400881e+05 3.370842 2.114757 2079.899736 1.510697e+04 \n",
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"std 3.671272e+05 0.930062 0.770163 918.440897 4.142051e+04 \n",
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"min 7.500000e+04 0.000000 0.000000 290.000000 5.200000e+02 \n",
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"25% 3.219500e+05 3.000000 1.750000 1427.000000 5.040000e+03 \n",
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"50% 4.500000e+05 3.000000 2.250000 1910.000000 7.618000e+03 \n",
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"75% 6.450000e+05 4.000000 2.500000 2550.000000 1.068800e+04 \n",
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"max 7.700000e+06 33.000000 8.000000 13540.000000 1.651359e+06 \n",
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"\n",
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" floors waterfront view condition grade \\\n",
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"count 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 \n",
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"mean 1.494309 0.007542 0.234303 3.409430 7.656873 \n",
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"std 0.539989 0.086517 0.766318 0.650743 1.175459 \n",
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"min 1.000000 0.000000 0.000000 1.000000 1.000000 \n",
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"25% 1.000000 0.000000 0.000000 3.000000 7.000000 \n",
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"50% 1.500000 0.000000 0.000000 3.000000 7.000000 \n",
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"75% 2.000000 0.000000 0.000000 4.000000 8.000000 \n",
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"max 3.500000 1.000000 4.000000 5.000000 13.000000 \n",
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"\n",
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" sqft_above sqft_basement yr_built yr_renovated zipcode \\\n",
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"count 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 \n",
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"mean 1788.390691 291.509045 1971.005136 84.402258 98077.939805 \n",
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"std 828.090978 442.575043 29.373411 401.679240 53.505026 \n",
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"min 290.000000 0.000000 1900.000000 0.000000 98001.000000 \n",
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"25% 1190.000000 0.000000 1951.000000 0.000000 98033.000000 \n",
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"50% 1560.000000 0.000000 1975.000000 0.000000 98065.000000 \n",
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"75% 2210.000000 560.000000 1997.000000 0.000000 98118.000000 \n",
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"max 9410.000000 4820.000000 2015.000000 2015.000000 98199.000000 \n",
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"\n",
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" lat long sqft_living15 sqft_lot15 \n",
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"count 21613.000000 21613.000000 21613.000000 21613.000000 \n",
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"mean 47.560053 -122.213896 1986.552492 12768.455652 \n",
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"std 0.138564 0.140828 685.391304 27304.179631 \n",
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"min 47.155900 -122.519000 399.000000 651.000000 \n",
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"25% 47.471000 -122.328000 1490.000000 5100.000000 \n",
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"50% 47.571800 -122.230000 1840.000000 7620.000000 \n",
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"75% 47.678000 -122.125000 2360.000000 10083.000000 \n",
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"max 47.777600 -121.315000 6210.000000 871200.000000 \n",
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"\n",
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"Количество строк и столбцов data_frame: (21613, 20)\n",
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"\n",
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"Названия столбцов: Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
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" 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
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" 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
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" 'sqft_living15', 'sqft_lot15'],\n",
|
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" dtype='object')\n",
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"\n",
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"Типы данных каждого столбца:\n",
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"date object\n",
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"price float64\n",
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"bedrooms int64\n",
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"bathrooms float64\n",
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"sqft_living int64\n",
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"sqft_lot int64\n",
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"floors float64\n",
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"waterfront int64\n",
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"view int64\n",
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"condition int64\n",
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"grade int64\n",
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"sqft_above int64\n",
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"sqft_basement int64\n",
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"yr_built int64\n",
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"yr_renovated int64\n",
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"zipcode int64\n",
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"lat float64\n",
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"long float64\n",
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"sqft_living15 int64\n",
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"sqft_lot15 int64\n",
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"dtype: object \n",
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"\n",
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"Количество пропущенных значений в каждом столбце:\n",
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"date 0\n",
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"price 0\n",
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"bedrooms 0\n",
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"bathrooms 0\n",
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"sqft_living 0\n",
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"sqft_lot 0\n",
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"floors 0\n",
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"waterfront 0\n",
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"view 0\n",
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"condition 0\n",
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"grade 0\n",
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"sqft_above 0\n",
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"sqft_basement 0\n",
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"yr_built 0\n",
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"yr_renovated 0\n",
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"zipcode 0\n",
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"lat 0\n",
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"long 0\n",
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"sqft_living15 0\n",
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"sqft_lot15 0\n",
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"dtype: int64 \n",
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"\n"
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]
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}
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],
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"source": [
|
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"# Общая информация о data_frame\n",
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"print(\"Общая информация о data_frame:\")\n",
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"print(data_frame.info(), \"\\n\")\n",
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"\n",
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"# Первые строки\n",
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"print(\"Первые строки data_frame:\")\n",
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"print(data_frame.head(), \"\\n\")\n",
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"\n",
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"# Описание данных\n",
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"print(\"Описание данных data_frame:\")\n",
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"print(data_frame.describe(), \"\\n\")\n",
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"\n",
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"# Количество строк и столбцов \n",
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"print(f\"Количество строк и столбцов data_frame: {data_frame.shape}\\n\")\n",
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"\n",
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"# Названия столбцов\n",
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"print(f\"Названия столбцов: {data_frame.columns}\\n\")\n",
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"\n",
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"# Типы данных каждого столбца\n",
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"print(\"Типы данных каждого столбца:\")\n",
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"print(data_frame.dtypes, \"\\n\")\n",
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"\n",
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"# Количество пропущенных значений\n",
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"print(\"Количество пропущенных значений в каждом столбце:\")\n",
|
||
"print(data_frame.isnull().sum(), \"\\n\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Получение сведений о колонках"
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]
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},
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{
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||
"cell_type": "code",
|
||
"execution_count": 5,
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||
"metadata": {},
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||
"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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||
"text": [
|
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"Список всех столбцов: Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',\n",
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" 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
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" 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
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" 'sqft_living15', 'sqft_lot15'],\n",
|
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" dtype='object')\n",
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"\n",
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"Типы данных каждого столбца:\n",
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"date object\n",
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"price float64\n",
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"bedrooms int64\n",
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"bathrooms float64\n",
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"sqft_living int64\n",
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"sqft_lot int64\n",
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"floors float64\n",
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"waterfront int64\n",
|
||
"view int64\n",
|
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"condition int64\n",
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"grade int64\n",
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"sqft_above int64\n",
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"sqft_basement int64\n",
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"yr_built int64\n",
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"yr_renovated int64\n",
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"zipcode int64\n",
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"lat float64\n",
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"long float64\n",
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"sqft_living15 int64\n",
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"sqft_lot15 int64\n",
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"dtype: object \n",
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"\n",
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"Описание всех столбцов DataFrame:\n",
|
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" date price bedrooms bathrooms \\\n",
|
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"count 21613 2.161300e+04 21613.000000 21613.000000 \n",
|
||
"unique 372 NaN NaN NaN \n",
|
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"top 20140623T000000 NaN NaN NaN \n",
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"freq 142 NaN NaN NaN \n",
|
||
"mean NaN 5.400881e+05 3.370842 2.114757 \n",
|
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"std NaN 3.671272e+05 0.930062 0.770163 \n",
|
||
"min NaN 7.500000e+04 0.000000 0.000000 \n",
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"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",
|
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"\n",
|
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" 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",
|
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"\n",
|
||
" condition grade sqft_above sqft_basement yr_built \\\n",
|
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"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",
|
||
"20141102T000000 1\n",
|
||
"20150131T000000 1\n",
|
||
"20150524T000000 1\n",
|
||
"20140517T000000 1\n",
|
||
"20140727T000000 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": null,
|
||
"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",
|
||
"price_per_bedroom 90000.0\n",
|
||
"total_rooms 3.0\n",
|
||
"is_expensive False\n",
|
||
"floor_area_ratio 0.5\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 ... yr_renovated \\\n",
|
||
"id ... \n",
|
||
"7129300520 5650 1.0 0 0 3 ... 0 \n",
|
||
"6414100192 7242 2.0 0 0 3 ... 1991 \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",
|
||
"\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",
|
||
"\n",
|
||
"[2 rows x 24 columns] \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": null,
|
||
"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": 8,
|
||
"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": 9,
|
||
"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": 10,
|
||
"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": 11,
|
||
"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": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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"
|
||
},
|
||
{
|
||
"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.xticks(rotation=45)\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=\"pink\", alpha=0.5)\n",
|
||
"plt.title(\"Диаграмма рассеяния: Цена vs Площадь\")\n",
|
||
"plt.xlabel(\"Площадь (sqft)\")\n",
|
||
"plt.ylabel(\"Цена\")\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|