1857 lines
291 KiB
Plaintext
1857 lines
291 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": [
|
||
"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": [
|
||
"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",
|
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"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",
|
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" 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": 6,
|
||
"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": 7,
|
||
"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": 13,
|
||
"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.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()\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
|
||
}
|