{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "e626969d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import random" ] }, { "cell_type": "markdown", "id": "74cd3978", "metadata": {}, "source": [ "請從給定的網址讀取本次測驗的資料集：https://github.com/TA-aiacademy/course_3.0/releases/download/Python/housing.csv" ] }, { "cell_type": "code", "execution_count": 2, "id": "61abd321", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('https://github.com/TA-aiacademy/course_3.0/releases/download/Python/housing.csv')" ] }, { "cell_type": "markdown", "id": "d618af3c", "metadata": {}, "source": [ "首先，先查看整份資料集相關資訊。 \n", "hint：info" ] }, { "cell_type": "code", "execution_count": 3, "id": "4b0c2f80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 19743 entries, 0 to 19742\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 longitude 19405 non-null float64\n", " 1 latitude 19382 non-null float64\n", " 2 housing_median_age 19743 non-null float64\n", " 3 total_rooms 19743 non-null float64\n", " 4 total_bedrooms 19743 non-null float64\n", " 5 population 19743 non-null float64\n", " 6 households 19743 non-null float64\n", " 7 median_income 19743 non-null float64\n", " 8 median_house_value 19743 non-null float64\n", " 9 ocean_proximity 19575 non-null object \n", " 10 level 19743 non-null object \n", "dtypes: float64(9), object(2)\n", "memory usage: 1.7+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "id": "f5713a4e", "metadata": {}, "source": [ "從上面可以看出，longitude、latitude 和 ocean_proximity 具有缺值。 \n", "接下來，試著印出資料集的前 8 筆資料。" ] }, { "cell_type": "code", "execution_count": 4, "id": "5d90d28e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevel
0-118.3833.8036.04421.0702.01433.0624.08.0838500001.0NEAR OCEANH
1-122.2737.4333.01601.0223.0629.0215.015.0001500001.0NEAR OCEANH
2-118.4133.754.0311.051.0128.046.09.8091500001.0NEAR OCEANH
3-118.3333.7733.04244.0595.01534.0557.09.8214500001.0NEAR OCEANH
4-118.3233.7533.02996.0398.01048.0387.09.2670500001.0NEAR OCEANH
5-118.3233.7537.01080.0135.0366.0142.011.6677500001.0NEAR OCEANH
6-118.3233.7424.06097.0794.02248.0806.010.1357500001.0NEAR OCEANH
7-118.3233.7737.0627.095.0259.0106.06.8870500001.0<1H OCEANH
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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "0 -118.38 33.80 36.0 4421.0 702.0 \n", "1 -122.27 37.43 33.0 1601.0 223.0 \n", "2 -118.41 33.75 4.0 311.0 51.0 \n", "3 -118.33 33.77 33.0 4244.0 595.0 \n", "4 -118.32 33.75 33.0 2996.0 398.0 \n", "5 -118.32 33.75 37.0 1080.0 135.0 \n", "6 -118.32 33.74 24.0 6097.0 794.0 \n", "7 -118.32 33.77 37.0 627.0 95.0 \n", "\n", " population households median_income median_house_value ocean_proximity \\\n", "0 1433.0 624.0 8.0838 500001.0 NEAR OCEAN \n", "1 629.0 215.0 15.0001 500001.0 NEAR OCEAN \n", "2 128.0 46.0 9.8091 500001.0 NEAR OCEAN \n", "3 1534.0 557.0 9.8214 500001.0 NEAR OCEAN \n", "4 1048.0 387.0 9.2670 500001.0 NEAR OCEAN \n", "5 366.0 142.0 11.6677 500001.0 NEAR OCEAN \n", "6 2248.0 806.0 10.1357 500001.0 NEAR OCEAN \n", "7 259.0 106.0 6.8870 500001.0 <1H OCEAN \n", "\n", " level \n", "0 H \n", "1 H \n", "2 H \n", "3 H \n", "4 H \n", "5 H \n", "6 H \n", "7 H " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(8)" ] }, { "cell_type": "markdown", "id": "b37147a5", "metadata": {}, "source": [ "再來，試著檢查資料集的大小 (形狀)，了解資料及共有幾筆資料和幾個欄位。" ] }, { "cell_type": "code", "execution_count": 5, "id": "7f2d91ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(19743, 11)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "markdown", "id": "b07b844e", "metadata": {}, "source": [ "觀察了資料後，第一步，我們要先處理缺失值。 \n", "首先先找出含有 na 的全部資料。" ] }, { "cell_type": "code", "execution_count": 6, "id": "c1f5ef48", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevel
64-121.98NaN19.0755.093.0267.099.015.0000500001.0<1H OCEANH
84-118.49NaN31.04066.0951.01532.0868.04.8125500001.0<1H OCEANH
90NaN37.7952.01817.0535.0800.0487.03.9750500001.0NEAR BAYH
99NaN37.4435.01140.0193.0486.0199.04.6908500001.0NEAR OCEANH
114NaN37.5046.030.04.013.05.015.0001500001.0NEAR BAYH
....................................
19625-119.77NaN39.01287.0332.01386.0306.01.522746900.0INLANDL
19640-119.20NaN32.01355.0363.01427.0384.01.344445600.0INLANDL
19649-114.63NaN15.01448.0378.0949.0300.00.858545000.0INLANDL
19670-119.00NaN40.0850.0227.0764.0186.00.940743600.0INLANDL
19742-122.74NaN16.0255.073.085.038.01.660714999.0INLANDL
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860 rows × 11 columns

\n", "
" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "64 -121.98 NaN 19.0 755.0 93.0 \n", "84 -118.49 NaN 31.0 4066.0 951.0 \n", "90 NaN 37.79 52.0 1817.0 535.0 \n", "99 NaN 37.44 35.0 1140.0 193.0 \n", "114 NaN 37.50 46.0 30.0 4.0 \n", "... ... ... ... ... ... \n", "19625 -119.77 NaN 39.0 1287.0 332.0 \n", "19640 -119.20 NaN 32.0 1355.0 363.0 \n", "19649 -114.63 NaN 15.0 1448.0 378.0 \n", "19670 -119.00 NaN 40.0 850.0 227.0 \n", "19742 -122.74 NaN 16.0 255.0 73.0 \n", "\n", " population households median_income median_house_value \\\n", "64 267.0 99.0 15.0000 500001.0 \n", "84 1532.0 868.0 4.8125 500001.0 \n", "90 800.0 487.0 3.9750 500001.0 \n", "99 486.0 199.0 4.6908 500001.0 \n", "114 13.0 5.0 15.0001 500001.0 \n", "... ... ... ... ... \n", "19625 1386.0 306.0 1.5227 46900.0 \n", "19640 1427.0 384.0 1.3444 45600.0 \n", "19649 949.0 300.0 0.8585 45000.0 \n", "19670 764.0 186.0 0.9407 43600.0 \n", "19742 85.0 38.0 1.6607 14999.0 \n", "\n", " ocean_proximity level \n", "64 <1H OCEAN H \n", "84 <1H OCEAN H \n", "90 NEAR BAY H \n", "99 NEAR OCEAN H \n", "114 NEAR BAY H \n", "... ... ... \n", "19625 INLAND L \n", "19640 INLAND L \n", "19649 INLAND L \n", "19670 INLAND L \n", "19742 INLAND L \n", "\n", "[860 rows x 11 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.isna().any(axis=1)]" ] }, { "cell_type": "markdown", "id": "02ed764b", "metadata": {}, "source": [ "從上面可以知道，所有有缺失值的資料筆數總共是 860 筆。 \n", "我們也可以只取某一欄位含有缺失值的資料出來觀察，接下來，請試著取出 ocean_proximity 為缺失值的資料，並存到另一變數當中。" ] }, { "cell_type": "code", "execution_count": 7, "id": "a6b97490", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevel
162-118.2134.2035.03646.0552.01409.0534.06.3794500001.0NaNH
165-118.1834.1743.04269.0591.01467.0582.09.0702500001.0NaNH
166-122.0037.2336.03191.0430.01234.0440.09.0704500001.0NaNH
237-118.4033.8936.02127.0314.0807.0306.08.1596500001.0NaNH
278-117.8733.5944.02499.0396.0910.0374.06.6544500001.0NaNH
....................................
17148-118.2534.0128.0481.0136.0596.0128.01.239690300.0NaNL
17160-118.2433.9537.0441.0125.0390.098.01.651390200.0NaNL
18214-117.7133.6126.03046.0726.0888.0663.02.684874100.0NaNL
18491-118.2934.0642.03894.02293.06846.02156.01.555370000.0NaNL
19547-123.7241.0919.01970.0431.01166.0363.01.820850000.0NaNL
\n", "

168 rows × 11 columns

\n", "
" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "162 -118.21 34.20 35.0 3646.0 552.0 \n", "165 -118.18 34.17 43.0 4269.0 591.0 \n", "166 -122.00 37.23 36.0 3191.0 430.0 \n", "237 -118.40 33.89 36.0 2127.0 314.0 \n", "278 -117.87 33.59 44.0 2499.0 396.0 \n", "... ... ... ... ... ... \n", "17148 -118.25 34.01 28.0 481.0 136.0 \n", "17160 -118.24 33.95 37.0 441.0 125.0 \n", "18214 -117.71 33.61 26.0 3046.0 726.0 \n", "18491 -118.29 34.06 42.0 3894.0 2293.0 \n", "19547 -123.72 41.09 19.0 1970.0 431.0 \n", "\n", " population households median_income median_house_value \\\n", "162 1409.0 534.0 6.3794 500001.0 \n", "165 1467.0 582.0 9.0702 500001.0 \n", "166 1234.0 440.0 9.0704 500001.0 \n", "237 807.0 306.0 8.1596 500001.0 \n", "278 910.0 374.0 6.6544 500001.0 \n", "... ... ... ... ... \n", "17148 596.0 128.0 1.2396 90300.0 \n", "17160 390.0 98.0 1.6513 90200.0 \n", "18214 888.0 663.0 2.6848 74100.0 \n", "18491 6846.0 2156.0 1.5553 70000.0 \n", "19547 1166.0 363.0 1.8208 50000.0 \n", "\n", " ocean_proximity level \n", "162 NaN H \n", "165 NaN H \n", "166 NaN H \n", "237 NaN H \n", "278 NaN H \n", "... ... ... \n", "17148 NaN L \n", "17160 NaN L \n", "18214 NaN L \n", "18491 NaN L \n", "19547 NaN L \n", "\n", "[168 rows x 11 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "na_df = df[df.ocean_proximity.isna()]\n", "na_df" ] }, { "cell_type": "markdown", "id": "bbb93fbf", "metadata": {}, "source": [ "觀察完有缺失值的資料後，我們要開始對缺失值進行處理。 \n", "首先，ocean_proximity 為類別型資料，請試著列出該欄位所有類別和類別的個數。" ] }, { "cell_type": "code", "execution_count": 8, "id": "71e63c9c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<1H OCEAN 8579\n", "INLAND 6261\n", "NEAR OCEAN 2533\n", "NEAR BAY 2197\n", "ISLAND 5\n", "Name: ocean_proximity, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ocean_proximity.value_counts()" ] }, { "cell_type": "markdown", "id": "62ac9093", "metadata": {}, "source": [ "請試著用個數最多的類別填補 ocean_proximity 的缺失值" ] }, { "cell_type": "code", "execution_count": 9, "id": "e49151a4", "metadata": {}, "outputs": [], "source": [ "df['ocean_proximity'].fillna(value='<1H OCEAN', inplace=True)" ] }, { "cell_type": "markdown", "id": "5eeac081", "metadata": {}, "source": [ "剩下含有缺失值的 longitude、latitude 和 total_bedrooms 為數值型資料。 \n", "接著，請清除所有具缺失值的資料。" ] }, { "cell_type": "code", "execution_count": 10, "id": "6f58a287", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevel
0-118.3833.8036.04421.0702.01433.0624.08.0838500001.0NEAR OCEANH
1-122.2737.4333.01601.0223.0629.0215.015.0001500001.0NEAR OCEANH
2-118.4133.754.0311.051.0128.046.09.8091500001.0NEAR OCEANH
3-118.3333.7733.04244.0595.01534.0557.09.8214500001.0NEAR OCEANH
4-118.3233.7533.02996.0398.01048.0387.09.2670500001.0NEAR OCEANH
....................................
19737-117.1632.7152.0845.0451.01230.0375.01.091822500.0NEAR OCEANL
19738-118.3334.1539.0493.0168.0259.0138.02.366717500.0<1H OCEANL
19739-123.1740.3136.098.028.018.08.00.536014999.0INLANDL
19740-117.0236.4019.0619.0239.0490.0164.02.100014999.0INLANDL
19741-117.8634.2452.0803.0267.0628.0225.04.193214999.0INLANDL
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19044 rows × 11 columns

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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "0 -118.38 33.80 36.0 4421.0 702.0 \n", "1 -122.27 37.43 33.0 1601.0 223.0 \n", "2 -118.41 33.75 4.0 311.0 51.0 \n", "3 -118.33 33.77 33.0 4244.0 595.0 \n", "4 -118.32 33.75 33.0 2996.0 398.0 \n", "... ... ... ... ... ... \n", "19737 -117.16 32.71 52.0 845.0 451.0 \n", "19738 -118.33 34.15 39.0 493.0 168.0 \n", "19739 -123.17 40.31 36.0 98.0 28.0 \n", "19740 -117.02 36.40 19.0 619.0 239.0 \n", "19741 -117.86 34.24 52.0 803.0 267.0 \n", "\n", " population households median_income median_house_value \\\n", "0 1433.0 624.0 8.0838 500001.0 \n", "1 629.0 215.0 15.0001 500001.0 \n", "2 128.0 46.0 9.8091 500001.0 \n", "3 1534.0 557.0 9.8214 500001.0 \n", "4 1048.0 387.0 9.2670 500001.0 \n", "... ... ... ... ... \n", "19737 1230.0 375.0 1.0918 22500.0 \n", "19738 259.0 138.0 2.3667 17500.0 \n", "19739 18.0 8.0 0.5360 14999.0 \n", "19740 490.0 164.0 2.1000 14999.0 \n", "19741 628.0 225.0 4.1932 14999.0 \n", "\n", " ocean_proximity level \n", "0 NEAR OCEAN H \n", "1 NEAR OCEAN H \n", "2 NEAR OCEAN H \n", "3 NEAR OCEAN H \n", "4 NEAR OCEAN H \n", "... ... ... \n", "19737 NEAR OCEAN L \n", "19738 <1H OCEAN L \n", "19739 INLAND L \n", "19740 INLAND L \n", "19741 INLAND L \n", "\n", "[19044 rows x 11 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.dropna()\n", "df" ] }, { "cell_type": "markdown", "id": "b841d2c6", "metadata": {}, "source": [ "最後，再顯示一次所有資料的相關資資訊，確定是否還有缺失值。" ] }, { "cell_type": "code", "execution_count": 11, "id": "72379b1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 19044 entries, 0 to 19741\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 longitude 19044 non-null float64\n", " 1 latitude 19044 non-null float64\n", " 2 housing_median_age 19044 non-null float64\n", " 3 total_rooms 19044 non-null float64\n", " 4 total_bedrooms 19044 non-null float64\n", " 5 population 19044 non-null float64\n", " 6 households 19044 non-null float64\n", " 7 median_income 19044 non-null float64\n", " 8 median_house_value 19044 non-null float64\n", " 9 ocean_proximity 19044 non-null object \n", " 10 level 19044 non-null object \n", "dtypes: float64(9), object(2)\n", "memory usage: 1.7+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "id": "4c5209d7", "metadata": {}, "source": [ "使用 describe 顯示各個欄位的統計量" ] }, { "cell_type": "code", "execution_count": 12, "id": "837c1da6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
count19044.00000019044.00000019044.00000019044.00000019044.00000019044.00000019044.00000019044.00000019044.000000
mean-119.56791235.62883028.6191452640.316110538.8858961427.430582500.3367463.870969207054.636106
std2.0003902.13373612.5764012184.806006422.6438791138.903177383.4480111.895007115475.650978
min-124.35000032.5400001.0000002.0000001.0000003.0000001.0000000.49990014999.000000
25%-121.79000033.93000018.0000001451.000000296.000000786.750000280.0000002.564300119800.000000
50%-118.49000034.25000029.0000002126.500000435.0000001167.000000410.0000003.534100180000.000000
75%-118.01000037.71000037.0000003150.000000648.0000001726.000000606.0000004.738600264900.000000
max-114.31000041.95000052.00000037937.0000006445.00000035682.0000006082.00000015.000100500001.000000
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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms \\\n", "count 19044.000000 19044.000000 19044.000000 19044.000000 \n", "mean -119.567912 35.628830 28.619145 2640.316110 \n", "std 2.000390 2.133736 12.576401 2184.806006 \n", "min -124.350000 32.540000 1.000000 2.000000 \n", "25% -121.790000 33.930000 18.000000 1451.000000 \n", "50% -118.490000 34.250000 29.000000 2126.500000 \n", "75% -118.010000 37.710000 37.000000 3150.000000 \n", "max -114.310000 41.950000 52.000000 37937.000000 \n", "\n", " total_bedrooms population households median_income \\\n", "count 19044.000000 19044.000000 19044.000000 19044.000000 \n", "mean 538.885896 1427.430582 500.336746 3.870969 \n", "std 422.643879 1138.903177 383.448011 1.895007 \n", "min 1.000000 3.000000 1.000000 0.499900 \n", "25% 296.000000 786.750000 280.000000 2.564300 \n", "50% 435.000000 1167.000000 410.000000 3.534100 \n", "75% 648.000000 1726.000000 606.000000 4.738600 \n", "max 6445.000000 35682.000000 6082.000000 15.000100 \n", "\n", " median_house_value \n", "count 19044.000000 \n", "mean 207054.636106 \n", "std 115475.650978 \n", "min 14999.000000 \n", "25% 119800.000000 \n", "50% 180000.000000 \n", "75% 264900.000000 \n", "max 500001.000000 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "daa49cc0", "metadata": {}, "source": [ "請依照 median_house_value 由大至小排序。" ] }, { "cell_type": "code", "execution_count": 13, "id": "ea991192", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevel
0-118.3833.8036.04421.0702.01433.0624.08.0838500001.0NEAR OCEANH
624-118.4834.0536.02143.0434.0751.0396.06.7496500001.0<1H OCEANH
613-122.1437.4552.03841.0537.01391.0540.07.8647500001.0NEAR BAYH
614-122.1537.4652.01803.0257.0683.0259.010.9508500001.0NEAR BAYH
615-122.1537.4639.0906.0109.0353.0112.010.3942500001.0NEAR BAYH
....................................
19737-117.1632.7152.0845.0451.01230.0375.01.091822500.0NEAR OCEANL
19738-118.3334.1539.0493.0168.0259.0138.02.366717500.0<1H OCEANL
19739-123.1740.3136.098.028.018.08.00.536014999.0INLANDL
19740-117.0236.4019.0619.0239.0490.0164.02.100014999.0INLANDL
19741-117.8634.2452.0803.0267.0628.0225.04.193214999.0INLANDL
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19044 rows × 11 columns

\n", "
" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "0 -118.38 33.80 36.0 4421.0 702.0 \n", "624 -118.48 34.05 36.0 2143.0 434.0 \n", "613 -122.14 37.45 52.0 3841.0 537.0 \n", "614 -122.15 37.46 52.0 1803.0 257.0 \n", "615 -122.15 37.46 39.0 906.0 109.0 \n", "... ... ... ... ... ... \n", "19737 -117.16 32.71 52.0 845.0 451.0 \n", "19738 -118.33 34.15 39.0 493.0 168.0 \n", "19739 -123.17 40.31 36.0 98.0 28.0 \n", "19740 -117.02 36.40 19.0 619.0 239.0 \n", "19741 -117.86 34.24 52.0 803.0 267.0 \n", "\n", " population households median_income median_house_value \\\n", "0 1433.0 624.0 8.0838 500001.0 \n", "624 751.0 396.0 6.7496 500001.0 \n", "613 1391.0 540.0 7.8647 500001.0 \n", "614 683.0 259.0 10.9508 500001.0 \n", "615 353.0 112.0 10.3942 500001.0 \n", "... ... ... ... ... \n", "19737 1230.0 375.0 1.0918 22500.0 \n", "19738 259.0 138.0 2.3667 17500.0 \n", "19739 18.0 8.0 0.5360 14999.0 \n", "19740 490.0 164.0 2.1000 14999.0 \n", "19741 628.0 225.0 4.1932 14999.0 \n", "\n", " ocean_proximity level \n", "0 NEAR OCEAN H \n", "624 <1H OCEAN H \n", "613 NEAR BAY H \n", "614 NEAR BAY H \n", "615 NEAR BAY H \n", "... ... ... \n", "19737 NEAR OCEAN L \n", "19738 <1H OCEAN L \n", "19739 INLAND L \n", "19740 INLAND L \n", "19741 INLAND L \n", "\n", "[19044 rows x 11 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.sort_values(by=['median_house_value'], ascending=False)\n", "df" ] }, { "cell_type": "markdown", "id": "3b37b0ac", "metadata": {}, "source": [ "請畫出 median_house_value 的直方圖 (hist)、機率密度統計圖 (kde)、盒型圖 (box)" ] }, { "cell_type": "code", "execution_count": 14, "id": "92c6c79f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.median_house_value.plot.hist()" ] }, { "cell_type": "code", "execution_count": 15, "id": "4972021f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.median_house_value.plot.kde()" ] }, { "cell_type": "code", "execution_count": 16, "id": "2177abd5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.median_house_value.plot.box()" ] }, { "cell_type": "markdown", "id": "c7cb67a9", "metadata": {}, "source": [ "請依照 median_house_value 的高低， \n", "將低於(含) 25 百分位的分為 'L'； \n", "25 百分位至 75 百分位的分為 'M'； \n", "高於 (含) 75 百分位的分為 'H'，並存至 'level' 欄位中" ] }, { "cell_type": "code", "execution_count": 17, "id": "1d2be08c", "metadata": {}, "outputs": [], "source": [ "def value_level(row):\n", " if row['median_house_value'] <= df.median_house_value.quantile(.25):\n", " return 'L'\n", " elif row['median_house_value'] >= df.median_house_value.quantile(.75):\n", " return 'H'\n", " return 'M'\n", "\n", "df['level'] = df.apply(value_level, axis=1)" ] }, { "cell_type": "markdown", "id": "69e37b10", "metadata": {}, "source": [ "請顯示 ocean_proximity 和 level 的統計量" ] }, { "cell_type": "code", "execution_count": 18, "id": "374c0448", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ocean_proximitylevel
count1904419044
unique53
top<1H OCEANM
freq84529515
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" ], "text/plain": [ " ocean_proximity level\n", "count 19044 19044\n", "unique 5 3\n", "top <1H OCEAN M\n", "freq 8452 9515" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe(include='O')" ] }, { "cell_type": "markdown", "id": "409946fd", "metadata": {}, "source": [ "請使用 pivot_table ，設定 level 的各個類別為列、ocean_proximity 的各個類別為欄， \n", "並且計算每個組別下 median_house_value 的平均。" ] }, { "cell_type": "code", "execution_count": 19, "id": "0863dd9f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ocean_proximity<1H OCEANINLANDISLANDNEAR BAYNEAR OCEAN
level
H370457.166792350386.717472380440.0381572.051078377976.590387
L100190.64856782888.727556NaN94795.47325193442.121212
M191283.848998163309.594595NaN190795.656566188655.060034
\n", "
" ], "text/plain": [ "ocean_proximity <1H OCEAN INLAND ISLAND NEAR BAY \\\n", "level \n", "H 370457.166792 350386.717472 380440.0 381572.051078 \n", "L 100190.648567 82888.727556 NaN 94795.473251 \n", "M 191283.848998 163309.594595 NaN 190795.656566 \n", "\n", "ocean_proximity NEAR OCEAN \n", "level \n", "H 377976.590387 \n", "L 93442.121212 \n", "M 188655.060034 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.pivot_table(values='median_house_value', index='level', columns='ocean_proximity', aggfunc = 'mean')" ] }, { "cell_type": "markdown", "id": "c6044d98", "metadata": {}, "source": [ "上述發現， ISLAND 類別中有 NaN ，請列出所有 ocean_proximity 為 ISLAND 的資料觀察原因。" ] }, { "cell_type": "code", "execution_count": 20, "id": "e5ac7f7e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevel
1212-118.3233.3452.0996.0264.0341.0160.02.7361450000.0ISLANDH
1211-118.3233.3527.01675.0521.0744.0331.02.1579450000.0ISLANDH
1535-118.3333.3452.02359.0591.01100.0431.02.8333414700.0ISLANDH
3677-118.3233.3352.02127.0512.0733.0288.03.3906300000.0ISLANDH
4034-118.4833.4329.0716.0214.0422.0173.02.6042287500.0ISLANDH
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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "1212 -118.32 33.34 52.0 996.0 264.0 \n", "1211 -118.32 33.35 27.0 1675.0 521.0 \n", "1535 -118.33 33.34 52.0 2359.0 591.0 \n", "3677 -118.32 33.33 52.0 2127.0 512.0 \n", "4034 -118.48 33.43 29.0 716.0 214.0 \n", "\n", " population households median_income median_house_value \\\n", "1212 341.0 160.0 2.7361 450000.0 \n", "1211 744.0 331.0 2.1579 450000.0 \n", "1535 1100.0 431.0 2.8333 414700.0 \n", "3677 733.0 288.0 3.3906 300000.0 \n", "4034 422.0 173.0 2.6042 287500.0 \n", "\n", " ocean_proximity level \n", "1212 ISLAND H \n", "1211 ISLAND H \n", "1535 ISLAND H \n", "3677 ISLAND H \n", "4034 ISLAND H " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.ocean_proximity=='ISLAND']" ] }, { "cell_type": "markdown", "id": "e86135d3", "metadata": {}, "source": [ "請使用 groupby ，算出 level 欄位各個類別的 median_house_value 平均值" ] }, { "cell_type": "code", "execution_count": 21, "id": "0f195198", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "level\n", "H 372901.363503\n", "L 86632.619467\n", "M 184384.077772\n", "Name: median_house_value, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(by='level')['median_house_value'].mean()" ] }, { "cell_type": "markdown", "id": "a90d3513", "metadata": {}, "source": [ "最後，請使用 left join 的方式，將 level 欄位各個類別的 median_house_value 平均值併入到資料集當中。" ] }, { "cell_type": "code", "execution_count": 22, "id": "00d16950", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitylevelmean
0-118.3833.8036.04421.0702.01433.0624.08.0838500001.0NEAR OCEANH372901.363503
1-118.4834.0536.02143.0434.0751.0396.06.7496500001.0<1H OCEANH372901.363503
2-122.1437.4552.03841.0537.01391.0540.07.8647500001.0NEAR BAYH372901.363503
3-122.1537.4652.01803.0257.0683.0259.010.9508500001.0NEAR BAYH372901.363503
4-122.1537.4639.0906.0109.0353.0112.010.3942500001.0NEAR BAYH372901.363503
.......................................
19039-117.1632.7152.0845.0451.01230.0375.01.091822500.0NEAR OCEANL86632.619467
19040-118.3334.1539.0493.0168.0259.0138.02.366717500.0<1H OCEANL86632.619467
19041-123.1740.3136.098.028.018.08.00.536014999.0INLANDL86632.619467
19042-117.0236.4019.0619.0239.0490.0164.02.100014999.0INLANDL86632.619467
19043-117.8634.2452.0803.0267.0628.0225.04.193214999.0INLANDL86632.619467
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19044 rows × 12 columns

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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "0 -118.38 33.80 36.0 4421.0 702.0 \n", "1 -118.48 34.05 36.0 2143.0 434.0 \n", "2 -122.14 37.45 52.0 3841.0 537.0 \n", "3 -122.15 37.46 52.0 1803.0 257.0 \n", "4 -122.15 37.46 39.0 906.0 109.0 \n", "... ... ... ... ... ... \n", "19039 -117.16 32.71 52.0 845.0 451.0 \n", "19040 -118.33 34.15 39.0 493.0 168.0 \n", "19041 -123.17 40.31 36.0 98.0 28.0 \n", "19042 -117.02 36.40 19.0 619.0 239.0 \n", "19043 -117.86 34.24 52.0 803.0 267.0 \n", "\n", " population households median_income median_house_value \\\n", "0 1433.0 624.0 8.0838 500001.0 \n", "1 751.0 396.0 6.7496 500001.0 \n", "2 1391.0 540.0 7.8647 500001.0 \n", "3 683.0 259.0 10.9508 500001.0 \n", "4 353.0 112.0 10.3942 500001.0 \n", "... ... ... ... ... \n", "19039 1230.0 375.0 1.0918 22500.0 \n", "19040 259.0 138.0 2.3667 17500.0 \n", "19041 18.0 8.0 0.5360 14999.0 \n", "19042 490.0 164.0 2.1000 14999.0 \n", "19043 628.0 225.0 4.1932 14999.0 \n", "\n", " ocean_proximity level mean \n", "0 NEAR OCEAN H 372901.363503 \n", "1 <1H OCEAN H 372901.363503 \n", "2 NEAR BAY H 372901.363503 \n", "3 NEAR BAY H 372901.363503 \n", "4 NEAR BAY H 372901.363503 \n", "... ... ... ... \n", "19039 NEAR OCEAN L 86632.619467 \n", "19040 <1H OCEAN L 86632.619467 \n", "19041 INLAND L 86632.619467 \n", "19042 INLAND L 86632.619467 \n", "19043 INLAND L 86632.619467 \n", "\n", "[19044 rows x 12 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp = pd.DataFrame(df.groupby(by='level')['median_house_value'].mean()).rename(columns={'median_house_value':'mean'})\n", "df = pd.merge(df, tmp, on='level', how='left')\n", "df" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 5 }