「新国民生活指標データ」を例に説明します。
# 数値計算やデータフレーム操作に関するライブラリをインポートする
import numpy as np
import pandas as pd
from pandas.tools import plotting # 高度なプロットを行うツールのインポート
# 「#」(シャープ)以降の文字はプログラムに影響しません。
# 図やグラフを図示するためのライブラリをインポートする。
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
%matplotlib inline
# URL によるリソースへのアクセスを提供するライブラリをインポートする。
# import urllib # Python 2 の場合
import urllib.request # Python 3 の場合
url = 'https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/PLIlive_dataJ.txt'
# 指定したURLからリソースをダウンロードし、名前をつける。
# urllib.urlretrieve(url, 'PLIlive_dataJ.txt') # Python 2 の場合
urllib.request.urlretrieve(url, 'PLIlive_dataJ.txt') # Python 3 の場合
('PLIlive_dataJ.txt', <http.client.HTTPMessage at 0x10b7d17b8>)
# データの読み込み
df = pd.read_csv('PLIlive_dataJ.txt', sep='\t', index_col=0)
df
NonRep | OverMin | Rent | HomeOwn | CompPol | NumClime | NumLarc | TrafAcci | Fire | DspRubb | ... | Sunshine | NumMat | AreaResi | Transpt | AreaPark | Sewarage | Recycle | AmtRubb | AvgMin | Pavement | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Pref | |||||||||||||||||||||
Hokkaido | 7.67 | 94.3 | 1510 | 54.0 | 15.0 | 7.6 | 206 | 451.8 | 77.3 | 44.78 | ... | 56.16 | 11.8 | 306 | 90.9 | 25.5 | 77.86 | 4.9 | 1655.0 | 24 | 19.9 |
Aomori | 7.80 | 95.9 | 1480 | 71.6 | 32.8 | 4.5 | 103 | 558.2 | 88.1 | 66.29 | ... | 61.99 | 12.6 | 356 | 87.1 | 12.9 | 36.00 | 3.2 | 1363.6 | 23 | 24.9 |
Iwate | 6.93 | 95.0 | 1643 | 72.8 | 22.0 | 6.3 | 115 | 388.9 | 65.6 | 79.81 | ... | 72.20 | 12.4 | 365 | 80.2 | 9.6 | 35.50 | 11.3 | 940.0 | 24 | 14.4 |
Miyagi | 7.26 | 93.9 | 2257 | 60.7 | 34.3 | 9.1 | 272 | 471.1 | 81.7 | 83.99 | ... | 67.29 | 10.8 | 358 | 85.1 | 11.7 | 40.10 | 16.2 | 1102.9 | 29 | 26.6 |
Akita | 5.92 | 97.1 | 1554 | 79.6 | 23.1 | 9.1 | 88 | 364.3 | 84.8 | 70.43 | ... | 63.55 | 13.3 | 389 | 84.9 | 15.8 | 31.90 | 10.4 | 1203.8 | 22 | 15.7 |
Yamagata | 5.75 | 96.8 | 1701 | 79.2 | 31.0 | 5.6 | 137 | 496.6 | 94.8 | 81.28 | ... | 74.52 | 11.7 | 398 | 79.7 | 13.4 | 44.60 | 16.5 | 950.2 | 23 | 20.7 |
Fukushima | 7.11 | 93.7 | 1811 | 68.6 | 25.1 | 6.5 | 232 | 618.6 | 94.3 | 83.07 | ... | 74.51 | 11.0 | 374 | 81.9 | 9.2 | 36.90 | 9.5 | 1044.0 | 26 | 14.7 |
Ibaraki | 5.36 | 93.2 | 2063 | 70.5 | 41.8 | 12.5 | 245 | 732.6 | 95.1 | 79.28 | ... | 72.37 | 10.2 | 433 | 72.7 | 7.9 | 47.60 | 12.6 | 1105.5 | 31 | 11.4 |
Tochigi | 5.19 | 93.0 | 2123 | 69.2 | 58.4 | 8.5 | 209 | 700.2 | 92.6 | 82.42 | ... | 73.74 | 10.3 | 402 | 61.9 | 10.4 | 43.10 | 16.2 | 1055.4 | 28 | 20.8 |
Gunma | 5.99 | 93.3 | 1992 | 70.4 | 51.0 | 6.7 | 275 | 854.7 | 80.9 | 84.11 | ... | 74.30 | 10.6 | 363 | 61.4 | 10.5 | 41.10 | 12.3 | 1069.0 | 28 | 16.8 |
Saitama | 4.13 | 90.1 | 3085 | 61.9 | 59.2 | 15.0 | 246 | 596.9 | 70.7 | 85.12 | ... | 58.62 | 9.3 | 245 | 75.4 | 5.2 | 69.60 | 15.0 | 938.1 | 46 | 15.7 |
Chiba | 4.40 | 91.2 | 2908 | 61.1 | 40.7 | 13.1 | 342 | 535.4 | 70.0 | 83.33 | ... | 59.61 | 9.7 | 278 | 84.0 | 5.9 | 60.49 | 19.4 | 1012.1 | 45 | 22.5 |
Tokyo | 6.86 | 78.8 | 4379 | 39.6 | 55.4 | 15.7 | 366 | 528.6 | 76.6 | 82.68 | ... | 44.01 | 8.7 | 156 | 95.4 | 5.1 | 95.85 | 7.9 | 1249.7 | 39 | 58.9 |
Kanagawa | 4.61 | 87.8 | 3507 | 51.5 | 43.2 | 12.6 | 194 | 749.8 | 57.5 | 92.85 | ... | 55.36 | 9.0 | 195 | 90.5 | 4.4 | 89.15 | 10.1 | 1143.4 | 47 | 52.7 |
Niigata | 5.40 | 95.7 | 1847 | 76.9 | 28.9 | 9.8 | 164 | 542.9 | 78.9 | 80.93 | ... | 60.99 | 12.5 | 348 | 84.5 | 7.4 | 39.60 | 6.9 | 1228.6 | 25 | 19.2 |
Toyama | 4.15 | 96.7 | 1874 | 79.8 | 17.0 | 6.2 | 281 | 688.2 | 62.4 | 85.53 | ... | 68.90 | 13.9 | 401 | 80.5 | 11.9 | 51.50 | 13.9 | 972.8 | 28 | 34.6 |
Ishikawa | 3.85 | 95.6 | 2041 | 69.9 | 28.3 | 9.3 | 190 | 736.9 | 56.9 | 55.78 | ... | 55.26 | 13.2 | 293 | 84.0 | 10.2 | 51.60 | 6.8 | 1434.9 | 26 | 23.5 |
Fukui | 4.61 | 95.7 | 1640 | 76.5 | 31.0 | 6.0 | 184 | 555.4 | 64.6 | 81.53 | ... | 63.05 | 12.1 | 328 | 82.5 | 12.8 | 54.00 | 14.0 | 1005.6 | 25 | 25.9 |
Yamanashi | 3.82 | 93.7 | 2123 | 69.6 | 37.2 | 12.4 | 188 | 729.4 | 79.1 | 85.20 | ... | 76.25 | 11.4 | 340 | 77.0 | 7.4 | 36.00 | 12.2 | 1005.1 | 25 | 27.0 |
Nagano | 6.42 | 95.7 | 1881 | 72.9 | 49.4 | 8.5 | 145 | 606.0 | 82.9 | 76.39 | ... | 74.38 | 12.5 | 352 | 77.1 | 8.4 | 50.00 | 17.0 | 963.3 | 27 | 12.9 |
Gifu | 5.13 | 95.4 | 1761 | 73.9 | 46.7 | 5.5 | 225 | 591.0 | 84.4 | 79.65 | ... | 70.72 | 12.3 | 293 | 77.2 | 6.8 | 47.20 | 15.3 | 1028.3 | 29 | 18.2 |
Shizuoka | 4.15 | 92.6 | 2155 | 65.9 | 32.6 | 11.5 | 155 | 814.0 | 81.7 | 85.85 | ... | 65.51 | 10.4 | 269 | 84.5 | 5.3 | 42.50 | 13.3 | 1038.7 | 28 | 24.2 |
Aichi | 3.90 | 91.4 | 2125 | 57.8 | 60.5 | 6.2 | 279 | 648.6 | 78.7 | 83.23 | ... | 61.23 | 10.8 | 275 | 80.1 | 7.3 | 59.30 | 11.6 | 1068.3 | 32 | 31.1 |
Mie | 4.31 | 96.0 | 1620 | 77.8 | 46.5 | 6.5 | 161 | 558.6 | 80.9 | 67.79 | ... | 66.61 | 12.0 | 297 | 77.1 | 6.7 | 32.10 | 10.0 | 1235.5 | 30 | 19.0 |
Shiga | 4.89 | 95.9 | 1677 | 76.5 | 53.2 | 8.4 | 211 | 609.7 | 81.4 | 76.57 | ... | 63.08 | 12.0 | 296 | 77.6 | 10.6 | 61.30 | 12.6 | 1034.5 | 33 | 26.2 |
Kyoto | 4.68 | 88.8 | 2659 | 58.1 | 51.5 | 12.1 | 224 | 674.6 | 46.5 | 84.55 | ... | 50.95 | 10.2 | 188 | 91.0 | 5.0 | 76.99 | 3.9 | 1250.5 | 33 | 32.5 |
Osaka | 5.13 | 85.2 | 2750 | 47.9 | 48.2 | 9.6 | 386 | 627.2 | 73.3 | 91.16 | ... | 45.91 | 8.9 | 159 | 90.8 | 4.8 | 79.31 | 5.6 | 1413.6 | 41 | 72.1 |
Hyogo | 4.36 | 90.8 | 2514 | 59.8 | 46.0 | 6.1 | 206 | 670.0 | 71.8 | 51.59 | ... | 55.91 | 10.2 | 220 | 87.4 | 9.2 | 79.95 | 7.6 | 2466.5 | 36 | 33.0 |
Nara | 5.18 | 93.5 | 2043 | 70.0 | 47.9 | 6.5 | 171 | 537.0 | 73.3 | 85.53 | ... | 62.63 | 11.2 | 253 | 81.7 | 10.0 | 56.00 | 16.4 | 995.9 | 41 | 28.4 |
Wakayama | 6.55 | 93.0 | 1673 | 72.8 | 32.9 | 7.5 | 175 | 735.7 | 73.9 | 79.70 | ... | 64.57 | 10.7 | 219 | 76.6 | 4.5 | 17.70 | 8.5 | 1135.5 | 26 | 49.6 |
Tottori | 3.73 | 94.3 | 1730 | 73.8 | 21.8 | 11.7 | 256 | 450.3 | 88.5 | 83.83 | ... | 62.03 | 11.6 | 329 | 85.4 | 10.1 | 39.50 | 6.4 | 1121.9 | 25 | 31.0 |
Shimane | 5.35 | 95.2 | 1489 | 76.2 | 23.6 | 7.9 | 151 | 371.1 | 86.5 | 69.92 | ... | 64.80 | 11.5 | 300 | 74.7 | 13.6 | 28.50 | 10.5 | 972.8 | 21 | 16.5 |
Okayama | 4.57 | 93.1 | 1822 | 66.7 | 39.1 | 5.1 | 187 | 628.7 | 85.2 | 79.91 | ... | 68.75 | 11.3 | 273 | 76.5 | 9.5 | 47.30 | 11.6 | 1056.3 | 27 | 16.6 |
Hiroshima | 4.94 | 94.3 | 2047 | 60.4 | 35.0 | 8.3 | 228 | 633.5 | 78.8 | 76.10 | ... | 62.11 | 11.3 | 227 | 83.0 | 8.6 | 57.51 | 10.9 | 992.8 | 28 | 29.8 |
Yamaguchi | 4.83 | 94.9 | 1562 | 66.4 | 33.0 | 7.7 | 154 | 605.9 | 69.1 | 75.99 | ... | 68.79 | 11.4 | 282 | 77.2 | 9.0 | 50.50 | 7.6 | 1163.4 | 24 | 26.8 |
Tokushima | 7.39 | 94.3 | 1642 | 70.1 | 43.0 | 5.9 | 116 | 720.2 | 84.2 | 77.56 | ... | 69.28 | 11.4 | 299 | 82.0 | 5.9 | 18.60 | 10.6 | 1001.5 | 25 | 19.7 |
Kagawa | 4.92 | 95.3 | 1786 | 70.5 | 36.1 | 7.8 | 186 | 669.6 | 81.2 | 73.83 | ... | 67.69 | 11.9 | 290 | 75.0 | 8.4 | 34.00 | 8.7 | 1036.2 | 25 | 24.6 |
Ehime | 6.04 | 95.1 | 1639 | 66.7 | 55.9 | 10.3 | 223 | 707.4 | 73.5 | 79.26 | ... | 67.39 | 11.2 | 237 | 77.6 | 9.0 | 36.80 | 7.6 | 1073.1 | 21 | 18.8 |
Kochi | 6.58 | 91.9 | 1684 | 67.3 | 50.5 | 8.0 | 173 | 676.0 | 76.4 | 77.46 | ... | 68.24 | 10.7 | 192 | 85.1 | 13.1 | 24.70 | 7.9 | 991.8 | 24 | 19.7 |
Fukuoka | 4.77 | 91.3 | 1908 | 53.4 | 37.3 | 10.0 | 322 | 918.9 | 74.1 | 84.66 | ... | 59.98 | 10.1 | 288 | 83.5 | 7.9 | 67.09 | 8.5 | 1135.3 | 31 | 15.0 |
Saga | 5.25 | 95.2 | 1544 | 72.4 | 50.6 | 8.2 | 148 | 599.0 | 97.1 | 80.82 | ... | 74.51 | 10.5 | 326 | 74.4 | 7.4 | 29.60 | 11.7 | 947.0 | 23 | 25.0 |
Nagasaki | 5.04 | 91.8 | 1684 | 64.9 | 28.4 | 7.9 | 109 | 472.5 | 79.0 | 75.60 | ... | 64.72 | 9.9 | 246 | 84.4 | 12.1 | 45.60 | 7.1 | 1168.5 | 28 | 34.3 |
Kumamoto | 6.23 | 92.6 | 1754 | 66.4 | 23.0 | 7.6 | 152 | 612.0 | 80.1 | 82.49 | ... | 70.04 | 10.2 | 335 | 80.2 | 6.8 | 44.50 | 8.9 | 927.4 | 23 | 23.1 |
Oita | 4.77 | 95.0 | 1599 | 64.5 | 57.9 | 6.8 | 128 | 551.9 | 72.3 | 69.59 | ... | 70.88 | 11.1 | 293 | 82.9 | 7.8 | 38.80 | 7.9 | 1262.5 | 25 | 31.8 |
Miyazaki | 2.64 | 93.9 | 1587 | 68.9 | 50.6 | 8.3 | 209 | 360.2 | 82.3 | 66.98 | ... | 78.52 | 10.0 | 323 | 76.8 | 14.1 | 38.50 | 11.8 | 1151.2 | 21 | 19.6 |
Kagoshima | 4.17 | 94.1 | 1673 | 69.3 | 30.1 | 7.8 | 145 | 641.7 | 75.1 | 63.87 | ... | 69.89 | 10.1 | 295 | 83.2 | 9.3 | 39.00 | 3.1 | 1178.6 | 21 | 16.1 |
Okinawa | 8.23 | 86.3 | 1889 | 55.9 | 24.4 | 10.6 | 380 | 241.5 | 58.4 | 68.99 | ... | 58.37 | 8.4 | 282 | 78.5 | 7.1 | 52.00 | 5.0 | 1040.6 | 24 | 40.7 |
47 rows × 23 columns
df.T
Pref | Hokkaido | Aomori | Iwate | Miyagi | Akita | Yamagata | Fukushima | Ibaraki | Tochigi | Gunma | ... | Ehime | Kochi | Fukuoka | Saga | Nagasaki | Kumamoto | Oita | Miyazaki | Kagoshima | Okinawa |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
NonRep | 7.67 | 7.80 | 6.93 | 7.26 | 5.92 | 5.75 | 7.11 | 5.36 | 5.19 | 5.99 | ... | 6.04 | 6.58 | 4.77 | 5.25 | 5.04 | 6.23 | 4.77 | 2.64 | 4.17 | 8.23 |
OverMin | 94.30 | 95.90 | 95.00 | 93.90 | 97.10 | 96.80 | 93.70 | 93.20 | 93.00 | 93.30 | ... | 95.10 | 91.90 | 91.30 | 95.20 | 91.80 | 92.60 | 95.00 | 93.90 | 94.10 | 86.30 |
Rent | 1510.00 | 1480.00 | 1643.00 | 2257.00 | 1554.00 | 1701.00 | 1811.00 | 2063.00 | 2123.00 | 1992.00 | ... | 1639.00 | 1684.00 | 1908.00 | 1544.00 | 1684.00 | 1754.00 | 1599.00 | 1587.00 | 1673.00 | 1889.00 |
HomeOwn | 54.00 | 71.60 | 72.80 | 60.70 | 79.60 | 79.20 | 68.60 | 70.50 | 69.20 | 70.40 | ... | 66.70 | 67.30 | 53.40 | 72.40 | 64.90 | 66.40 | 64.50 | 68.90 | 69.30 | 55.90 |
CompPol | 15.00 | 32.80 | 22.00 | 34.30 | 23.10 | 31.00 | 25.10 | 41.80 | 58.40 | 51.00 | ... | 55.90 | 50.50 | 37.30 | 50.60 | 28.40 | 23.00 | 57.90 | 50.60 | 30.10 | 24.40 |
NumClime | 7.60 | 4.50 | 6.30 | 9.10 | 9.10 | 5.60 | 6.50 | 12.50 | 8.50 | 6.70 | ... | 10.30 | 8.00 | 10.00 | 8.20 | 7.90 | 7.60 | 6.80 | 8.30 | 7.80 | 10.60 |
NumLarc | 206.00 | 103.00 | 115.00 | 272.00 | 88.00 | 137.00 | 232.00 | 245.00 | 209.00 | 275.00 | ... | 223.00 | 173.00 | 322.00 | 148.00 | 109.00 | 152.00 | 128.00 | 209.00 | 145.00 | 380.00 |
TrafAcci | 451.80 | 558.20 | 388.90 | 471.10 | 364.30 | 496.60 | 618.60 | 732.60 | 700.20 | 854.70 | ... | 707.40 | 676.00 | 918.90 | 599.00 | 472.50 | 612.00 | 551.90 | 360.20 | 641.70 | 241.50 |
Fire | 77.30 | 88.10 | 65.60 | 81.70 | 84.80 | 94.80 | 94.30 | 95.10 | 92.60 | 80.90 | ... | 73.50 | 76.40 | 74.10 | 97.10 | 79.00 | 80.10 | 72.30 | 82.30 | 75.10 | 58.40 |
DspRubb | 44.78 | 66.29 | 79.81 | 83.99 | 70.43 | 81.28 | 83.07 | 79.28 | 82.42 | 84.11 | ... | 79.26 | 77.46 | 84.66 | 80.82 | 75.60 | 82.49 | 69.59 | 66.98 | 63.87 | 68.99 |
Sidewalk | 21.70 | 10.90 | 8.30 | 13.80 | 8.30 | 15.00 | 8.00 | 7.80 | 11.00 | 6.80 | ... | 8.20 | 9.10 | 13.30 | 11.90 | 8.70 | 8.40 | 10.10 | 9.70 | 10.00 | 24.10 |
MedFacil | 57.30 | 30.10 | 34.10 | 49.40 | 31.40 | 40.90 | 38.10 | 30.90 | 47.90 | 44.90 | ... | 53.10 | 47.50 | 61.80 | 45.90 | 58.00 | 44.10 | 46.50 | 45.10 | 46.40 | 51.50 |
OverOrd | 53.71 | 57.73 | 55.65 | 45.44 | 63.89 | 54.28 | 47.43 | 40.75 | 41.22 | 41.66 | ... | 45.62 | 39.98 | 39.20 | 43.73 | 37.99 | 39.73 | 47.11 | 38.52 | 39.88 | 30.35 |
Sunshine | 56.16 | 61.99 | 72.20 | 67.29 | 63.55 | 74.52 | 74.51 | 72.37 | 73.74 | 74.30 | ... | 67.39 | 68.24 | 59.98 | 74.51 | 64.72 | 70.04 | 70.88 | 78.52 | 69.89 | 58.37 |
NumMat | 11.80 | 12.60 | 12.40 | 10.80 | 13.30 | 11.70 | 11.00 | 10.20 | 10.30 | 10.60 | ... | 11.20 | 10.70 | 10.10 | 10.50 | 9.90 | 10.20 | 11.10 | 10.00 | 10.10 | 8.40 |
AreaResi | 306.00 | 356.00 | 365.00 | 358.00 | 389.00 | 398.00 | 374.00 | 433.00 | 402.00 | 363.00 | ... | 237.00 | 192.00 | 288.00 | 326.00 | 246.00 | 335.00 | 293.00 | 323.00 | 295.00 | 282.00 |
Transpt | 90.90 | 87.10 | 80.20 | 85.10 | 84.90 | 79.70 | 81.90 | 72.70 | 61.90 | 61.40 | ... | 77.60 | 85.10 | 83.50 | 74.40 | 84.40 | 80.20 | 82.90 | 76.80 | 83.20 | 78.50 |
AreaPark | 25.50 | 12.90 | 9.60 | 11.70 | 15.80 | 13.40 | 9.20 | 7.90 | 10.40 | 10.50 | ... | 9.00 | 13.10 | 7.90 | 7.40 | 12.10 | 6.80 | 7.80 | 14.10 | 9.30 | 7.10 |
Sewarage | 77.86 | 36.00 | 35.50 | 40.10 | 31.90 | 44.60 | 36.90 | 47.60 | 43.10 | 41.10 | ... | 36.80 | 24.70 | 67.09 | 29.60 | 45.60 | 44.50 | 38.80 | 38.50 | 39.00 | 52.00 |
Recycle | 4.90 | 3.20 | 11.30 | 16.20 | 10.40 | 16.50 | 9.50 | 12.60 | 16.20 | 12.30 | ... | 7.60 | 7.90 | 8.50 | 11.70 | 7.10 | 8.90 | 7.90 | 11.80 | 3.10 | 5.00 |
AmtRubb | 1655.00 | 1363.60 | 940.00 | 1102.90 | 1203.80 | 950.20 | 1044.00 | 1105.50 | 1055.40 | 1069.00 | ... | 1073.10 | 991.80 | 1135.30 | 947.00 | 1168.50 | 927.40 | 1262.50 | 1151.20 | 1178.60 | 1040.60 |
AvgMin | 24.00 | 23.00 | 24.00 | 29.00 | 22.00 | 23.00 | 26.00 | 31.00 | 28.00 | 28.00 | ... | 21.00 | 24.00 | 31.00 | 23.00 | 28.00 | 23.00 | 25.00 | 21.00 | 21.00 | 24.00 |
Pavement | 19.90 | 24.90 | 14.40 | 26.60 | 15.70 | 20.70 | 14.70 | 11.40 | 20.80 | 16.80 | ... | 18.80 | 19.70 | 15.00 | 25.00 | 34.30 | 23.10 | 31.80 | 19.60 | 16.10 | 40.70 |
23 rows × 47 columns
# 相関行列
pd.DataFrame(np.corrcoef(df.dropna().iloc[:, :].as_matrix().tolist()),
columns=df.index, index=df.index)
Pref | Hokkaido | Aomori | Iwate | Miyagi | Akita | Yamagata | Fukushima | Ibaraki | Tochigi | Gunma | ... | Ehime | Kochi | Fukuoka | Saga | Nagasaki | Kumamoto | Oita | Miyazaki | Kagoshima | Okinawa |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Pref | |||||||||||||||||||||
Hokkaido | 1.000000 | 0.990436 | 0.942096 | 0.919749 | 0.978805 | 0.937078 | 0.940955 | 0.928415 | 0.918560 | 0.920831 | ... | 0.950980 | 0.937957 | 0.929341 | 0.945681 | 0.969379 | 0.927043 | 0.981225 | 0.974799 | 0.966304 | 0.930259 |
Aomori | 0.990436 | 1.000000 | 0.968665 | 0.946766 | 0.990976 | 0.968250 | 0.970735 | 0.962769 | 0.954915 | 0.957785 | ... | 0.977475 | 0.968513 | 0.961627 | 0.977099 | 0.986424 | 0.962443 | 0.995859 | 0.985439 | 0.988932 | 0.942309 |
Iwate | 0.942096 | 0.968665 | 1.000000 | 0.994594 | 0.988507 | 0.998542 | 0.994181 | 0.992117 | 0.992583 | 0.981639 | ... | 0.980899 | 0.983770 | 0.971900 | 0.991050 | 0.992086 | 0.993192 | 0.983558 | 0.991243 | 0.987221 | 0.984786 |
Miyagi | 0.919749 | 0.946766 | 0.994594 | 1.000000 | 0.972193 | 0.993335 | 0.991157 | 0.990544 | 0.993368 | 0.981341 | ... | 0.976227 | 0.982273 | 0.971048 | 0.983942 | 0.983798 | 0.991107 | 0.969663 | 0.982871 | 0.977012 | 0.992574 |
Akita | 0.978805 | 0.990976 | 0.988507 | 0.972193 | 1.000000 | 0.984403 | 0.980466 | 0.973543 | 0.969767 | 0.961472 | ... | 0.974106 | 0.970357 | 0.957109 | 0.981315 | 0.993477 | 0.974066 | 0.993668 | 0.996322 | 0.987716 | 0.970265 |
Yamagata | 0.937078 | 0.968250 | 0.998542 | 0.993335 | 0.984403 | 1.000000 | 0.997605 | 0.996868 | 0.996703 | 0.989304 | ... | 0.986261 | 0.988677 | 0.980521 | 0.995625 | 0.990412 | 0.997352 | 0.983389 | 0.987644 | 0.989881 | 0.979328 |
Fukushima | 0.940955 | 0.970735 | 0.994181 | 0.991157 | 0.980466 | 0.997605 | 1.000000 | 0.999175 | 0.997492 | 0.995720 | ... | 0.993984 | 0.994631 | 0.990923 | 0.998314 | 0.990413 | 0.998463 | 0.985838 | 0.986920 | 0.993700 | 0.977918 |
Ibaraki | 0.928415 | 0.962769 | 0.992117 | 0.990544 | 0.973543 | 0.996868 | 0.999175 | 1.000000 | 0.999043 | 0.997413 | ... | 0.992193 | 0.994018 | 0.991435 | 0.997409 | 0.985523 | 0.999196 | 0.979960 | 0.980549 | 0.990277 | 0.973784 |
Tochigi | 0.918560 | 0.954915 | 0.992583 | 0.993368 | 0.969767 | 0.996703 | 0.997492 | 0.999043 | 1.000000 | 0.995688 | ... | 0.988672 | 0.992923 | 0.987802 | 0.995060 | 0.983918 | 0.999239 | 0.975245 | 0.977553 | 0.986439 | 0.975355 |
Gunma | 0.920831 | 0.957785 | 0.981639 | 0.981341 | 0.961472 | 0.989304 | 0.995720 | 0.997413 | 0.995688 | 1.000000 | ... | 0.994858 | 0.995967 | 0.997398 | 0.995709 | 0.978943 | 0.996363 | 0.975619 | 0.970344 | 0.988602 | 0.961051 |
Saitama | 0.842029 | 0.882798 | 0.965951 | 0.984065 | 0.919644 | 0.966357 | 0.962664 | 0.967346 | 0.976544 | 0.960123 | ... | 0.943050 | 0.958578 | 0.944588 | 0.952444 | 0.945359 | 0.970491 | 0.919916 | 0.936268 | 0.936637 | 0.966872 |
Chiba | 0.863108 | 0.898145 | 0.974053 | 0.990945 | 0.933292 | 0.973563 | 0.970997 | 0.973961 | 0.981383 | 0.965913 | ... | 0.951795 | 0.964584 | 0.951963 | 0.960043 | 0.955479 | 0.975774 | 0.932350 | 0.950396 | 0.946541 | 0.979251 |
Tokyo | 0.827394 | 0.862360 | 0.953388 | 0.976315 | 0.905791 | 0.950538 | 0.945272 | 0.949005 | 0.960238 | 0.938893 | ... | 0.922901 | 0.940632 | 0.922171 | 0.932124 | 0.932604 | 0.952919 | 0.902601 | 0.925142 | 0.917446 | 0.965138 |
Kanagawa | 0.851841 | 0.892045 | 0.968005 | 0.984358 | 0.924699 | 0.968678 | 0.965742 | 0.970023 | 0.978950 | 0.964304 | ... | 0.950046 | 0.965341 | 0.950802 | 0.957485 | 0.952090 | 0.974176 | 0.927932 | 0.939842 | 0.944003 | 0.965491 |
Niigata | 0.964402 | 0.985098 | 0.995610 | 0.987690 | 0.993509 | 0.995278 | 0.995489 | 0.991715 | 0.989554 | 0.985207 | ... | 0.991559 | 0.990799 | 0.981722 | 0.995275 | 0.998535 | 0.992193 | 0.995576 | 0.996128 | 0.996965 | 0.979729 |
Toyama | 0.921373 | 0.956500 | 0.989009 | 0.989382 | 0.967494 | 0.994414 | 0.997965 | 0.999112 | 0.998017 | 0.997649 | ... | 0.990727 | 0.992430 | 0.992065 | 0.995100 | 0.980899 | 0.997913 | 0.974927 | 0.976270 | 0.986845 | 0.973092 |
Ishikawa | 0.967945 | 0.987709 | 0.986819 | 0.978763 | 0.987356 | 0.988439 | 0.992662 | 0.988883 | 0.985550 | 0.987061 | ... | 0.996524 | 0.994401 | 0.988208 | 0.994791 | 0.997007 | 0.989403 | 0.997169 | 0.990724 | 0.999284 | 0.968849 |
Fukui | 0.950767 | 0.977599 | 0.994948 | 0.989632 | 0.985615 | 0.997296 | 0.999155 | 0.997360 | 0.995427 | 0.993525 | ... | 0.995069 | 0.995027 | 0.989806 | 0.998570 | 0.994435 | 0.997344 | 0.990912 | 0.990388 | 0.996589 | 0.977529 |
Yamanashi | 0.909871 | 0.948609 | 0.989052 | 0.991554 | 0.963002 | 0.993797 | 0.995356 | 0.997533 | 0.999233 | 0.995775 | ... | 0.987964 | 0.993689 | 0.988346 | 0.993156 | 0.980910 | 0.998616 | 0.970966 | 0.971572 | 0.983991 | 0.971093 |
Nagano | 0.923598 | 0.959139 | 0.994637 | 0.993454 | 0.974059 | 0.997860 | 0.997511 | 0.998482 | 0.999508 | 0.994234 | ... | 0.988905 | 0.993341 | 0.986272 | 0.995770 | 0.986996 | 0.999489 | 0.978399 | 0.980006 | 0.988386 | 0.975266 |
Gifu | 0.943845 | 0.971355 | 0.993341 | 0.991817 | 0.980294 | 0.996079 | 0.999178 | 0.997920 | 0.996804 | 0.995180 | ... | 0.995634 | 0.996830 | 0.991610 | 0.997688 | 0.992754 | 0.997790 | 0.987572 | 0.988003 | 0.994704 | 0.979769 |
Shizuoka | 0.909037 | 0.948469 | 0.983803 | 0.986185 | 0.958441 | 0.989577 | 0.992790 | 0.995109 | 0.996756 | 0.995945 | ... | 0.989904 | 0.995889 | 0.990825 | 0.992368 | 0.979993 | 0.997045 | 0.970861 | 0.966825 | 0.984601 | 0.963095 |
Aichi | 0.921823 | 0.952408 | 0.990205 | 0.995570 | 0.967485 | 0.992875 | 0.995643 | 0.996089 | 0.997582 | 0.993366 | ... | 0.989227 | 0.993976 | 0.987740 | 0.991588 | 0.985460 | 0.996477 | 0.975060 | 0.979285 | 0.985303 | 0.982690 |
Mie | 0.978148 | 0.994094 | 0.986055 | 0.974016 | 0.993258 | 0.986387 | 0.989367 | 0.983972 | 0.979401 | 0.979930 | ... | 0.992331 | 0.988112 | 0.980852 | 0.992096 | 0.997109 | 0.983926 | 0.999592 | 0.994402 | 0.998263 | 0.967459 |
Shiga | 0.949980 | 0.976786 | 0.991436 | 0.987504 | 0.981829 | 0.994822 | 0.998687 | 0.997135 | 0.994958 | 0.995374 | ... | 0.997581 | 0.997312 | 0.993351 | 0.998667 | 0.993456 | 0.996825 | 0.990696 | 0.988156 | 0.997088 | 0.974870 |
Kyoto | 0.911547 | 0.941141 | 0.986684 | 0.994826 | 0.961216 | 0.986949 | 0.987421 | 0.987898 | 0.992118 | 0.983573 | ... | 0.980102 | 0.988442 | 0.976571 | 0.982601 | 0.982518 | 0.990582 | 0.967414 | 0.973331 | 0.977208 | 0.982003 |
Osaka | 0.924021 | 0.945208 | 0.984656 | 0.994508 | 0.964241 | 0.983440 | 0.985818 | 0.984254 | 0.987251 | 0.979260 | ... | 0.979563 | 0.985688 | 0.974506 | 0.979345 | 0.984057 | 0.985593 | 0.970019 | 0.979102 | 0.976870 | 0.989768 |
Hyogo | 0.994968 | 0.989077 | 0.953103 | 0.937014 | 0.981534 | 0.947523 | 0.951038 | 0.939985 | 0.933708 | 0.933518 | ... | 0.962005 | 0.953999 | 0.940807 | 0.955226 | 0.980883 | 0.940818 | 0.987064 | 0.980605 | 0.974591 | 0.944106 |
Nara | 0.918455 | 0.949695 | 0.993005 | 0.997369 | 0.969749 | 0.993644 | 0.992837 | 0.993167 | 0.996326 | 0.987542 | ... | 0.983336 | 0.990359 | 0.979225 | 0.988658 | 0.986382 | 0.995214 | 0.973156 | 0.979239 | 0.982307 | 0.983668 |
Wakayama | 0.954456 | 0.980535 | 0.978895 | 0.972121 | 0.975072 | 0.983954 | 0.991295 | 0.988972 | 0.985407 | 0.991850 | ... | 0.999151 | 0.997379 | 0.994636 | 0.995226 | 0.990198 | 0.989603 | 0.991512 | 0.979853 | 0.997462 | 0.956511 |
Tottori | 0.961325 | 0.978726 | 0.995161 | 0.991342 | 0.990862 | 0.993986 | 0.994942 | 0.990680 | 0.988589 | 0.983033 | ... | 0.988221 | 0.987274 | 0.979121 | 0.991676 | 0.995748 | 0.989952 | 0.991033 | 0.997338 | 0.992429 | 0.988833 |
Shimane | 0.962193 | 0.981112 | 0.997259 | 0.990289 | 0.994544 | 0.995333 | 0.993724 | 0.989374 | 0.987847 | 0.980150 | ... | 0.985855 | 0.985718 | 0.974594 | 0.991744 | 0.997214 | 0.989758 | 0.992263 | 0.997762 | 0.992671 | 0.985486 |
Okayama | 0.942285 | 0.971134 | 0.992404 | 0.990485 | 0.979051 | 0.995445 | 0.998437 | 0.997464 | 0.996768 | 0.995423 | ... | 0.996190 | 0.998150 | 0.992147 | 0.997913 | 0.993112 | 0.998161 | 0.987649 | 0.985956 | 0.995233 | 0.976182 |
Hiroshima | 0.915662 | 0.948223 | 0.988473 | 0.994406 | 0.963692 | 0.991360 | 0.993850 | 0.994726 | 0.996991 | 0.992549 | ... | 0.987845 | 0.993999 | 0.986721 | 0.990153 | 0.983829 | 0.996108 | 0.972117 | 0.975200 | 0.983498 | 0.979241 |
Yamaguchi | 0.972752 | 0.992149 | 0.984401 | 0.972819 | 0.988801 | 0.986585 | 0.990946 | 0.986574 | 0.981891 | 0.984906 | ... | 0.995687 | 0.991657 | 0.986784 | 0.994452 | 0.995612 | 0.986723 | 0.998504 | 0.990317 | 0.999540 | 0.962781 |
Tokushima | 0.939124 | 0.973701 | 0.983904 | 0.976417 | 0.973211 | 0.990203 | 0.994620 | 0.994489 | 0.992155 | 0.996071 | ... | 0.997105 | 0.997350 | 0.994668 | 0.998375 | 0.987192 | 0.995407 | 0.986545 | 0.976113 | 0.995587 | 0.954675 |
Kagawa | 0.939856 | 0.970893 | 0.990309 | 0.987536 | 0.976578 | 0.994631 | 0.998432 | 0.997942 | 0.996728 | 0.997262 | ... | 0.997242 | 0.998660 | 0.994452 | 0.998749 | 0.990974 | 0.998398 | 0.986805 | 0.982981 | 0.995445 | 0.971133 |
Ehime | 0.950980 | 0.977475 | 0.980899 | 0.976227 | 0.974106 | 0.986261 | 0.993984 | 0.992193 | 0.988672 | 0.994858 | ... | 1.000000 | 0.998159 | 0.996973 | 0.996397 | 0.989271 | 0.991815 | 0.989930 | 0.980994 | 0.996937 | 0.961700 |
Kochi | 0.937957 | 0.968513 | 0.983770 | 0.982273 | 0.970357 | 0.988677 | 0.994631 | 0.994018 | 0.992923 | 0.995967 | ... | 0.998159 | 1.000000 | 0.995589 | 0.996156 | 0.989045 | 0.995138 | 0.985302 | 0.978056 | 0.994375 | 0.964851 |
Fukuoka | 0.929341 | 0.961627 | 0.971900 | 0.971048 | 0.957109 | 0.980521 | 0.990923 | 0.991435 | 0.987802 | 0.997398 | ... | 0.996973 | 0.995589 | 1.000000 | 0.992381 | 0.976889 | 0.990010 | 0.977042 | 0.966960 | 0.989128 | 0.952587 |
Saga | 0.945681 | 0.977099 | 0.991050 | 0.983942 | 0.981315 | 0.995625 | 0.998314 | 0.997409 | 0.995060 | 0.995709 | ... | 0.996397 | 0.996156 | 0.992381 | 1.000000 | 0.991242 | 0.997444 | 0.989606 | 0.984837 | 0.996791 | 0.966971 |
Nagasaki | 0.969379 | 0.986424 | 0.992086 | 0.983798 | 0.993477 | 0.990412 | 0.990413 | 0.985523 | 0.983918 | 0.978943 | ... | 0.989271 | 0.989045 | 0.976889 | 0.991242 | 1.000000 | 0.987392 | 0.996600 | 0.995649 | 0.995942 | 0.977144 |
Kumamoto | 0.927043 | 0.962443 | 0.993192 | 0.991107 | 0.974066 | 0.997352 | 0.998463 | 0.999196 | 0.999239 | 0.996363 | ... | 0.991815 | 0.995138 | 0.990010 | 0.997444 | 0.987392 | 1.000000 | 0.980392 | 0.979859 | 0.990692 | 0.972589 |
Oita | 0.981225 | 0.995859 | 0.983558 | 0.969663 | 0.993668 | 0.983389 | 0.985838 | 0.979960 | 0.975245 | 0.975619 | ... | 0.989930 | 0.985302 | 0.977042 | 0.989606 | 0.996600 | 0.980392 | 1.000000 | 0.993382 | 0.997223 | 0.963183 |
Miyazaki | 0.974799 | 0.985439 | 0.991243 | 0.982871 | 0.996322 | 0.987644 | 0.986920 | 0.980549 | 0.977553 | 0.970344 | ... | 0.980994 | 0.978056 | 0.966960 | 0.984837 | 0.995649 | 0.979859 | 0.993382 | 1.000000 | 0.989660 | 0.984687 |
Kagoshima | 0.966304 | 0.988932 | 0.987221 | 0.977012 | 0.987716 | 0.989881 | 0.993700 | 0.990277 | 0.986439 | 0.988602 | ... | 0.996937 | 0.994375 | 0.989128 | 0.996791 | 0.995942 | 0.990692 | 0.997223 | 0.989660 | 1.000000 | 0.964749 |
Okinawa | 0.930259 | 0.942309 | 0.984786 | 0.992574 | 0.970265 | 0.979328 | 0.977918 | 0.973784 | 0.975355 | 0.961051 | ... | 0.961700 | 0.964851 | 0.952587 | 0.966971 | 0.977144 | 0.972589 | 0.963183 | 0.984687 | 0.964749 | 1.000000 |
47 rows × 47 columns
corrcoef = np.corrcoef(df.dropna().iloc[:, :].as_matrix().tolist())
fig = plt.figure(figsize=(12, 10))
plt.imshow(corrcoef, interpolation='nearest', cmap=plt.cm.coolwarm)
plt.colorbar()
tick_marks = np.arange(len(corrcoef))
plt.xticks(tick_marks, df.index, rotation=90)
plt.yticks(tick_marks, df.index)
plt.tight_layout()
# 相関行列
pd.DataFrame(np.corrcoef(df.dropna().iloc[:, :].T.as_matrix().tolist()),
columns=df.columns, index=df.columns)
NonRep | OverMin | Rent | HomeOwn | CompPol | NumClime | NumLarc | TrafAcci | Fire | DspRubb | ... | Sunshine | NumMat | AreaResi | Transpt | AreaPark | Sewarage | Recycle | AmtRubb | AvgMin | Pavement | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
NonRep | 1.000000 | -0.110529 | -0.101532 | -0.119425 | -0.232210 | -0.165721 | -0.024362 | -0.239581 | 0.138387 | -0.109146 | ... | -0.032424 | -0.006240 | 0.103259 | 0.106801 | 0.199225 | -0.152304 | -0.176556 | -0.045065 | -0.224092 | 0.011666 |
OverMin | -0.110529 | 1.000000 | -0.815368 | 0.859007 | -0.305812 | -0.583851 | -0.647593 | -0.031722 | 0.335028 | -0.257343 | ... | 0.654443 | 0.809590 | 0.649520 | -0.398914 | 0.434398 | -0.652436 | 0.230194 | -0.182505 | -0.587400 | -0.677050 |
Rent | -0.101532 | -0.815368 | 1.000000 | -0.702218 | 0.374497 | 0.692562 | 0.560380 | 0.175525 | -0.335108 | 0.360928 | ... | -0.619212 | -0.577670 | -0.507189 | 0.377264 | -0.481145 | 0.739477 | 0.104236 | 0.153990 | 0.805577 | 0.548556 |
HomeOwn | -0.119425 | 0.859007 | -0.702218 | 1.000000 | -0.268300 | -0.481268 | -0.608404 | -0.105420 | 0.355555 | -0.073719 | ... | 0.651371 | 0.732096 | 0.650671 | -0.497908 | 0.245703 | -0.726973 | 0.288859 | -0.311068 | -0.529916 | -0.570858 |
CompPol | -0.232210 | -0.305812 | 0.374497 | -0.268300 | 1.000000 | 0.187540 | 0.193997 | 0.322823 | 0.033263 | 0.245359 | ... | -0.034850 | -0.372956 | -0.364463 | -0.219886 | -0.417345 | 0.213781 | 0.238792 | -0.001798 | 0.430505 | 0.116044 |
NumClime | -0.165721 | -0.583851 | 0.692562 | -0.481268 | 0.187540 | 1.000000 | 0.462426 | 0.068642 | -0.241715 | 0.309111 | ... | -0.429000 | -0.518124 | -0.304864 | 0.228029 | -0.379465 | 0.429633 | -0.001633 | -0.079865 | 0.481630 | 0.261887 |
NumLarc | -0.024362 | -0.647593 | 0.560380 | -0.608404 | 0.193997 | 0.462426 | 1.000000 | 0.076333 | -0.243967 | 0.284085 | ... | -0.466745 | -0.506737 | -0.278294 | 0.107785 | -0.267303 | 0.546709 | 0.039511 | 0.048163 | 0.490038 | 0.439677 |
TrafAcci | -0.239581 | -0.031722 | 0.175525 | -0.105420 | 0.322823 | 0.068642 | 0.076333 | 1.000000 | -0.051674 | 0.299187 | ... | 0.033341 | -0.055598 | -0.141291 | -0.168509 | -0.401152 | 0.085953 | 0.042051 | 0.028329 | 0.215567 | -0.003796 |
Fire | 0.138387 | 0.335028 | -0.335108 | 0.355555 | 0.033263 | -0.241715 | -0.243967 | -0.051674 | 1.000000 | 0.021125 | ... | 0.531183 | 0.129167 | 0.486350 | -0.385563 | 0.210601 | -0.456012 | 0.270003 | -0.184735 | -0.325480 | -0.375884 |
DspRubb | -0.109146 | -0.257343 | 0.360928 | -0.073719 | 0.245359 | 0.309111 | 0.284085 | 0.299187 | 0.021125 | 1.000000 | ... | 0.052171 | -0.251970 | -0.052499 | -0.116998 | -0.564799 | 0.052476 | 0.416914 | -0.621829 | 0.385439 | 0.239702 |
Sidewalk | 0.109900 | -0.570085 | 0.440115 | -0.636986 | -0.077704 | 0.329577 | 0.605952 | -0.240657 | -0.332218 | -0.087841 | ... | -0.644849 | -0.366169 | -0.308143 | 0.478489 | 0.083688 | 0.664294 | -0.300921 | 0.297209 | 0.298661 | 0.617484 |
MedFacil | -0.123396 | -0.789458 | 0.732584 | -0.811927 | 0.322297 | 0.452314 | 0.544918 | 0.222074 | -0.500119 | 0.178667 | ... | -0.739708 | -0.602521 | -0.789977 | 0.512694 | -0.358547 | 0.721005 | -0.227939 | 0.325385 | 0.606221 | 0.711647 |
OverOrd | 0.030921 | 0.841950 | -0.621736 | 0.749379 | -0.411705 | -0.551721 | -0.511028 | -0.134822 | 0.198471 | -0.288879 | ... | 0.347852 | 0.981755 | 0.601846 | -0.122375 | 0.485633 | -0.417699 | 0.153506 | -0.055518 | -0.491764 | -0.479586 |
Sunshine | -0.032424 | 0.654443 | -0.619212 | 0.651371 | -0.034850 | -0.429000 | -0.466745 | 0.033341 | 0.531183 | 0.052171 | ... | 1.000000 | 0.315737 | 0.671132 | -0.688192 | 0.164938 | -0.721119 | 0.405222 | -0.449767 | -0.567962 | -0.631671 |
NumMat | -0.006240 | 0.809590 | -0.577670 | 0.732096 | -0.372956 | -0.518124 | -0.506737 | -0.055598 | 0.129167 | -0.251970 | ... | 0.315737 | 1.000000 | 0.518617 | -0.107467 | 0.427450 | -0.417325 | 0.120037 | -0.066589 | -0.475717 | -0.445027 |
AreaResi | 0.103259 | 0.649520 | -0.507189 | 0.650671 | -0.364463 | -0.304864 | -0.278294 | -0.141291 | 0.486350 | -0.052499 | ... | 0.671132 | 0.518617 | 1.000000 | -0.495494 | 0.379794 | -0.470946 | 0.355798 | -0.264593 | -0.477433 | -0.614682 |
Transpt | 0.106801 | -0.398914 | 0.377264 | -0.497908 | -0.219886 | 0.228029 | 0.107785 | -0.168509 | -0.385563 | -0.116998 | ... | -0.688192 | -0.107467 | -0.495494 | 1.000000 | 0.026411 | 0.487938 | -0.436826 | 0.408156 | 0.264131 | 0.468986 |
AreaPark | 0.199225 | 0.434398 | -0.481145 | 0.245703 | -0.417345 | -0.379465 | -0.267303 | -0.401152 | 0.210601 | -0.564799 | ... | 0.164938 | 0.427450 | 0.379794 | 0.026411 | 1.000000 | -0.110887 | -0.065608 | 0.185767 | -0.454130 | -0.354230 |
Sewarage | -0.152304 | -0.652436 | 0.739477 | -0.726973 | 0.213781 | 0.429633 | 0.546709 | 0.085953 | -0.456012 | 0.052476 | ... | -0.721119 | -0.417325 | -0.470946 | 0.487938 | -0.110887 | 1.000000 | -0.067907 | 0.403357 | 0.711380 | 0.480201 |
Recycle | -0.176556 | 0.230194 | 0.104236 | 0.288859 | 0.238792 | -0.001633 | 0.039511 | 0.042051 | 0.270003 | 0.416914 | ... | 0.405222 | 0.120037 | 0.355798 | -0.436826 | -0.065608 | -0.067907 | 1.000000 | -0.452109 | 0.283089 | -0.289639 |
AmtRubb | -0.045065 | -0.182505 | 0.153990 | -0.311068 | -0.001798 | -0.079865 | 0.048163 | 0.028329 | -0.184735 | -0.621829 | ... | -0.449767 | -0.066589 | -0.264593 | 0.408156 | 0.185767 | 0.403357 | -0.452109 | 1.000000 | 0.140968 | 0.243804 |
AvgMin | -0.224092 | -0.587400 | 0.805577 | -0.529916 | 0.430505 | 0.481630 | 0.490038 | 0.215567 | -0.325480 | 0.385439 | ... | -0.567962 | -0.475717 | -0.477433 | 0.264131 | -0.454130 | 0.711380 | 0.283089 | 0.140968 | 1.000000 | 0.440712 |
Pavement | 0.011666 | -0.677050 | 0.548556 | -0.570858 | 0.116044 | 0.261887 | 0.439677 | -0.003796 | -0.375884 | 0.239702 | ... | -0.631671 | -0.445027 | -0.614682 | 0.468986 | -0.354230 | 0.480201 | -0.289639 | 0.243804 | 0.440712 | 1.000000 |
23 rows × 23 columns
corrcoef = np.corrcoef(df.dropna().iloc[:, :].T.as_matrix().tolist())
plt.figure(figsize=(12, 10))
plt.imshow(corrcoef, interpolation='nearest', cmap=plt.cm.coolwarm)
plt.colorbar()
tick_marks = np.arange(len(corrcoef))
plt.xticks(tick_marks, df.columns, rotation=90)
plt.yticks(tick_marks, df.columns)
plt.tight_layout()