{"id":70961,"date":"2018-09-04T21:51:42","date_gmt":"2018-09-04T12:51:42","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=70961"},"modified":"2024-05-06T11:51:27","modified_gmt":"2024-05-06T02:51:27","slug":"%e3%80%90numpy%e5%85%a5%e9%96%80-np-histogram%e3%80%91%e3%83%92%e3%82%b9%e3%83%88%e3%82%b0%e3%83%a9%e3%83%a0%e3%82%92%e4%bd%9c%e6%88%90%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95%e3%81%a8plt-hist","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/70961","title":{"rendered":"\u3010NumPy\u5165\u9580 np.histogram\u3011\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3068plt.hist"},"content":{"rendered":"

\u3053\u306e\u8a18\u4e8b\u3067\u306fnp.ndarray\u304b\u3089\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3059\u308b\u95a2\u6570<\/strong><\/span>\u3001np.histogram<\/span><\/strong>\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n

\u30c7\u30fc\u30bf\u89e3\u6790\u30fb\u6a5f\u68b0\u5b66\u7fd2\u306b\u304a\u3044\u3066\u3001\u30c7\u30fc\u30bf\u306e\u5168\u4f53\u50cf\u3092\u4fef\u77b0\u3059\u308b\u306e\u306f\u975e\u5e38\u306b\u5927\u5207<\/strong><\/span>\u306a\u30b9\u30c6\u30c3\u30d7\u3067\u3059\u3002<\/p>\n

\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u306f\u30c7\u30fc\u30bf\u306e\u5168\u4f53\u50cf\u3092\u898b\u308b\u3068\u304d\u306b\u91cd\u8981\u306a\u30c4\u30fc\u30eb\u306a\u306e\u3067\u3001\u662f\u975e\u899a\u3048\u3066\u4f7f\u3044\u3053\u306a\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n

\u3010\u57fa\u672c\u3011np.histogram\u3067\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u4f5c\u6210
\n\u3010\u57fa\u672c\u3011plt.hist\u3067\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u30d7\u30ed\u30c3\u30c8
\n\u3010\u767a\u5c55\u3011np.histogram\/plt.hist\u3067bin\u6570\u306e\u5909\u66f4\u3084\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u306e\u6a19\u6e96\u5316<\/p>\n

np.histogram\u306e\u4f7f\u3044\u65b9<\/h2>\n

\u203b\u3053\u306e\u8a18\u4e8b\u306e\u30b3\u30fc\u30c9\u306f\u3001jupyter notebook<\/a>\u3084juputer lab<\/a>\u3092\u4f7f\u3063\u3066\u66f8\u304b\u308c\u3066\u3044\u307e\u3059\u3002
\n\u30b3\u30fc\u30c9\u3092\u8a66\u3059\u3068\u304d\u306f\u662f\u975e\u3053\u308c\u3089\u3084IPython\u3092\u4f7f\u3063\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

# \u30b3\u30fc\u30c9 In [1]:\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt<\/pre>\n
\u57fa\u672c\u7684\u306anp.histogram\u306e\u4f7f\u3044\u65b9<\/h3>\n
np.histogram\u306f\u3001\u914d\u5217\u304b\u3089histogram\u3092\u4f5c\u6210\u3059\u308b\u95a2\u6570<\/strong><\/span>\u3067\u3059\u3002<\/p>\n
\u3053\u306e\u95a2\u6570\u306fbin\u3054\u3068\u306e\u8981\u7d20\u6570\uff08\u5ea6\u6570\uff09<\/strong><\/span>\u3068bin\uff08\u968e\u7d1a\uff09\u306e\u5883\u754c\u306e\u5024<\/strong><\/span>\u3092\u914d\u5217\u3067\u8fd4\u3057\u307e\u3059\u3002<\/p>\n
\u5b9f\u969b\u306b\u3053\u306e\u95a2\u6570\u3092\u4f7f\u3063\u3066\u51fa\u529b\u5024\u3092\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [2]:\r\n# \u6a19\u6e96\u6b63\u898f\u5206\u5e03\u306b\u5f93\u3046\u4e71\u6570\u30921000\u500b\u751f\u6210\u3057\u3066a\u306b\u4ee3\u5165\r\na = np.random.randn(1000)\r\n\r\n# histgram\u3068bin\u306e\u5883\u754c\u5024\u3092a_hist\u3068a_bins\u306b\u4ee3\u5165\r\na_hist, a_bins = np.histogram(a)\r\n\r\nprint(\"\u30aa\u30ea\u30b8\u30ca\u30eb\u306e\u914d\u5217a (a\u306e\u5148\u982d100\u500b\u306e\u8981\u7d20\u3060\u3051print):\\n\", a[:100], \"\\n\")\r\nprint(\"a\u306e\u5ea6\u6570:\\n\", a_hist, \"\\n\")\r\nprint(\"a_hist\u306eshape:\\n\", a_hist.shape, \"\\n\")\r\nprint(\"a\u306e\u968e\u7d1a\u3092\u533a\u5207\u308b\u5024:\\n\", a_bins, \"\\n\")\r\nprint(\"a_bins\u306eshape:\\n\", a_bins.shape, \"\\n\")<\/pre>\n
# \u51fa\u529b\u7d50\u679c [2]:\r\n\u30aa\u30ea\u30b8\u30ca\u30eb\u306e\u914d\u5217a (a\u306e\u5148\u982d100\u500b\u306e\u8981\u7d20\u3060\u3051print):\r\n [ 1.40359326 -0.09107017 -0.70222454 -0.69154174  1.00746289 -1.29444826\r\n -0.4051456   0.27589838 -0.19342482 -1.03965977  0.29958986  0.36877692\r\n  0.28560059  0.06115003 -0.52473576 -0.05330536  0.2121698   1.08919852\r\n -0.99183916  0.989039   -1.82295716  0.211349    0.38667401  0.71567032\r\n -0.35889123  0.25584366 -1.6000845  -1.21105517  2.14720227  0.21357017\r\n -0.40273383  1.10581592  1.65872923  0.02276412 -0.21102715 -1.06041993\r\n -1.39724982  0.37385592 -0.00544005  1.02367865 -0.96347618  1.50610189\r\n -0.27578184 -0.53272795 -0.330013   -1.02940385  0.89334504 -1.2223676\r\n -0.09373311 -0.29055041 -0.08257971 -0.76857742 -0.68267469  0.85012477\r\n  1.25693121  1.59618945 -0.41102347 -0.29874702  0.22457684 -0.4659283\r\n  0.32884514  0.12333912 -1.08921716 -2.18078513  0.91119611  0.26030972\r\n -1.28055916 -1.67538201  0.12342149 -0.38635714  0.43704228 -0.58893319\r\n -0.2152168   1.37031416 -0.67441627  0.33147404 -1.80944471 -0.36292766\r\n -1.2828725   1.9418853  -0.72268891  0.05362563  1.38053317 -1.23291616\r\n  0.43747315 -1.48471547 -0.22619044 -0.13380173 -0.20752616 -0.10938818\r\n  0.52836161 -0.35618303 -1.32338092 -0.10313023  0.74400153  1.13902877\r\n -1.30455422 -0.38157293  0.67187669  1.59665266]\r\n\r\na\u306e\u5ea6\u6570:\r\n [ 11  55 117 176 247 193 119  61  19   2]\r\n\r\na_hist\u306eshape:\r\n (10,)\r\n\r\na\u306e\u968e\u7d1a\u3092\u533a\u5207\u308b\u5024:\r\n [-2.65685307 -2.07874915 -1.50064522 -0.9225413  -0.34443738  0.23366654\r\n  0.81177046  1.38987438  1.9679783   2.54608223  3.12418615]\r\n\r\na_bins\u306eshape:\r\n (11,)\r\n\r\n<\/pre>\n
\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u30d7\u30ed\u30c3\u30c8<\/h3>\n
\u4eca\u4f5c\u3063\u305fhistogram\u3092\u30b0\u30e9\u30d5\u306b<\/strong><\/span>\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
a_bins\u306b\u306fbin\u306e\u5883\u754c\u306e\u5024\uff08bin\u540c\u58eb\u306e\u533a\u5207\u308a\u76ee\uff09\u304c\u5165\u3063\u3066\u3044\u308b<\/strong><\/span>\u306e\u3067\u3001\u3053\u306e\u307e\u307e\u3060\u3068a_bins\u3068a_hist\u306e\u8981\u7d20\u6570\u304c\u9055\u3044\u307e\u3059<\/strong><\/span>\u3002<\/p>\n
\u305d\u308c\u305e\u308c\u306ebin\u6bce\u306b\u771f\u3093\u4e2d\u306e\u5024\u3092\u8a08\u7b97\u3057\u3066X\u8ef8\u306e\u5024\u306b\u4f7f\u3044\u307e\u3059\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [3]:\r\nX = []\r\nfor i in range(1, len(a_bins)):\r\n    X.append((a_bins[i-1]+a_bins[i])\/2)\r\nplt.bar(X,a_hist)<\/pre>\n
# \u51fa\u529b\u7d50\u679c Out [3]:\r\n<\/pre>\n
\n
<\/div>\n
<\/div>\n<\/div>\n
matplotlib\u3067\u3082hisgogram\u304c\u4f5c\u308c\u308b<\/h2>\n
plt.hist\u306e\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9<\/h3>\n
\u5b9f\u306fhistgram\u306fmatplotlib\u306e\u95a2\u6570\u306b\u3082\u3042\u308a\u307e\u3059<\/strong><\/span>\u3002<\/p>\n
plt.hist\u95a2\u6570<\/strong><\/span>\u306fnp.histogram\u3068\u540c\u3058\u3082\u306e\u3092\u4f5c\u308c\u308b\u3060\u3051\u3067\u306a\u304f\u3001\u30b0\u30e9\u30d5\u307e\u3067\u4f5c\u6210\u3057\u3066\u304f\u308c\u308b\u306e\u3067\u3001\u8a66\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [4]:\r\n(a_hist2, a_bins2, _) = plt.hist(a)<\/pre>\n
\n
<\/div>\n
<\/div>\n<\/div>\n
\u5b9f\u969b\u306b\u3001numpy\u3067\u4f5c\u3063\u305f\u3082\u306e\u3068matplotlib\u3067\u4f5c\u3063\u305f\u3082\u306e\u3067\u3001bin\u306e\u5883\u754c\u306e\u5024\u304c\u540c\u3058\u304b\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [5]:\r\na_bins==a_bins2<\/pre>\n
# \u51fa\u529b\u7d50\u679c Out [5]:\r\narray([ True,  True,  True,  True,  True,  True,  True,  True,  True,\r\n        True,  True])<\/pre>\n
\u540c\u3058\u5024\u306b\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u306d\u3002<\/p>\n
\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u306f\u30b0\u30e9\u30d5\u306b\u3057\u305f\u307b\u3046\u304c\u308f\u304b\u308a\u3084\u3059\u3044\u306e\u3067\u3001\u3053\u3053\u304b\u3089\u306fmatplotlib\u306ehist\u95a2\u6570\u3092\u4f7f\u3063\u3066\u7d39\u4ecb\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n
plt.hist\u3067\u7d39\u4ecb\u3057\u3066\u884c\u304d\u307e\u3059\u304c\u3001np.histogram\u3067\u3082\u30b0\u30e9\u30d5\u304c\u4f5c\u308c\u306a\u3044\u3060\u3051\u3067\u540c\u3058\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3088\uff01<\/p>\n
\u30b0\u30e9\u30d5\u304c\u5fc5\u8981\u306a\u3089matplotlib\u3001\u3044\u3089\u306a\u3044\u306e\u306a\u3089NumPy\u3092\u4f7f\u3046\u3068\u3044\u3044\u3067\u3057\u3087\u3046\u3002<\/p>\n
bins\uff08\u968e\u7d1a\uff09\u306e\u6570\u3092\u6c7a\u3081\u308b<\/h3>\n
\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u306e\u968e\u7d1a\u6570\u3092\u5909\u3048\u308b<\/strong><\/span>\u306e\u306f\u7c21\u5358\u3067\u3059\u3002bins\u30d1\u30e9\u30e1\u30fc\u30bf<\/strong><\/span>\u306b\u9069\u5f53\u306a\u6570\u5b57\u3092\u6e21\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
\u6307\u5b9a\u3057\u306a\u3044\u3068bins=10\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u8a66\u3057\u306bbins=5\u306b\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [6]:\r\n#a_hist2, a_bins2 = np.histogram(a, bins=5)\r\n(a_hist2, a_bins2, _) = plt.hist(a, bins=5)\r\n\r\nprint(\"a\u306e\u5ea6\u6570:\\n\", a_hist2, \"\\n\")\r\nprint(\"a_hist2\u306eshape:\\n\", a_hist2.shape, \"\\n\")\r\nprint(\"a\u306e\u968e\u7d1a\u3092\u533a\u5207\u308b\u5024:\\n\", a_bins2, \"\\n\")\r\nprint(\"a_bins2\u306eshape:\\n\", a_bins2.shape, \"\\n\")<\/pre>\n
# \u51fa\u529b\u7d50\u679c [6]:\r\na\u306e\u5ea6\u6570:\r\n [ 66. 293. 440. 180.  21.]\r\n\r\na_hist2\u306eshape:\r\n (5,)\r\n\r\na\u306e\u968e\u7d1a\u3092\u533a\u5207\u308b\u5024:\r\n [-2.65685307 -1.50064522 -0.34443738  0.81177046  1.9679783   3.12418615]\r\n\r\na_bins2\u306eshape:\r\n (6,)\r\n\r\n<\/pre>\n
\n
<\/div>\n
<\/div>\n<\/div>\n
\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u6a19\u6e96\u5316\u3059\u308b<\/h3>\n
\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u306e\u5408\u8a08\u9762\u7a4d\u304c1\u306b\u306a\u308b\u3088\u3046\u306b\u6a19\u6e96\u5316\u3059\u308b<\/strong><\/span>\u5fc5\u8981\u304c\u3042\u308b\u6642\u306f\u3001density\u30d1\u30e9\u30e1\u30fc\u30bf<\/strong><\/span>\u3092True\u306b\u3057\u307e\u3059\u3002<\/p>\n
\u300c\u3053\u308c\u306f\u7e26\u8ef8\u3092\u78ba\u7387\u5206\u5e03\u95a2\u6570\u3068\u601d\u3063\u3066\u898f\u683c\u5316\u3059\u308b\u30aa\u30d7\u30b7\u30e7\u30f3\u203b\u300d\u3067\u3059\u3002<\/p>\n
\u203b \u53c2\u8003 <\/p>\n
# \u30b3\u30fc\u30c9 In [7]:\r\n#a_hist3, a_bins3 = np.histogram(a, density=True)\r\n(a_hist3, a_bins3, _) = plt.hist(a, density=True)\r\n\r\nprint(\"a\u306e\u5ea6\u6570:\\n\", a_hist3, \"\\n\")\r\nprint(\"a_hist3\u306eshape:\\n\", a_hist3.shape, \"\\n\")\r\nprint(\"a\u306e\u968e\u7d1a\u3092\u533a\u5207\u308b\u5024:\\n\", a_bins3, \"\\n\")\r\nprint(\"a_bins3\u306eshape:\\n\", a_bins3.shape, \"\\n\")<\/pre>\n
# \u51fa\u529b\u7d50\u679c [7]:\r\na\u306e\u5ea6\u6570:\r\n [0.01902772 0.0951386  0.20238576 0.30444353 0.42725882 0.33385001\r\n 0.20584534 0.10551736 0.03286606 0.00345959]\r\n\r\na_hist3\u306eshape:\r\n (10,)\r\n\r\na\u306e\u968e\u7d1a\u3092\u533a\u5207\u308b\u5024:\r\n [-2.65685307 -2.07874915 -1.50064522 -0.9225413  -0.34443738  0.23366654\r\n  0.81177046  1.38987438  1.9679783   2.54608223  3.12418615]\r\n\r\na_bins3\u306eshape:\r\n (11,)\r\n\r\n<\/pre>\n
\n
<\/div>\n
<\/div>\n
\u307e\u3068\u3081<\/h2>\n
\u3053\u306e\u8a18\u4e8b\u3067\u306fNumPy\u3092\u4f7f\u3063\u305f\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u306e\u4f5c\u308a\u65b9\u3092\u7d39\u4ecb\u3057\u3001\u540c\u69d8\u306e\u6a5f\u80fd\u3092\u3082\u3063\u305fmatplotlib\u306e\u95a2\u6570\u306b\u3064\u3044\u3066\u3082\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u3093\u3060\u3089\u3001\u307e\u305a\u306f\u7279\u5fb4\u3054\u3068\u306b\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u4f5c\u6210<\/strong><\/span>\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
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