{"id":69045,"date":"2018-08-21T15:55:44","date_gmt":"2018-08-21T06:55:44","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=69045"},"modified":"2024-05-06T11:50:51","modified_gmt":"2024-05-06T02:50:51","slug":"%e3%80%90numpy%e5%85%a5%e9%96%80-np-mean%e3%80%91%e9%85%8d%e5%88%97%e3%81%ae%e8%a6%81%e7%b4%a0%e3%81%ae%e5%b9%b3%e5%9d%87%e3%82%92%e6%b1%82%e3%82%81%e3%82%8b%e6%96%b9%e6%b3%95","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/69045","title":{"rendered":"\u3010NumPy\u5165\u9580 np.mean\u3011\u914d\u5217\u306e\u8981\u7d20\u306e\u5e73\u5747\u3092\u6c42\u3081\u308b\u65b9\u6cd5"},"content":{"rendered":"

\u3053\u306e\u8a18\u4e8b\u3067\u306fnp.array\u306e\u8981\u7d20\u306e\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u95a2\u6570<\/strong><\/span>\u3001np.mean<\/strong><\/span>\u95a2\u6570\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n

\u307e\u305f\u3001\u3053\u306e\u95a2\u6570\u306fnp.array\u306e\u30e1\u30bd\u30c3\u30c9\u3068\u3057\u3066\u3082\u5b9f\u88c5<\/strong><\/span>\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

NumPy\u3067\u306f\u3001\u751f\u306ePython\u3067\u5b9f\u88c5\u3055\u308c\u305f\u95a2\u6570\u3067\u306f\u306a\u304f\u3001NumPy<\/strong><\/span>\u306b\u7528\u610f\u3055\u308c\u305f\u95a2\u6570\u3092\u4f7f\u3046\u3053\u3068\u3067\u9ad8\u901f\u306a\u8a08\u7b97\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n

\u3053\u306e\u8a18\u4e8b\u3067NumPy\u3092\u4f7f\u3063\u305f\u52b9\u7387\u7684\u306a\u8a08\u7b97\u306e\u4ed5\u65b9\u306b\u3064\u3044\u3066\u52c9\u5f37\u3057\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n

np.mean\u306e\u5f15\u6570\u3068\u8fd4\u308a\u5024<\/h2>\n

numpy.<\/code>mean<\/code>(<\/span>a<\/em>,\u00a0axis=None<\/em>,\u00a0dtype=None<\/em>,\u00a0out=None<\/em>,\u00a0keepdims=<no value><\/em>)<\/span><\/p>\n

axis\u3067\u6307\u5b9a\u3057\u305f\u8ef8\u306b\u6cbf\u3063\u305f\u7b97\u8853\u5e73\u5747\uff08\u3088\u304f\u4f7f\u3046\u666e\u901a\u306e\u5e73\u5747\uff09\u3092\u8a08\u7b97<\/strong><\/span>\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<\/colgroup>\n\n\n\n
Parameters:<\/th>\n\n
\n
a<\/strong>\u00a0:<\/span>\u00a0array_like\uff08np.array\u3084list\uff09<\/span><\/span><\/dt>\n
\n

\u5e73\u5747\u5024\u3092\u8a08\u7b97\u3057\u305f\u3044\u914d\u5217<\/p>\n<\/dd>\n

axis<\/strong>\u00a0:<\/span> None\uff08\u6307\u5b9a\u3057\u306a\u3044\uff09 or int\u578b or int\u578b\u306e\u5165\u3063\u305ftuple\u578b, optional<\/span><\/span><\/dt>\n
\n
\n

mean\u304c\u8a08\u7b97\u3055\u308c\u308b\u7bc4\u56f2\uff08\u8ef8\uff09\u3092\u6307\u5b9a\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf<\/p>\n<\/div>\n<\/dd>\n

dtype<\/strong>\u00a0:<\/span>\u00a0data-type, optional<\/span><\/span><\/dt>\n
\n

\u8fd4\u308a\u5024\u306e\u914d\u5217\u306e\u578b\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/p>\n

\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408\u306ffloat64\u306b\u306a\u308a\u307e\u3059\u3002<\/dd>\n

out<\/strong>\u00a0:<\/span>\u00a0ndarray, optional<\/span><\/span><\/dt>\n
\n

\u51fa\u529b\u3055\u308c\u308b\u914d\u5217\u3092\u4ee3\u7528\u3059\u308b\u914d\u5217\u3092\u6307\u5b9a\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u65e2\u5b58\u306e\u914d\u5217\u3092\u6307\u5b9a\u3059\u308b\u3068\u3001\u65b0\u3057\u3044\u914d\u5217\u3092\u4f5c\u3089\u305a\u306b\u65e2\u5b58\u306e\u914d\u5217\u3092\u4f7f\u3044\u307e\u308f\u3057\u307e\u3059\u3002<\/dd>\n

keepdims<\/strong>\u00a0:<\/span>\u00a0bool, optional<\/span><\/span><\/dt>\n<\/dl>\n<\/td>\n<\/tr>\n
Returns:<\/th>\n\n
\n
m<\/strong>\u00a0:<\/span>\u00a0ndarray, see dtype parameter above<\/span><\/span><\/dt>\n
\n

\u5e73\u5747\u5024\u3092\u53ce\u3081\u305f\u914d\u5217\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n

out\u306b\u914d\u5217\u3092\u6307\u5b9a\u3057\u305f\u5834\u5408\u306f\u3001\u305d\u306e\u914d\u5217\u3078\u306e\u53c2\u7167\u304c\u8fd4\u3055\u308c\u307e\u3059\u3002<\/dd>\n<\/dl>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

mean\u30e1\u30bd\u30c3\u30c9<\/strong><\/span>\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3082\u307b\u307c\u540c\u69d8\u3067\u3059\u3002<\/p>\n

\u7b2c\u4e00\u5f15\u6570\u306e\u4ee3\u308f\u308a\u306b\u3001\u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u305f\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u81ea\u4f53\u304c\u4f7f\u308f\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u4f7f\u3044\u65b9<\/h2>\n
\n
\n
\n

\u4ee5\u964d\u306e\u30b3\u30fc\u30c9\u306fJupyter<\/strong><\/span>\u3067\u5b9f\u884c\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

\u307e\u305a\u306fNumPy\u3092import<\/strong><\/span>\u3057\u3066\u3001\u89e3\u8aac\u306e\u305f\u3081\u306e\u30b5\u30f3\u30d7\u30eb\u914d\u5217\u3092\u7528\u610f<\/span><\/strong>\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
\n
\n
\n
\n
In [1]:\r\n\r\nimport numpy as np\r\n\r\nIn [2]:\r\n\r\nx = np.arange(10)\r\nx2 = np.reshape(x,(2,5))\r\n\r\nprint(\"x\\n\",x)\r\nprint(\"x2\\n\",x2)\r\n<\/pre>\n
Out:<\/p>\n<\/div>\n
\n
x\r\n [0 1 2 3 4 5 6 7 8 9]\r\nx2\r\n [[0 1 2 3 4]\r\n [5 6 7 8 9]]\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9<\/h3>\n
np.mean\u3082arr.mean\u3082\u4f7f\u3044\u65b9\u306f\u540c\u3058<\/strong><\/span>\u3067\u3059\u3002<\/p>\n
\u307e\u305f\u3001\u4f7f\u3044\u65b9\u306fnp.sum\u3068\u307b\u307c\u540c\u3058\u306a\u306e\u3067\u3001\u3053\u3061\u3089\u304c\u308f\u304b\u3063\u3066\u308b\u5834\u5408\u306f\u7c21\u5358\u306b\u4f7f\u3044\u3053\u306a\u305b\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n
\u307e\u305a\u306faxis\u3092\u6307\u5b9a\u3057\u306a\u3044\u5834\u5408<\/strong><\/span>\u3001\u914d\u5217\u5185\u306e\u8981\u7d20\u5168\u3066\u3092\u4f7f\u3063\u3066\u5e73\u5747\u5024\u3092\u8a08\u7b97<\/strong><\/span>\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
In [3]:\r\n\r\nnp.mean(x)\r\n\r\nOut[3]:\r\n\r\n4.5\r\n\r\nIn [4]:\r\n\r\nx.mean()\r\n\r\nOut[4]:\r\n\r\n4.5\r\n\r\nIn [5]:\r\n\r\nnp.mean(x2)\r\n\r\nOut[5]:\r\n\r\n4.5\r\n\r\nIn [6]:\r\n\r\nx2.mean()\r\n\r\nOut[6]:\r\n\r\n4.5\r\n\r\n<\/pre>\n<\/div>\n
\n
\n
axis\u3067\u5e73\u5747\u5024\u3092\u51fa\u3059\u8ef8\u3092\u6307\u5b9a<\/h3>\n
axis=0\u3068\u3059\u308b\u3068\u3001\u5217\u3054\u3068<\/strong><\/span>\u306b\u5e73\u5747\u5024\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/p>\n
axis=1\u3068\u3059\u308b\u3068\u3001\u884c\u3054\u3068<\/strong><\/span>\u306b\u5e73\u5747\u5024\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
In [7]:\r\n\r\nnp.mean(x2, axis=0)\r\n\r\nOut[7]:\r\n\r\narray([2.5, 3.5, 4.5, 5.5, 6.5])\r\n\r\nIn [8]:\r\n\r\nx2.mean(axis=0)\r\n\r\nOut[8]:\r\n\r\narray([2.5, 3.5, 4.5, 5.5, 6.5])\r\n\r\nIn [9]:\r\n\r\nnp.mean(x2, axis=1)\r\n\r\nOut[9]:\r\n\r\narray([2., 7.])\r\n\r\nIn [10]:\r\n\r\nx2.mean(axis=1)\r\n\r\nOut[10]:\r\n\r\narray([2., 7.])\r\n\r\n<\/pre>\n<\/div>\n
\n
\n
out\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u51fa\u529b\u5024\u3092\u5165\u308c\u308b\u914d\u5217\u3092\u7528\u610f<\/h3>\n
sum\u306e\u51fa\u529b\u914d\u5217\u3068\u540c\u3058shape\u306e\u914d\u5217\u3092\u7528\u610f<\/strong><\/span>\u3057\u3066\u3001out\u306b\u6e21\u3059\u3068\u305d\u306e\u914d\u5217\u3092\u4e0a\u66f8\u304d<\/strong><\/span>\u3057\u3066\u7b54\u3048\u3092\u8fd4\u3057\u3066\u304f\u308c\u307e\u3059\u3002<\/p>\n
\u65b0\u3057\u3044\u914d\u5217\u3092\u4f5c\u3089\u306a\u3044\u5206\u3060\u3051\u9ad8\u901f<\/strong><\/span>\u306b\u52d5\u4f5c\u3057\u307e\u3059\u3002<\/p>\n
\u3053\u306e\u3042\u305f\u308a\u306fnp.sum\u3068\u540c\u69d8\u3067\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
\n
\n
In [11]:\r\n\r\nx3 = np.zeros(2)\r\nx3\r\n\r\nOut[11]:\r\n\r\narray([0., 0.])\r\n\r\nIn [12]:\r\n\r\nnp.mean(x2, axis=1, out=x3)\r\nx3\r\n\r\nOut[12]:\r\n\r\narray([2., 7.])\r\n\r\nIn [13]:\r\n\r\nx2.mean(axis=1, out=x3)\r\nx3\r\n\r\nOut[13]:\r\n\r\narray([2., 7.])<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
dtype\u3067\u51fa\u529b\u914d\u5217\u306e\u578b\u3092\u6307\u5b9a<\/h3>\n
dtype\u3092\u6307\u5b9a\u3057\u306a\u3044\u3068\u3001\u9069\u5f53\u306a\u578b\u304c\u5272\u308a\u5f53\u3066\u3089\u308c\u307e\u3059\u3002<\/p>\n
\u4f8b\u3048\u3070x3\u306e\u4e2d\u8eab\u3092\u898b\u3066\u307f\u308b\u3068\u3001float64<\/strong><\/span>\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u306d\u3002<\/p>\n
\u3053\u308c\u306f\u5e73\u5747\u5024\u3092\u8a08\u7b97\u3057\u305f\u914d\u5217\u304cfloat64\u578b\u3060\u3063\u305f\u305f\u3081\u3067\u3059\u3002<\/p>\n
\u3053\u308c\u3092\u6562\u3048\u3066\u5909\u66f4\u3057\u305f\u3044\u5834\u5408\u306fdtype\u306b\u578b\u3092\u6307\u5b9a<\/strong><\/span>\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
In [14]:\r\n\r\nx3.dtype\r\n\r\nOut[14]:\r\n\r\ndtype('float64')\r\n\r\nIn [15]:\r\n\r\nx4 = np.mean(x2, axis=1, dtype=np.float32)\r\nx4\r\n\r\nOut[15]:\r\n\r\narray([2., 7.], dtype=float32)\r\n\r\n<\/pre>\n<\/div>\n
\n
\n
NaN\u304c\u5165\u3063\u305f\u914d\u5217\u306e\u8981\u7d20\u548c\u3092\u8a08\u7b97\u3059\u308b<\/h2>\n
\u914d\u5217\u5185\u306bNaN\u304c\u5165\u3063\u3066\u3044\u308b\u5834\u5408\u3001\u901a\u5e38\u306enp.mean\u3084arr.mean\u3067\u306f\u8a08\u7b97\u7d50\u679c\u304cNaN<\/strong><\/span>\u306b\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
\n
\n
In [16]:\r\n\r\nx5 = np.array([np.nan, 1.0, 2.0])\r\n\r\nIn [17]:\r\n\r\nnp.mean(x5)\r\n\r\nOut[17]:\r\n\r\nnan\r\n\r\nIn [18]:\r\n\r\nx5.mean()\r\n\r\nOut[18]:\r\n\r\nnan<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
\u3053\u306e\u5834\u5408\u3001NaN\u3092\u7121\u8996\u3057\u3066\u5e73\u5747\u5024\u3092\u51fa\u3059<\/strong><\/span>\u306b\u306f\u3001np.nanmean<\/strong><\/span>\u95a2\u6570\u3092\u4f7f\u3044\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
In [19]:\r\n\r\nnp.nanmean(x5)\r\n\r\nOut[19]:\r\n\r\n1.5\r\n\r\n<\/pre>\n<\/div>\n
\n
\n
nanmean\u306fnan\u306e\u5206\u306f\u7121\u8996\u3057\u3066\u8a08\u7b97\u3057\u3066\u3044\u308b\u305f\u3081\u3001x5\u306e\u8981\u7d20\u6570\u304c3\u3067\u3059\u304c\u3001nan\u306e\u5206\u3092\u629c\u3044\u305f\u8981\u7d20\u6570\uff082\uff09\u3067\u5272\u3063\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n
\u307e\u305f\u3001np.nansum\u95a2\u6570\u306f\u901a\u5e38\u306enp.sum\u95a2\u6570\u3068\u6bd4\u3079\u3066\u8a08\u7b97\u901f\u5ea6\u304c\u9045\u3044<\/strong><\/span>\u306e\u3067\u3001nan\u304c\u5165\u3063\u3066\u3044\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u5834\u5408\u306b\u3060\u3051\u4f7f\u3046\u4e8b\u3092\u304a\u3059\u3059\u3081\u3057\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
In\u00a0[20]:<\/div>\n
\n
\n
\n
%%timeit \r\nnp.nanmean(x2)<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
[\u8a08\u7b97\u901f\u5ea6]<\/div>\n
\n
11.1 \u00b5s \u00b1 88.2 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
In\u00a0[21]:<\/div>\n
\n
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%%timeit \r\nnp.mean(x2)<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
\n
\n
[\u8a08\u7b97\u901f\u5ea6]<\/div>\n
\n
6.57 \u00b5s \u00b1 55 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\u307e\u3068\u3081<\/h2>\n
\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001np.array\u304b\u3089\u5e73\u5747\u5024\u3092\u8a08\u7b97\u3059\u308b\u6a5f\u80fd\u3001np.mean\u3068np.array\u306e\u30e1\u30bd\u30c3\u30c9arr.mean\u3092\u7d39\u4ecb<\/strong><\/span>\u3057\u307e\u3057\u305f\u3002<\/p>\n
mean\u95a2\u6570\u3001mean\u30e1\u30bd\u30c3\u30c9\u306a\u3069\u306eNumPy\u306e\u4e2d\u306e\u95a2\u6570\u3067\u8a08\u7b97\u3092\u5b8c\u7d50\u3055\u305b\u308b\u4e8b\u304c\u3067\u304d\u308c\u3070\u3001\u975e\u5e38\u306b\u9ad8\u901f\u306a\u8a08\u7b97\u304c\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u662f\u975e\u3053\u306e\u8a18\u4e8b\u3067\u4f7f\u3044\u65b9\u3092\u899a\u3048\u3066\u3001\u79d1\u5b66\u8a08\u7b97\u3084\u6a5f\u68b0\u5b66\u7fd2\u306e\u5b9f\u88c5\u306b\u5f79\u7acb\u3066\u3066\u304f\u3060\u3055\u3044\uff01<\/p>\n
\u4eca\u56de\u306e\u8a18\u4e8b\u306f\u4e0b\u8a18\u306e\u8a18\u4e8b\u3092\u53c2\u8003\u306b\u3057\u3066\u3044\u307e\u3059\u3002
\n\u53c2\u8003\u8a18\u4e8b\uff1a
\n<\/p>\n","protected":false},"excerpt":{"rendered":"
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