{"id":70996,"date":"2018-09-04T23:01:26","date_gmt":"2018-09-04T14:01:26","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=70996"},"modified":"2024-05-06T11:51:26","modified_gmt":"2024-05-06T02:51:26","slug":"%e3%80%90numpy%e5%85%a5%e9%96%80-np-sort%e3%80%91%e9%85%8d%e5%88%97%e3%82%92%e6%98%87%e9%a0%86%e3%81%ab%e3%82%bd%e3%83%bc%e3%83%88suru","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/70996","title":{"rendered":"\u3010NumPy\u5165\u9580 np.sort\u3011\u914d\u5217\u3092\u6607\u9806\u306b\u30bd\u30fc\u30c8\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u5b66\u307c\u3046"},"content":{"rendered":"

\u3053\u3093\u306b\u3061\u306f\u3001\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc<\/a>\u306e\u30d5\u30af\u30ed\u30a6\u3067\u3059\uff01<\/p>\n

\u8981\u7d20\u3092\u5c0f\u3055\u3044\u9806\u306b\u4e26\u3079\u308b\uff08\u30bd\u30fc\u30c8\u3059\u308b\uff09<\/strong><\/span>\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u304c\u3001\u305d\u308c\u3089\u306e\u5b9f\u88c5\u306f\u3061\u3087\u3063\u3068\u5927\u5909\u3067\u3059\u3002<\/p>\n

NumPy\u3067\u306f\u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u7c21\u5358\u306b\u4f7f\u3048\u308b\u95a2\u6570\u3001np.sort<\/strong><\/span>\u304c\u7528\u610f\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

np.sort\u3092\u4f7f\u3048\u3070\u3001\u30af\u30a4\u30c3\u30af\u30bd\u30fc\u30c8<\/span><\/strong>\u3084\u30de\u30fc\u30b8\u30bd\u30fc\u30c8<\/strong><\/span>\u306a\u3069\u306e\u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u7c21\u5358\u306b\u5207\u308a\u66ff\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u306e\u3067\u3001\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6c17\u306b\u305b\u305a\u306b\u30bd\u30fc\u30c8\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u8a18\u4e8b\u3067np.sort\u306e\u4f7f\u3044\u65b9\u3092\u899a\u3048\u3066\u3001\u662f\u975e\u4f7f\u3063\u3066\u307f\u3066\u304f\u3060\u3055\u3044\uff01<\/p>\n

np.sort\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\u3092\u4f7f\u3063\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

# \u30b3\u30fc\u30c9 In [1]:\nimport numpy as np<\/pre>\n
\u4e00\u6b21\u5143\u914d\u5217\u306e\u30bd\u30fc\u30c8<\/h3>\n
np.sort\u95a2\u6570\u306e\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u306f\u3001Python\u306esorted<\/strong><\/span>\u3068\u540c\u69d8\u3067\u3059\u3002<\/p>\n
np.sort\u3092\u9069\u7528\u3059\u308b\u3068\u3001\u6607\u9806\u306b\u30bd\u30fc\u30c8\u3055\u308c\u305f\u914d\u5217<\/strong><\/span>\u304c\u8fd4\u3063\u3066\u304d\u307e\u3059\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [2]:\na = np.arange(0,10)\nnp.random.shuffle(a)\nprint(\"original a:n\", a,\"n\")\n\nsorted_a = np.sort(a)\nprint(\"sorted a:n\", sorted_a,\"n\")<\/pre>\n
# \u51fa\u529b\u7d50\u679c [2]:\noriginal a:\n [1 5 6 7 2 8 9 4 0 3] \n\nsorted a:\n [0 1 2 3 4 5 6 7 8 9] \n\n<\/pre>\n
\u9006\u9806\u306b\u30bd\u30fc\u30c8<\/h3>\n
reversed<\/strong><\/span>\u306e\u3088\u3046\u306a\u9006\u9806\u306b\u30bd\u30fc\u30c8\u3059\u308b\u95a2\u6570\u306f\u3042\u308a\u307e\u305b\u3093\u304c\u3001numpy.ndarray\u3092\u9006\u306b\u3059\u308b\u65b9\u6cd5<\/strong><\/span>\u306f\u3042\u308a\u307e\u3059\u3002<\/p>\n
\u25cf\u30b9\u30e9\u30a4\u30b9\u8a18\u6cd5\u3067\u9006\u9806\u306b\u3059\u308b<\/p>\n
\u914d\u5217a\u3092\u9006\u9806<\/strong><\/span>\u306b\u3057\u305f\u3044\u306a\u3089\u2192 a[::-1]<\/span><\/strong><\/p>\n
\u3053\u308c\u3092sort\u3068\u4f75\u7528\u3059\u308b\u3053\u3068\u3067\u9006\u9806\u306b\u30bd\u30fc\u30c8\u3057\u305f\u7d50\u679c\u3092\u5f97\u308c\u307e\u3059\u3002\u8a66\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [3]:\nreversed_a = np.sort(a)[::-1]\nprint(\"reversed a:n\", reversed_a)<\/pre>\n
# \u51fa\u529b\u7d50\u679c [3]:\nreversed a:\n [9 8 7 6 5 4 3 2 1 0]\n<\/pre>\n
\u591a\u6b21\u5143\u914d\u5217\u306e\u30bd\u30fc\u30c8<\/h3>\n
np.sum\u306a\u3069\u3068\u540c\u69d8\u306b\u3001axis\u30d1\u30e9\u30e1\u30fc\u30bf<\/strong><\/span>\u3092\u4f7f\u3063\u3066\u30bd\u30fc\u30c8\u3059\u308b\u969b\u306b\u5f93\u3046\u8ef8\u3092\u6307\u5b9a\u3067\u304d\u307e\u3059\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [4]:\nb = np.reshape(a, (2,5))\nprint(\"original b:n\", b,\"n\")\n\n# \u6700\u5f8c\u306e\u8ef8\u306b\u5f93\u3063\u3066\u30bd\u30fc\u30c8(axis=-1, \u3064\u307e\u308a\u3053\u3053\u3067\u306faxis=1)\nsorted_b = np.sort(b)\nprint(\"sorted b:n\", sorted_b,\"n\")\n\n# \u5217\u3054\u3068\u306b\u30bd\u30fc\u30c8\nsorted_b_axis0 = np.sort(b, axis=0)\nprint(\"sorted b (axis=0):n\", sorted_b_axis0,\"n\")\n\n# \u884c\u3054\u3068\u306b\u30bd\u30fc\u30c8\nsorted_b_axis1 = np.sort(b, axis=1)\nprint(\"sorted b (axis=1):n\", sorted_b_axis1,\"n\")<\/pre>\n
# \u51fa\u529b\u7d50\u679c [4]:\noriginal b:\n [[1 5 6 7 2]\n [8 9 4 0 3]] \n\nsorted b:\n [[1 2 5 6 7]\n [0 3 4 8 9]] \n\nsorted b (axis=0):\n [[1 5 4 0 2]\n [8 9 6 7 3]] \n\nsorted b (axis=1):\n [[1 2 5 6 7]\n [0 3 4 8 9]] \n\n<\/pre>\n
\u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u5909\u66f4<\/h3>\n
np.sort\u306b\u306fkind\u30d1\u30e9\u30e1\u30fc\u30bf<\/span><\/strong>\u304c\u3042\u308a\u3001\u3053\u3053\u3067\u306f\u2018quicksort\u2019, \u2018mergesort\u2019, \u2018heapsort\u2019, \u2018stable\u2019<\/span><\/strong>\u306e\u56db\u7a2e\u985e\u306e\u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6307\u5b9a\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306fkind=\u2018quicksort\u2019<\/strong><\/span>\u3068\u306a\u3063\u3066\u3044\u308b\u306e\u3067\u3001\u30af\u30a4\u30c3\u30af\u30bd\u30fc\u30c8\u4ee5\u5916\u3092\u4f7f\u3044\u305f\u3044\u5834\u5408\u306f\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5909\u66f4\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
# \u30b3\u30fc\u30c9 In [5]:\nx = np.linspace(-9,10,10000)\n\n# \u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3088\u3063\u3066\u5b9f\u884c\u901f\u5ea6\u304c\u9055\u3044\u307e\u3059\u3002\n%timeit np.sort(x, kind=\"quicksort\")\n%timeit np.sort(x, kind=\"mergesort\")\n%timeit np.sort(x, kind=\"heapsort\")\n%timeit np.sort(x, kind=\"stable\")<\/pre>\n
# \u51fa\u529b\u7d50\u679c [5]:\n67.4 \u00b5s \u00b1 136 ns per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n78 \u00b5s \u00b1 381 ns per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n429 \u00b5s \u00b1 240 ns per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n78.1 \u00b5s \u00b1 176 ns per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n<\/pre>\n
\u4efb\u610f\u306e\u6307\u6a19\u3092\u4f7f\u3063\u3066\u30bd\u30fc\u30c8<\/h2>\n
python\u306esort\u30e1\u30bd\u30c3\u30c9\u3084sorted\u95a2\u6570\u306b\u306fkey\u30aa\u30d7\u30b7\u30e7\u30f3<\/span><\/strong>\u304c\u3042\u308a\u307e\u3057\u305f\u3002<\/p>\n
\u3053\u308c\u306fkey\u306b\u6307\u5b9a\u3057\u305f\u95a2\u6570\u306b\u5f93\u3063\u3066\u30bd\u30fc\u30c8\u3059\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3059\u3002<\/p>\n
\u3053\u308c\u306b\u8fd1\u3044\u65b9\u6cd5\u3092numpy\u3067\u884c\u3046\u306b\u306f\u3069\u3046\u3059\u308c\u3070\u3044\u3044\u3067\u3057\u3087\u3046\u304b\u3002<\/p>\n
\u7d76\u5bfe\u5024\u3092\u4f7f\u3063\u3066\u6607\u9806\u30bd\u30fc\u30c8\u3092\u3057\u305f\u3044\u3068\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
numpy.ndarray\u306e\u5168\u3066\u306e\u8981\u7d20\u306b\u5bfe\u3057\u3066\u9069\u7528\u3067\u304d\u308b\u95a2\u6570\u3067\u30bd\u30fc\u30c8\u306e\u57fa\u6e96\u3092\u518d\u73fe\u3067\u304d\u308b\u306a\u3089\u3001\u4ee5\u4e0b\u306e\u6d41\u308c\u3067\u30bd\u30fc\u30c8\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \n
  1. \u30bd\u30fc\u30c8\u3057\u305f\u3044\u914d\u5217c\u306b\u3001\u30bd\u30fc\u30c8\u306e\u57fa\u6e96\u306b\u3057\u305f\u3044\u95a2\u6570\u3092\u9069\u7528<\/li>\n
  2. 1\u3067\u3067\u304d\u305f\u914d\u5217\u3092argsort<\/li>\n
  3. 2\u3067\u3067\u304d\u305f\u914d\u5217\u3092\u4f7f\u3063\u3066c\u3092\u4e26\u3073\u66ff\u3048\u308b<\/li>\n<\/ol>\n
    # \u30b3\u30fc\u30c9 In [6]:\nc = np.linspace(-9,10,10)\nprint(\"original c:n\", c,\"n\")\n\nc_key = np.abs(c)\nprint(\"\u5143\u306e\u914d\u5217\u306b\u30bd\u30fc\u30c8\u306e\u57fa\u6e96\u306b\u3057\u305f\u3044\u95a2\u6570\u3092\u9069\u7528:n\", c_key,\"n\")\n\nc_index = np.argsort(c_key)\nprint(\"c_key\u3092argsort:n\", c_index, \"n\")\n\nsorted_c = c[c_index]\nprint(\"c_index\u3092\u5143\u306b\u30bd\u30fc\u30c8\u3057\u305f\u3044\u914d\u5217c\u3092\u4e26\u3073\u66ff\u3048\u308b:n\", sorted_c,\"n\")<\/pre>\n
    # \u51fa\u529b\u7d50\u679c [6]:\noriginal c:\n [-9.         -6.88888889 -4.77777778 -2.66666667 -0.55555556  1.55555556\n  3.66666667  5.77777778  7.88888889 10.        ] \n\n\u5143\u306e\u914d\u5217\u306b\u30bd\u30fc\u30c8\u306e\u57fa\u6e96\u306b\u3057\u305f\u3044\u95a2\u6570\u3092\u9069\u7528:\n [ 9.          6.88888889  4.77777778  2.66666667  0.55555556  1.55555556\n  3.66666667  5.77777778  7.88888889 10.        ] \n\nc_key\u3092argsort:\n [4 5 3 6 2 7 1 8 0 9] \n\nc_index\u3092\u5143\u306b\u30bd\u30fc\u30c8\u3057\u305f\u3044\u914d\u5217c\u3092\u4e26\u3073\u66ff\u3048\u308b:\n [-0.55555556  1.55555556 -2.66666667  3.66666667 -4.77777778  5.77777778\n -6.88888889  7.88888889 -9.         10.        ] \n\n<\/pre>\n
    \u4e00\u3064\u306e\u95a2\u6570\u306b\u307e\u3068\u3081\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u611f\u3058\u3067\u3059\u3002<\/p>\n
    # \u30b3\u30fc\u30c9 In [7]:\ndef keysort(arr, key):\n    arr_key = key(arr)\n    arr_index = np.argsort(arr_key)\n    return arr[arr_index]\n\nsorted_abs = keysort(c, np.abs)\nprint(\"\u7d76\u5bfe\u5024\u3092\u5143\u306b\u30bd\u30fc\u30c8:n\", sorted_abs, \"n\")<\/pre>\n
    # \u51fa\u529b\u7d50\u679c [7]:\n\u7d76\u5bfe\u5024\u3092\u5143\u306b\u30bd\u30fc\u30c8:\n [-0.55555556  1.55555556 -2.66666667  3.66666667 -4.77777778  5.77777778\n -6.88888889  7.88888889 -9.         10.        ] \n\n<\/pre>\n
    \u3069\u3046\u306b\u304bnumpy\u306e\u95a2\u6570\u3060\u3051\u3067\u76ee\u7684\u306e\u30bd\u30fc\u30c8\u65b9\u6cd5\u304c\u518d\u73fe\u3067\u304d\u308b\u306a\u3089\u3070\u3001\u3053\u306e\u3088\u3046\u306a\u65b9\u6cd5\u3082\u4f7f\u3048\u307e\u3059\u3002<\/p>\n
    \u3069\u3046\u3057\u3066\u3082\u96e3\u3057\u3044\u5834\u5408\u306f\u3001python\u306esorted\u95a2\u6570\u3092\u4f7f\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u3087\u3046\u3002<\/p>\n
    # \u30b3\u30fc\u30c9 In [8]:\nsorted(c, key=abs)<\/pre>\n
    # \u51fa\u529b\u7d50\u679c Out [8]:\n[-0.5555555555555554,\n 1.5555555555555554,\n -2.666666666666666,\n 3.666666666666668,\n -4.777777777777778,\n 5.777777777777779,\n -6.888888888888889,\n 7.888888888888889,\n -9.0,\n 10.0]<\/pre>\n
    \u305f\u3060\u3057\u3001python\u306esorted\u306fnumpy\u3060\u3051\u3067\u5b9f\u88c5\u3057\u305fkeysort\u3088\u308a\u9045\u3044\u306e\u3067\u6ce8\u610f\u3067\u3059\u3002<\/p>\n
    # \u30b3\u30fc\u30c9 In [9]:\nx = np.linspace(-9,10,10000)\n\n%timeit keysort(x, np.abs)\n%timeit sorted(x, key=abs)<\/pre>\n
    # \u51fa\u529b\u7d50\u679c [9]:\n483 \u00b5s \u00b1 225 ns per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n1.13 ms \u00b1 12.8 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n<\/pre>\n
    \u307e\u3068\u3081<\/h2>\n
    \u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001np.sort\u3092\u4e2d\u5fc3\u306b\u3001np.ndarray\u3092\u30bd\u30fc\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u89e3\u8aac\u3057\u307e\u3057\u305f\u3002<\/p>\n
    \u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u5b9f\u88c5\u306b\u3088\u3063\u3066\u306f\u901f\u5ea6\u304c\u5927\u5e45\u306b\u5909\u308f\u308b<\/strong><\/span>\u3053\u3068\u306e\u3042\u308b\u3082\u306e\u306a\u306e\u3067\u3001NumPy\u306b\u7d44\u307f\u8fbc\u307e\u308c\u3066\u3044\u308bnp.sort\u3092\u4f7f\u3063\u3066\u697d\u306b\u9ad8\u901f\u306a\u95a2\u6570\u3092\u4f7f\u3046\u306e\u304c\u304a\u3059\u3059\u3081\u3067\u3059\u3002<\/p>\n
    \u69d8\u3005\u306a\u3068\u3053\u308d\u3067\u5fc5\u8981\u306b\u306a\u308b\u30bd\u30fc\u30c8\u3001\u7c21\u5358\u3067\u3059\u304c\u3057\u3063\u304b\u308a\u899a\u3048\u3066\u4f7f\u3044\u3053\u306a\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
    \u3053\u3093\u306b\u3061\u306f\u3001\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc\u306e\u30d5\u30af\u30ed\u30a6\u3067\u3059\uff01 \u8981\u7d20\u3092\u5c0f\u3055\u3044\u9806\u306b\u4e26\u3079\u308b\uff08\u30bd\u30fc\u30c8\u3059\u308b\uff09\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u304c\u3001\u305d\u308c\u3089\u306e\u5b9f\u88c5\u306f\u3061\u3087\u3063\u3068\u5927\u5909\u3067\u3059\u3002 NumPy\u3067\u306f\u30bd\u30fc\u30c8\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u7c21\u5358\u306b\u4f7f\u3048\u308b\u95a2\u6570\u3001np.sort\u304c\u7528 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