{"id":1930,"date":"2019-07-09T22:43:43","date_gmt":"2019-07-09T13:43:43","guid":{"rendered":"https:\/\/tnishimaki.com\/?p=1930"},"modified":"2025-06-12T19:29:06","modified_gmt":"2025-06-12T10:29:06","slug":"python%e3%81%ab%e3%82%88%e3%82%8b%e9%87%8d%e5%9b%9e%e5%b8%b0%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/analysis-navi.com\/?p=1930","title":{"rendered":"Python\u306b\u3088\u308b\u91cd\u56de\u5e30\u5206\u6790"},"content":{"rendered":"\n

Python\u3067\u91cd\u56de\u5e30\u5206\u6790<\/a>\u3092\u884c\u3063\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n

\u5148\u306bPython\u306b\u3088\u308b\u5358\u56de\u5e30\u5206\u6790<\/a>\u306e\u8a18\u4e8b\u3092\u8aad\u3093\u3067\u3044\u305f\u3060\u3044\u305f\u307b\u3046\u304c\u5206\u304b\u308a\u3084\u3059\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\u3001\u3053\u3061\u3089\u306e\u307f\u8aad\u3093\u3067\u3044\u305f\u3060\u3044\u3066\u3082\u5206\u304b\u308b\u3088\u3046\u306b\u306f\u3057\u3066\u304a\u308a\u307e\u3059\u3002
\u307e\u305f\u3001\u4eca\u56de\u3082statsmodels<\/strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\nhttps:\/\/analysis-navi.com\/?p=1495\n<\/div><\/figure>\n\n\n\n\n\n\n

\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\u30fb\u91cd\u56de\u5e30\u5206\u6790\u306e\u5b9f\u884c<\/h2>\n\n\n\n

\u4eca\u56de\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u3001\u300ctemperature\u300d(=\u6c17\u6e29)\u300cprice\u300d(=\u30a2\u30a4\u30b9\u306e\u5024\u6bb5)\u300crainy\u300d(=\u96e8\u304b\u3069\u3046\u304b)\u306e3\u30c7\u30fc\u30bf\u304b\u3089\u3001\u300csales\u300d(=\u30a2\u30a4\u30b9\u306e\u58f2\u4e0a)\u3092\u4e88\u6e2c\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n

– \u65e2\u77e5\u306e\u30c7\u30fc\u30bf(sales_data.csv)<\/strong>
\"\"<\/p>\n\n\n\n

– \u4e88\u6e2c\u5bfe\u8c61\u30c7\u30fc\u30bf(sales_future.csv)<\/strong>
\"\"<\/p>\n\n\n\n

\u307e\u305a\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u3093\u3067\u3001\u56de\u5e30\u5206\u6790\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002
\u3053\u3053\u306f\u5358\u56de\u5e30\u5206\u6790\u3068\u307b\u3068\u3093\u3069\u4e00\u7dd2\u3067\u3059\u3002<\/p>\n\n\n\n

#CSV\u8aad\u307f\u8fbc\u307f\ndf = pd.read_csv(\"sales_data_j.csv\")\ndf_test = pd.read_csv(\"sales_future_j.csv\")\n\n#\u76ee\u7684\u5909\u6570\u540d\u306e\u6307\u5b9a\ny_name = \"sales\"\n\n#\u5f93\u5c5e\u5909\u6570\uff08\u4f7f\u7528\u5217\uff09\u306e\u9078\u629e\nX_name = [\"temperature\", \"price\", \"rainy\"]\n\n#X\u3068y\u306b\u5206\u96e2\nX = df[X_name]\ny = df[y_name]\n\n#\u56de\u5e30\u5206\u6790\nimport statsmodels.api as sm\n\nmodel = sm.OLS(y, sm.add_constant(X))\nresult = model.fit(disp=0)\nprint(result.summary())<\/pre>\n\n\n\n
\u56de\u5e30\u5206\u6790\u306b\u7528\u3044\u308b\u5217\u3092X_name\u306b\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002
\u305d\u3057\u3066\u3001\u6700\u5f8c\u306eprint\u3092\u5b9f\u884c\u3059\u308b\u3068\u30fb\u30fb\u30fb<\/p>\n\n\n\n
                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                  sales   R-squared:                       0.840\nModel:                            OLS   Adj. R-squared:                  0.825\nMethod:                 Least Squares   F-statistic:                     55.83\nDate:                Sun, 07 Jul 2019   Prob (F-statistic):           8.19e-13\nTime:                        17:07:05   Log-Likelihood:                -225.71\nNo. Observations:                  36   AIC:                             459.4\nDf Residuals:                      32   BIC:                             465.8\nDf Model:                           3                                         \nCovariance Type:            nonrobust                                         \n===============================================================================\n                  coef    std err          t      P>|t|      [0.025      0.975]\n-------------------------------------------------------------------------------\nconst        -340.9653    303.596     -1.123      0.270    -959.371     277.440\ntemperature    48.2076      4.818     10.005      0.000      38.393      58.022\nprice           1.1667      1.314      0.888      0.381      -1.511       3.844\nrainy         193.0358     77.940      2.477      0.019      34.277     351.794\n==============================================================================\nOmnibus:                        1.521   Durbin-Watson:                   1.822\nProb(Omnibus):                  0.467   Jarque-Bera (JB):                1.072\nSkew:                          -0.112   Prob(JB):                        0.585\nKurtosis:                       2.185   Cond. No.                     2.82e+03\n==============================================================================<\/pre>\n\n\n\n
\u300c\u6708\u5e73\u5747\u6c17\u6e29\u300d\u306e\u5024\u3092\\(x_1\\)\u3001\u300c\u5024\u6bb5\u300d\u306e\u5024\u3092\\(x_2\\)\u3001\u300c\u96e8\u300d\u306e\u5024\u3092\\(x_3\\)\u3001\u3068\u3059\u308b\u3068\u3001\\(y=48.2 x_1+1.2 x_2+193.0 x_3-341.0\\)\u3068\u3044\u3046\u95a2\u4fc2\u304c\u3042\u308b<\/span><\/strong>\u3068\u3044\u3046\u95a2\u4fc2\u304c\u5206\u304b\u308a\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u4e0a\u90e8\u306e\u300cAdj. R-squared\u300d\u304c\u300c\u81ea\u7531\u5ea6\u8abf\u6574\u6e08\u307f\\(R^2\\)\u5024\u300d<\/span><\/strong>\u306b\u5f53\u305f\u308a\u30010.825\u3068\u8a08\u7b97\u3055\u308c\u3066\u3044\u307e\u3059\u3002
\u4e2d\u6bb5\u306e\u8868\u5185\u306e\u300cP>|t|\u300d\u304c\u3001\u5909\u6570\u3054\u3068\u306ep\u5024<\/span><\/strong>\u3092\u8868\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u3044\u305a\u308c\u3082Excel\u306b\u3088\u308b\u91cd\u56de\u5e30\u5206\u6790<\/a>\u3068\u540c\u3058\u7d50\u679c\u306e\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u5909\u6570\u306e\u9078\u629e<\/h2>\n\n\n\n
\u91cd\u56de\u5e30\u5206\u6790\u3067\u5927\u5207\u306a\u306e\u306f\u3001\u3069\u306e\u5909\u6570\u304c\u5927\u4e8b\u3067\u3001\u3069\u306e\u5909\u6570\u304c\u4e0d\u8981\u306a\u306e\u304b\u3092\u304d\u3061\u3093\u3068\u898b\u6975\u3081\u308b<\/span><\/strong>\u4e8b\u3067\u3059\u3002<\/p>\n\n\n\n
\u4f8b\u3048\u3070\u3001\u4e0a\u306e\u7d50\u679c\u3092\u898b\u3066\u307f\u308b\u3068\u3001\u300cprice\u300d\u306ep\u5024\u304c\u4f4e\u3044\u306e\u3067\u3001\u300cprice\u300d\u5217\u3092\u6d88\u3057\u3066\u91cd\u56de\u5e30\u5206\u6790\u3057\u3066\u307f\u305f\u3044\u3067\u3059\u306d\u3002
\u305d\u306e\u6642\u3001Excel\u3060\u3068\u3001\u81ea\u3089\u4e0d\u8981\u306a\u5217\u3092\u6d88\u3057\u3066\u3001\u518d\u5ea6\u3001\u300c\u30c7\u30fc\u30bf\u5206\u6790\u300d\u30e1\u30cb\u30e5\u30fc\u304b\u3089\u4f7f\u7528\u5217\u3092\u9078\u629e\u3057\u3066\u91cd\u56de\u5e30\u5206\u6790\u3092\u5b9f\u884c\u30fb\u30fb\u30fb\u3068\u3044\u3046\u624b\u9806\u3092\u8e0f\u307e\u306a\u3051\u308c\u3070\u3044\u3051\u307e\u305b\u3093\u3002
\u3057\u304b\u3057python\u3067\u306f\u300c\u4f7f\u3046\u5217\u306e\u540d\u524d\u300d\u3092\u5909\u66f4\u3057\u3066\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u884c\u3057\u3066\u3042\u3052\u308b\u3060\u3051<\/span><\/strong>\u3067\u3059\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306e\u4f8b\u3067\u8a00\u3048\u3070\u3001\u4e0a\u8a18\u30d7\u30ed\u30b0\u30e9\u30e0\u306eX_name\u3092
X_name = [\"temperature\", \"price\", \"rainy\"]<\/code>
\u304b\u3089
X_name = [\"temperature\", \"rainy\"]<\/code>
\u3068\u5909\u66f4\u3057\u3001\u5b9f\u884c\u3059\u308b\u3060\u3051\u3067OK\u3067\u3059\u3002<\/p>\n\n\n\n
\u3059\u308b\u3068\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                  sales   R-squared:                       0.836\nModel:                            OLS   Adj. R-squared:                  0.826\nMethod:                 Least Squares   F-statistic:                     83.88\nDate:                Tue, 09 Jul 2019   Prob (F-statistic):           1.15e-13\nTime:                        10:18:19   Log-Likelihood:                -226.15\nNo. Observations:                  36   AIC:                             458.3\nDf Residuals:                      33   BIC:                             463.0\nDf Model:                           2                                         \nCovariance Type:            nonrobust                                         \n===============================================================================\n                  coef    std err          t      P>|t|      [0.025      0.975]\n-------------------------------------------------------------------------------\nconst         -86.2107     98.682     -0.874      0.389    -286.980     114.559\ntemperature    47.6485      4.762     10.007      0.000      37.961      57.336\nrainy         178.0815     75.852      2.348      0.025      23.759     332.404\n==============================================================================\nOmnibus:                        3.161   Durbin-Watson:                   1.821\nProb(Omnibus):                  0.206   Jarque-Bera (JB):                1.483\nSkew:                           0.031   Prob(JB):                        0.476\nKurtosis:                       2.008   Cond. No.                         94.6\n==============================================================================<\/pre>\n\n\n\n
\u3053\u3061\u3089\u3082\u3001Excel\u306e\u6642\u3068\u540c\u69d8\u306e\u7d50\u679c\u304c\u5f97\u3089\u308c\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u3002
\u300cAdj. R-squared\u300d\u304c\u5148\u7a0b\u3088\u308a\u50c5\u304b\u306b\u4e0a\u304c\u308a\u3001\u300ctemperature\u300d\u3068\u300crainy\u300d\u306ep\u5024\u3082\u4f4e\u3044\u3053\u3068\u304b\u3089\u3001\u300cprice\u300d\u3092\u7528\u3044\u306a\u3044\u307b\u3046\u304c\u826f\u3044\u63a8\u6e2c\u3060\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u306e\u63a8\u6e2c<\/h2>\n\n\n\n
\u305d\u308c\u3067\u306f\u3001\u300ctemperature\u300d\u3068\u300crainy\u300d\u3092\u7528\u3044\u3066\u3001\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u3092\u3057\u3066\u307f\u307e\u3059\u3002
\u3068\u3044\u3063\u3066\u3082\u5148\u306e\u56de\u5e30\u5206\u6790\u306e\u7d50\u679c\u3068\u3001\u8aad\u307f\u8fbc\u3093\u3060\u300csales_future_j.csv\u300d\u3092\u4f7f\u3063\u3066\u6a5f\u68b0\u7684\u306b\u8a08\u7b97\u3059\u308b\u3060\u3051\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u30bd\u30fc\u30b9\u306f\u4ee5\u4e0b\u3002<\/p>\n\n\n\n
y_result=[]\n#\u672a\u77e5\u30c7\u30fc\u30bf\u306e\u63a8\u6e2c(\u91cd\u56de\u5e30)\nfor i in range(len(df_test.index)):\n    y_tmp = result.params.const\n    for j in range(len(X_name)):\n        x_name = X_name[j]\n        y_tmp += result.params[x_name] * df_test[x_name][i]\n\n    y_result.append(y_tmp)\nprint(y_result)<\/pre>\n\n\n\n
[692.242462219598, 804.8170009756097, 399.80442653785354, 811.3638076424675, 890.5843696800758]<\/pre>\n\n\n\n
\u6700\u5f8c\u306e\u300cy_result\u300d\u306b\u7d50\u679c\u304c\u683c\u7d0d\u3055\u308c\u307e\u3057\u305f\u3002
\u307e\u305f\u3001\u3053\u306e\u7d50\u679c\u3092Excel\u306b\u66f8\u304d\u51fa\u3057\u305f\u3044\u306e\u306a\u3089\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3057\u307e\u3059\u3002\uff08\u5358\u56de\u5e30\u306e\u6642\u3068\u4e00\u7dd2\u3067\u3059\u3002\uff09<\/p>\n\n\n\n
#\u7d50\u679c\u66f8\u304d\u51fa\u3057\ndf_test_y = pd.DataFrame(y_result,columns=[\"y\"])\ndf_result = pd.concat([df_test,df_test_y],axis=1)\ndf_result.to_csv(\"result.csv\",index=None)<\/pre>\n\n\n\n
\u4f5c\u6210\u3055\u308c\u305fresult.csv\u3092\u898b\u3066\u307f\u308b\u3068\u30fb\u30fb\u30fb\u3002
\"\"<\/p>\n\n\n\n
\u7121\u4e8b\u306b\u5148\u7a0b\u306e\u7d50\u679c\u304ccsv\u306b\u53cd\u6620\u3055\u308c\u3066\u3044\u308b\u3088\u3046\u3067\u3059\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
\u3055\u3066\u3001\u4eca\u56de\u306fPython\u3067\u91cd\u56de\u5e30\u5206\u6790\u3092\u884c\u3063\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u91cd\u56de\u5e30\u5206\u6790\u306f\u4eca\u56de\u6271\u3063\u305f\u5909\u6570\u9078\u629e\u4ee5\u5916\u306b\u3082\u3001\u30c7\u30fc\u30bf\u306e\u6b20\u640d\u5024\u3084\u5916\u308c\u5024\u3092\u9664\u5916\u3057\u305f\u308a\u3001\u307e\u305f\u300c\u307b\u3068\u3093\u3069\u540c\u3058\u5217\u300d\u304c\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u304b\u78ba\u8a8d\u3057\u305f\u308a\u3068\u8003\u3048\u308b\u3053\u3068\u306f\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u3002
\u305d\u3046\u8003\u3048\u308b\u3068\u3001Excel\u3067\u3082\u8a08\u7b97\u306f\u53ef\u80fd\u3067\u3059\u304c\u3001\u5727\u5012\u7684\u306bPython\u306e\u65b9\u304c\u4fbf\u5229\u3067\u81ea\u7531\u5ea6\u306e\u9ad8\u3044\u5206\u6790\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/strong><\/p>\n\n\n\n
\u5165\u529b\u3059\u308bcsv\u306e\u5f62\u5f0f\u3092\u5408\u308f\u305b\u308c\u3070\u3001\u3042\u3068\u306f\u30bd\u30fc\u30b9\u306e\u30b3\u30d4\u30da\u3067\u52d5\u304f\u3068\u601d\u308f\u308c\u307e\u3059\u306e\u3067\u3001\u3054\u6d3b\u7528\u304f\u3060\u3055\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
Python\u3067\u91cd\u56de\u5e30\u5206\u6790\u3092\u884c\u3063\u3066\u307f\u307e\u3059\u3002 \u5148\u306bPython\u306b\u3088\u308b\u5358\u56de\u5e30\u5206\u6790\u306e\u8a18\u4e8b\u3092\u8aad\u3093\u3067\u3044\u305f\u3060\u3044\u305f\u307b\u3046\u304c\u5206\u304b\u308a\u3084\u3059\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c\u3001\u3053\u3061\u3089\u306e\u307f\u8aad\u3093\u3067\u3044\u305f\u3060\u3044\u3066\u3082\u5206\u304b\u308b\u3088\u3046\u306b\u306f\u3057\u3066\u304a\u308a\u307e\u3059\u3002\u307e\u305f\u3001\u4eca\u56de\u3082statsmode […]<\/p>\n","protected":false},"author":1,"featured_media":5082,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"swell_btn_cv_data":"","vkexunit_cta_each_option":"","footnotes":""},"categories":[129],"tags":[101,131,134],"class_list":["post-1930","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-study","tag-ml","tag-stats","tag-python"],"_links":{"self":[{"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/posts\/1930","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1930"}],"version-history":[{"count":18,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/posts\/1930\/revisions"}],"predecessor-version":[{"id":5162,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/posts\/1930\/revisions\/5162"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=\/wp\/v2\/media\/5082"}],"wp:attachment":[{"href":"https:\/\/analysis-navi.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1930"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1930"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/analysis-navi.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1930"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}