{"id":3921,"date":"2024-07-18T05:00:00","date_gmt":"2024-07-17T20:00:00","guid":{"rendered":"https:\/\/www.uncovertruth.co.jp\/dx-accelerator\/?p=3921"},"modified":"2024-09-27T17:21:27","modified_gmt":"2024-09-27T08:21:27","slug":"064","status":"publish","type":"post","link":"https:\/\/www.uncovertruth.co.jp\/dx-accelerator\/blog\/articles\/tableau\/064\/","title":{"rendered":"\u3010Tableau\u3011\u3059\u3050\u3067\u304d\u308b\uff01Tabpy\u306e\u4f7f\u3044\u65b9\u3092\u89e3\u8aac"},"content":{"rendered":"\n

\u306f\u3058\u3081\u306b<\/h2>\n\n\n\n

Python\u3092\u4f7f\u3063\u3066Tableau\u306e\u30c7\u30fc\u30bf\u3092\u52a0\u5de5\u3067\u304d\u308b\u3053\u3068\u306f\u3054\u5b58\u3058\u3067\u3057\u3087\u3046\u304b\u3002\u4eca\u56de\u306fTabpy\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u52a0\u5de5\u304b\u3089\u96c6\u8a08\u307e\u3067\u3092\u884c\u3046\u65b9\u6cd5\u3092\u3054\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

Tabpy\u306e\u4f7f\u3044\u65b9<\/h2>\n\n\n\n

Tabpy\u3068\u306f<\/mark><\/h3>\n\n\n\n

Tableau\u4e0a\u3067Python\u304c\u4f7f\u3048\u308b\u3088\u3046\u306b\u306a\u308bTableau\u306e\u62e1\u5f35\u6a5f\u80fd\u3067\u3059\u3002<\/strong>Tabpy(Tableau Python Server)\u3068\u3044\u3046\u540d\u524d\u304b\u3089\u3082\u898b\u3066\u53d6\u308c\u308b\u3088\u3046\u306b\u3001Tableau\u304c\u30b5\u30fc\u30d0\u30fc\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u3053\u3068\u3067Python\u306b\u3088\u308b\u8a08\u7b97\u3092\u53ef\u80fd\u306b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\u5c0e\u5165\u65b9\u6cd5<\/mark><\/h3>\n\n\n\n

\u2460Python\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h4>\n\n\n\n

PC\u3078Python\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002\u30b7\u30f3\u30d7\u30eb\u306bPython\u3092\u5165\u308c\u308b\u4ed6\u306b\u3001Python\u306b\u95a2\u308f\u308b\u4fbf\u5229\u306a\u3082\u306e\u304c\u307e\u3068\u307e\u3063\u305fAnaconda<\/a>\u3068\u3044\u3046\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u65b9\u3092\u304a\u52e7\u3081\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\u2781Tabpy Server\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/h4>\n\n\n\n

2\u3064\u306e\u65b9\u6cd5\u304c\u3042\u308a\u307e\u3059\u3002
\u30fbGithub\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/strong>
\u3053\u3061\u3089<\/a>\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9
\u30fbAnaconda Prompt\u3067\u4ee5\u4e0b\u3092\u5b9f\u884c<\/strong><\/p>\n\n\n

\npip install tabpy\n<\/pre><\/div>\n\n\n
<\/p>\n\n\n\n
\u2462Tabpy Server\u3092\u7acb\u3061\u4e0a\u3052\u308b<\/h4>\n\n\n\n
\u30fbGithub\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u305f\u5834\u5408<\/strong>
Tabpy\u30d5\u30a9\u30eb\u30c0\u304b\u3089\u7acb\u3061\u4e0a\u3052\u307e\u3059\u3002
\u30fbAnaconda Prompt\u3092\u4f7f\u3063\u305f\u5834\u5408<\/strong>
\u300cTabpy\u300d\u3068\u5b9f\u884c<\/p>\n\n\n\n
\u2463Tabpy Server\u3068Tableau Desktop\u306e\u63a5\u7d9a<\/h4>\n\n\n\n
Tableau\u3067\u30d8\u30eb\u30d7\u27a1\u8a2d\u5b9a\u304a\u3088\u3073\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u27a1\u62e1\u5f35\u6a5f\u80fd\u63a5\u7d9a\u306e\u7ba1\u7406\u3078\u3068\u9032\u3080<\/p>\n\n\n
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\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n\n
<\/p>\n\n\n\n
\u30b5\u30fc\u30d0\u30fc\u306b\u300clocalhost\u300d\u3001\u30dd\u30fc\u30c8\u306b\u300c9004\u300d\u3068\u5165\u529b\u3057\u3066\u30b5\u30a4\u30f3\u30a4\u30f3\u3057\u307e\u3059\u3002<\/p>\n\n\n
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\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n
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\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n\n
\u5b9f\u969b\u306b\u4f7f\u3063\u3066\u307f\u3088\u3046\uff01<\/h2>\n\n\n\n
\u57fa\u672c\u7684\u306a4\u7a2e\u985e\u306e\u95a2\u6570<\/mark><\/h3>\n\n\n\n
\u2460SCRIPT_REAL \u95a2\u6570<\/h4>\n\n\n\n
\u6570\u5024\u3092\u8fd4\u3059\u95a2\u6570\u3067\u3059\u3002\u4e3b\u306b\u6570\u5024\u4e88\u6e2c\u3084\u30c7\u30fc\u30bf\u5909\u63db\u306b\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n
\nSCRIPT_REAL("import numpy as np; result = np.mean(_arg1); result", SUM([\u6307\u6a19]))\n<\/pre><\/div>\n\n\n
\u3053\u306e\u4f8b\u3067\u306f\u3001Sales\u5217\u306e\u5e73\u5747\u5024\u3092\u8a08\u7b97\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u2781SCRIPT_STR \u95a2\u6570<\/h4>\n\n\n\n
\u6587\u5b57\u5217\u3092\u8fd4\u3059\u95a2\u6570\u3067\u3059\u3002\u4e3b\u306b\u6587\u5b57\u5217\u64cd\u4f5c\u3084\u30ab\u30c6\u30b4\u30ea\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u306b\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n
\nSCRIPT_STR("import pandas as pd; result = 'High' if _arg1 > pd.Series(_arg2).quantile(0.75) else 'Low'", [Sales], [Sales])\n<\/pre><\/div>\n\n\n
\u3053\u306e\u4f8b\u3067\u306f\u3001Sales\u306e\u5024\u304c\u7b2c75\u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb\u3088\u308a\u3082\u9ad8\u3044\u5834\u5408\u306b”High”\u3092\u8fd4\u3057\u3001\u305d\u3046\u3067\u306a\u3044\u5834\u5408\u306b”Low”\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u2462SCRIPT_BOOL \u95a2\u6570<\/h4>\n\n\n\n
\u771f\u507d\u5024\u3092\u8fd4\u3059\u95a2\u6570\u3067\u3059\u3002\u4e3b\u306b\u6761\u4ef6\u4ed8\u304d\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3084\u6761\u4ef6\u4ed8\u304d\u96c6\u8a08\u306b\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n
\nSCRIPT_BOOL("import numpy as np; result = True if _arg1 > np.mean(_arg2) else False", [Sales], WINDOW_AVG([Sales]))\n<\/pre><\/div>\n\n\n
\u3053\u306e\u4f8b\u3067\u306f\u3001Sales\u304c\u305d\u306e\u30a6\u30a3\u30f3\u30c9\u30a6\u306e\u5e73\u5747\u5024\u3088\u308a\u3082\u5927\u304d\u3044\u5834\u5408\u306bTrue\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u2783SCRIPT_INT \u95a2\u6570<\/h4>\n\n\n\n
\u6574\u6570\u5024\u3092\u8fd4\u3059\u95a2\u6570\u3067\u3059\u3002\u4e3b\u306b\u96e2\u6563\u5024\u306e\u8a08\u7b97\u3084\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u4ed8\u4e0e\u306b\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n
\nSCRIPT_INT("import pandas as pd; result = pd.cut(_arg1, bins=[0, 100, 200, 300], labels=[1, 2, 3])", [Sales])\n<\/pre><\/div>\n\n\n
<\/p>\n\n\n\n
\u6765\u6708\u306e\u58f2\u308a\u4e0a\u3052\u4e88\u6e2c<\/mark><\/h3>\n\n\n\n
\u305d\u308c\u3067\u306f\u4e0a\u8a18\u3067\u7d39\u4ecb\u3057\u305f\u95a2\u6570\u3092\u4f7f\u3063\u3066\u7c21\u5358\u306a\u6765\u6708\u306e\u58f2\u4e0a\u4e88\u6e2c\u3092\u51fa\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n
\nSCRIPT_REAL("\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\n\n# \u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\ndata = pd.DataFrame({'\u58f2\u4e0a': _arg1, '\u30aa\u30fc\u30c0\u30fc\u65e5': _arg2})\ndata['\u30aa\u30fc\u30c0\u30fc\u65e5'] = pd.to_numeric(data['\u30aa\u30fc\u30c0\u30fc\u65e5'])\n\n# \u30e2\u30c7\u30eb\u306e\u8a13\u7df4\nX = data[['\u30aa\u30fc\u30c0\u30fc\u65e5']]\ny = data['\u58f2\u4e0a']\nmodel = LinearRegression()\nmodel.fit(X, y)\n\n# \u6b21\u306e\u6708\u306e\u4e88\u6e2c\nfuture_month = [[data['\u30aa\u30fc\u30c0\u30fc\u65e5'].max() + 1]]\npredicted_sales = model.predict(future_month)\nreturn predicted_sales[0]\n", SUM([\u58f2\u4e0a]), DATEPART('\u30aa\u30fc\u30c0\u30fc\u65e5', [\u30aa\u30fc\u30c0\u30fc\u65e5]))\n<\/pre><\/div>\n\n\n
\u3053\u306e\u3088\u3046\u306b\u300c\u58f2\u4e0a\u300d\u3068\u300c\u8cfc\u5165\u65e5\u300d\u3092\u6e21\u3059\u3068\u6765\u6708\u306e\u58f2\u308a\u4e0a\u3052\u4e88\u6e2c\u3092\u8fd4\u3057\u3066\u304f\u308c\u308b\u3088\u3046\u306b\u66f8\u3051\u307e\u3059\u3002<\/p>\n\n\n
\n
\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n\n
<\/p>\n\n\n\n
\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/mark><\/h3>\n\n\n\n
\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3092\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002<\/p>\n\n\n
\nSCRIPT_INT("\nfrom sklearn.cluster import KMeans\nimport pandas as pd\n\n# \u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406\ndata = pd.DataFrame({'\u58f2\u4e0a': _arg1, '\u5229\u76ca': _arg2})\nkmeans = KMeans(n_clusters=3)\ndata['cluster'] = kmeans.fit_predict(data[['\u58f2\u4e0a', '\u5229\u76ca']])\n\n# \u30af\u30e9\u30b9\u30bf\u3092\u8fd4\u3059\nreturn data['cluster']\n", AVG([\u58f2\u4e0a]), AVG([\u5229\u76ca]))\n<\/pre><\/div>\n\n\n
<\/p>\n\n\n
\n
\"\"\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n<\/div>\n\n\n
<\/p>\n\n\n\n
Tableau\u306b\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u6a5f\u80fd\u304c\u4ed8\u3044\u3066\u3044\u307e\u3059\u304c\u3001\u3042\u307e\u308a\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u3044\u3058\u308c\u306a\u304b\u3063\u305f\u308a\u3001Kmeans\u4ee5\u5916\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3082\u8a66\u3057\u305f\u3044\u3068\u304d\u306b\u4f7f\u3048\u307e\u3059\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
Tableau\u4e0a\u3067Python\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u96c6\u8a08\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306fNumpy\u3084Pandas\u306a\u3069\u306e\u4e00\u822c\u7684\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u304c\u3001Jupyter Notebook\u4e0a\u3067\u5b9a\u7fa9\u3057\u305f\u95a2\u6570\u3092\u5f15\u3063\u5f35\u3063\u3066\u304d\u3066Tableau\u4e0a\u3067\u6d3b\u7528\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u662f\u975e\u304a\u8a66\u3057\u304f\u3060\u3055\u3044\uff01<\/p>\n\n\n\n
<\/p>\n\n\n\n
\n
\u30c7\u30fc\u30bf\u6d3b\u7528\u3067\u304a\u56f0\u308a\u306e\u65b9\u3078<\/mark><\/h3>\n\n\n\n
\u79c1\u305f\u3061DX-Accelerator\u4e8b\u696d\u3067\u306f\u3001\u30c7\u30fc\u30bf\u6d3b\u7528\u306b\u3064\u3044\u3066\u306e\u69d8\u3005\u306a\u30b9\u30ad\u30eb\u3092\u6301\u3063\u305f\u4eba\u6750\u304c\u5e38\u99d0\u3067\u30c7\u30fc\u30bf\u6d3b\u7528\u652f\u63f4\u3092\u884c\u3046\u30b5\u30fc\u30d3\u30b9\u3092\u63d0\u4f9b\u3057\u3066\u3044\u307e\u3059\u3002
\u5f53\u4e8b\u696d\u306f\u30ed\u30fc\u30f3\u30c1\u304b\u3089\u7d043\u5e74\uff0824\u5e749\u6708\u6642\u70b9\uff09\u3067\u3059\u304c\u3001\u3053\u308c\u307e\u3067\u306b\u69d8\u3005\u306a\u696d\u754c\u30fb\u696d\u7a2e\u306e\u304a\u5ba2\u3055\u307e\u306e\u304a\u624b\u4f1d\u3044\u3092\u3055\u305b\u3066\u3044\u305f\u3060\u3044\u3066\u304a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u5c11\u3057\u3067\u3082\u8208\u5473\u3092\u6301\u3063\u3066\u304f\u3060\u3055\u3063\u305f\u308a\u3001\u3059\u3067\u306b\u3054\u76f8\u8ac7\u3092\u3057\u305f\u3044\u3053\u3068\u304c\u3042\u308b\u65b9\u306f\u304a\u6c17\u8efd\u306b\u3054\u76f8\u8ac7\u304f\u3060\u3055\u3044\u3002\u73fe\u5728\u3042\u306a\u305f\u306e\u7d44\u7e54\u306e\u30d5\u30a7\u30fc\u30ba\u304c\u3069\u3053\u306b\u3042\u308b\u304b\u306f\u95a2\u4fc2\u3042\u308a\u307e\u305b\u3093\u3002\u307e\u305a\u306f\u304a\u8a71\u3092\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n
\n
\u3054\u76f8\u8ac7\u306f\u3053\u3061\u3089<\/strong><\/a><\/div>\n<\/div>\n\n\n\n
<\/p>\n\n\n\n
\u3082\u3046\u5c11\u3057\u30b5\u30fc\u30d3\u30b9\u306b\u3064\u3044\u3066\u77e5\u308a\u305f\u3044\u65b9\u306f\u30b5\u30fc\u30d3\u30b9\u7d39\u4ecb\u8cc7\u6599\u3082\u3054\u7528\u610f\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
<\/p>\n\n\n\n
\n
\u30b5\u30fc\u30d3\u30b9\u7d39\u4ecb\u8cc7\u6599<\/strong><\/a><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n
<\/p>\n\n\n\n
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\"\"<\/figure>\n<\/div>\n\n\n\n
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\u3053\u306e\u8a18\u4e8b\u3092\u66f8\u3044\u305f\u4eba<\/p>\n\n\n\n
\u7389\u4e95 \u96c4\u5e0c
\uff08\u305f\u307e\u3044 \u3086\u3046\u304d\uff09<\/p>\n<\/div>\n<\/div>\n\n\n\n
\u682a\u5f0f\u4f1a\u793eUNCOVER TRUTH
\u30d3\u30b8\u30cd\u30b9\u30c7\u30d9\u30ed\u30c3\u30d7\u30e1\u30f3\u30c8\u30b0\u30eb\u30fc\u30d7 DX-Accelerator\u30c1\u30fc\u30e0<\/strong>
2023\u5e74\u3001UNCOVER TRUTH\u306b\u4e2d\u9014\u5165\u793e\u3002
\u7dcf\u5408\u4eba\u6750\u7d39\u4ecb\u30b5\u30fc\u30d3\u30b9\u4f1a\u793e\u306e\u55b6\u696d\u8077\u3092\u7d4c\u3066\u3001\u30c7\u30fc\u30bf\u6d3b\u7528\u4f1a\u793e\u306b\u5165\u8077\u3002
\u5316\u7ca7\u54c1\u4f1a\u793e\u7b49\u306e\u30c7\u30fc\u30bf\u5206\u6790\u3084\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9\u69cb\u7bc9\u3092\u652f\u63f4\u3002\u826f\u304f\u4f7f\u3046\u30c4\u30fc\u30eb\u306fBigQuery\u3068Tableau\u3002<\/p>\n<\/div><\/div>\n\n\n\n
<\/p>\n\n\n\n
\u304a\u5f79\u7acb\u3061\u8cc7\u6599\u3092\u3054\u6d3b\u7528\u304f\u3060\u3055\u3044\uff01<\/mark><\/h2>\n\n\n\n
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\uff11\uff0eGA4 \u30b5\u30f3\u30d7\u30eb\u30ec\u30dd\u30fc\u30c8\uff5cLooker Studio\u3000\u7121\u6599\u30d7\u30ec\u30bc\u30f3\u30c8<\/mark><\/strong>
GA4\u306e\u57fa\u672cKPI\u304c\u7db2\u7f85\u3055\u308c\u305f\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3067\u3059\uff01\u30c7\u30fc\u30bf\u3092\u5dee\u3057\u66ff\u3048\u308c\u3070\u3059\u3050\u306b\u3054\u6d3b\u7528\u3044\u305f\u3060\u3051\u307e\u3059\u3002<\/p>\n\n\n\n
<\/p>\n\n\n\n
 
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GA4 \u30b5\u30f3\u30d7\u30eb\u30ec\u30dd\u30fc\u30c8\uff5cLooker Studio\u3000\u7121\u6599\u30d7\u30ec\u30bc\u30f3\u30c8<\/figure>\n
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GA4 \u30b5\u30f3\u30d7\u30eb\u30ec\u30dd\u30fc\u30c8\uff5cLooker Studio\u3000\u7121\u6599\u30d7\u30ec\u30bc\u30f3\u30c8 <\/div>\n<\/div><\/a><\/div>\n<\/div><\/div>\n\n\n\n
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\uff12\uff0e\u30b5\u30f3\u30d7\u30eb\u7d4c\u55b6\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9 | Tableau\u7121\u6599\u30d7\u30ec\u30bc\u30f3\u30c8<\/mark><\/strong>
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\u30b5\u30f3\u30d7\u30eb\u7d4c\u55b6\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9 | Tableau\u7121\u6599\u30d7\u30ec\u30bc\u30f3\u30c8<\/figure>\n
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\u30b5\u30f3\u30d7\u30eb\u7d4c\u55b6\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9 | Tableau\u7121\u6599\u30d7\u30ec\u30bc\u30f3\u30c8 <\/div>\n<\/div><\/a><\/div>\n<\/div><\/div>\n\n\n\n
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