{"id":3554,"date":"2023-07-16T20:39:21","date_gmt":"2023-07-16T20:39:21","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e5%85%a5%e5%8a%9b%e3%81%ab%e7%84%a1%e9%99%90%e3%81%ae-nan-%e3%81%be%e3%81%9f%e3%81%af-dtype-%e3%81%ab%e5%af%be%e3%81%97%e3%81%a6%e5%a4%a7%e3%81%8d%e3%81%99%e3%81%8d%e3%82%99%e3%82%8b%e5%80%a4\/"},"modified":"2023-07-16T20:39:21","modified_gmt":"2023-07-16T20:39:21","slug":"%e5%85%a5%e5%8a%9b%e3%81%ab%e7%84%a1%e9%99%90%e3%81%ae-nan-%e3%81%be%e3%81%9f%e3%81%af-dtype-%e3%81%ab%e5%af%be%e3%81%97%e3%81%a6%e5%a4%a7%e3%81%8d%e3%81%99%e3%81%8d%e3%82%99%e3%82%8b%e5%80%a4","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e5%85%a5%e5%8a%9b%e3%81%ab%e7%84%a1%e9%99%90%e3%81%ae-nan-%e3%81%be%e3%81%9f%e3%81%af-dtype-%e3%81%ab%e5%af%be%e3%81%97%e3%81%a6%e5%a4%a7%e3%81%8d%e3%81%99%e3%81%8d%e3%82%99%e3%82%8b%e5%80%a4\/","title":{"rendered":"\u4fee\u6b63\u65b9\u6cd5: \u5165\u529b\u306b nan\u3001\u7121\u9650\u5927\u3001\u307e\u305f\u306f dtype ('float64') \u306b\u306f\u5927\u304d\u3059\u304e\u308b\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059"},"content":{"rendered":"

<\/p>\n

\n

Python \u306e\u4f7f\u7528\u6642\u306b\u767a\u751f\u3059\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u4e00\u822c\u7684\u306a\u30a8\u30e9\u30fc\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n

 ValueError: Input contains infinity or a value too large for dtype('float64').\n<\/strong><\/span><\/pre>\n
\u3053\u306e\u30a8\u30e9\u30fc\u306f\u901a\u5e38\u3001scikit-learn \u30e2\u30b8\u30e5\u30fc\u30eb\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3088\u3046\u3068\u3057\u305f\u3068\u304d\u306b\u3001\u5165\u529b\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3044\u308b DataFrame \u307e\u305f\u306f\u884c\u5217\u306b NaN \u5024\u307e\u305f\u306f\u7121\u9650\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5834\u5408\u306b\u767a\u751f\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u30a8\u30e9\u30fc\u3092\u5b9f\u969b\u306b\u89e3\u6c7a\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u30a8\u30e9\u30fc\u3092\u518d\u73fe\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x1<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n                   ' x2<\/span> ': [1, 3, 3, 5, 2, 2, 1, np.inf, 0, 3, 4],\n                   ' y<\/span> ': [np.nan, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n    x1 x2 y\n0 1 1.0 NaN\n1 2 3.0 78.0\n2 2 3.0 85.0\n3 4 5.0 88.0\n4 2 2.0 72.0\n5 1 2.0 69.0\n6 5 1.0 94.0\n7 4 lower 94.0\n8 2 0.0 88.0\n9 4 3.0 92.0\n10 4 4.0 90.0<\/span><\/span><\/strong><\/pre>\n
\u3053\u3053\u3067\u3001 scikit-learn<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u8fd1\u4f3c\u3057\u3088\u3046\u3068\u3057\u3066\u3044\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df[[' x1<\/span> ', ' x2<\/span> ']], df. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n\n#print model intercept and coefficients\n<\/span>print<\/span> (model. intercept_<\/span> , model. coef_<\/span> )\n\n<\/span>ValueError: Input contains infinity or a value too large for dtype('float64').<\/span>\n<\/span><\/strong><\/pre>\n
\u4f7f\u7528\u3057\u3066\u3044\u308b DataFrame \u306b\u7121\u9650\u5024\u3068 NaN \u5024\u306e\u4e21\u65b9\u304c\u3042\u308b\u305f\u3081\u3001\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30a8\u30e9\u30fc\u3092\u4fee\u6b63\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n
\u3053\u306e\u30a8\u30e9\u30fc\u3092\u89e3\u6c7a\u3059\u308b\u65b9\u6cd5\u306f\u3001\u307e\u305a\u3001\u7121\u9650\u5024\u307e\u305f\u306f NaN \u5024\u3092\u542b\u3080\u3059\u3079\u3066\u306e\u884c\u3092 DataFrame \u304b\u3089\u524a\u9664\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n
 #remove rows with any values that are not finite\n<\/span>df_new = df[np. isfinite<\/span> (df). all<\/span> ( 1<\/span> )]\n\n#view updated DataFrame\n<\/span>print<\/span> (df_new)\n\n    x1 x2 y\n1 2 3.0 78.0\n2 2 3.0 85.0\n3 4 5.0 88.0\n4 2 2.0 72.0\n5 1 2.0 69.0\n6 5 1.0 94.0\n8 2 0.0 88.0\n9 4 3.0 92.0\n10 4 4.0 90.0\n<\/span><\/span><\/strong><\/pre>\n
\u7121\u9650\u307e\u305f\u306f NaN \u5024\u3092\u542b\u3080 2 \u884c\u304c\u524a\u9664\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/p>\n
\u3053\u3053\u3067\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u9069\u5408\u306b\u9032\u3080\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df_new[[' x1<\/span> ', ' x2<\/span> ']], df_new. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n\n#print model intercept and coefficients\n<\/span>print<\/span> (model. intercept_<\/span> , model. coef_<\/span> )\n\n69.85144124168515 [5.72727273 -0.93791574]\n<\/span><\/span><\/strong><\/pre>\n
\u6700\u521d\u306b\u7121\u9650\u5024\u307e\u305f\u306f NaN \u5024\u3092\u6301\u3064\u884c\u3092 DataFrame \u304b\u3089\u524a\u9664\u3057\u305f\u305f\u3081\u3001\u4eca\u56de\u306f\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3057\u306a\u3044\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u306e\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30a8\u30e9\u30fc\u3092\u4fee\u6b63\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 Python \u3067\u4fee\u6b63\u3059\u308b\u65b9\u6cd5: \u30aa\u30d6\u30b8\u30a7\u30af\u30c8 ‘numpy.ndarray’ \u306f\u547c\u3073\u51fa\u3057\u53ef\u80fd\u3067\u306f\u3042\u308a\u307e\u305b\u3093<\/a>
\u4fee\u6b63\u65b9\u6cd5: TypeError: \u30aa\u30d6\u30b8\u30a7\u30af\u30c8 ‘numpy.float64’ \u306f\u547c\u3073\u51fa\u3057\u53ef\u80fd\u3067\u306f\u3042\u308a\u307e\u305b\u3093<\/a>
\u4fee\u6b63\u65b9\u6cd5: \u578b\u30a8\u30e9\u30fc: \u6587\u5b57\u5217\u307e\u305f\u306f\u30d0\u30a4\u30c8 \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u304c\u5fc5\u8981\u3067\u3059<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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