{"id":2421,"date":"2023-07-22T08:36:27","date_gmt":"2023-07-22T08:36:27","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas%e6%95%b0%e6%8d%ae%e8%bd%ac%e6%8d%a2%e4%b8%ba%e5%af%b9%e8%b1%a1%e7%9a%84numpy-dtype%e6%a3%80%e6%9f%a5%e8%be%93%e5%85%a5%e6%95%b0%e6%8d%ae%e4%b8%8enp-asarraydata\/"},"modified":"2023-07-22T08:36:27","modified_gmt":"2023-07-22T08:36:27","slug":"pandas%e6%95%b0%e6%8d%ae%e8%bd%ac%e6%8d%a2%e4%b8%ba%e5%af%b9%e8%b1%a1%e7%9a%84numpy-dtype%e6%a3%80%e6%9f%a5%e8%be%93%e5%85%a5%e6%95%b0%e6%8d%ae%e4%b8%8enp-asarraydata","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas%e6%95%b0%e6%8d%ae%e8%bd%ac%e6%8d%a2%e4%b8%ba%e5%af%b9%e8%b1%a1%e7%9a%84numpy-dtype%e6%a3%80%e6%9f%a5%e8%be%93%e5%85%a5%e6%95%b0%e6%8d%ae%e4%b8%8enp-asarraydata\/","title":{"rendered":"\u5982\u4f55\u4fee\u590d\uff1apandas \u6570\u636e\u8f6c\u6362\u4e3a numpy \u5bf9\u8c61\u7c7b\u578b\u3002\u4f7f\u7528 np.asarray(data) \u68c0\u67e5\u8f93\u5165\u6570\u636e\u3002"},"content":{"rendered":"

<\/p>\n

\n

\u4f7f\u7528Python\u65f6\u53ef\u80fd\u4f1a\u9047\u5230\u7684\u9519\u8bef\u662f\uff1a<\/span><\/p>\n

 ValueError<\/span> : Pandas data cast to numpy dtype of object. Check input data with\nnp.asarray(data).\n<\/strong><\/pre>\n
\u5f53\u60a8\u5c1d\u8bd5\u5728 Python \u4e2d\u62df\u5408\u56de\u5f52\u6a21\u578b\u5e76\u4e14\u5728\u62df\u5408\u6a21\u578b\u4e4b\u524d\u65e0\u6cd5\u5c06 calcategori \u53d8\u91cf\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf<\/a>\u65f6\uff0c\u4f1a\u51fa\u73b0\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u7ea0\u6b63\u6b64\u9519\u8bef\u3002<\/span><\/p>\n
\u5982\u4f55\u91cd\u73b0\u9519\u8bef<\/strong><\/span><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' points<\/span> ': [14, 19, 8, 12, 17, 19, 22, 25]})\n\n#view DataFrame\n<\/span>df\n\n\tteam assists rebounds points\n0 A 5 11 14\n1 To 7 8 19\n2 A 7 10 8\n3 to 9 6 12\n4 B 12 6 17\n5 B 9 5 19\n6 B 9 9 22\n7 B 4 12 25<\/span><\/span><\/strong><\/pre>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df['points']\n\n#define predictor variables\n<\/span>x = df[['team', 'assists', 'rebounds']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#attempt to fit regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\nValueError<\/span> : Pandas data cast to numpy dtype of object. Check input data with\nnp.asarray(data).\n<\/span><\/span><\/strong><\/pre>\n
\u6211\u4eec\u6536\u5230\u9519\u8bef\uff0c\u56e0\u4e3a\u201c\u56e2\u961f\u201d\u53d8\u91cf\u662f\u5206\u7c7b\u53d8\u91cf\uff0c\u5e76\u4e14\u5728\u62df\u5408\u56de\u5f52\u6a21\u578b\u4e4b\u524d\u6211\u4eec\u6ca1\u6709\u5c06\u5176\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\u3002<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d\u9519\u8bef<\/strong><\/span><\/h3>\n
\u4fee\u590d\u6b64\u9519\u8bef\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u4f7f\u7528pandas.get_dummies()<\/a>\u51fd\u6570\u5c06\u201cteam\u201d\u53d8\u91cf\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\u3002<\/span><\/p>\n
\u6ce8\u610f<\/strong>\uff1a\u67e5\u770b\u672c\u6559\u7a0b<\/a>\uff0c\u5feb\u901f\u56de\u987e\u4e00\u4e0b\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u865a\u62df\u53d8\u91cf\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u5c06\u201cteam\u201d\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' points<\/span> ': [14, 19, 8, 12, 17, 19, 22, 25]})\n\n#convert \"team\" to dummy variable\n<\/span>df = pd. get_dummies<\/span> (df, columns=[' team<\/span> '], drop_first= True<\/span> )\n\n#view updated DataFrame\n<\/span>df\n\n        assists rebounds points team_B\n0 5 11 14 0\n1 7 8 19 0\n2 7 10 8 0\n3 9 6 12 0\n4 12 6 17 1\n5 9 5 19 1\n6 9 9 22 1\n7 4 12 25 1<\/span><\/span><\/strong><\/pre>\n
 \u201cteam\u201d\u5217\u4e2d\u7684\u503c\u5df2\u4ece\u201cA\u201d\u548c\u201cB\u201d\u8f6c\u6362\u4e3a0\u548c1\u3002<\/span><\/span><\/p>\n
\u6211\u4eec\u73b0\u5728\u53ef\u4ee5\u4f7f\u7528\u65b0\u53d8\u91cf\u201cteam_B\u201d\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df['points']\n\n#define predictor variables\n<\/span>x = df[['team_B', 'assists', 'rebounds']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view summary of model fit\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared points: 0.701\nModel: OLS Adj. R-squared: 0.476\nMethod: Least Squares F-statistic: 3.119\nDate: Thu, 11 Nov 2021 Prob (F-statistic): 0.150\nTime: 14:49:53 Log-Likelihood: -19.637\nNo. Observations: 8 AIC: 47.27\nDf Residuals: 4 BIC: 47.59\nDf Model: 3                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 27.1891 17.058 1.594 0.186 -20.171 74.549\nteam_B 9.1288 3.032 3.010 0.040 0.709 17.548\nassists -1.3445 1.148 -1.171 0.307 -4.532 1.843\nrebounds -0.5174 1.099 -0.471 0.662 -3.569 2.534\n==================================================== ============================\nOmnibus: 0.691 Durbin-Watson: 3.075\nProb(Omnibus): 0.708 Jarque-Bera (JB): 0.145\nSkew: 0.294 Prob(JB): 0.930\nKurtosis: 2.698 Cond. No. 140.\n==================================================== ============================\n<\/span><\/span><\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u8fd9\u6b21\u6211\u4eec\u80fd\u591f\u62df\u5408\u56de\u5f52\u6a21\u578b\uff0c\u6ca1\u6709\u4efb\u4f55\u9519\u8bef\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u4fee\u590d Python \u4e2d\u7684\u5176\u4ed6\u5e38\u89c1\u9519\u8bef\uff1a<\/span><\/p>\n
\u5982\u4f55\u4fee\u590d Pandas \u4e2d\u7684 KeyError<\/a>
 \u5982\u4f55\u4fee\u590d\uff1aValueError\uff1a\u65e0\u6cd5\u5c06 float NaN \u8f6c\u6362\u4e3a int<\/a>
 \u5982\u4f55\u4fee\u590d\uff1aValueError\uff1a\u64cd\u4f5c\u6570\u65e0\u6cd5\u4e0e\u5f62\u72b6\u4e00\u8d77\u5e7f\u64ad<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u4f7f\u7528Python\u65f6\u53ef\u80fd\u4f1a\u9047\u5230\u7684\u9519\u8bef\u662f\uff1a ValueError : Pandas data cast to nu […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-2421","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u4fee\u590d\uff1apandas \u6570\u636e\u8f6c\u6362\u4e3a numpy \u5bf9\u8c61\u7c7b\u578b\u3002\u4f7f\u7528 np.asarray(data) \u68c0\u67e5\u8f93\u5165\u6570\u636e\u3002 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u4fee\u590d\u4ee5\u4e0b\u9519\u8bef\uff1apandas \u6570\u636e\u8f6c\u6362\u4e3a numpy \u5bf9\u8c61\u7c7b\u578b\u3002\u4f7f\u7528 np.asarray(data) \u68c0\u67e5\u8f93\u5165\u6570\u636e\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/pandas\u6570\u636e\u8f6c\u6362\u4e3a\u5bf9\u8c61\u7684numpy-dtype\u68c0\u67e5\u8f93\u5165\u6570\u636e\u4e0enp-asarraydata\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4fee\u590d\uff1apandas \u6570\u636e\u8f6c\u6362\u4e3a numpy \u5bf9\u8c61\u7c7b\u578b\u3002\u4f7f\u7528 np.asarray(data) \u68c0\u67e5\u8f93\u5165\u6570\u636e\u3002 - 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