{"id":2418,"date":"2023-07-22T08:36:27","date_gmt":"2023-07-22T08:36:27","guid":{"rendered":"https:\/\/statorials.org\/ko\/np-asarraydata%e1%84%85%e1%85%b3%e1%86%af-%e1%84%89%e1%85%a1%e1%84%8b%e1%85%ad%e1%86%bc%e1%84%92%e1%85%a1%e1%84%8b%e1%85%a7-%e1%84%80%e1%85%a2%e1%86%a8%e1%84%8e%e1%85%a6-%e1%84%80%e1%85%a5%e1%86%b7\/"},"modified":"2023-07-22T08:36:27","modified_gmt":"2023-07-22T08:36:27","slug":"np-asarraydata%e1%84%85%e1%85%b3%e1%86%af-%e1%84%89%e1%85%a1%e1%84%8b%e1%85%ad%e1%86%bc%e1%84%92%e1%85%a1%e1%84%8b%e1%85%a7-%e1%84%80%e1%85%a2%e1%86%a8%e1%84%8e%e1%85%a6-%e1%84%80%e1%85%a5%e1%86%b7","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/np-asarraydata%e1%84%85%e1%85%b3%e1%86%af-%e1%84%89%e1%85%a1%e1%84%8b%e1%85%ad%e1%86%bc%e1%84%92%e1%85%a1%e1%84%8b%e1%85%a7-%e1%84%80%e1%85%a2%e1%86%a8%e1%84%8e%e1%85%a6-%e1%84%80%e1%85%a5%e1%86%b7\/","title":{"rendered":"\ud574\uacb0 \ubc29\ubc95: pandas \ub370\uc774\ud130\uac00 numpy \uac1c\uccb4 \uc720\ud615\uc73c\ub85c \ubcc0\ud658\ub429\ub2c8\ub2e4. np.asarray(data)\ub85c \uc785\ub825 \ub370\uc774\ud130\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."},"content":{"rendered":"

<\/p>\n

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Python\uc744 \uc0ac\uc6a9\ud560 \ub54c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 ValueError<\/span> : Pandas data cast to numpy dtype of object. Check input data with\nnp.asarray(data).\n<\/strong><\/pre>\n
 \uc774 \uc624\ub958\ub294 Python\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\ub824\uace0 \uc2dc\ub3c4\ud558\uace0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uae30 \uc804\uc5d0 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \ub354\ubbf8 \ubcc0\uc218<\/a> \ub85c \ubcc0\ud658\ud560 \uc218 \uc5c6\uc744 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc624\ub958\ub97c \uc7ac\ud604\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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
 “\ud300” \ubcc0\uc218\uac00 \ubc94\uc8fc\ud615\uc774\uace0 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc804\uc5d0 \uc774\ub97c \ub354\ubbf8 \ubcc0\uc218\ub85c \ubcc0\ud658\ud558\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc5d0 \uc624\ub958\uac00 \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h3>\n
 \uc774 \uc624\ub958\ub97c \ud574\uacb0\ud558\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc740 pandas.get_dummies()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec “team” \ubcc0\uc218\ub97c \ub354\ubbf8 \ubcc0\uc218\ub85c \ubcc0\ud658\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : \ud68c\uadc0 \ubaa8\ub378\uc758 \ub354\ubbf8 \ubcc0\uc218\uc5d0 \ub300\ud574 \ube60\ub974\uac8c \ubcf5\uc2b5\ud558\ub824\uba74 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744<\/a> \ud655\uc778\ud558\uc138\uc694.<\/span><\/p>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 “team”\uc744 \ub354\ubbf8 \ubcc0\uc218\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 ‘\ud300’ \uc5f4\uc758 \uac12\uc774 ‘A’\uc640 ‘B’\uc5d0\uc11c 0\uacfc 1\ub85c \ubcc0\ud658\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \uc774\uc81c \uc0c8\ub85c\uc6b4 \ubcc0\uc218 “team_B”\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uc774\ubc88\uc5d0\ub294 \uc624\ub958 \uc5c6\uc774 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc758 \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas\uc5d0\uc11c KeyError\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95<\/a>
 \ud574\uacb0 \ubc29\ubc95: ValueError: float NaN\uc744 int\ub85c \ubcc0\ud658\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/a>
 \ud574\uacb0 \ubc29\ubc95: ValueError: \ud53c\uc5f0\uc0b0\uc790\ub97c \ubaa8\uc591\uacfc \ud568\uaed8 \ube0c\ub85c\ub4dc\uce90\uc2a4\ud2b8\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Python\uc744 \uc0ac\uc6a9\ud560 \ub54c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. ValueError : Pandas data cast to numpy dtype of object. Check input data with np.asarray(data). \uc774 \uc624\ub958\ub294 Python\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\ub824\uace0 \uc2dc\ub3c4\ud558\uace0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uae30 \uc804\uc5d0 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \ub354\ubbf8 \ubcc0\uc218 \ub85c \ubcc0\ud658\ud560 \uc218 \uc5c6\uc744 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc624\ub958\ub97c \uc7ac\ud604\ud558\ub294 \ubc29\ubc95 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-2418","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\n\ud574\uacb0 \ubc29\ubc95: Pandas \ub370\uc774\ud130\uac00 numpy \uac1c\uccb4 \uc720\ud615\uc73c\ub85c \ubcc0\ud658\ub429\ub2c8\ub2e4. np.asarray(data)\ub85c \uc785\ub825 \ub370\uc774\ud130\ub97c \ud655\uc778\ud569\ub2c8\ub2e4. - 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 \ucc38\uace0<\/strong> : \uc5ec\uae30\uc5d0\uc11c<\/a> statsmodels \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 ols()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
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