Question

试图弄清楚为什么我一直把消息列为该问题的标题。我想我已经清除了数据，删除了NaN。有人可以帮我吗？

查看一个包含11K行的数据集，我正在尝试使代码训练数据来预测辍学学生的水平。使用一台普通的Windows笔记本电脑，同时还可以进行更好的数据分析。

# divide the data set into categorial and non categorial features and apply models to get the insight of the data
print("\nDEFINING CATEGORICAL AND NUMERICAL FEATURES")
categorical_features = X.select_dtypes(include=['object']).columns
print(categorical_features)
numerical_features = X.select_dtypes(exclude = ["object"]).columns
print(numerical_features)

print("\nDIVIDE THE DATA SET INTO CATEGORIAL AND NON CATEGORIAL FEATURES AND APPLY MODELS TO GET THE INSIGHT OF THE DATA")
print("Numerical features : " + str(len(numerical_features)))
print("Categorical features : " + str(len(categorical_features)))

print("\nFILLING THE MISSING VALUE OF TEST WITH THEIR MEAN VALUE, FOR BETTER ACCURACY")

test = test.select_dtypes(exclude=[np.object])
test.info()
test = test.fillna(test.mean(), inplace=True)
print("\nAPPLYING MODEL RANDOM FOREST REGRESSOR")

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from warnings import simplefilter
# ignore all future warnings
simplefilter(action='ignore', category=FutureWarning)

# pull data into target (y) and predictors (X)
predictor_cols = ['F18 ECTS på kurser med beståede talkarakter']

# -------------------------------------------

# Create training predictors data
train_X = X[predictor_cols]
my_model = RandomForestRegressor()
my_model.fit(train_X, y)
my_model.score(train_X, y)
print(predictor_cols)
print(my_model.score(train_X, y))

test = pd.read_csv("…_test.csv")

# -------------------------------------------

print("\nPRINT PREDICTED FACTORS")
test_X = test[predictor_cols]

#  model to make predictions

predicted_factor = my_model.predict(test_X)

#  at the predicted prices to ensure something sensible.

print(predicted_factor)

让我的大多数代码运行正常，除了：

APPLYING MODEL RANDOM FOREST REGRESSOR
Traceback (most recent call last):
  File "C:/Users/jcst/PycharmProjects/Frafaldsanalyse/DefiningCatAndNumFeatures_4_new.py", line 142, in <module>
    my_model.fit(train_X, y)
  File "C:\Users\jcst\PycharmProjects\Frafaldsanalyse\venv\lib\site-packages\sklearn\ensemble\forest.py", line 250, in fit
    X = check_array(X, accept_sparse="csc", dtype=DTYPE)
  File "C:\Users\jcst\PycharmProjects\Frafaldsanalyse\venv\lib\site-packages\sklearn\utils\validation.py", line 573, in check_array
    allow_nan=force_all_finite == 'allow-nan')
  File "C:\Users\jcst\PycharmProjects\Frafaldsanalyse\venv\lib\site-packages\sklearn\utils\validation.py", line 56, in _assert_all_finite
    raise ValueError(msg_err.format(type_err, X.dtype))
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

Process finished with exit code 1

Answer 1

如上所述，您的数据集X_train或y必须包含nan个。再次检查以查看其来源。通常是由于被0除或数学函数域错误（如负值的对数）引起的。

您可能会在之后遇到其他事情：

您正在使用test = test.fillna(test.mean(), inplace=True)

您应该使用test = test.fillna(test.mean())

或test.fillna(test.mean(), inplace=True)

指定inplace=True时，该函数返回None，因此test为None。

由于您稍后将通过读取DataFrame来覆盖test，因此您无需使用所有功能即可完成所有操作。也许您在这里有意想不到的行为。

ValueError：输入包含NaN，无穷或.....（'float32'）

1 个答案: