我正在尝试将一些数据传递给skLearning，但我只得到错误消息“包含NaN/inf或错误的dtype”。

我得到的数据主要是我从CSV文件中获得的天气数据，并合并到Pandas中。

我已经检查了NaN和Inf值以及错误的数据类型。

np.isfinite(X_train).all()
True

np.any(np.isnan(X_train))
False

X_train.dtype
dtype('float64')

我还尝试只使用一些列和行来缩小范围，但即使只有两列和一些行，我也会得到相同的错误。

我已经将Pandas Dataframe 转换为麻木，然后再将其传递给skLearning，我尝试对其进行重新索引。我已经将其写入CSV文件，并检查它是否有奇怪的入口。我能找到的所有解决方案对我都不起作用。

我尝试使用的代码是这样的：

X = climate.drop(columns=['e_bins'])
y = climate.e_bins
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_score(y_test, y_pred)

我现在在这个问题上工作了一段时间，现在成功了。如有任何帮助或想法，欢迎光临！谢谢!

这是我收到的全部错误：

ValueError                                Traceback (most recent call last)
<ipython-input-197-14fc949c5576> in <module>
     17 X_test = scaler.transform(X_test)
     18 knn = neighbors.KNeighborsClassifier(n_neighbors=5)
---> 19 knn.fit(X_train, y_train)
     20 #y_pred = knn.predict(X_test)
     21 #accuracy_score(y_test, y_pred)

~AppDataLocalContinuumanaconda3libsite-packagessklearnneighborsbase.py in fit(self, X, y)
    890         """
    891         if not isinstance(X, (KDTree, BallTree)):
--> 892             X, y = check_X_y(X, y, "csr", multi_output=True)
    893 
    894         if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:

~AppDataLocalContinuumanaconda3libsite-packagessklearnutilsvalidation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    720     if multi_output:
    721         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
--> 722                         dtype=None)
    723     else:
    724         y = column_or_1d(y, warn=True)

~AppDataLocalContinuumanaconda3libsite-packagessklearnutilsvalidation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    540         if force_all_finite:
    541             _assert_all_finite(array,
--> 542                                allow_nan=force_all_finite == 'allow-nan')
    543 
    544     if ensure_min_samples > 0:

~AppDataLocalContinuumanaconda3libsite-packagessklearnutilsvalidation.py in _assert_all_finite(X, allow_nan)
     54                 not allow_nan and not np.isfinite(X).all()):
     55             type_err = 'infinity' if allow_nan else 'NaN, infinity'
---> 56             raise ValueError(msg_err.format(type_err, X.dtype))
     57     # for object dtype data, we only check for NaNs (GH-13254)
     58     elif X.dtype == np.dtype('object') and not allow_nan:

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').