如何在数据集中找到值错误？

我正在尝试将 .csv 表中的数据缩放到 0 到 1 之间的范围。我已经多次收到输入数据包含 NaN、无穷大或值太大的错误。

“ValueError：输入包含 NaN、无穷大或对于 dtype('float64') 来说太大的值。”

到现在为止，我总是能够弄清楚错误来自哪里，例如一个空单元格，有时表格中的空白或与 UTF-8 不兼容的字符。直到现在，我总能让它发挥作用。

这次我再次收到错误，但我无法找到错误。有没有办法找出哪个数据点是“NaN、无穷大或值太大”？由于我有很多数据点，我无法手动通过它。如果您有建议，我会非常高兴 - 即使这只是Excel中查找导致错误的值的一个技巧。您可以在下面找到我的代码和错误。不幸的是，我无法提供数据集，因为它包含机密信息。

代码：

import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# Load training data set from CSV file
training_data_df = pd.read_csv("mtth_train.csv")

# Load testing data set from CSV file
test_data_df = pd.read_csv("mtth_test.csv")

# Data needs to be scaled to a small range like 0 to 1 
scaler = MinMaxScaler(feature_range= (0, 1))

# Scale both the training inputs and outputs
scaled_training = scaler.fit_transform(training_data_df)
scaled_testing = scaler.transform(test_data_df)

# Print out the adjustment that the scaler applied to the total_earnings column of data
print("Note: Parameters were scaled by multiplying by {:.10f} and adding {:.6f}".format(scaler.scale_[8], scaler.min_[8]))

# Create new pandas DataFrame objects from the scaled data
scaled_training_df = pd.DataFrame(scaled_training, columns=training_data_df.columns.values)
scaled_testing_df = pd.DataFrame(scaled_testing, columns=test_data_df.columns.values)

# Save scaled data dataframes to new CSV files
scaled_training_df.to_csv("mtth_train_scaled", index=False)
scaled_testing_df.to_csv("mtth_test_scaled.csv", index=False)

错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-4e3503c96698> in <module>()
     14 # Scale both the training inputs and outputs
     15 scaled_training = scaler.fit_transform(training_data_df)
---> 16 scaled_testing = scaler.transform(test_data_df)
     17 
     18 # Print out the adjustment that the scaler applied to the total_earnings column of data

~/anaconda3_501/lib/python3.6/site-packages/sklearn/preprocessing/data.py in transform(self, X)
    365         check_is_fitted(self, 'scale_')
    366 
--> 367         X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)
    368 
    369         X *= self.scale_

~/anaconda3_501/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    451                              % (array.ndim, estimator_name))
    452         if force_all_finite:
--> 453             _assert_all_finite(array)
    454 
    455     shape_repr = _shape_repr(array.shape)

~/anaconda3_501/lib/python3.6/site-packages/sklearn/utils/validation.py in _assert_all_finite(X)
     42             and not np.isfinite(X).all()):
     43         raise ValueError("Input contains NaN, infinity"
---> 44                          " or a value too large for %r." % X.dtype)
     45 
     46 

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