当我缩放数据时，输入包含 NaN、无穷大或对于 dtype('float64') 来说太大的值

Question

I am trying to normalize my data like this :

scaler = MinMaxScaler()
trainX=scaler.fit_transform(X_data_train)

and I get this error :

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

X_data_train is a pandas DataFrame of size (95538, 550) . What is really odd is that when I write

print (X_data_train.min().min())

it gives -5482.4473 and similarly for the max, I get 28738212.0, which does not seem for me to be extra-high values...

Moreover, based on the command given by the 54+ voted answer , I did check I have no NaN or Infinity for sure. Moreover, I don't have blanks in my csv or things like that, as I checked the dimensions

So, where is the problem ??

Answer 1

You can also check NaN s and inf :

df = pd.DataFrame({'B':[4,5,4,5,5,np.inf],
                   'C':[7,8,9,4,2,3],
                   'D':[np.nan,3,5,7,1,0],
                   'E':[5,3,6,9,2,4]})

print (df)
          B  C    D  E
0  4.000000  7  NaN  5
1  5.000000  8  3.0  3
2  4.000000  9  5.0  6
3  5.000000  4  7.0  9
4  5.000000  2  1.0  2
5       inf  3  0.0  4

nan = df[df.isnull().any(axis=1)]
print (nan)
     B  C   D  E
0  4.0  7 NaN  5

inf = df[df.eq(np.inf).any(axis=1)]
print (inf)
     B  C    D  E
5  inf  3  0.0  4

If want find all index with at least one NaN s in rows:

print (df.index[np.isnan(df).any(axis=1)])
Int64Index([0], dtype='int64')

And columns:

print (df.columns[np.isnan(df).any()])
Index(['D'], dtype='object')