熊猫仅在NaN浮动0.0的位置删除行

Question

I have a dataframe where each column represents a user. I am trying to eliminate a any column that has nothing but NaN's and 0.000000's. So that the that username 1 or the first column would not be included in the dataframe, but the others would be.

This is the dataframe:

  username  1         2        3         4          5    
      date
2019-01-16  NaN       9.16667  NaN       NaN        1.000000
2019-01-17  NaN       NaN      NaN       1.000000   1.000000
2019-01-18  NaN       1.00000  0.956522  1.000000   1.000000
2019-01-19  0.000000  NaN      1.000000  NaN        NaN  
2019-01-20  0.000000  NaN      0.961538  NaN        NaN     

The Percentages are stored as float64:

type(df['1'].iloc[0])
numpy.float64

Answer 1

You could start by replacing 0 by NaN and then drop columns which contain only NaNs :

df.loc[:,~df.replace(0,np.nan).isna().all()]

username            2         3    4    5
0        date      NaN       NaN  NaN  NaN
1  2019-01-16  9.16667       NaN  NaN  1.0
2  2019-01-17      NaN       NaN  1.0  1.0
3  2019-01-18  1.00000  0.956522  1.0  1.0
4  2019-01-19      NaN  1.000000  NaN  NaN
5  2019-01-20      NaN  0.961538  NaN  NaN

Answer 2

You can first convert 0 values to NaN via mask and then dropna :

df = df.mask(df.eq(0)).dropna(how='all', axis=1)

This does convert 0 values to NaN even for non-deleted columns. It's not clear whether this is what you want, but probably advisable for consistency.