CODEWITHSUNDEEP
pythonpandasdataframemask
Joe
Joe

Reputation: 12417

Mask 2 df by columns position

I would like to mask a df using another one regardless the name of the columns, but considering only their positions. For example, given these 2 df:

df = pd.DataFrame({'D': [10,20,0], 'E': [0, 30, 10], 'F': [0, 0, 10]})
df2 = pd.DataFrame({'A': [1,1,1], 'C': [1, 1, 1], 'E': [1, 1, 1]})

and using this mask:

mask = (df != 0)

I would like:

     A    C    E
0  NaN  1.0  1.0
1  NaN  NaN  1.0
2  1.0  NaN  NaN

Where D masks A, E(of the first df) masks C and F masks E(of the second df).

I can do this removing columns names:

df.columns = [''] * len(df.columns)
mask = (df != 0)
df2.columns = [''] * len(df2.columns)
dfn = df2.mask(mask)

But is there a better way? Thanks in advance

Upvotes: 1

Views: 68

Answers (1)

jezrael
jezrael

Reputation: 862431

You are close, is possible convert boolean DataFrame to boolean 2d array:

mask = (df != 0).values
dfn = df2.mask(mask)
print (dfn)

     A    C    E
0  NaN  1.0  1.0
1  NaN  NaN  1.0
2  1.0  NaN  NaN

Upvotes: 3

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