Masking multiple columns on a pandas dataframe in Python

Question

I am looking to apply multiply masks on each column of a pandas dataset (respectively to its properties) in Python.

In the next step, I want to find (a) row(s) in the dataframe that fits all conditions.

Therefore I have:

df
Out[27]:
   DE  FL  GA  IA  ID
0   0   1   0   0   0
1   1   0   1   0   1
2   0   0   1   0   0
3   0   1   0   0   0
4   0   0   0   0   0

mask_list = []
for i in range(0,5):

    if i % 2==0:
        mask_list.append(df[[i]]>0)
    else:
        mask_list.append(df[[i]]<1)

concat_frame = pa.DataFrame()
for mask in mask_list:
    concat_frame =pa.concat((concat_frame, mask), axis=1)

concat_frame
Out[48]:
      DE     FL     GA    IA     ID
0  False   False False  True  False
1  True    True  True   True  True
2  False   True  True   True  False
3  False   False False  True  False
4  False   True  False  True  False

[5 rows x 5 columns]

Expected outcome:

Out[60]:
   DE   FL  GA  IA  ID
1   1   0   1   0   1

How can I apply the concat_mask on df, so that I select rows, in which all Boolean criteria are matched (are True)?

Asked By: Jonas

||

Source

Answer 1

You can use the pandas all method and Boolean logic. As EdChum commented, I am a bit unclear still on your exact example, but a similar example is:

In [1]: df = DataFrame([[1,2],[-3,5]], index=[0,1], columns=['a','b'])
In [2]: df
Out [2]:
   a  b
0  1  2
1 -3  5

In [3]: msk = (df>1) & (df<5)
In [4]: msk
Out [4]:
      a    b
0 False  True
1 False False

In [5]: msk.all(axis=1)
Out [5]:
0  False
1  False
dtype: bool

If you wanted to index the original dataframe by the mask you could do:

In [6]: df[msk]
Out [6]:
     a   b
0  NaN   2
1  NaN NaN

Or as you originally indicated, rows where all the rows are true:

In [7]: idx = msk.all(axis=1)
In [8]: df[idx]
Out [8]:
Empty DataFrame
Columns: [a,b]
Index: []

Or if one row was true

In [9]: idx[0] = True
In [10]: df[idx]
Out [10]:
  a b
0 1 2

For the original question after clarification from the comments, where we want different filtering criteria for different columns:

In [10]: msk1 = df[['a']] < 0
In [11]: msk2 = df[['b']] > 3
In [12]: msk = concat((msk1, msk2), axis=1)
In [12]: slct = msk.all(axis=1)
In [13]: df.ix[slct]
Out [13]:
   a b
1 -3 5

Answered By: mgilbert

Answer 2

df[df[['DE', 'GA', 'ID']].all(axis=1) * (1 - df[['FL', 'IA']]).all(axis=1)]

The hard part here is understanding why you’re using even/odd column positions to determine the treatment. Based on your code, it looks like you want columns 0, 2, and 4 to actually be 1 minus their current values. However, based on what you claim is the expected output, it actually seems like you want colums 1 and 3 to have 1 minus their current values.

My code above reflects the latter assumption. The general idea still works; just tune it to reflect whatever columns you actually need to have 1 minus the value of, assuming you make your desired output more rigorously defined.

Probably that needs to be cleaned up and turned into a proper helper function first that explicitly shows which columns need to have 1 minus their value, versus which columns can be left alone.

Answered By: ely

Masking multiple columns on a pandas dataframe in Python

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