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 it's 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]

update expected outcome:

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

Here comes the question :
how can i apply the concat_mask on df , so that i select rows, in which all Boolean criteria are matched (are True)?

Solution

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

Edit: Just to address 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

Comments

[Jonas]

thank You that helped. I wanted to do: index = s_mask.all(axis=1) select = df.ix[index] out: Out[21]: a b 1 -3 5 . Can You insert that into your answer? Then i will marked it as answered :)

[Jonas]

Thanks for Your answer. In the proper code i actually iterate throw all columns and apply various of diffenrent conditions to mask each column. This is all what the code ment to express. And i actually want to extract data of the original df dataframe....

[mgilbert]

Not exactly sure I undersand, if I do the above I would get an empty dataframe since index is False for both rows, does your s_mask refer to msk in my example? so I'm unsure how your getting Out[21] as in your comment?

[ely]

The approach that I give does extract the data from the original DataFrame as needed.

[Jonas]

oh yes, i jumped ahead there. I applied: msk1 = df[[0]]<0 msk2 = df[[1]]>3 s_mask = pandas.concat((msk1, msk2), axis=1)

[mgilbert]

Hey @Antihead let me know if that last edit addressed your concern

[kva1966]

General caution for combining masks: Not immediately obvious to me initially is that because of precedence rules, combining masks using the operators & and |, requires the individual relational expressions to be parenthesised because of precedence rules, otherwise you either get errors, or worse misleading, crazy bitwise-op'ed results because & and | have higher precedence than the relational ops! (ref: docs.python.org/3/reference/…)