CODEWITHSUNDEEP
pythonpython-2.7pandas
Asif Rehan
Asif Rehan

Reputation: 1025

Generate condition for selecting rows in pandas.DataFrame

For the dataframe df, I am selecting the rows that have True values either in column 'a' or 'b'.

>>> df
Out[127]: 
       a      b
0  False  False
1   True   True
2   True  False
>>> con = (df['a'] == True) | (df['b'] == True)
>>> con
Out[129]: 
0    False
1     True
2     True
dtype: bool
>>> df[con]
Out[130]: 
      a      b
1  True   True
2  True  False

There are only two columns in the dataframe. For the actual code, the number of such columns is a variable. How can the condition con be generated on-the-fly?

Say, when df has 26 columns from a through z, I want something like 

>>> con = (df['a'] == True) | (df['b'] == True) | ... (df['y'] == True) | (df['z'] == True)

which I can use to get the desired rows

Upvotes: 1

Views: 541

Answers (1)

DSM
DSM

Reputation: 353549

You can use DataFrame.any:

>>> df = pd.DataFrame(np.random.choice([True]+[False]*5, size=(6,5)), columns=list("abcde"))
>>> df
       a      b      c      d      e
0  False  False  False  False  False
1  False  False   True  False  False
2  False  False   True  False  False
3  False  False  False  False  False
4  False  False  False  False   True
5  False  False  False  False  False
>>> df.any(axis=1)
0    False
1     True
2     True
3    False
4     True
5    False
dtype: bool
>>> df[df.any(axis=1)]
       a      b      c      d      e
1  False  False   True  False  False
2  False  False   True  False  False
4  False  False  False  False   True

And as always you can use df.loc[df.any(axis=1)] if you want to ensure you have a handle on the original.

Upvotes: 3

Related Questions