python - Filtering pandas dataframe with multiple Boolean columns

Question

I am trying to filter a df using several Boolean variables that are a part of the df, but have been unable to do so.

Sample data:

A | B | C | D
John Doe | 45 | True | False
Jane Smith | 32 | False | False
Alan Holmes | 55 | False | True
Eric Lamar | 29 | True | True

The dtype for columns C and D is Boolean. I want to create a new df (df1) with only the rows where either C or D is True. It should look like this:

A | B | C | D
John Doe | 45 | True | False
Alan Holmes | 55 | False | True
Eric Lamar | 29 | True | True

I've tried something like this, which faces issues because it cant handle the Boolean type:

df1 = df[(df['C']=='True') or (df['D']=='True')]

Any ideas?

Answer 1

In [82]: d
Out[82]:
             A   B      C      D
0     John Doe  45   True  False
1   Jane Smith  32  False  False
2  Alan Holmes  55  False   True
3   Eric Lamar  29   True   True

Solution 1:

In [83]: d.loc[d.C | d.D]
Out[83]:
             A   B      C      D
0     John Doe  45   True  False
2  Alan Holmes  55  False   True
3   Eric Lamar  29   True   True

Solution 2:

In [94]: d[d[['C','D']].any(1)]
Out[94]:
             A   B      C      D
0     John Doe  45   True  False
2  Alan Holmes  55  False   True
3   Eric Lamar  29   True   True

Solution 3:

In [95]: d.query("C or D")
Out[95]:
             A   B      C      D
0     John Doe  45   True  False
2  Alan Holmes  55  False   True
3   Eric Lamar  29   True   True

PS If you change your solution to:

df[(df['C']==True) | (df['D']==True)]

it'll work too

Pandas docs - boolean indexing

---

why we should NOT use "PEP complaint" df["col_name"] is True instead of df["col_name"] == True?

In [11]: df = pd.DataFrame({"col":[True, True, True]})

In [12]: df
Out[12]:
    col
0  True
1  True
2  True

In [13]: df["col"] is True
Out[13]: False               # <----- oops, that's not exactly what we wanted