Python pandas apply function if a column value is not NULL
The problem is that pd.notnull(['foo', 'bar'])
operates elementwise and returns array([ True, True], dtype=bool)
. Your if condition trys to convert that to a boolean, and that's when you get the exception.
To fix it, you could simply wrap the isnull statement with np.all
:
df[['A','C']].apply(lambda x: my_func(x) if(np.all(pd.notnull(x[1]))) else x, axis = 1)
Now you'll see that np.all(pd.notnull(['foo', 'bar']))
is indeed True
.
I had a column contained lists and NaN
s. So, the next one worked for me.
df.C.map(lambda x: my_func(x) if type(x) == list else x)
Also another way is to just use row.notnull().all()
(without numpy
), here is an example:
df.apply(lambda row: func1(row) if row.notnull().all() else func2(row), axis=1)
Here is a complete example on your df:
>>> d = {'A': [None, 2, 3, 4], 'B': [11, None, 33, 4], 'C': [None, ['a','b'], None, 4]}
>>> df = pd.DataFrame(d)
>>> df
A B C
0 NaN 11.0 None
1 2.0 NaN [a, b]
2 3.0 33.0 None
3 4.0 4.0 4
>>> def func1(r):
... return 'No'
...
>>> def func2(r):
... return 'Yes'
...
>>> df.apply(lambda row: func1(row) if row.notnull().all() else func2(row), axis=1)
0 Yes
1 Yes
2 Yes
3 No
And a friendlier screenshot :-)