pandas dataframe drop columns by number of nan
There is a thresh
param for dropna
, you just need to pass the length of your df - the number of NaN
values you want as your threshold:
In [13]:
dff.dropna(thresh=len(dff) - 2, axis=1)
Out[13]:
A B
0 0.517199 -0.806304
1 -0.643074 0.229602
2 0.656728 0.535155
3 NaN -0.162345
4 -0.309663 -0.783539
5 1.244725 -0.274514
6 -0.254232 NaN
7 -1.242430 0.228660
8 -0.311874 -0.448886
9 -0.984453 -0.755416
So the above will drop any column that does not meet the criteria of the length of the df (number of rows) - 2 as the number of non-Na values.
You can use a conditional list comprehension:
>>> dff[[c for c in dff if dff[c].isnull().sum() < 2]]
A B
0 -0.819004 0.919190
1 0.922164 0.088111
2 0.188150 0.847099
3 NaN -0.053563
4 1.327250 -0.376076
5 3.724980 0.292757
6 -0.319342 NaN
7 -1.051529 0.389843
8 -0.805542 -0.018347
9 -0.816261 -1.627026