Reputation: 137
If I have a DF:
Name1 Name2 NUll Name3 NULL Name4
abc abc null abc
abc abc null abc
abc abc null abc
abc abc null abc
Can I use dropna, to keep Name3 as a column with all empty values? Yet still drop both Null columns. Thank you
Upvotes: 2
Views: 10127
Reputation: 12325
I was importing some data from a google sheet which had empty column names, this did the required for me:
# drop those with empty column names
self.df.drop([""], axis=1, inplace=True)
Upvotes: 1
Reputation: 18551
What about using DataFrame.drop
?
In [3]: df = pd.read_clipboard()
Out[3]:
Name1 Name2 NUll Name3 NULL Name4
0 abc abc null abc
1 abc abc null abc
2 abc abc null abc
3 abc abc null abc
In [4]: df.drop(["NUll", "NULL"], axis=1)
Out[4]:
Name1 Name2 Name3 Name4
0 abc abc null abc
1 abc abc null abc
2 abc abc null abc
3 abc abc null abc
Upvotes: 2