Remove unwanted columns in a python dataframe

Question

I'm new to python/panda. This dataframe is part of a lab exercise.

Following is the dataframe. The result has 20 rows and 384 columns.

 country almond angelica anise anise_seed apple apple_brandy / 55620 Switzerland No No No No No No 55621 Switzerland Yes No No No Yes No 55622 Switzerland No No No No No No 55623 Switzerland No No No No No No 55624 Switzerland No No No No No No 55625 Switzerland No No No No No No 55626 Switzerland No No No No No No 55627 Switzerland No No No No No No 55628 Switzerland Yes No No No No No

I want to remove all the columns where value is 'No' and create a subset of this dataframe without these columns. From the above data set it means only columns 'almond' and 'apple' have a value other than 'No'. So the subset should look like,

 country almond apple 55620 Switzerland No No 55621 Switzerland Yes Yes 55622 Switzerland No No 55623 Switzerland No No 55624 Switzerland No No 55625 Switzerland No No 55626 Switzerland No No 55627 Switzerland No No 55628 Switzerland Yes No

Can you show us what you have tried so far?

CarlosSR
– CarlosSR

2020-05-11 11:04:58 +00:00
Commented May 11, 2020 at 11:04 — CarlosSR
– CarlosSR, Commented May 11, 2020 at 11:04

jezrael · Accepted Answer · 2020-05-11 10:00:49Z

First check by eq for equality and then test if at least one matched value in DataFrame.any:

df = df.loc[:, df.ne('No').any()] print (df) country almond apple 55620 Switzerland No No 55621 Switzerland Yes Yes 55622 Switzerland No No 55623 Switzerland No No 55624 Switzerland No No 55625 Switzerland No No 55626 Switzerland No No 55627 Switzerland No No 55628 Switzerland Yes No

Thanks, that worked! May I know where/how 'eq' was used here?
@pradeept - Sure, it is better for easy chain by anoter functions and also better readable, because less ()

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