Select everything but a list of columns from pandas dataframe

Question

Is it possible to select the negation of a given list from pandas dataframe?. For instance, say I have the following dataframe

T1_V2  T1_V3 T1_V4 T1_V5 T1_V6 T1_V7 T1_V8
1     15      3      2     N     B     N         
4     16     14      5     H     B     N            
1     10     10      5     N     K     N  

and I want to get out all columns but column T1_V6. I would normally do that this way:

df = df[["T1_V2","T1_V3","T1_V4","T1_V5","T1_V7","T1_V8"]]

My question is on whether there is a way to this the other way around, something like this

df = df[!["T1_V6"]]

Answer 1

Do:

df[df.columns.difference(["T1_V6"])]

Notes from comments:

• This will sort the columns. If you don't want to sort call difference with sort=False

• The difference won't raise error if the dropped column name doesn't exist. If you want to raise error in case the column doesn't exist then use drop as suggested in other answers: df.drop(["T1_V6"]) `

Answer 2

For completeness, you can also easily use drop for this:

df.drop(["T1_V6"], axis=1)

Answer 3

Another way to exclude columns that you don't want:

df[df.columns[~df.columns.isin(['T1_V6'])]]

Answer 4

I would suggest using DataFrame.drop():

columns_to _exclude = ['T1_V6']
old_dataframe = #Has all columns
new_dataframe = old_data_frame.drop(columns_to_exclude, axis = 1)

You could use inplace to make changes to the original dataframe itself

old_dataframe.drop(columns_to_exclude, axis = 1, inplace = True)
#old_dataframe is changed