Fix Python – Deleting multiple columns based on column names in Pandas


Asked By – Peadar Coyle

I have some data and when I import it, I get the following unneeded columns. I’m looking for an easy way to delete all of these.

'Unnamed: 24', 'Unnamed: 25', 'Unnamed: 26', 'Unnamed: 27',
'Unnamed: 28', 'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31',
'Unnamed: 32', 'Unnamed: 33', 'Unnamed: 34', 'Unnamed: 35',
'Unnamed: 36', 'Unnamed: 37', 'Unnamed: 38', 'Unnamed: 39',
'Unnamed: 40', 'Unnamed: 41', 'Unnamed: 42', 'Unnamed: 43',
'Unnamed: 44', 'Unnamed: 45', 'Unnamed: 46', 'Unnamed: 47',
'Unnamed: 48', 'Unnamed: 49', 'Unnamed: 50', 'Unnamed: 51',
'Unnamed: 52', 'Unnamed: 53', 'Unnamed: 54', 'Unnamed: 55',
'Unnamed: 56', 'Unnamed: 57', 'Unnamed: 58', 'Unnamed: 59',
'Unnamed: 60'

They are indexed by 0-indexing so I tried something like

df.drop(df.columns[[22, 23, 24, 25, 
26, 27, 28, 29, 30, 31, 32 ,55]], axis=1, inplace=True)

But this isn’t very efficient. I tried writing some for loops but this struck me as bad Pandas behaviour. Hence i ask the question here.

I’ve seen some examples which are similar (Drop multiple columns in pandas) but this doesn’t answer my question.

Now we will see solution for issue: Deleting multiple columns based on column names in Pandas


I don’t know what you mean by inefficient but if you mean in terms of typing it could be easier to just select the cols of interest and assign back to the df:

df = df[cols_of_interest]

Where cols_of_interest is a list of the columns you care about.

Or you can slice the columns and pass this to drop:

df.drop(df.ix[:,'Unnamed: 24':'Unnamed: 60'].head(0).columns, axis=1)

The call to head just selects 0 rows as we’re only interested in the column names rather than data


Another method: It would be simpler to use the boolean mask from str.contains and invert it to mask the columns:

In [2]:
df = pd.DataFrame(columns=['a','Unnamed: 1', 'Unnamed: 1','foo'])

Empty DataFrame
Columns: [a, Unnamed: 1, Unnamed: 1, foo]
Index: []

In [4]:

array([ True, False, False,  True], dtype=bool)

In [5]:

Empty DataFrame
Columns: [a, foo]
Index: []

This question is answered By – EdChum

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