python - Deleting multiple columns based on column names in Pandas

106
votes

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 pandas) but this doesn't answer my question.

10
pythonpandas
What do you mean, efficient? Is it running too slow? If your problem is that you don't want to get the indices of all the columns that you want to delete, please note that you can just give df.drop a list of column names: df.drop(['Unnamed: 24', 'Unnamed: 25', ...], axis=1) - Carsten
Would it not be easier to just subset the columns of interest: i.e. df = df[cols_of_interest], otherwise you could slice the df by columns and get the columns df.drop(df.ix[:,'Unnamed: 24':'Unnamed: 60'].head(0).columns, axis=1) - EdChum
I meant inefficient in terms of typing or 'bad code smell' - Peadar Coyle
Might be worth noting that in most cases it's easier just to keep the columns you want then delete the ones that you don't: df = df['col_list'] - sparrow

10 Answers

68
votes

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

update

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'])
df

Out[2]:
Empty DataFrame
Columns: [a, Unnamed: 1, Unnamed: 1, foo]
Index: []

In [4]:
~df.columns.str.contains('Unnamed:')

Out[4]:
array([ True, False, False,  True], dtype=bool)

In [5]:
df[df.columns[~df.columns.str.contains('Unnamed:')]]

Out[5]:
Empty DataFrame
Columns: [a, foo]
Index: []
243
votes

The by far the simplest approach is:

yourdf.drop(['columnheading1', 'columnheading2'], axis=1, inplace=True)
46
votes

My personal favorite, and easier than the answers I have seen here (for multiple columns):

df.drop(df.columns[22:56], axis=1, inplace=True)
21
votes

This is probably a good way to do what you want. It will delete all columns that contain 'Unnamed' in their header.

for col in df.columns:
    if 'Unnamed' in col:
        del df[col]
14
votes

You can do this in one line and one go:

df.drop([col for col in df.columns if "Unnamed" in col], axis=1, inplace=True)

This involves less moving around/copying of the object than the solutions above.

11
votes

Not sure if this solution has been mentioned anywhere yet but one way to do is is pandas.Index.difference.

>>> df = pd.DataFrame(columns=['A','B','C','D'])
>>> df
Empty DataFrame
Columns: [A, B, C, D]
Index: []
>>> to_remove = ['A','C']
>>> df = df[df.columns.difference(to_remove)]
>>> df
Empty DataFrame
Columns: [B, D]
Index: []
5
votes

Simple and Easy. Remove all columns after the 22th. 

df.drop(columns=df.columns[22:]) # love it
4
votes

You can just pass the column names as a list with specifying the axis as 0 or 1

  • axis=1: Along the Rows
  • axis=0: Along the Columns

  • By default axis=0

    data.drop(["Colname1","Colname2","Colname3","Colname4"],axis=1)

1
votes

The below worked for me:

for col in df:
    if 'Unnamed' in col:
        #del df[col]
        print col
        try:
            df.drop(col, axis=1, inplace=True)
        except Exception:
            pass
0
votes

df = df[[col for col in df.columns if not ('Unnamed' in col)]]