[python] Deleting multiple columns based on column names in Pandas

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.

This question is related to python pandas

The answer is


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


The by far the simplest approach is:

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

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

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

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.


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

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)


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: []

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]

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)