CODEWITHSUNDEEP
pythonpandas
Peadar Coyle
Peadar Coyle

Reputation: 2243

Deleting multiple columns based on column names

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.

Upvotes: 134

Views: 271158

Answers (11)

Mykola Zotko
Mykola Zotko

Reputation: 17804

You can drop all columns that start with 'Unnamed':

df.loc[:, ~df.columns.str.startswith('Unnamed')]

Upvotes: 0

Philipp Schwarz
Philipp Schwarz

Reputation: 20724

By far the simplest approach is:

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

Upvotes: 294

sheldonzy
sheldonzy

Reputation: 5941

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)

Upvotes: 57

EdChum
EdChum

Reputation: 394003

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: []

Upvotes: 71

Niedson
Niedson

Reputation: 71

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

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

Upvotes: 7

Swaroop Maddu
Swaroop Maddu

Reputation: 4844

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)

Upvotes: 8

Sarah
Sarah

Reputation: 1982

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

Upvotes: 0

px06
px06

Reputation: 2326

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: []

Upvotes: 13

Peter
Peter

Reputation: 294

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.

Upvotes: 18

Shivgan
Shivgan

Reputation: 11

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

Upvotes: 1

knightofni
knightofni

Reputation: 1956

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]

Upvotes: 22

Related Questions