Deleting multiple columns based on column names in Pandas
PythonPandasPython Problem Overview
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 (https://stackoverflow.com/questions/26347412/drop-multiple-columns-pandas) but this doesn't answer my question.
Python Solutions
Solution 1 - Python
By far the simplest approach is:
yourdf.drop(['columnheading1', 'columnheading2'], axis=1, inplace=True)
Solution 2 - Python
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: []
Solution 3 - Python
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)
Solution 4 - Python
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]
Solution 5 - Python
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.
Solution 6 - Python
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: []
Solution 7 - Python
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)
Solution 8 - Python
Simple and Easy. Remove all columns after the 22th.
df.drop(columns=df.columns[22:]) # love it
Solution 9 - Python
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
Solution 10 - Python
df = df[[col for col in df.columns if not ('Unnamed' in col)]]