Reputation: 5382
I have a pandas.dataframe
:
import pandas as pd
df = pd.DataFrame( {'one': pd.Series([1., 2., 3.],
index=['a', 'b', 'c']),
'two': pd.Series([1., 2., 3., 4.],
index=['a', 'b', 'c', 'd']),
'three': pd.Series([0., 6., 1.],
index=['b', 'c', 'd']),
'two_': pd.Series([1., 2., 5, 4.],
index=['a', 'b', 'c', 'd'])})
or
print (df)
# one three two two_
#a 1 NaN 1 1
#b 2 0 2 2
#c 3 6 3 5
#d NaN 1 4 4
and I have a map which renames certain columns as such
name_map = {'one': 'one', 'two': 'two_'}
df.rename(columns=name_map)
# one three two_ two_
# a 1 NaN 1 1
# b 2 0 2 2
# c 3 6 3 5
# d NaN 1 4 4
(occasionally name_map
might map a column to itself, e.g. 'one' -> 'one'). What I want in the end is the object
# one_ three two_
#a 1 NaN 1
#b 2 0 2
#c 3 6 3
#d NaN 1 4
How I should remove potential duplicates before renaming?
Upvotes: 3
Views: 1430
Reputation: 16174
I think the easiest way would be to drop the columns which are not present in the name_map
values list (since you want to remove the first two
column)
In [74]: df
Out[74]:
one two two_
a 1 1 1
b 2 2 2
c 3 3 5
d NaN 4 4
In [76]: df.drop([col for col in df.columns if col not in name_map.keys()], axis=1)
Out[76]:
one two
a 1 1
b 2 2
c 3 3
d NaN 4
Upvotes: 0
Reputation: 77027
First get the common columns list(set(name_map.values()) & set(df.columns))
and drop()
them. And, then rename()
it using columns=name_map
In [16]: (df.drop(list(set(name_map.values()) & set(df.columns)), axis=1)
.rename(columns=name_map))
Out[16]:
one_ two_
a 1 1
b 2 2
c 3 3
d NaN 4
Upvotes: 3
Reputation: 5382
I have one method, but it seems a bit messy (dealing with the NaN values contributes to the messiness)
potential_duplicates = [ new
for old,new in name_map.items()
if new in list(df) # if the new column name exists
and
pd.np.any( df[old][df[old]==df[old]] # if said column differs from the one to be renames
!= df[new][df[new]==df[new]] ) ]
df.drop( potential_duplicates, axis = 1, inplace=True)
df.rename( columns=name_map)
# one_ two_
#a 1 1
#b 2 2
#c 3 3
#d NaN 4
Upvotes: 0