Changing a specific column name in pandas DataFrame
Question:
I was looking for an elegant way to change a specified column name in a DataFrame.
play data …
import pandas as pd
d = {
'one': [1, 2, 3, 4, 5],
'two': [9, 8, 7, 6, 5],
'three': ['a', 'b', 'c', 'd', 'e']
}
df = pd.DataFrame(d)
The most elegant solution I have found so far …
names = df.columns.tolist()
names[names.index('two')] = 'new_name'
df.columns = names
I was hoping for a simple one-liner … this attempt failed …
df.columns[df.columns.tolist().index('one')] = 'another_name'
Any hints gratefully received.
Answers:
A one liner does exist:
In [27]: df=df.rename(columns = {'two':'new_name'})
In [28]: df
Out[28]:
one three new_name
0 1 a 9
1 2 b 8
2 3 c 7
3 4 d 6
4 5 e 5
Following is the docstring for the rename method.
Definition: df.rename(self, index=None, columns=None, copy=True, inplace=False)
Docstring:
Alter index and / or columns using input function or
functions. Function / dict values must be unique (1-to-1). Labels not
contained in a dict / Series will be left as-is.
Parameters
----------
index : dict-like or function, optional
Transformation to apply to index values
columns : dict-like or function, optional
Transformation to apply to column values
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new DataFrame. If True then value of copy is
ignored.
See also
--------
Series.rename
Returns
-------
renamed : DataFrame (new object)
Since inplace argument is available, you don’t need to copy and assign the original data frame back to itself, but do as follows:
df.rename(columns={'two':'new_name'}, inplace=True)
What about?
df.columns[2] = "new_name"
Pandas 0.21 now has an axis parameter
The rename method has gained an axis parameter to match most of the rest of the pandas API.
So, in addition to this:
df.rename(columns = {'two':'new_name'})
You can do:
df.rename({'two':'new_name'}, axis=1)
or
df.rename({'two':'new_name'}, axis='columns')
For renaming the columns here is the simple one which will work for both Default(0,1,2,etc;) and existing columns but not much useful for a larger data sets(having many columns).
For a larger data set we can slice the columns that we need and apply the below code:
df.columns = ['new_name','new_name1','old_name']
Following short code can help:
df3 = df3.rename(columns={c: c.replace(' ', '') for c in df3.columns})
Remove spaces from columns.
If you know which column # it is (first / second / nth) then this solution posted on a similar question works regardless of whether it is named or unnamed, and in one line: https://stackoverflow.com/a/26336314/4355695
df.rename(columns = {list(df)[1]:'new_name'}, inplace=True)
# 1 is for second column (0,1,2..)
Another option would be to simply copy & drop the column:
df = pd.DataFrame(d)
df['new_name'] = df['two']
df = df.drop('two', axis=1)
df.head()
After that you get the result:
one three new_name
0 1 a 9
1 2 b 8
2 3 c 7
3 4 d 6
4 5 e 5
pandas version 0.23.4
df.rename(index=str,columns={'old_name':'new_name'},inplace=True)
For the record:
omitting index=str will give error replace has an unexpected argument
‘columns’
size = 10
df.rename(columns={df.columns[i]: someList[i] for i in range(size)}, inplace = True)
I was looking for an elegant way to change a specified column name in a DataFrame.
play data …
import pandas as pd
d = {
'one': [1, 2, 3, 4, 5],
'two': [9, 8, 7, 6, 5],
'three': ['a', 'b', 'c', 'd', 'e']
}
df = pd.DataFrame(d)
The most elegant solution I have found so far …
names = df.columns.tolist()
names[names.index('two')] = 'new_name'
df.columns = names
I was hoping for a simple one-liner … this attempt failed …
df.columns[df.columns.tolist().index('one')] = 'another_name'
Any hints gratefully received.
A one liner does exist:
In [27]: df=df.rename(columns = {'two':'new_name'})
In [28]: df
Out[28]:
one three new_name
0 1 a 9
1 2 b 8
2 3 c 7
3 4 d 6
4 5 e 5
Following is the docstring for the rename method.
Definition: df.rename(self, index=None, columns=None, copy=True, inplace=False)
Docstring:
Alter index and / or columns using input function or
functions. Function / dict values must be unique (1-to-1). Labels not
contained in a dict / Series will be left as-is.
Parameters
----------
index : dict-like or function, optional
Transformation to apply to index values
columns : dict-like or function, optional
Transformation to apply to column values
copy : boolean, default True
Also copy underlying data
inplace : boolean, default False
Whether to return a new DataFrame. If True then value of copy is
ignored.
See also
--------
Series.rename
Returns
-------
renamed : DataFrame (new object)
Since inplace argument is available, you don’t need to copy and assign the original data frame back to itself, but do as follows:
df.rename(columns={'two':'new_name'}, inplace=True)
What about?
df.columns[2] = "new_name"
Pandas 0.21 now has an axis parameter
The rename method has gained an axis parameter to match most of the rest of the pandas API.
So, in addition to this:
df.rename(columns = {'two':'new_name'})
You can do:
df.rename({'two':'new_name'}, axis=1)
or
df.rename({'two':'new_name'}, axis='columns')
For renaming the columns here is the simple one which will work for both Default(0,1,2,etc;) and existing columns but not much useful for a larger data sets(having many columns).
For a larger data set we can slice the columns that we need and apply the below code:
df.columns = ['new_name','new_name1','old_name']
Following short code can help:
df3 = df3.rename(columns={c: c.replace(' ', '') for c in df3.columns})
Remove spaces from columns.
If you know which column # it is (first / second / nth) then this solution posted on a similar question works regardless of whether it is named or unnamed, and in one line: https://stackoverflow.com/a/26336314/4355695
df.rename(columns = {list(df)[1]:'new_name'}, inplace=True)
# 1 is for second column (0,1,2..)
Another option would be to simply copy & drop the column:
df = pd.DataFrame(d)
df['new_name'] = df['two']
df = df.drop('two', axis=1)
df.head()
After that you get the result:
one three new_name
0 1 a 9
1 2 b 8
2 3 c 7
3 4 d 6
4 5 e 5
pandas version 0.23.4
df.rename(index=str,columns={'old_name':'new_name'},inplace=True)
For the record:
omitting index=str will give error replace has an unexpected argument
‘columns’
size = 10
df.rename(columns={df.columns[i]: someList[i] for i in range(size)}, inplace = True)