Changing a specific column name in pandas DataFrame
PythonPandasPython Problem Overview
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.
Python Solutions
Solution 1 - Python
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)
Solution 2 - Python
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)
Solution 3 - Python
What about?
df.columns[2] = "new_name"
Solution 4 - Python
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..)
Solution 5 - Python
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')
Solution 6 - Python
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']
Solution 7 - Python
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'
Solution 8 - Python
Following short code can help:
df3 = df3.rename(columns={c: c.replace(' ', '') for c in df3.columns})
Remove spaces from columns.
Solution 9 - Python
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
Solution 10 - Python
size = 10
df.rename(columns={df.columns[i]: someList[i] for i in range(size)}, inplace = True)