在pandas DataFrame中更改特定的列名称

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.values[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')

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..)

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.

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’

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