The rename
method takes a dictionary for the index which applies to index values.
You want to rename to index level’s name:
df.index.names = ['Date']
A good way to think about this is that columns and index are the same type of object (Index
or MultiIndex
), and you can interchange the two via transpose.
This is a little bit confusing since the index names have a similar meaning to columns, so here are some more examples:
In [1]: df = pd.DataFrame([[1, 2, 3], [4, 5 ,6]], columns=list('ABC'))
In [2]: df
Out[2]:
A B C
0 1 2 3
1 4 5 6
In [3]: df1 = df.set_index('A')
In [4]: df1
Out[4]:
B C
A
1 2 3
4 5 6
You can see the rename on the index, which can change the value 1:
In [5]: df1.rename(index={1: 'a'})
Out[5]:
B C
A
a 2 3
4 5 6
In [6]: df1.rename(columns={'B': 'BB'})
Out[6]:
BB C
A
1 2 3
4 5 6
Whilst renaming the level names:
In [7]: df1.index.names = ['index']
df1.columns.names = ['column']
Note: this attribute is just a list, and you could do the renaming as a list comprehension/map.
In [8]: df1
Out[8]:
column B C
index
1 2 3
4 5 6