Use set_levels:
In [22]:
df.columns.set_levels(['b1','c1','f1'],level=1,inplace=True)
df
Out[22]:
a d
b1 c1 f1
0 1 2 3
1 10 20 30
2 100 200 300
rename sets the name for the index, it doesn't rename the column names:
In [26]:
df.columns = df.columns.rename("b1", level=1)
df
Out[26]:
a d
b1 b c f
0 1 2 3
1 10 20 30
2 100 200 300
This is why you get the error
Answer from EdChum on Stack Overflow Top answer 1 of 8
91
Use set_levels:
In [22]:
df.columns.set_levels(['b1','c1','f1'],level=1,inplace=True)
df
Out[22]:
a d
b1 c1 f1
0 1 2 3
1 10 20 30
2 100 200 300
rename sets the name for the index, it doesn't rename the column names:
In [26]:
df.columns = df.columns.rename("b1", level=1)
df
Out[26]:
a d
b1 b c f
0 1 2 3
1 10 20 30
2 100 200 300
This is why you get the error
2 of 8
69
In pandas 0.21.0+ use parameter level=1:
d = dict(zip(df.columns.levels[1], ["b1", "c1", "f1"]))
print (d)
{'c': 'c1', 'b': 'b1', 'f': 'f1'}
df = df.rename(columns=d, level=1)
print (df)
a d
b1 c1 f1
0 1 2 3
1 10 20 30
2 100 200 300
Pandas
pandas.pydata.org › pandas-docs › version › 0.22 › generated › pandas.MultiIndex.rename.html
pandas.MultiIndex.rename — pandas 0.22.0 documentation
>>> Index([1, 2, 3, 4]).set_names('foo') Int64Index([1, 2, 3, 4], dtype='int64') >>> Index([1, 2, 3, 4]).set_names(['foo']) Int64Index([1, 2, 3, 4], dtype='int64') >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'), (2, u'one'), (2, u'two')], names=['foo', 'bar']) >>> idx.set_names(['baz', 'quz']) MultiIndex(levels=[[1, 2], [u'one', u'two']], labels=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[u'baz', u'quz']) >>> idx.set_names('baz', level=0) MultiIndex(levels=[[1, 2], [u'one', u'two']], labels=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[u'baz', u'bar'])
ENH: Rename multi-level columns or indices using their tupelized names
It's currently quite difficult to rename a single column (or row) in a multi-indexed DataFrame. The problem is illustrated also in this SO article. The current approach requires at least 3 step... More on github.com
3
November 25, 2020python - Pandas - Create Multiindex columns during rename - Stack Overflow
I'm trying to find a simple way to rename a flat column index to a hierarchical multindex column set. I've come across one way, but it seems a bit kludgy - is there a better way to do this in Panda... More on stackoverflow.com
python - pandas multiindex columns rename - Stack Overflow
hi I would like to rename the columns of my df. it has a multiindex columns and I would like to change the second level of it ie I have : ('GDP US Chained 2012 Dollars SAAR', 'GDP CHWG Index') ('G... More on stackoverflow.com
python pandas: rename single column label in multi-index dataframe - Stack Overflow
2 Renaming single column of multi-index pandas column More on stackoverflow.com
DEV Community
dev.to › vikramaruchamy › how-to-rename-multi-index-columns-in-pandas-dataframe-170
How to Rename Multi index columns in Pandas Dataframe - DEV Community
April 13, 2022 - In this short tutorial, you've learnt how to rename the columns of the multi-index pandas data-frame using the set_levels() method.
Saturn Cloud
saturncloud.io › blog › how-to-rename-multiindex-columns-in-pandas-a-guide-for-data-scientists
How to Rename MultiIndex Columns in Pandas A Guide for Data Scientists | Saturn Cloud Blog
June 19, 2023 - Testing: Before applying renaming to the entire dataset, conduct testing on a small subset to validate the correctness of the renaming logic. This reduces the risk of errors on the entire dataset. Working with MultiIndex columns in Pandas can be challenging, especially when dealing with large datasets.
GitHub
github.com › pandas-dev › pandas › issues › 38069
ENH: Rename multi-level columns or indices using their tupelized names · Issue #38069 · pandas-dev/pandas
November 25, 2020 - df = pd.DataFrame([[1,2,3],[3,4,5],[5,6,7], [7,8,9]]) df.columns = pd.MultiIndex.from_tuples([('i','a'),('i','b'),('ii','a')]) # Rename using tuples: df.rename(columns={('i','b'):('ii','b')}) # Rename using current column name (equivalent to 1a) col = df[('i','b')] df.rename(columns={col.name: ('ii','b')})
Author pandas-dev
TutorialsPoint
tutorialspoint.com › python_pandas › python_pandas_renaming_multiindex_labels.htm
Python Pandas - Renaming MultiIndex Labels
To rename the labels of the index or columns in a MultiIndexed object, you can use the pandas DataFame.rename() method.
w3resource
w3resource.com › python-exercises › pandas › index › pandas-indexing-exercise-15.php
Pandas: Rename names of columns and specific labels of the Main Index of MultiIndex dataframe - w3resource
Write a Pandas program to update the names of both columns and the top level of a MultiIndex, using a dictionary mapping for renaming.
DataScientYst
datascientyst.com › pandas-rename-column-names
How to rename column in Pandas
January 10, 2023 - (1) rename single column df.rename(columns = {'$b':'B'}, inplace = True) (2) rename multiple columns column_map = {'A': 'First', 'B': 'Second'} df = df.rename(columns=column_map) (3) rename multi-index columns cols = pd.MultiIndex.
DevPress
devpress.csdn.net › bigdata › 62f253767e6682346618506d.html
How to Rename Multi index columns in Pandas Dataframe_python_weixin_0010034-大数据
In this short tutorial, you've learnt how to rename the columns of the multi-index pandas data-frame using the set_levels() method.
datagy
datagy.io › home › pandas tutorials › pandas dataframes › pandas rename index: how to rename a pandas dataframe index
Pandas Rename Index: How to Rename a Pandas Dataframe Index • datagy
December 15, 2022 - Learn how to rename a Pandas index, including a single index or multi-index, as well as how to drop an index name altogether.
Top answer 1 of 2
2
You could try:
df.columns = pd.MultiIndex.from_tuples(df.rename(columns = nested_columns).columns)
df
Output:
One Two
a c b d
0 27 67 35 36
1 80 42 93 20
2 64 9 18 83
3 85 69 60 84
2 of 2
1
IIUC, rename
flat_df.rename(columns=nested_columns)
Out[224]:
One Two
a c b d
0 36 19 53 46
1 17 85 63 36
2 40 80 75 86
3 31 83 75 16
Updated
flat_df.columns.map(nested_columns.get)
Out[15]:
MultiIndex(levels=[['One', 'Two'], ['a', 'b', 'c', 'd']],
labels=[[0, 0, 1, 1], [0, 2, 1, 3]])
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.rename.html
pandas.DataFrame.rename — pandas 3.0.3 documentation
In case of a MultiIndex, only rename labels in the specified level. errors{‘ignore’, ‘raise’}, default ‘ignore’ · If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed.
DNMTechs
dnmtechs.com › renaming-multiindex-columns-in-pandas
Renaming MultiIndex Columns in Pandas – DNMTechs – Sharing and Storing Technology Knowledge
This example creates a DataFrame with MultiIndex columns and then renames the first level of the MultiIndex columns using the set_levels() method. The resulting DataFrame will have the new first level names and the original second level names. import pandas as pd # Create a DataFrame with ...
Pandas
pandas.pydata.org › docs › user_guide › advanced.html
MultiIndex / advanced indexing — pandas 3.0.3 documentation
The rename() method is used to rename the labels of a MultiIndex, and is typically used to rename the columns of a DataFrame.
Solix Technologies
solix.com › products › answers › renaming-multiindex-columns-in-pandas-a-data-science-guide
Renaming MultiIndex Columns In Pandas - A Data Science Guide | Solix Technologies, Inc.
June 29, 2025 - One of the simplest ways to rename your MultiIndex columns is by using the setnames method. This method allows you to explicitly name the levels of your MultiIndex. Heres a practical example · import pandas as pd Create a sample DataFrame with MultiIndexarrays = A, A, B, B, one, two, one, ...
PYnative
pynative.com › home › python › pandas › rename columns in pandas dataframe
Rename columns in Pandas DataFrame
March 9, 2023 - This is a new approach.( This approach makes this method match the rest of the pandas API) . Use the axis parameter of a df.rename() to rename columns and row index. The axis can be a row or column.
Apache
spark.apache.org › docs › latest › api › python › reference › pyspark.pandas › api › pyspark.pandas.MultiIndex.rename.html
pyspark.pandas.MultiIndex.rename — PySpark 4.1.1 documentation
>>> df = ps.DataFrame({'a': ['A', 'C'], 'b': ['A', 'B']}, columns=['a', 'b']) >>> df.index.rename("c") Index([0, 1], dtype='int64', name='c') >>> df.set_index("a", inplace=True) >>> df.index.rename("d") Index(['A', 'C'], dtype='object', name='d') You can also change the index name in place. >>> df.index.rename("e", inplace=True) >>> df.index Index(['A', 'C'], dtype='object', name='e') ... >>> psidx = ps.MultiIndex.from_tuples([('a', 'x'), ('b', 'y')]) >>> psidx.names = ['hello', 'pandas-on-Spark'] >>> psidx MultiIndex([('a', 'x'), ('b', 'y')], names=['hello', 'pandas-on-Spark'])
Stack Overflow
stackoverflow.com › questions › 74517802 › pandas-multiindex-columns-rename
python - pandas multiindex columns rename - Stack Overflow
df.columns = pd.MultiIndex.from_tuples([ (col0, col1.lower().replace('index', ''), *more_cols) for col0, col1, *more_cols in df.columns], names=df.columns.names)
Pandas
pandas.pydata.org › pandas-docs › version › 0.18 › generated › pandas.MultiIndex.rename.html
pandas.MultiIndex.rename — pandas 0.18.1 documentation
>>> Index([1, 2, 3, 4]).set_names('foo') Int64Index([1, 2, 3, 4], dtype='int64') >>> Index([1, 2, 3, 4]).set_names(['foo']) Int64Index([1, 2, 3, 4], dtype='int64') >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'), (2, u'one'), (2, u'two')], names=['foo', 'bar']) >>> idx.set_names(['baz', 'quz']) MultiIndex(levels=[[1, 2], [u'one', u'two']], labels=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[u'baz', u'quz']) >>> idx.set_names('baz', level=0) MultiIndex(levels=[[1, 2], [u'one', u'two']], labels=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[u'baz', u'bar'])
Top answer 1 of 6
25
That is indeed something missing in rename (ideally it should let you specify the level).
Another way is by setting the levels of the columns index, but then you need to know all values for that level:
In [41]: df.columns.levels[0]
Out[41]: Index([u'1', u'2'], dtype='object')
In [43]: df.columns = df.columns.set_levels(['one', 'two'], level=0)
In [44]: df
Out[44]:
one two
A B A B
0 0.899686 0.466577 0.867268 0.064329
1 0.162480 0.455039 0.736870 0.759595
2 0.620960 0.922119 0.060141 0.669997
3 0.871107 0.043799 0.080080 0.577421
In [45]: df.columns.levels[0]
Out[45]: Index([u'one', u'two'], dtype='object')
2 of 6
10
As of pandas 0.22.0 (and probably much earlier), you can specify the level:
df = df.rename(columns={'1': one, '2': two}, level=0)
or, alternatively (new notation since pandas 0.21.0):
df = df.rename({'1': one, '2': two}, axis='columns', level=0)
But actually, it works even when omitting the level:
df = df.rename(columns={'1': one, '2': two})
In that case, all column levels are checked for occurrences to be renamed.