437

How do I get the index column name in Python's pandas? Here's an example dataframe:

             Column 1
Index Title          
Apples              1
Oranges             2
Puppies             3
Ducks               4

What I'm trying to do is get/set the dataframe's index title. Here is what I tried:

import pandas as pd

data = {'Column 1'   : [1., 2., 3., 4.], 
        'Index Title': ["Apples", "Oranges", "Puppies", "Ducks"]}
df = pd.DataFrame(data)
df.index = df["Index Title"]
del df["Index Title"]

Anyone know how to do this?

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10 Answers 10

Reset to default
625

You can just get/set the index via its name property

In [7]: df.index.name
Out[7]: 'Index Title'

In [8]: df.index.name = 'foo'

In [9]: df.index.name
Out[9]: 'foo'

In [10]: df
Out[10]: 
         Column 1
foo              
Apples          1
Oranges         2
Puppies         3
Ducks           4
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6 Comments

As of now (0.16) it does not work. Or rather - it does work, but as soon as DataFrame gets modifies, it erases index name.
it should be possible to specify index name at DataFrame creation time. e.g. pd.DataFrame(values,index={"INDEX_NAME":index_values}). I do not understand why this is not allowed or implemented?
you can directly construct with an Index to add a name
If it is a Multiindex, use df.index.names instead of df.index.name.
To get rid of the index name, df.index.name = None.
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142

You can use rename_axis, for removing set to None:

d = {'Index Title': ['Apples', 'Oranges', 'Puppies', 'Ducks'],'Column 1': [1.0, 2.0, 3.0, 4.0]}
df = pd.DataFrame(d).set_index('Index Title')
print (df)
             Column 1
Index Title          
Apples            1.0
Oranges           2.0
Puppies           3.0
Ducks             4.0

print (df.index.name)
Index Title

print (df.columns.name)
None

The new functionality works well in method chains.

df = df.rename_axis('foo')
print (df)
         Column 1
foo              
Apples        1.0
Oranges       2.0
Puppies       3.0
Ducks         4.0

You can also rename column names with parameter axis:

d = {'Index Title': ['Apples', 'Oranges', 'Puppies', 'Ducks'],'Column 1': [1.0, 2.0, 3.0, 4.0]}
df = pd.DataFrame(d).set_index('Index Title').rename_axis('Col Name', axis=1)
print (df)
Col Name     Column 1
Index Title          
Apples            1.0
Oranges           2.0
Puppies           3.0
Ducks             4.0

print (df.index.name)
Index Title

print (df.columns.name)
Col Name

print df.rename_axis('foo').rename_axis("bar", axis="columns")
bar      Column 1
foo              
Apples        1.0
Oranges       2.0
Puppies       3.0
Ducks         4.0

print df.rename_axis('foo').rename_axis("bar", axis=1)
bar      Column 1
foo              
Apples        1.0
Oranges       2.0
Puppies       3.0
Ducks         4.0

From version pandas 0.24.0+ is possible use parameter index and columns:

df = df.rename_axis(index='foo', columns="bar")
print (df)
bar      Column 1
foo              
Apples        1.0
Oranges       2.0
Puppies       3.0
Ducks         4.0

Removing index and columns names means set it to None:

df = df.rename_axis(index=None, columns=None)
print (df)
         Column 1
Apples        1.0
Oranges       2.0
Puppies       3.0
Ducks         4.0

If MultiIndex in index only:

mux = pd.MultiIndex.from_arrays([['Apples', 'Oranges', 'Puppies', 'Ducks'],
                                  list('abcd')], 
                                  names=['index name 1','index name 1'])


df = pd.DataFrame(np.random.randint(10, size=(4,6)), 
                  index=mux, 
                  columns=list('ABCDEF')).rename_axis('col name', axis=1)
print (df)
col name                   A  B  C  D  E  F
index name 1 index name 1                  
Apples       a             5  4  0  5  2  2
Oranges      b             5  8  2  5  9  9
Puppies      c             7  6  0  7  8  3
Ducks        d             6  5  0  1  6  0

print (df.index.name)
None

print (df.columns.name)
col name

print (df.index.names)
['index name 1', 'index name 1']

print (df.columns.names)
['col name']

df1 = df.rename_axis(('foo','bar'))
print (df1)
col name     A  B  C  D  E  F
foo     bar                  
Apples  a    5  4  0  5  2  2
Oranges b    5  8  2  5  9  9
Puppies c    7  6  0  7  8  3
Ducks   d    6  5  0  1  6  0

df2 = df.rename_axis('baz', axis=1)
print (df2)
baz                        A  B  C  D  E  F
index name 1 index name 1                  
Apples       a             5  4  0  5  2  2
Oranges      b             5  8  2  5  9  9
Puppies      c             7  6  0  7  8  3
Ducks        d             6  5  0  1  6  0

df2 = df.rename_axis(index=('foo','bar'), columns='baz')
print (df2)
baz          A  B  C  D  E  F
foo     bar                  
Apples  a    5  4  0  5  2  2
Oranges b    5  8  2  5  9  9
Puppies c    7  6  0  7  8  3
Ducks   d    6  5  0  1  6  0

Removing index and columns names means set it to None:

df2 = df.rename_axis(index=(None,None), columns=None)
print (df2)

           A  B  C  D  E  F
Apples  a  6  9  9  5  4  6
Oranges b  2  6  7  4  3  5
Puppies c  6  3  6  3  5  1
Ducks   d  4  9  1  3  0  5

For MultiIndex in index and columns is necessary working with .names instead .name and set by list or tuples:

mux1 = pd.MultiIndex.from_arrays([['Apples', 'Oranges', 'Puppies', 'Ducks'],
                                  list('abcd')], 
                                  names=['index name 1','index name 1'])


mux2 = pd.MultiIndex.from_product([list('ABC'),
                                  list('XY')], 
                                  names=['col name 1','col name 2'])

df = pd.DataFrame(np.random.randint(10, size=(4,6)), index=mux1, columns=mux2)
print (df)
col name 1                 A     B     C   
col name 2                 X  Y  X  Y  X  Y
index name 1 index name 1                  
Apples       a             2  9  4  7  0  3
Oranges      b             9  0  6  0  9  4
Puppies      c             2  4  6  1  4  4
Ducks        d             6  6  7  1  2  8

Plural is necessary for check/set values:

print (df.index.name)
None

print (df.columns.name)
None

print (df.index.names)
['index name 1', 'index name 1']

print (df.columns.names)
['col name 1', 'col name 2']

df1 = df.rename_axis(('foo','bar'))
print (df1)
col name 1   A     B     C   
col name 2   X  Y  X  Y  X  Y
foo     bar                  
Apples  a    2  9  4  7  0  3
Oranges b    9  0  6  0  9  4
Puppies c    2  4  6  1  4  4
Ducks   d    6  6  7  1  2  8

df2 = df.rename_axis(('baz','bak'), axis=1)
print (df2)
baz                        A     B     C   
bak                        X  Y  X  Y  X  Y
index name 1 index name 1                  
Apples       a             2  9  4  7  0  3
Oranges      b             9  0  6  0  9  4
Puppies      c             2  4  6  1  4  4
Ducks        d             6  6  7  1  2  8

df2 = df.rename_axis(index=('foo','bar'), columns=('baz','bak'))
print (df2)
baz          A     B     C   
bak          X  Y  X  Y  X  Y
foo     bar                  
Apples  a    2  9  4  7  0  3
Oranges b    9  0  6  0  9  4
Puppies c    2  4  6  1  4  4
Ducks   d    6  6  7  1  2  8

Removing index and columns names means set it to None:

df2 = df.rename_axis(index=(None,None), columns=(None,None))
print (df2)

           A     B     C   
           X  Y  X  Y  X  Y
Apples  a  2  0  2  5  2  0
Oranges b  1  7  5  5  4  8
Puppies c  2  4  6  3  6  5
Ducks   d  9  6  3  9  7  0

And @Jeff solution:

df.index.names = ['foo','bar']
df.columns.names = ['baz','bak']
print (df)

baz          A     B     C   
bak          X  Y  X  Y  X  Y
foo     bar                  
Apples  a    3  4  7  3  3  3
Oranges b    1  2  5  8  1  0
Puppies c    9  6  3  9  6  3
Ducks   d    3  2  1  0  1  0
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3 Comments

Also worth noting, as phil suggests in an other df.index.rename('foo', inplace=True) see pandas.pydata.org/pandas-docs/stable/generated/…
I have imported a dictionary to be used as a dataframe therefore the index column was set automatically to None with rows being numbered 1-10. But I would like to assign the "names" column as the index. And if possible do this during plotting. Is it possible to do this and is it possible to do this on the fly as we plot?
jezrael gave the right answer for the original question, which was about the column index names, not the row index names. Plus a complete explanation. Basically, to Radical Edward have the index column name, just need to use df.index.names
33

df.index.name should do the trick.

Python has a dir function that let's you query object attributes. dir(df.index) was helpful here.

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Comments

23

If you do not want to create a new row but simply put it in the empty cell then use:

df.columns.name = 'foo'

Otherwise use: 

df.index.name = 'foo'
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1 Comment

Just found it it's a name for columns' names. No wonder that setting df.index.name gives you a new level. Thank you!
22

Use df.index.rename('foo', inplace=True) to set the index name.

Seems this api is available since pandas 0.13.

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2 Comments

Wow... an elegant solution!
currently better not to use inplace as it's going to be deprecated, but otherwise good
16

Setting the index name can also be accomplished at creation:

pd.DataFrame(data={'age': [10,20,30], 'height': [100, 170, 175]}, index=pd.Series(['a', 'b', 'c'], name='Tag'))
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1 Comment

RangeIndex also works df = pd.DataFrame(..., index=pd.RangeIndex(10, name='No'))
6

df.columns.values also give us the column names

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Comments

6

The solution for multi-indexes is inside jezrael's cyclopedic answer, but it took me a while to find it so I am posting a new answer:

df.index.names gives the names of a multi-index (as a Frozenlist).

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1 Comment

df.index.name can help as well
3
1. Use pd.Index to name an index (or column) from construction

Pandas has Index (MultiIndex) objects that accepts names. Passing those as index or column on dataframe construction constructs frames with named indices/columns.

data = {'Column 1': [1,2,3,4], 'Index Title': ["Apples","Oranges","Puppies","Ducks"]}

# for RangeIndex
df = pd.DataFrame(data, index=pd.Index(range(4), name='foo'))
#                             ^^^^^^^^  <---- here

# for Index
df = pd.DataFrame(data, index=pd.Index(data['Index Title'], name='foo'))
#                             ^^^^^^^^  <---- here

# for columns
df = pd.DataFrame(data, columns=pd.Index(data.keys(), name='foo'))
#                               ^^^^^^^^  <---- here

# for MultiIndex
df = pd.DataFrame(data, index=pd.MultiIndex.from_arrays([['Fruit', 'Fruit', 'Animal', 'Animal'], data['Index Title']], names=['foo', 'bar']))
#                             ^^^^^^^^^^^^^  <---- here
2. Change MultiIndex level name

If the dataframe has MultiIndex and an index name at a specific level has to be changed, index.set_names may be used. For example, to change the name of the second index level, use the following. Don't forget inplace=True.

df.index.set_names('foo', level=1, inplace=True)

# equivalently, rename could be used with a dict
df.index.rename({'Index Title 2': 'foo'}, inplace=True)

res1

set_names can also be used for just regular index (set level=None). However, rename_axis is probably easier.

df.index.set_names('foo', level=None, inplace=True)

# equivalent to the following
df.index.name = 'foo'
df = df.rename_axis('foo')

res2

There's a corresponding columns.set_names for columns.

df.columns.set_names('foo', level=None, inplace=True)
# equivalent to 
df = df.rename_axis(columns='foo')

# for MultiIndex columns
df.columns.set_names('foo', level=0, inplace=True)

res3

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Comments

2

To just get the index column names df.index.names will work for both a single Index or MultiIndex as of the most recent version of pandas.

As someone who found this while trying to find the best way to get a list of index names + column names, I would have found this answer useful:

names = list(filter(None, df.index.names + df.columns.values.tolist()))

This works for no index, single column Index, or MultiIndex. It avoids calling reset_index() which has an unnecessary performance hit for such a simple operation. I'm surprised there isn't a built in method for this (that I've come across). I guess I run into needing this more often because I'm shuttling data from databases where the dataframe index maps to a primary/unique key, but is really just another column to me.

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