How do I get the index column name in python 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 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"] print df Anyone know how to do this?
9 Answers
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 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 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.
Use df.index.rename('foo', inplace=True) to set the index name.
Seems this api is available since pandas 0.13.
1If 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' 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')) df.columns.values also give us the column names
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).
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.
