Python | Pandas Series.xs
Last Updated :
28 Jan, 2019
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.xs()
function return a cross-section from the Series/DataFrame for the given key value.
Syntax:Series.xs(key, axis=0, level=None, drop_level=True)
Parameters :
key : Label contained in the index, or partially in a MultiIndex.
axis : Axis to retrieve cross-section on.
level : In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.
drop_level : If False, returns object with same levels as self.
Returns : Series or DataFrame
Example #1: Use Series.xs()
function to return a cross-section of the given Series object for the passed key value.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' ])
sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
print (sr)
|
Output :
Now we will use Series.xs()
function to return the cross-section for the given series object.
Output :
As we can see in the output, the Series.xs()
function has returned ‘Lisbon’ as the cross-section for the given Series object.
Example #2 : Use Dataframe.xs()
function to return a cross-section of the given Dataframe object for the passed key value.
import pandas as pd
df = pd.DataFrame({ 'num_legs' : [ 4 , 4 , 2 , 2 ],
'num_wings' : [ 0 , 0 , 2 , 2 ],
'class' : [ 'Mammal' , 'Mammal' , 'Mammal' , 'Bird' ],
'animal' : [ 'Cow' , 'Elephant' , 'Deer' , 'Sparrow' ],
'locomotion' : [ 'Walks' , 'Walks' , 'Walks' , 'Flies' ]})
df = df.set_index([ 'class' , 'animal' , 'locomotion' ])
print (df)
|
Output :
Now we will use Dataframe.xs()
function to return the cross-section for the given Dataframe object.
Output :
As we can see in the output, the Dataframe.xs()
function has returned the cross-section of the given Dataframe object for the passed key value.
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