Python | Pandas Series.xs

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. Python3

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])

# Creating the row axis labels
sr.index = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] 

# Print the series
print(sr)

Output : Now we will use Series.xs() function to return the cross-section for the given series object. Python3 1==

# return cross-section corresponding to
# the 'City 4' label
sr.xs(key = 'City 4')

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. Python3

# importing pandas as pd
import pandas as pd

# Creating the Dataframe
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']})

# setting the index
df = df.set_index(['class', 'animal', 'locomotion'])

# Print the Dataframe
print(df)

Output : Now we will use Dataframe.xs() function to return the cross-section for the given Dataframe object. Python3 1==

# return cross-section corresponding to
# the 'Mammal' label
sr.xs(key = 'Mammal')

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.