Pandas DataFrame.xs

Pandas DataFrame.xs

The DataFrame.xs method in pandas is used to retrieve a cross-section (row or column) of a DataFrame. This is particularly useful when working with hierarchical (MultiIndex) data.

Syntax

The syntax for DataFrame.xs is:

DataFrame.xs(key, axis=0, level=None, drop_level=True)

Here, DataFrame refers to the pandas DataFrame from which a cross-section is being retrieved.

Parameters

Returns

A Series or DataFrame representing the cross-section.

Examples

Accessing a Row by Label

Use xs to access a specific row by its label.

Python Program

import pandas as pd

# Create a DataFrame with a MultiIndex
arrays = [['A', 'A', 'B', 'B'], ['X', 'Y', 'X', 'Y']]
index = pd.MultiIndex.from_arrays(arrays, names=('Group', 'Subgroup'))
data = {
    'Value1': [10, 20, 30, 40],
    'Value2': [50, 60, 70, 80]
}
df = pd.DataFrame(data, index=index)

# Access a cross-section for group 'A'
print("Cross-section for Group 'A':")
print(df.xs('A'))

Output

Cross-section for Group 'A':
          Value1  Value2
Subgroup                
X             10      50
Y             20      60

Accessing Data Along a Specific Level

Use the level parameter to access data at a specific level of the MultiIndex.

Python Program

import pandas as pd

# Create a DataFrame with a MultiIndex
arrays = [['A', 'A', 'B', 'B'], ['X', 'Y', 'X', 'Y']]
index = pd.MultiIndex.from_arrays(arrays, names=('Group', 'Subgroup'))
data = {
    'Value1': [10, 20, 30, 40],
    'Value2': [50, 60, 70, 80]
}
df = pd.DataFrame(data, index=index)

# Access data along the 'Subgroup' level
print("Cross-section for Subgroup 'X':")
print(df.xs('X', level='Subgroup'))

Output

Cross-section for Subgroup 'X':
       Value1  Value2
Group                
A          10      50
B          30      70

Accessing Data Along Columns

Set axis=1 to retrieve a cross-section along columns.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Group': ['A', 'A', 'B', 'B'],
    'Subgroup': ['X', 'Y', 'X', 'Y'],
    'Value1': [10, 20, 30, 40],
    'Value2': [50, 60, 70, 80]
}
df = pd.DataFrame(data)

# Set 'Group' and 'Subgroup' as the index
df.set_index(['Group', 'Subgroup'], inplace=True)

# Access a column using xs
print("Cross-section for column 'Value1':")
print(df.xs('Value1', axis=1))

Output

Cross-section for column 'Value1':
Group  Subgroup
A      X           10
       Y           20
B      X           30
       Y           40
Name: Value1, dtype: int64

Using drop_level=False

Set drop_level=False to retain the selected level in the output.

Python Program

import pandas as pd

# Create a DataFrame with a MultiIndex
arrays = [['A', 'A', 'B', 'B'], ['X', 'Y', 'X', 'Y']]
index = pd.MultiIndex.from_arrays(arrays, names=('Group', 'Subgroup'))
data = {
    'Value1': [10, 20, 30, 40],
    'Value2': [50, 60, 70, 80]
}
df = pd.DataFrame(data, index=index)

# Access a cross-section without dropping the level
print("Cross-section for Group 'A' without dropping the level:")
print(df.xs('A', drop_level=False))

Output

Cross-section for Group 'A' without dropping the level:
                 Value1  Value2
Group Subgroup                
A     X             10      50
      Y             20      60

Summary

In this tutorial, we explored the DataFrame.xs method in pandas. Key takeaways include:

• Using xs to retrieve cross-sections of a DataFrame along rows or columns.
• Accessing data at specific levels in MultiIndex DataFrames.
• Controlling whether to drop the selected level using drop_level.

The DataFrame.xs method is a powerful tool for working with MultiIndex DataFrames and extracting specific subsets of data.