Pandas DataFrame.expanding: Expanding Window Calculations on a DataFrame

Pandas DataFrame.expanding

The DataFrame.expanding method in pandas is used to perform expanding window calculations on a DataFrame. It allows you to apply operations (e.g., cumulative sum, cumulative mean, etc.) over an expanding window of rows or columns.

Syntax

The syntax for DataFrame.expanding is:

DataFrame.expanding(min_periods=1, axis=0, method='single')

Here, DataFrame refers to the pandas DataFrame on which the expanding window calculations are performed.

Parameters

ParameterDescription
min_periodsSpecifies the minimum number of observations in the window required to have a value. Defaults to 1.
axisSpecifies the axis along which the expanding window is applied. Use 0 or 'index' for rows, and 1 or 'columns' for columns. Defaults to 0.
methodSpecifies the method for handling overlapping windows. Options include 'single' or 'table'. Defaults to 'single'.

Returns

An Expanding object, which can be used to apply aggregation or transformation operations over the expanding window.

Examples

Expanding Sum Calculation on a DataFrame

This example demonstrates how to use expanding to calculate the cumulative sum of a DataFrame column.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'Values': [1, 2, 3, 4, 5]})

# Calculate the expanding sum
result = df['Values'].expanding().sum()
print(result)

Output

0     1.0
1     3.0
2     6.0
3    10.0
4    15.0
Name: Values, dtype: float64

Expanding Mean Calculation on a DataFrame

This example shows how to use expanding to calculate the cumulative mean of a DataFrame column.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'Values': [10, 20, 30, 40, 50]})

# Calculate the expanding mean
result = df['Values'].expanding().mean()
print(result)

Output

0    10.0
1    15.0
2    20.0
3    25.0
4    30.0
Name: Values, dtype: float64

Expanding Window Calculation with Minimum Periods

This example demonstrates how to use expanding with the min_periods parameter to control the minimum number of observations required for a calculation.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'Values': [1, 2, 3, 4, 5]})

# Calculate the expanding sum with min_periods=3
result = df['Values'].expanding(min_periods=3).sum()
print(result)

Output

0     NaN
1     NaN
2     6.0
3    10.0
4    15.0
Name: Values, dtype: float64

Expanding Window Calculation on Rows of a DataFrame

This example shows how to use expanding to calculate the cumulative sum along the rows of a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Calculate the expanding sum along rows
result = df.expanding(axis=1).sum()
print(result)

Output

   A   B   C
0  1   5  12
1  2   7  15
2  3   9  18

Summary

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

  • Using expanding to perform cumulative calculations (e.g., sum, mean) on a DataFrame.
  • Controlling the minimum number of observations with the min_periods parameter.
  • Applying expanding window calculations along rows or columns using the axis parameter.
  • Understanding the flexibility of expanding for cumulative operations.

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