Pandas: Calculate the expanding minimum/maximum of a DataFrame

Updated: February 21, 2024 By: Guest Contributor Post a comment


Pandas, a versatile and powerful data manipulation library in Python, allows analysts and data scientists to perform extensive analysis and transformations on datasets efficiently. One of the less talked about, yet highly powerful functionalities in Pandas, is the ability to calculate the expanding minimum and maximum. This feature enables the tracking of minimum or maximum values in a dataset, growing with each additional data point. It’s particularly useful in time series analysis, for detecting trends, or for identifying the overall range of data as more observations are made.

Understanding Expanding Operations

Before diving into specific examples, let’s clarify what expanding operations are. An expanding operation takes into account all previous data points from the start upto the current point to compute a specific statistic. For example, the expanding minimum of a dataset is the smallest value encountered from the beginning to the current point.

Getting Started with the Data

import pandas as pd

# Creating a simple DataFrame
data = {'Value': [10, 20, 15, 12, 18, 9, 30, 8]}
df = pd.DataFrame(data)

This will output:

0     10
1     20
2     15
3     12
4     18
5      9
6     30
7      8

Calculating the Expanding Minimum

To calculate the expanding minimum, we use the expanding() method followed by min(). This automatically considers all previous rows in the calculation continuously.

expanding_min = df['Value'].expanding().min()

This returns:

0    10.0
1    10.0
2    10.0
3    10.0
4    10.0
5     9.0
6     9.0
7     8.0
Name: Value, dtype: float64

Calculating the Expanding Maximum

Similarly, to calculate the expanding maximum, switch to max() after expanding().

expanding_max = df['Value'].expanding().max()

This demonstrates :

0    10.0
1    20.0
2    20.0
3    20.0
4    20.0
5    20.0
6    30.0
7    30.0
Name: Value, dtype: float64

Advanced Usage

Expanding functions become even more powerful when combined with other transformations or used in complex time series analysis. Let’s say we wish to compute the expanding minimum, but only after the first 3 data points.

expanding_min_after_3 = df['Value'][3:].expanding().min()

This adjustment shifts our focus to later data points:

3    12.0
4    12.0
5     9.0
6     9.0
7     8.0
Name: Value, dtype: float64

Using Rolling and Expanding Together

For deeper analysis, one might want to compare rolling and expanding statistics. Rolling calculations use a fixed window size, while expanding captures everything from the start. This can unveil trends that rolling averages alone might miss.

# Comparing rolling with expanding
rolling_min = df['Value'].rolling(window=3).min()
expanding_min = df['Value'].expanding().min()

print(pd.DataFrame({'Rolling Min': rolling_min, 'Expanding Min': expanding_min}))

Visualizing Expanding Statistics

Finally, for a more intuitive understanding, visualizations can greatly help. Using Matplotlib or Seaborn to plot the original data against its expanding minimum and maximum highlights overall trends and extremes easily.

Here, we only scratched the surface of what’s possible with expanding operations in Pandas. As you gain familiarity, you’ll find them indispensable for comprehensive data analysis, especially in time series forecasting.


Expanding minimum and maximum calculations in Pandas offer a dynamic way to understand dataset trends and variations over time. Starting from simple applications to more complex analyses involving other statistical methods, these tools empower data scientists to derive meaningful insights from evolving datasets. Embracing these functionalities opens up new dimensions in data exploration and analysis.