Understanding IntervalIndex in Pandas (5 examples)

Updated: February 23, 2024 By: Guest Contributor Post a comment


Pandas is a powerful library in Python that provides extensive capabilities to work with structured data seamlessly. One of the lesser-known, yet incredibly powerful features available in pandas is the IntervalIndex. This feature can revolutionize the way you handle data that represents intervals, such as time periods, ranges of numbers, or any other form of start and end points. In this guide, we will journey through the concept of IntervalIndex, with practical examples demonstrating its usage and benefits.

What is IntervalIndex?

IntervalIndex is a type of index in pandas that allows for indexing with intervals. An interval is defined as a range between two endpoints, where each interval is unique and does not overlap. This index type is particularly useful when you want to work with data segments or periods in a performant and intuitive way.

Example 1: Creating an IntervalIndex

Let’s start with the basics by creating an IntervalIndex. This can be achieved by using the pandas.IntervalIndex.from_arrays method, which takes two arrays representing the left and right bounds of the intervals.

import pandas as pd

# Define the left and right bounds
left_bounds = [1, 3, 5, 7, 9]
right_bounds = [2, 4, 6, 8, 10]

# Create IntervalIndex
interval_index = pd.IntervalIndex.from_arrays(left_bounds, right_bounds, closed='right')


The output:

IntervalIndex([(1, 2], (3, 4], (5, 6], (7, 8], (9, 10]],

This code snippet effectively creates an IntervalIndex with intervals that are closed on the right. This means that the right endpoint is included in the interval.

Example 2: Using IntervalIndex in a DataFrame

Once you have an IntervalIndex, you can use it as the index for a DataFrame. This allows you to assign specific values to each interval, making it easy to work with time series and range-based data.

import pandas as pd

# Use the previously created IntervalIndex
data = [100, 200, 300, 400, 500]
df = pd.DataFrame(data, index=interval_index, columns=['Value'])


The output:

(1, 2]     100
(3, 4]     200
(5, 6]     300
(7, 8]     400
(9, 10]    500

This DataFrame associates each interval with a specific value. It is an intuitive way to represent data that maps values to ranges.

Example 3: Querying Data by Interval

IntervalIndex shines when it comes to querying data by intervals. You can easily select data that falls within a specific range using the loc attribute.

selected_data = df.loc[pd.Interval(3, 6, closed='right')]

The output:

(3, 4]     200
(5, 6]     300

Here, we select data from the DataFrame where the index falls within the interval (3, 6]. This approach allows for highly efficient and intuitive data selection based on ranges.

Example 4: Operations on IntervalIndex

IntervalIndex is not just about retrieving data; it supports numerous operations that enhance its usefulness. Let’s look at how you can perform mathematical operations on an IntervalIndex.

interval_sum = interval_index + 5

The output:

IntervalIndex([(6, 7], (8, 9], (10, 11], (12, 13], (14, 15]],

By adding 5 to the IntervalIndex, we shift all intervals by 5 units to the right. This operation demonstrates the flexibility of IntervalIndex in manipulating interval-based data.

Example 5: Cutting Continuous Data into Bins

One of the most powerful applications of IntervalIndex is its ability to cut continuous data into bins, enabling easy categorization and analysis of data.

import numpy as np

# Continuous data
continuous_data = np.arange(1, 11)

# Cut into bins
data_categories = pd.cut(continuous_data, bins=interval_index)


The output:

[(1, 2], (1, 2], NaN, (3, 4], (3, 4], NaN, (5, 6], (5, 6], NaN, (7, 8]]
Categories (4, interval[int64, right]): [(1, 2] < (3, 4] < (5, 6] < (7, 8]]

In this example, pd.cut is used to categorize continuous data into the intervals defined by interval_index. This method is incredibly useful for data analysis, as it groups data into manageable and meaningful categories.


The IntervalIndex feature in pandas offers a sophisticated and efficient way to manage data that falls within intervals. Through the examples provided, we have seen how it simplifies data analysis and manipulation, providing powerful querying capabilities and allowing for intuitive operations on range-based data. Its application in categorizing continuous data into bins further highlights its versatility and value in data science endeavors.