When working with large datasets in Python, we often need to segment continuous numerical data into discrete bins. This is where pandas.cut() comes in handy. It’s a powerful function that lets us create bins from numerical values efficiently. In this guide, I’ll break down how pandas.cut() works, provide practical examples, and discuss the best ways to use it.
Understanding pandas.cut(): What Does It Do?
The pandas.cut() function is used to segment and sort data values into discrete intervals, essentially converting continuous data into categorical data. This technique is often called “binning” or “bucketing”. It’s useful when analyzing frequency distributions.
Basic Syntax of pandas.cut()
The method has the following syntax:
pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')Here’s what each parameter does:
- x: The array or Series containing numerical data.
- bins: Specifies how the data is partitioned, either as an integer (number of bins) or as a sequence of limits.
- right: If
True, bins will be closed on the right (default behavior). - labels: Allows assigning custom labels to the bins.
- retbins: If
True, returns the actual bin edges. - precision: Defines decimal precision for bin labels.
- include_lowest: If
True, includes the first bin’s lower boundary. - duplicates: Determines how to handle duplicate bin edges.
Example 1: Basic Binning
Let’s start with a simple example where we divide a dataset into three bins:
import pandas as pd
data = [5, 15, 25, 35, 45, 55, 65, 75, 85, 95]
bins = [0, 25, 50, 100]
categories = pd.cut(data, bins)
print(categories)Output:
[(0, 25], (0, 25], (25, 50], (25, 50], (50, 100], (50, 100], (50, 100], (50, 100], (50, 100], (50, 100]]Each number is classified into a bin range, represented as an interval.
Example 2: Assigning Labels to Bins
Instead of returning numeric bins, we can label them for better readability:
labels = ["Low", "Medium", "High"]
categorized_data = pd.cut(data, bins, labels=labels)
print(categorized_data)Output:
['Low', 'Low', 'Medium', 'Medium', 'High', 'High', 'High', 'High', 'High', 'High']Now the data is categorized as “Low”, “Medium”, and “High” instead of numerical intervals.
Example 3: Returning Bin Boundaries
If we want to see the actual bin boundaries used in categorization, we can set retbins=True:
categories, bin_edges = pd.cut(data, bins, labels=labels, retbins=True)
print("Categories:", categories)
print("Bin edges:", bin_edges)Output:
Categories: ['Low', 'Low', 'Medium', 'Medium', 'High', 'High', 'High', 'High', 'High', 'High']
Bin edges: [ 0 25 50 100]Example 4: Creating Equal-Sized Bins
Instead of manually specifying bin ranges, we can divide the data automatically into equal-sized bins:
equal_bins = pd.cut(data, 4) # 4 equal-sized bins
print(equal_bins)Output:
[(4.91, 28.75], (4.91, 28.75], (4.91, 28.75], (28.75, 52.5], (28.75, 52.5],
(52.5, 76.25], (52.5, 76.25], (76.25, 100.0], (76.25, 100.0], (76.25, 100.0]]Comparison of pandas.cut() Features
| Feature | Usage |
|---|---|
| Fixed Bins | Manually defining bin edges. |
| Auto-Generated Bins | Dividing data into a set number of bins. |
| Labels | Assigning custom names to bins. |
| Bin Boundaries | Returning bin edges. |
Best Practices for Using pandas.cut()
To make the most out of pandas.cut(), follow these best practices:
- Use labeled bins when working with categorical data.
- Enable
retbins=Trueif you need the bin boundaries for analysis. - Ensure no duplicate bin edges by using unique bin values.
- Use automatic binning for equal distribution of numerical data.
Binning is a powerful tool for data preprocessing and analysis. With pandas.cut(), you can effectively segment data into meaningful groups, making it easier to analyze distributions and gain insights.