Data Binning with Python pd.cut and pd.qcut

Introduction

Data preprocessing is a crucial step in data analysis and machine learning. Binning is a commonly used data preprocessing method. Data binning converts continuous variables into discrete variables. By dividing a set of continuous values into different intervals (bins), continuous data is converted into ordered discrete data, facilitating further analysis and modeling.

The Python pandas library provides two functions, pd.cut and pd.qcut, for binning data. This article will explain the usage and examples of these two functions in detail, and analyze their differences and applicable scenarios.

1. pd.cut Function

1.1 Function Introduction

pd.cut is used to bin continuous data, assigning a set of values to corresponding intervals based on the specified interval.

1.2 Function Syntax

pd.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise')

Parameter Description:

• x: The one-dimensional array, Series, or column of a DataFrame to be binned.
• bins: An int representing the number of bins to be divided into, or a list or array for customizing the binning range.
• right: A bool indicating whether the bins are right-closed.
• labels: The labels for the bins, which can be a list or array and must match the length of bins.
• Other parameters: retbins, precision, include_lowest, and duplicates are optional and are used to set the return result type and specific behavior.

1.3 Example

First, we import the necessary libraries and create sample data.

import pandas as pd
import numpy as np

#Create sample data
data = pd.DataFrame({'score': np.random.randint(0, 100, 10)})
print(data)

Running result:

 score
0 77
1 95
2 73
3 97
4 82
5 60
6 42
7 20
8 86
9 51

Next, use the pd.cut function to bin the data.

# Divide the data into 5 bins
bins = [0, 50, 60, 70, 80, 100]
labels = ['D', 'C', 'B', 'A', 'S']
data['grade'] = pd.cut(data['score'], bins=bins, labels=labels)
print(data)

Running results:

 score grade
0 77 A
1 95 S
2 73 B
3 97 S
4 82 A
5 60 C
6 42 D
7 20 NaN
8 86 S
9 51 D

As can be seen from the above results, the original data has been divided into different bins based on the binning rules, and corresponding labels (grade column) have been added to each data point. Furthermore, data outside the specified bins are set to missing values (NaN).

2. pd.qcut Function

2.1 Function Introduction

pd.qcut is a quantile-based binning method that bins data according to percentiles, achieving a uniform distribution.

2.2 Function Syntax

pd.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise')

Parameter Description:

• x: The one-dimensional array, Series, or column of a DataFrame to be binned.
• q: int or array-like value, specifies the number of quantiles.
• labels: The binned labels, which can be a list or array and must match the length of q.
• Other parameters are the same as those of the pd.cut function.

2.3 Example

We continue using the previous example data and bin it using the pd.qcut function.

# Divide the data into 3 intervals
data['grade_qcut'] = pd.qcut(data['score'], q=3)
print(data)

Running results:

 score grade grade_qcut
0 77 A (76.0, 97.0]
1 95 S (76.0, 97.0]
2 73 B (60.0, 76.0]
3 97 S (76.0, 97.0]
4 82 A (76.0, 97.0]
5 60 C (42.0, 60.0]
6 42 D (42.0, 60.0]
7 20 NaN (19.999, 42.0]
8 86 S (76.0, 97.0]
9 51 D (42.0, 60.0]

From the above results, we can see that the pd.qcut function bins the original data according to quantiles, and the amount of data in each bin is roughly equal.

3. Differences between pd.cut and pd.qcut and their applicable scenarios

To summarize the differences between pd.cut and pd.qcut and their applicable scenarios:

• pd.cut performs binning based on a numerical range. Users can customize the binning intervals, making it suitable for unevenly distributed data and allowing for flexible processing based on actual business needs.
• pd.qcut performs binning based on quantiles, achieving a roughly equal sample size and suitable for evenly distributing samples across bins.

Generally speaking, if the data If the data volume is large, consider using the pd.qcut function to achieve uniform sample distribution. If the data volume is small, use the pd.cut function for flexible processing based on actual business needs.

Conclusion

This article detailed the usage and examples of the pd.cut and pd.qcut functions in Python, and analyzed their differences and applicable scenarios. Data binning is a common data preprocessing method that discretizes continuous variables, facilitating further data analysis and modeling. In practical work, it is very important to choose the appropriate binning method based on the specific problem and data characteristics.