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Transforming Continuous Data into Discrete Categories in Pandas

   November 29, 2023
Python Pandas @ Freshers.in

In data analysis and preprocessing, one often needs to convert continuous data into discrete categories. This is especially useful in data modeling and visualization, where categorical data can simplify analysis. Pandas, the versatile Python library, provides efficient ways to perform this transformation. This article explores these techniques with real-world examples.

Methods to Convert Continuous to Discrete in Pandas

1. Using cut() Function

Pandas’ cut() function is ideal for segmenting and sorting data values into bins. This method is useful when you need to divide the range of data into custom-defined intervals.

Example of Using cut():

Suppose we have a DataFrame with continuous age data and we want to categorize these ages into groups.

import pandas as pd
# Learning @ Freshers.in 
data = {'Name': ['Sachin', 'Manju', 'Ram', 'Raju', 'David', 'Freshers_in', 'Wilson'],
        'Age': [22, 29, 35, 42, 19, 25, 33]}
df = pd.DataFrame(data)
# Defining bins
bins = [18, 25, 30, 40, 50]
# Categorizing ages
df['Age Group'] = pd.cut(df['Age'], bins)
print(df['Age Group'])

Output

0    (18, 25]
1    (25, 30]
2    (30, 40]
3    (40, 50]
4    (18, 25]
5    (18, 25]
6    (30, 40]
Name: Age Group, dtype: category
Categories (4, interval[int64, right]): [(18, 25] < (25, 30] < (30, 40] < (40, 50]]

2. Using qcut() Function

The qcut() function is used to divide data into quantiles. This method ensures that each bin has roughly the same number of data points and is useful when dealing with skewed distributions.

Example of Using qcut():

For instance, if we want to divide a dataset into quartiles:

# Sample data
df['Income'] = [55000, 48000, 90000, 120000, 45000, 78000, 91000]
# Dividing into quartiles
df['Income Quartile'] = pd.qcut(df['Income'], 4)
print(df['Income Quartile'])
Output
0      (51500.0, 78000.0]
1    (44999.999, 51500.0]
2      (78000.0, 90500.0]
3     (90500.0, 120000.0]
4    (44999.999, 51500.0]
5      (51500.0, 78000.0]
6     (90500.0, 120000.0]
Name: Income Quartile, dtype: category
Categories (4, interval[float64, right]): [(44999.999, 51500.0] < (51500.0, 78000.0] <
                                           (78000.0, 90500.0] < (90500.0, 120000.0]]

Best Practices for Discretizing Continuous Data

  • Understand Your Data: Before discretizing, analyze the distribution of your continuous data.
  • Define Appropriate Bins: In cut(), the choice of bins impacts the analysis; ensure they are meaningful for your data.
  • Use qcut() for Even Distribution: When each category needs a similar number of data points, use qcut().
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