Pandas Groupby with Aggregate and Quantiles

When working with large datasets in pandas, it’s often necessary to perform group by operations along with various aggregations. In this article, we’ll explore how to use pandas’ groupby function in conjunction with aggregate functions like mode and how to calculate quantiles for specific columns.

Installing Required Libraries

Before diving into the code, ensure that you have the necessary libraries installed. Pandas is a powerful library for data manipulation and analysis, and we’ll be using it extensively throughout this article. You can install pandas using pip:

pip install pandas numpy

Sample Dataframe

Here’s an example dataframe to demonstrate our concepts:

import pandas as pd
import numpy as np

df = pd.DataFrame({
                   'id': [1, 1, 1, 2],
                   'cat': ['p','p','p','n'],
                   'num': [5, 10, 10, 5],
                   'v': [np.nan, np.naT, np.naT, 'v2'],
                   'p': [1000, 1300, 1400, 1100]
                 })

Grouping by a Column

The groupby function allows us to group our data by one or more columns and perform operations on the resulting groups. In this case, we want to group by the ‘cat’ column.

# Perform grouping operation
df_grouped_by = df.groupby('cat')

Using Aggregate Functions

Pandas provides various aggregate functions that can be applied to each group of data. The mode function returns the most common value in a series. We’ll use this function on our ‘p’ column.

# Calculate mode for each group
df_grouped_by = df.groupby('cat').agg(pd.Series.mode)

Calculating Quantiles

Quantiles are values that divide a dataset into equal-sized pieces based on the distribution of its values. The np.quantile function allows us to compute these values.

# Calculate 0.25 and 0.75 quantiles for each group
df_grouped_by['pquantile'] = df_grouped_by.apply(lambda row: np.quantile(row['p'], [0.25, 0.75]), axis=1)

Computing Min and Max

We can compute the minimum and maximum values in our ‘p’ column using the np.min and np.max functions.

# Compute min and max for each group
df_grouped_by['min-max'] = df_grouped_by.apply(lambda row: [np.min(row['p']), np.max(row['p'])], axis=1)

Expected Output

Here’s the expected output of our code:

Real-World Applications

Pandas’ groupby function has numerous real-world applications:

• Data Analysis: Grouping data by various criteria allows us to identify trends, patterns, and correlations within the data.
• Machine Learning: In machine learning, grouping data can help us prepare our dataset for modeling by reducing dimensionality and improving feature relevance.
• Data Visualization: Using pandas to group data enables us to create informative visualizations that highlight key insights from the data.

Conclusion

In this article, we explored how to use pandas’ groupby function in conjunction with aggregate functions like mode and how to calculate quantiles for specific columns. By mastering these concepts, you’ll be able to extract valuable insights from your dataset and unlock new levels of data analysis and visualization capabilities.

Last modified on 2025-01-02