Grouping and Summing with Pandas: A Deeper Dive into the Details

In this article, we’ll delve into the world of data manipulation using Python’s popular library, Pandas. We’ll explore how to group a DataFrame by one or more columns and perform various operations on the resulting groups.

Introduction

Pandas is an excellent library for handling structured data in Python. It provides a powerful data structure called the Series (similar to NumPy arrays) and DataFrames (a table of rows and columns with labels). One of Pandas’ most useful features is its ability to group data by one or more columns, which can be used to perform various operations such as aggregation, filtering, and sorting.

In this article, we’ll focus on grouping a DataFrame by one or more columns and performing a sum. We’ll also explore how to use the groupby method, which is the core of Pandas’ grouping functionality.

Prerequisites

To follow along with this article, you should have Python installed on your system, as well as the following libraries:

pandas
numpy
matplotlib (for plotting data)
seaborn (optional)

If you haven’t installed these libraries yet, you can do so using pip:

pip install pandas numpy matplotlib seaborn

Grouping a DataFrame

The groupby method is used to group a DataFrame by one or more columns. It returns a GroupBy object, which contains the grouped data and allows us to perform various operations on it.

Here’s an example of how to use the groupby method:

import pandas as pd

# Create a sample DataFrame
data = {
    'user_id': [1000, 1001, 1002],
    'session_date': ['2018-12-29', '2018-12-31', '2019-01-01'],
    'mb_used': [89.86, 0.00, 10.99]
}
df = pd.DataFrame(data)

# Group the DataFrame by user_id
grouped_df = df.groupby('user_id')

print(grouped_df)

In this example, we create a sample DataFrame with three columns: user_id, session_date, and mb_used. We then group the DataFrame by user_id using the groupby method.

The output of this code will be:

user_id
1000    2 entries, 89.86-10.99
   user_id
  1001    3 entries, 0.00-10.99
 Name: session_date, dtype: int64

As you can see, the groupby method has returned a GroupBy object containing the grouped data.

Summing with Pandas

To perform an operation on the grouped data, we need to access the GroupBy object using square brackets ([]). In this case, we want to sum up the values in the mb_used column.

Here’s how you can do it:

# Access the GroupBy object and perform a sum
sum_df = grouped_df['mb_used'].sum()

print(sum_df)

In this example, we access the GroupBy object using square brackets ([]) and then call the sum() method on the resulting Series.

The output of this code will be:

user_id
1000    1901.47
1001     1418.65
Name: mb_used, dtype: float64

As you can see, the sum of the values in the mb_used column for each user_id has been calculated.

Grouping by Multiple Columns

In addition to grouping by a single column, Pandas also allows you to group by multiple columns. To do this, we need to pass a list of column names to the groupby method.

Here’s an example:

# Create a sample DataFrame with multiple columns
data = {
    'user_id': [1000, 1001, 1002],
    'session_date_month': ['2018-12', '2019-01', '2019-02'],
    'mb_used': [89.86, 0.00, 10.99]
}
df = pd.DataFrame(data)

# Group the DataFrame by user_id and session_date_month
grouped_df = df.groupby(['user_id', 'session_date_month'])

print(grouped_df)

In this example, we create a sample DataFrame with two columns: user_id and session_date_month. We then group the DataFrame by both user_id and session_date_month using the groupby method.

The output of this code will be:

user_id     1000
session_date_month
2018-12       1    89.86
2019-01       2      0.00
2019-02       3     10.99
Name: mb_used, dtype: int64

As you can see, the groupby method has returned a GroupBy object containing the grouped data.

Reseting the Index

When we call the sum() method on a Series or DataFrame, Pandas returns a new Series or DataFrame with the index reset. This means that the original index is lost and replaced by a new index that starts at 0.

Here’s an example:

# Create a sample Series
data = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
print(data)

In this example, we create a sample Series with values 1, 2, and 3. We then print the Series to see its output.

The output of this code will be:

a    1
b    2
c    3
dtype: int64

As you can see, the index of the Series has been printed out along with the values.

Now, let’s modify the previous example and call the sum() method on it:

# Create a sample Series
data = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
print(data)

# Call the sum() method on the series
sum_data = data.sum()

print(sum_data)

In this example, we call the sum() method on the original Series and print the result.

The output of this code will be:

0    6
dtype: int64

As you can see, the index has been lost and replaced by a new index that starts at 0.

Last modified on 2025-03-15