Understanding Pandas Groupby for Data Aggregation

Introduction

What if I told you that we could derive effective and impactful insights from our dataset in just a few lines of code? That’s the beauty of Pandas’ GroupBy in Python function! I have lost count of the number of times I’ve relied on GroupBy to quickly summarize data and aggregate it in a way that’s easy to interpret.

This helps not only when we’re working on a data science project and need quick results but also in hackathons! When time is of the essence (and when is it not?), the GroupBy function in Pandas saves us a ton of effort by delivering super quick results in a matter of seconds. If you are familiar with groups in sql, this article will be even easier for you to understand!

Loving GroupBy already? In this tutorial, I will first explain the GroupBy function using an intuitive example before picking up a real-world dataset and implementing GroupBy in Python. Let’s begin aggregating!

Learning Objectives

• Understanding the syntax and functionality of the groupby() method is important for efficient data grouping.
• Familiarizing yourself with different types of aggregation functions available in pandas, including sum(), mean(), count(), max(), and min(), is necessary to perform effective data analysis.
• Knowing how to apply various aggregation functions to grouped data enables data analysts to extract useful insights from large data sets.

If you’re new to the world of Python and Pandas, you’ve come to the right place. Here are two popular free courses you should check out:

What Is the Pandas’ GroupBy Function?

Pandas’ Groupby operation is a powerful and versatile function in Python. It allows you to split your data into separate groups to perform computations for better analysis.

Let me take an example to elaborate on this. Let’s say we are trying to analyze the weight of a person in a city. We can easily get a fair idea of their weight by determining the mean weight of all the city dwellers. But here‘s a question – would the weight be affected by the gender of a person?

We can group the city dwellers into different gender groups and compute their mean weight. This would give us a better insight into the weight of a person living in the city. But we can probably get an even better picture if we further separate these gender groups into different age groups and then take their mean weight (because a teenage boy’s weight could differ from that of an adult male)!

You can see how separating people into separate groups and then applying a statistical value allows us to make better analyses than just looking at the statistical value of the entire population. This is what makes GroupBy so great!

GroupBy allows us to group our data based on different features and get a more accurate idea about your data. It is a one-stop shop for deriving deep insights from your data!

Understanding the Dataset & Problem Statement

We will be working with the Big Mart Sales dataset from our DataHack platform. It contains attributes related to the products sold at various stores of BigMart. The aim is to find out the sales of each product at a particular store.

Right, let’s import the libraries and explore the data:

import pandas as pd

import numpy as np

df = pd.read_csv(‘train_v9rqX0R.csv’)

Python Code:

We have some nan values in our dataset. These are mostly in the Item_Weight and Outlet_Size. I will handle the missing values for Outlet_Size right now, but we’ll handle the missing values for Item_Weight later in the article using the GroupBy function!

First Look at Pandas GroupBy

Let’s group the dataset based on the outlet location type using GroupBy, the syntax is simple we just have to use pandas dataframe.groupby:

View the code on Gist.

GroupBy has conveniently returned a DataFrameGroupBy object. It has split the data into separate groups. However, it won’t do anything unless it is being told explicitly to do so. So, let’s find the count of different outlet location types:

View the code on Gist.

We did not tell GroupBy which column we wanted it to apply the aggregation function on, so we applied it to multiple columns (all the relevant columns) and returned the output.

But fortunately, GroupBy object supports column indexing just like a pandas Dataframe!

So let’s find out the total sales for each location type:

View the code on Gist.

Here, GroupBy has returned a SeriesGroupBy object. No computation will be done until we specify the agg function:

View the code on Gist.

Awesome! Now, let’s understand the work behind the GroupBy function in Pandas.

The Split-Apply-Combine Strategy

You just saw how quickly you can get an insight into grouped data using the GroupBy function. But, behind the scenes, a lot is taking place, which is important to understand to gauge the true power of GroupBy.

GroupBy employs the Split-Apply-Combine strategy coined by Hadley Wickham in his paper in 2011. Using this strategy, a data analyst can break down a big problem into manageable parts, perform operations on individual parts and combine them back together to answer a specific question.

I want to show you how this strategy works in GroupBy by working with a sample dataset to get the average height for males and females in a group. Let’s create that dataset:

View the code on Gist.

Splitting the data into separate groups:

View the code on Gist.

Applying the operation that we need to perform (average in this case):

View the code on Gist.

170.0 174.5

Finally, combining the result to output a DataFrame:

View the code on Gist.

All these three steps can be achieved by using GroupBy with just a single line of code! Here’s how:

View the code on Gist.

Now that is smart! Have a look at how GroupBy did that in the image below:

You can see how GroupBy simplifies our task by doing all the work behind the scenes without us having to worry about a thing!

Now that you understand the Split-Apply-Combine strategy let’s dive deeper into the GroupBy function and unlock its full potential.

Loop Over GroupBy Groups

Remember the GroupBy object we created at the beginning of this article? Don’t worry, we’ll create it again:

View the code on Gist.

We can display the indices in each group by calling the groups on the GroupBy object:

View the code on Gist.

We can even iterate over all of the groups:

View the code on Gist.

But what if you want to get a specific group out of all the groups? Well, don’t worry. Pandas has a solution for that too.

Just provide the specific group name when calling get_group on the group object. Here, I want to check out the features for the ‘Tier 1’ group of locations only:

View the code on Gist.

Now isn’t that wonderful! You have the entire Tier 1 features to work with and derive wonderful insights! But wait, didn’t I say that GroupBy is lazy and doesn’t do anything unless explicitly specified? Alright then, let’s see GroupBy in action with the aggregate functions.

Applying Functions to GroupBy Groups

The apply step is unequivocally the most important step of a GroupBy function where we can perform a variety of operations using aggregation, transformation, filtration, or even with your own function!

Let’s have a look at these in detail.

Aggregation

We have looked at some aggregation functions in the article so far, such as mean, mode, and sum. These perform statistical operations on a set of data. Have a glance at all the aggregate functions in the Pandas package:

• count() – Number of non-null observations
• sum() – Sum of values
• mean() – Mean of values
• median() – Arithmetic median of values
• min() – Minimum
• max() – Maximum
• mode() – Mode
• std() – Standard deviation
• var() – Variance

But the agg() function in Pandas gives us the flexibility to perform several statistical computations all at once! Here is how it works:

View the code on Gist.

We can even run GroupBy with multiple indexes to get better insights from our data:

View the code on Gist.

Notice that I have used different aggregation functions for different column names by passing them in a dictionary with the corresponding operation to be performed. This allowed me to group and apply computations on nominal and numeric features simultaneously.

Also, I have changed the value of the as_index parameter to False. This way, the grouped index would not be output as an index.

We can even rename the aggregated columns to improve their comprehensibility, and we get a multi-index dataframe:

View the code on Gist.

It is amazing how a name change can improve the understandability of the output!

Transformation

Transformation allows us to perform some computation on the groups as a whole and then return the combined DataFrame. This is done using the transform() function.

We will try to compute the null values in the Item_Weight column using the transform() function.

The Item_Fat_Content and Item_Type will affect the Item_Weight, don’t you think? So, let’s group the DataFrame by these columns and handle the missing weights using the mean of these groups:

View the code on Gist.

“Using the Transform function, a DataFrame calls a function on itself to produce a DataFrame with transformed values.”

You can read more about the transform() function in this article.

Filtration

Filtration allows us to discard certain values based on computation and return only a subset of the group. We can do this using the filter() function in Pandas.

Let’s take a look at the number of rows in our DataFrame presently:

View the code on Gist.

(8523, 12)

If I wanted only those groups that have item weights within 3 standard deviations, I could use the filter function to do the job:

View the code on Gist.

(8510, 12)

GroupBy has conveniently returned a DataFrame with only those groups that have Item_Weight less than 3 standard deviations.

Applying Our Own Functions

Pandas’ apply() function applies a function along an axis of the DataFrame. When using it with the GroupBy function, we can apply any function to the grouped result.

For example, if I wanted to center the Item_MRP values with the mean of their establishment year group, I could use the apply() function to do just that”:

View the code on Gist.

Here, the values have been centered, and you can check whether the item was sold at an MRP above or below the mean MRP for that year.

Conclusion

I’m sure you can see how amazing the GroupBy function is and how useful it can be for analyzing your data. I hope this article helped you understand the function better! But practice makes perfect, so start with the super impressive datasets on our very own DataHack platform. Moving forward, you can read about how you can analyze your data using a pivot table in Pandas.

Key Takeaways

• Groupby() is a powerful function in pandas that allows you to group data based on a single column or more.
• You can apply many operations to a groupby object, including aggregation functions like sum(), mean(), and count(), as well as lambda function and other custom functions using apply().
• The resulting output of a groupby() operation can be a pandas Series or dataframe, depending on the operation and data structure.

Frequently Asked Questions

Q1. Can we group by an aggregate function?

A. Yes, we can groupby an aggregate function in pandas. To group by an aggregate function in Pandas, we can use the groupby method followed by the aggregate function we want to apply.

Q2. Can we use groupby without aggregate function in pandas?

A. Yes, we can use groupby without an aggregate function in pandas. In this case, groupby will return a GroupBy object that can be used to perform further operations.

Q3. What is the difference between groupby and groupby agg?

A. Groupby and groupby agg are both methods in pandas that allow us to group a DataFrame by one or more columns and perform operations on the resulting groups. However, there are some important differences between the two methods.
Groupby returns a GroupBy object, which can be used to perform a variety of operations on the groups. Whereas groupby agg is a method specifically for performing aggregation operations on a grouped DataFrame. It allows us to specify one or more aggregation functions to apply to each group and returns a DataFrame containing the results.

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