How to Leverage Pandas GroupBy for Data Analysis

The pandas library is a giant in the data analysis space. It is such a huge library with many functions. There are some functions that are more widely used in data analysis than others. One of the most powerful pandas functions that you mostly cannot do without in the data analysis process is GroupBy. The GroupBy function is a powerful tool for data manipulation and analysis, allowing you to split, apply, and combine data in various ways. This function is one of those functions that you must put into use to fully appreciate its capabilities. In this article, we will explore 5 questions that can be answered using the GroupBy function, demonstrating its versatility and depth.

How GroupBy Works

Before we dive into the questions, let's briefly talk about how GroupBy works. There are many everyday tasks that are comparable to the workings of this function. For example, after laundry, you sort your clothes into different piles based on color or type. Once the sorting is over, further operations can be performed on the separated clothes, like packing them in different areas of the closet. GroupBy is also used to group data by one or more columns. Once grouped, we can perform various operations on each group, such as calculating summary statistics (like mean, median, or standard deviation), applying functions, or filtering data. This makes it easier to analyze and understand patterns within the data. Let's look at an example:

In this code, we have a DataFrame created from the student_info dictionary. We want to know the average grade per subject. We use GroupBy to group the data by subject. This creates separate groups for 'Math' and 'Science'. We apply the mean() function to the 'Grade' column within each group. This calculates the average grade for each subject. Now, let's tackle the five questions using this function:

1. For each gender, which major has the highest total study hours per week?

For these questions, we are going to use a dataset from Kaggle, which you can download to follow along. Let's first view the data:

To answer this question, we have to group the data by the 'Gender' and 'Major' columns and sum the 'StudyHoursPerWeek' column. Here is the solution below:

In this code, we create a function that we use to solve the question. The function is simply for organizing code. First, we group the data by the two columns: 'Gender' and 'Major'. Then we sum the StudyHoursPerWeek for each group. Since the question is asking for highest values within each group, we further group the previously calculated sums by 'Gender (level 0) and find the index that corresponds to the major with the maximum value within each group using the idxmax() method.

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2. For each major, what is the percentage contribution of its total study hours to the overall total study hours?

This question is asking that we return the percentage of the number of hours that each major is contributing to the total study hours. This means that we have to group the data by the 'Major' column and sum the study hours per week for each major and find the percentage of each major to the overall total. Here is the solution below:

Here, first we group the data by the 'Major' column and sum the study hours for each group. Then, we calculate the percentage contribution of each group by dividing its total study hours by the sum of all study hours and multiplying by 100. The apply() method is used to format the results to two decimal places. This returns a Series with major names as the index and their corresponding percentage contributions as values.

3. Which gender has the highest overall average attendance rate? Return the gender and the average attendance rate.

To answer this question, first, we need to group the data by the 'Gender' column and find attendance mean for each group. Then, we need to return the group with the highest average attendance and its value. Here is the solution below:

Here in this code, we first group the data by the 'Gender' column and calculate the mean attendance rate for each gender. Then, using idxmax(), we identify the gender with the highest average attendance rate. The idxmax() method returns the index label of the maximum value in a Series. The max() function is returning the maximum average attendance value.

4. What percentage of students have part-time jobs, and what percentage have no part-time jobs?

For this question, we are going to concentrate on the 'PartTimeJob' column. This column has 'Yes' and 'No' values for students with part-time jobs and students with no part-time jobs, respectively. We are going to group the data by the 'PartTimeJob' column and use the count method to count the total for each group.

In this code, we basically first group the data by the 'PartTimeJob' column and count the number of students in each category (having a part-time job or not). The percentage of students in each category is calculated by dividing the count of each category by the total number of students and multiplying by 100. The results are formatted to two decimal points.

5. For each gender, what is the mean GPA and variance of age?

This question is asking that we calculate the mean GPA and variance of age for a given group of data. This means that we are going to concentrate on the 'GPA' and 'Age' and 'Gender' columns. Since the question is asking for two different calculations on the two columns, we are going to use a custom function to perform these calculations. Using a custom function in this case simply demonstrates an approach that you can use to perform complex transformations on grouped data. This approach of using a custom function provides flexibility beyond standard aggregation functions as it allows to implement custom logic or conditions within the calculations.

In this code, the df.groupby('Gender').apply(custom_stats) part of the code groups the DataFrame by the 'Gender' column, then applies the custom_stats function to each group. This means the function is called once for each unique gender, and the calculated mean GPA and variance for that gender are returned. If you do not want to use a custom function, then you can use the agg() method. See below:

You can see that we achieve the same results.

Conclusion

This few examples demonstrate that the groupby method is a fundamental tool in data analysis, offering versatile ways to categorize and analyze data. Grouping data is major operation in data analysis that cannot be avoided. Like most functions in pandas, you have to use them often to appreciate their capabilities. The book "50 Days of Data Analysis with Python: The Ultimate Challenge Book for Beginners" offers many challenges that cover the group by function.

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