Subset DataFrame by one column then value in another column

Introduction

In this article, we will discuss how to subset a pandas DataFrame using two columns. The first column is used as the grouping variable, and the second column is used to select the top N values for each group.

Problem Statement

Given a DataFrame TeamFourFactorsRAPM with 44 columns, we want to subset it based on two columns: teamName (consisting of team names for all players in the NBA) and mp (consisting of how many minutes a player played throughout the season). We want to get the 8 players with the highest minutes played, for every team.

Solution

To solve this problem, we can use the group_by function from pandas to group the DataFrame by teamName, and then use the slice_max function to select the top N values (in this case, N=8) for each group. Finally, we can ungroup the DataFrame using the ungroup function.

Here is the code:

result <- TeamFourFactorsRAPM %>%
  group_by(teamName) %>%
  slice_max(mp, n = 8) %>%
  ungroup

This will give us a new DataFrame result that contains only the rows with the top 8 minutes played for each team.

Understanding the Code

Let’s break down the code:

group_by(teamName): This function groups the DataFrame by the values in the teamName column. The resulting group is called a “group”.
slice_max(mp, n = 8): This function selects the top N values (in this case, N=8) for the specified column (mp). The result is a new group that contains only these top N values.
ungroup: This function removes the grouping from the resulting DataFrame. Without this step, we would have a group by object with multiple groups.

Using Dplyr

The code above uses the dplyr package to perform the necessary operations on the DataFrame. The dplyr package is a popular package for data manipulation in R that provides an efficient and consistent way of performing various data operations such as grouping, filtering, sorting, etc.

Understanding the Data

To understand how this code works, we need to have some knowledge about the structure of our DataFrame TeamFourFactorsRAPM. The DataFrame has 44 columns, but only two are relevant for this problem: teamName and mp.

teamName: This column contains the names of all teams in the NBA. For each team, there is at least one row.
mp: This column represents the number of minutes played by a player throughout the season. We want to find the top 8 players with the highest minutes played for every team.

The DataFrame also has many other columns that represent different types of basketball statistics such as LA_RAPM, RA_EFG, etc.

How It Works

Here’s how the code works step by step:

The group_by function groups all rows in the DataFrame by the values in the teamName column. This creates multiple groups, one for each unique value of teamName.
Next, we use the slice_max function to select the top 8 values for the mp column within each group.
Finally, the ungroup function removes the grouping from the DataFrame, and we are left with a new DataFrame that contains only the rows with the top 8 minutes played for each team.

Conclusion

In this article, we discussed how to subset a pandas DataFrame by one column then value in another column using the dplyr package. We used the group_by, slice_max, and ungroup functions to achieve this task.

Understanding Group By and Slice Max Functions

The group by function groups rows based on the values of one or more columns. The slice max function returns the top N values for a specified column within each group.

Group by Function

The group by function is used to divide data into groups based on the value of some columns. This can be useful when we want to perform operations that are specific to certain groups of data.

Slice Max Function

The slice max function returns the top N values for a specified column within each group. It’s often used in combination with the group by function to get the top values for a particular column across multiple groups.

Ungroup Function

Finally, the ungroup function is used to remove grouping from an object such as a data frame that has been grouped using the group_by function.

Conclusion

In this article, we learned how to subset a pandas DataFrame by one column then value in another column. We also learned about group by and slice max functions.

Understanding How It Works

To understand how the code works, it’s essential to have some knowledge of data structures such as DataFrames and Series, as well as functions like group_by, slice_max, and ungroup.

The group by function groups all rows in a DataFrame into groups based on the values of one or more columns.

Once we’ve grouped our data using the group_by function, we can use the slice_max function to get the top N values for a specified column within each group.

Finally, after we’ve selected our desired values with the slice max function, we can ungroup our DataFrame by removing grouping from it with the ungroup function.

Last modified on 2025-04-11