Relative Minimum Values in Pandas

Introduction

Pandas is a powerful data analysis library for Python that provides efficient data structures and operations for working with structured data, including tabular data such as spreadsheets and SQL tables. In this article, we will explore how to calculate the relative minimum values in pandas.

Problem Statement

Given a pandas DataFrame df with columns Race_ID, Athlete_ID, and Finish_time, we want to add a new column Relative_time@t-1 which is the Athlete’s Finish_time in the last race relative to the fastest time in the last race.

Data Preparation

First, let us prepare our data. We will create a pandas DataFrame with the given data.

data = [[1,1,56.1,'56.3/56.2'],
        [1,3,60.2,'56.4/56.2'],
        [1,2,57.1,'56.2/56.2'],
        [1,4,57.2,'56.5/56.2'],
        [2,2,56.2,'62.1/60'],
        [2,1,56.3,'61.2/60'],
        [2,3,56.4,'60.4/60'],
        [2,4,56.5,'60/60'],
        [3,1,61.2,'54/52'],
        [3,2,62.1,'55/52'],
        [3,3,60.4,'53/52'],
        [3,4,60,'52/52'],
        [4,2,55,'0'],
        [4,1,54,'0'],
        [4,3,53,'0'],
        [4,4,52,'0']]

df = pd.DataFrame(data,columns=['Race_ID','Athlete_ID','Finish_time','Relative_time@t-1'],dtype=float)

Sorting the Data

To calculate the relative minimum values, we need to sort our data by Race_ID and Athlete_ID. We can use the sort_values function to do this.

df.sort_values(by = ['Race_ID', 'Athlete_ID'], ascending=[True, True], inplace=True)

Calculating the Fastest Time for Each Athlete

Next, we need to calculate the fastest time for each athlete. We can use the groupby function and then apply the min function to each group.

df['Fastest_time'] = df.groupby('Athlete_ID')['Finish_time'].transform(lambda x: x.min())

Calculating the Relative Minimum Values

Now that we have the fastest time for each athlete, we can calculate the relative minimum values. We need to shift the fastest times by 1 row and then divide our Finish_time column by this value.

df['Relative_time@t-1'] = (df.groupby('Athlete_ID')['Finish_time']
                          .shift(-1)
                          .div(df['Fastest_time'].map(
                              df.groupby('Race_ID')['Finish_time']
                              .min()
                              .shift(-1)))
                          .fillna(0))

Example Use Case

Let’s create a new DataFrame with the calculated Relative_time@t-1 column.

new_df = pd.DataFrame({'Athlete_ID': [1, 2, 3], 
                       'Finish_time': [56.1, 56.2, 60.4]}, 
                      columns=['Athlete_ID', 'Finish_time'])
new_df['Relative_time@t-1'] = new_df.groupby('Athlete_ID')['Finish_time'].transform(lambda x: (x - x.min()) / (x.max() - x.min()))
print(new_df)

Output:

Conclusion

In this article, we have seen how to calculate the relative minimum values in pandas using the groupby and min functions. We hope that this helps you with your data analysis tasks!

Last modified on 2023-12-05