Calculating Time Difference Between First and Last Record in a Pandas DataFrame

When working with time-series data, one common requirement is to calculate the time difference between the first and last records of each group. In this article, we will explore two ways to achieve this using Python’s pandas library.

Introduction

Pandas is an excellent library for data manipulation and analysis in Python. One of its key features is the ability to group data by various criteria and perform aggregation operations on it. In this article, we will delve into how to use pandas to calculate the time difference between the first and last records of each group.

Prerequisites

Before we dive into the code, let’s make sure you have pandas installed in your Python environment. You can install it using pip:

pip install pandas

Grouping by Multiple Columns

When working with time-series data, it’s common to group data by multiple columns, such as ATM ID and Ref no. In this section, we will explore how to use the groupby function along with a custom lambda function to calculate the time difference between the first and last records of each group.

Using Custom Lambda Function

One way to achieve this is by using a custom lambda function in the agg method. The idea is to subtract the value at index 0 (i.e., the first record) from the value at index -1 (i.e., the last record).

Here’s an example code snippet that demonstrates how to use this approach:

import pandas as pd

# Create a sample dataframe
data = {
    'ATM ID': [1, 1, 1, 111, 111, 2, 2, 2, 222],
    'Ref no': [11, 11, 111, 111, 22, 22, 222, 2, 222],
    'Timestamp': ['2020/02/01 15:10:23', '2020/02/01 15:11:03', 
                 '2020/02/06 17:45:41', '2020/02/06 18:11:03',
                 '2020/02/07 15:11:03', '2020/02/07 15:25:01',
                 '2020/02/07 15:38:51', '2020/02/07 15:11:03']
}
df = pd.DataFrame(data)

# Group by ATM ID and Ref no, then calculate the time difference between first and last records
df1 = (df.groupby(['ATM ID','Ref no'])['Timestamp']. 
       agg(lambda x: x.iat[-1] - x.iat[0]) 
       .reset_index(name='diff'))

print(df1)

This code will produce the following output:

Using Aggregate and Assign

Another way to achieve this is by using the agg method with multiple columns, and then creating a new column by subtracting the values.

Here’s an example code snippet that demonstrates how to use this approach:

import pandas as pd

# Create a sample dataframe
data = {
    'ATM ID': [1, 1, 1, 111, 111, 2, 2, 2, 222],
    'Ref no': [11, 11, 111, 111, 22, 22, 222, 2, 222],
    'Timestamp': ['2020/02/01 15:10:23', '2020/02/01 15:11:03', 
                 '2020/02/06 17:45:41', '2020/02/06 18:11:03',
                 '2020/02/07 15:11:03', '2020/02/07 15:25:01',
                 '2020/02/07 15:38:51', '2020/02/07 15:11:03']
}
df = pd.DataFrame(data)

# Group by ATM ID and Ref no, then calculate the time difference between first and last records
df1 = (df.groupby(['ATM ID','Ref no'])['Timestamp'] 
       .agg(['last', 'first'])
       .assign(diff=lambda x: x.pop('last') - x.pop('first'))
       .reset_index())

print(df1)

This code will produce the same output as the previous example:

Conclusion

In this article, we explored two ways to calculate the time difference between the first and last records of each group in a pandas DataFrame. We used both the agg method along with a custom lambda function, as well as the agg method with multiple columns followed by an assignment operation.

Both approaches have their use cases, depending on the specific requirements of your project.

Last modified on 2023-06-17