Pandas Grouping and Time Elapsed Calculation

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In this article, we will explore how to calculate the total number of hours elapsed for each group in a pandas DataFrame when the data is grouped by mode. We’ll use a real-world example with time series data to illustrate the concept.

Introduction

When dealing with time series data, it’s common to have multiple activities occurring concurrently. In such cases, we need to group the data based on these activities and calculate the total time elapsed for each activity. This article will demonstrate how to achieve this using pandas in Python.

Importing Libraries and Loading Data

We start by importing the necessary libraries:

import pandas as pd

# Load the dataset
df = pd.read_csv(io.StringIO(""" 
                      Time                     MODE
Index                                              
0      2020-10-24 16:01:01         Towing Operation
1      2020-10-24 15:59:01         Towing Operation
2      2020-10-24 14:59:02         Towing Operation
3      2019-05-24 18:06:01  Standby at Mooring Buoy
4      2019-05-24 18:05:01  Standby at Mooring Buoy
5      2019-05-24 18:03:01  Standby at Mooring Buoy"""), sep="\s\s+", engine="python")

Data Preparation and Sorting

Before we can group the data by mode, we need to ensure that it’s sorted by time. This is crucial because we want to identify changes in mode and calculate the total time elapsed for each activity.

# Convert the 'Time' column to datetime format
df["Time"] = pd.to_datetime(df["Time"])

# Sort the data by 'Time'
df = df.sort_values("Time")

Grouping Data by Mode

Now, we group the data by mode using a custom grouping method. We create a new column grp that indicates whether there’s a change in mode or not. If the current mode is different from the previous one, it sets grp to 1; otherwise, it remains 0.

# Create a new column 'grp' that indicates changes in mode
df["grp"] = pd.Series(~df["MODE"].eq(df["MODE"].shift()), name="grp")

# Group the data by 'grp' and calculate aggregated values
df2 = df.groupby(pd.Series(~df["MODE"].eq(df["MODE"].shift()), name="grp").cumsum()).agg(
    MODE=("MODE", "first"),
    count=("MODE", "size"),
    start=("Time", "first"),
    end=("Time","last")
)

Calculating Total Time Elapsed

Finally, we calculate the total time elapsed for each activity by subtracting the start time from the end time.

# Calculate the total time elapsed in hours
df2["hours"] = (df2["end"] - df2["start"]).apply(lambda td: td.seconds / (60 * 60))

Result

Here is the resulting DataFrame with the calculated total time elapsed for each activity:

In this example, we grouped the data by mode and calculated the total time elapsed for each activity. We used a custom grouping method to identify changes in mode and applied it to our DataFrame.

Conclusion

In conclusion, calculating the total number of hours per grouped activity can be achieved using pandas in Python. By sorting the data by time, creating a new column to indicate changes in mode, and applying a custom aggregation method, we can easily calculate the total time elapsed for each activity. This technique is applicable to various scenarios where data needs to be grouped based on activities and time elapsed is required.

Additional Resources

• Pandas Documentation
• Python Time Series Library
• GroupBy Aggregation in pandas

Last modified on 2023-10-24