How to Convert Time Durations to Numeric (Hours, Minutes, and Seconds) in Polars

Working with time durations in data analysis can be tricky, especially when these durations are represented in various formats like HH:MM:SS or other string representations. Converting these time durations into numeric values, such as total seconds, minutes, or hours, can simplify calculations and further analysis. Polars, a high-performance DataFrame library, provides powerful ways to handle and convert time durations.

In this article, we will discuss how to convert time durations into numeric values using Polars in Python. By the end of this guide, we'll be able to seamlessly handle time data for any numerical analysis.

Introduction to Time Durations in Data

Time durations are typically represented as strings (e.g., "02:30:15" for 2 hours, 30 minutes, and 15 seconds) in datasets. However, for calculations like determining averages, or performing comparisons, you need these values in a numeric form. Polars makes it easy to convert these durations into seconds, minutes, or hours, providing us with flexibility based on your needs.

Loading Data in Polars

Let's first load a sample DataFrame using Polars. We'll create a DataFrame with time durations stored as strings.

import polars as pl

# Sample DataFrame with time durations as strings
df = pl.DataFrame({
    "duration": ["02:30:15", "01:45:30", "00:15:00"]
})

print(df)

1. Converting Duration to Seconds

The most common way to convert time durations into a numeric form is by converting them to total seconds. This allows us to perform mathematical operations and comparisons easily.

Here's how we can achieve this using Polars:

# Function to convert HH:MM:SS to total seconds
def duration_to_seconds(duration):
    hours, minutes, seconds = map(int, duration.split(':'))
    return hours * 3600 + minutes * 60 + seconds

# Apply the conversion to the 'duration' column
df = df.with_columns([
    pl.col("duration").apply(duration_to_seconds).alias("total_seconds")
])

print(df)

In this example, the duration_to_seconds function splits the time string into hours, minutes, and seconds, and calculates the total duration in seconds.

2. Converting Duration to Minutes

To convert the duration into total minutes, simply divide the total seconds by 60:

# Convert to total minutes
df = df.with_columns([
    (pl.col("total_seconds") / 60).alias("total_minutes")
])

print(df)

Converting Duration to Hours

Similarly, to convert the duration into hours, divide the total seconds by 3600:

# Convert to total hours
df = df.with_columns([
    (pl.col("total_seconds") / 3600).alias("total_hours")
])

print(df)

Now, your durations have been converted into total hours for easy analysis.

Common Issues and Troubleshooting

a. Incorrect Time Format

If your time strings are not in the standard HH:MM:SS format (e.g., they include milliseconds or only have minutes and seconds), you'll need to modify the parsing function accordingly. Always ensure that the format matches your input data.

b. Handling Missing or Invalid Data

Ensure that your dataset doesn't contain missing values or invalid time formats. Polars offers various functions like fill_null() to handle missing data.

c. Performance Considerations

When working with large datasets, the apply() function may not be the most efficient. Polars is optimized for vectorized operations, so try to perform calculations without applying custom Python functions if possible.

Conclusion

Converting time durations to numeric values is essential for data analysis, enabling easier manipulation and statistical calculations. Using Polars, we can efficiently transform time strings into seconds, minutes, or hours. This guide has shown us how to load data, parse time strings, and perform the conversions in Polars, along with potential issues we may encounter.