Understanding Date and Time Manipulation in pandas

As a data analyst or scientist, working with date and time data is an essential part of your job. pandas is a powerful library that provides data structures and functions to efficiently handle structured data. However, when it comes to manipulating dates and times, pandas can be tricky to use. In this article, we’ll explore why you can’t add two datetime objects in pandas and how you can achieve the desired result using timedelta.

Introduction to Date and Time Data Types in pandas

In pandas, there are several data types for date and time information. The most common ones are datetime64[ns] (seconds since Unix epoch) and timedelta64[ns].

• datetime64[ns] represents the number of seconds that have elapsed since January 1, 1970 at 00:00:00 UTC.
• timedelta64[ns] represents a duration between two dates or times.

Converting to datetime and timedelta

When you convert your time column to datetime64[ns] using pd.to_datetime, it assumes the date part is always the current year. However, when you have milliseconds in another column, this can cause issues if not handled correctly.

# Convert time to datetime
df['Time'] = pd.to_datetime(df['Time'], format='%H:%M:%S')

To handle milliseconds, we need to convert them to a format that pandas understands. This is done using the pd.to_timedelta function.

# Convert ms to timedelta
df['ms'] = pd.to_timedelta(df['ms'], unit='ms')

The Problem with Adding Datetime Objects

When you try to add two datetime objects in pandas, it throws an error because they are not the same data type. datetime64[ns] and timedelta64[ns] represent different things: one is a point in time, while the other represents a duration.

# Error when adding datetime objects
df['Time'] = df['Time'] + df['ms']

To fix this issue, we need to convert both datetime and timedelta columns to timedelta64[ns].

Using timedelta for Date Manipulation

One way to achieve the desired result is by using a timedelta. We can calculate the total duration between the start of the day (00:00:00) and the current time, including milliseconds.

# Convert time to timedelta64[ns]
df['Time'] = pd.to_timedelta(df['Time'])

Now, we can add the millisecond column to the timedelta column using the following code:

# Calculate total duration in seconds
df['Total Duration (s)'] = df['Time'].apply(lambda x: x.total_seconds())

Then, append the millisecond values to the seconds part of the total duration.

# Append milliseconds to the total duration
df['Total Duration (ms)'] = df['ms']

The Ideal Solution

However, there’s a cleaner way to achieve this result without using timedelta64[ns]. We can simply convert both time columns to seconds and then concatenate them.

# Convert time to seconds
df['Time (s)'] = df['Time'].apply(lambda x: int(x.timestamp()))

Now, we can add the millisecond values directly.

# Add milliseconds to the total duration in seconds
df['Total Duration (ms)'] = df['Time (s)'] + df['ms']

Conclusion

Date and time manipulation can be complex when working with pandas. While it may seem straightforward at first, the nuances of how pandas handles datetime objects can lead to unexpected results. In this article, we explored why adding two datetime objects in pandas is not possible and showed you an alternative approach using timedelta.

Last modified on 2025-03-14