Adding and subtracting seconds column from timestamp column using pandas
by vsasikalabe Updated: Mar 1, 2023
This code helps in working with time data in a Pandas DataFrame by providing a way to add and subtract time durations from timestamp data. This is useful for performing various calculations and analysis involving time data, such as calculating the duration of events, calculating the time between events, or creating time-based plots and visualizations. The code also demonstrates how to use pandas functions and methods such as pd.to_timedelta, dt.strftime, and str.rstrip to manipulate and format time data in a Pandas DataFrame. By providing this functionality, the code can help streamline and simplify time-based data analysis tasks in Python.
These are several Pandas and Python functions and methods used in the code snippet provided:
- pd.to_timedelta: This function converts a string or numerical value representing a time duration into a pandas Timedelta object. In the code, it is used to convert the values in the "Seconds" column into Timedelta objects.
- dt.strftime: This method formats a datetime object as a string with a specified format string. In the code, it is used to format the final datetime object as a string with the format '%H:%M:%S:%f'.
- str.rstrip: This method removes trailing characters from a string. In the code, it is used to remove any trailing zeros from the resulting string format of the datetime object.
- pd.Timestamp: This class represents a specific moment in time with a high level of precision. In the code, it is used to convert the "desired_date" column, which combines the "Date" and "timestamp" columns into a datetime object, to a pandas Timestamp object.
This process is useful when working with time data in a Pandas DataFrame because it allows you to manipulate the time data to perform various calculations and analysis. For example, you could use this code to calculate the end time of an event by adding the duration of the event to the start time. Alternatively, you could use this code to calculate the time elapsed between two events by subtracting the start time of the second event from the end time of the first event. Overall, this code provides a powerful tool for working with time data in a Pandas DataFrame.
Here is the example of how to Adding and subtracting seconds column from timestamp column using pandas:
Preview of the output that you will get on running this code from your IDE.
In this solution we used pandas library of python.
df = example_table df['desired_date'] = pd.to_datetime(df['Date'] + ' ' + df['timestamp'],format='%m/%d/%y %H:%M:%S:%f') df['desired_date'] = ( df['desired_date'] + pd.to_timedelta(df['Seconds']) ).dt.strftime('%H:%M:%S:%f').str.rstrip('0') print(df) Date Start_Time timestamp Seconds ID desired_date 0 11/19/20 9:40:28 9:40:00:0 00:00:00.2 101 09:40:00:2 1 12/22/20 9:29:28 9:29:28:15 00:10:28.0 102 09:39:56:15 2 2/17/21 9:20:20 9:20:20:2 0:00:05.2 206 09:20:25:4
Follow the steps carefully to get the output easily.
- Install pandas on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add required dependencies and import them in Python file.
- Run the file to generate the output.
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I found this code snippet by searching for ' Adding and subtracting seconds column from timestamp column using pandas 'in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2022.3.
- The solution is tested on Python 3.11.1
- Pandas version-1.5.2.
Using this solution, we are able to add and subtract seconds column from timestamp column using pandas with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to add and subtract seconds column from timestamp column using pandas.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python 37360 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)
If you do not have pandas library that is required to run this code, you can install it by clicking on the above link.
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