datetime to timedelta in pandas DataFrame & Vice Versa in Python (Examples)
In this article you’ll learn how to convert datetime to timedelta in a pandas DataFrame and vice versa in the Python programming language.
The tutorial looks as follows:
Here’s how to do it:
Example Data & Libraries
First, let’s load the pandas library and the datetime module:
import pandas as pd # importing pandas library import datetime # importing datetime module
Next, let’s also create some example data in Python:
df = pd.DataFrame.from_dict({"col_1" : [datetime.datetime(2000, 10, 8, 15, 21, 5), # DataFrame initialization datetime.datetime(2005, 2, 8, 18, 21, 5), datetime.datetime(2010, 5, 8, 3, 10, 19), datetime.datetime(2017, 7, 8, 8, 4, 2)]}) print(df) # printing DataFrame # col_1 #0 2000-10-08 15:21:05 #1 2005-02-08 18:21:05 #2 2010-05-08 03:10:19 #3 2017-07-08 08:04:02
Since we have our DataFrame of datetime objects, we can move on to the examples now.
Example 1: Convert a Column of datetime Objects to timedelta
Since the timedelta objects are meant to show the difference between two dates, first we need to create a datetime object for a random date. See dt_origin below.
dt_origin = datetime.datetime(2000, 1, 1, 0, 0, 0) # start_date initialization
Now we can simply define our function which subtracts the two datetime objects to generate a timedelta object.
def datetime_to_timedelta(dt): # defining the dt to td function return dt - dt_origin # generating and returning the timedelta object
After defining our function, we can plug it into the apply() function to convert the datetime objects in col_1 to the timedelta objects in col_2.
df["col_2"] = df["col_1"].apply(lambda dt: datetime_to_timedelta(dt)) # creating the column of timedelta objects print(df) # printing the updated DataFrame # col_1 col_2 #0 2000-10-08 15:21:05 281 days 15:21:05 #1 2005-02-08 18:21:05 1865 days 18:21:05 #2 2010-05-08 03:10:19 3780 days 03:10:19 #3 2017-07-08 08:04:02 6398 days 08:04:02
Example 2: Convert a Column of timedelta Objects to datetime
In this case, we do the same thing but in reverse order. We add dt_origin to the td values to get the datetime objects in the beginning.
def timedelta_to_datetime(td): # defining the td to dt function dt = dt_origin + td # generating the datetime object return dt # returning the datetime object
After defining the function timedelta_to_datetime(), we can plug it into the apply() function to convert the timedelta objects in col_2 to the datetime objects in col_3. Please be aware that we obtained an identical column with column col_1.
df["col_3"] = df["col_2"].apply(lambda td: timedelta_to_datetime(td)) # creating the column of timedelta objects print(df) # printing the updated DataFrame # col_1 col_2 col_3 #0 2000-10-08 15:21:05 281 days 15:21:05 2000-10-08 15:21:05 #1 2005-02-08 18:21:05 1865 days 18:21:05 2005-02-08 18:21:05 #2 2010-05-08 03:10:19 3780 days 03:10:19 2010-05-08 03:10:19 #3 2017-07-08 08:04:02 6398 days 08:04:02 2017-07-08 08:04:02
Video, Further Resources & Summary
Do you need further information on the Python programming codes of this article? Then you may want to have a look at the following video on my YouTube channel. I demonstrate the Python programming codes of this tutorial in the video.
The YouTube video will be added soon.
Furthermore, you might want to have a look at some of the other tutorials on my website:
- Dates & Times in Python – datetime Module
- Introduction to timedelta Objects in Python
- Change datetime Format in pandas DataFrame in Python
- Convert Epoch Time to datetime Object & Vice Versa in Python
- Convert datetime Object to Date & Vice Versa in Python
- Convert pandas DataFrame Column to datetime in Python
- Python Programming Language
In summary: In this tutorial, I have explained how to transform datetime objects to timedelta objects in a pandas DataFrame and back in the Python programming language. Don’t hesitate to let me know in the comments below, in case you have any additional questions.
This page was created in collaboration with Ömer Ekiz. Have a look at Ömer’s author page to get more information about his professional background, a list of all his tutorials, as well as an overview on his other tasks on Statistics Globe.
I’m Joachim Schork. On this website, I provide statistics tutorials as well as code in Python and R programming.
