As a data scientist working with time-series data for one of my clients in Chicago, I recently faced a challenge where I needed to convert date strings to timestamps. After experimenting with different methods, I discovered the most effective ways to convert a string to a Timestamp. I will explain how to convert a string to a timestamp in Python in this tutorial with suitable examples.
Convert a String to a Timestamp in Python
When dealing with date and time data, it’s common to encounter dates stored as strings. However, to perform time-series analysis or calculations, we often need to convert these string representations to timestamps. Python provides built-in libraries and functions to handle this conversion seamlessly.
For example, let’s say you have a dataset containing user registration dates for your US-based application. The dates are stored as strings in the format “YYYY-MM-DD HH:MM: SS”. To analyze user behavior over time, you need to convert these string dates to timestamps.
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Use strptime() in Python
Python’s module datetime offers a convenient method strptime() that allows you to convert a string to a datetime object according to the DataCamp tutorial. Here’s how you can use it:
from datetime import datetime
date_string = "2023-05-15 09:30:00"
datetime_object = datetime.strptime(date_string, "%Y-%m-%d %H:%M:%S")
timestamp = datetime_object.timestamp()
print(timestamp)Output:
1684123200.0I have executed the above example code and added the screenshot below.

In this example, we have a date string "2023-05-15 09:30:00" representing May 15, 2023, at 9:30 AM. We use the strptime() method to parse the string and specify the format using directives like %Y for the year, %m for the month, %d for the day, %H for the hour, %M for the minutes, and %S for the seconds.
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Handle Different Date Formats
In real-world scenarios, date strings can come in various formats. Python’s strptime() method allows you to handle different formats by specifying the appropriate directives. Here are a few common examples:
Example 1: MM/DD/YYYY Format
from datetime import datetime
date_string = "05/15/2023"
datetime_object = datetime.strptime(date_string, "%m/%d/%Y")
timestamp = datetime_object.timestamp()
print(timestamp)Output:
1684089000.0I have executed the above example code and added the screenshot below.

In this example, the date string follows the format “MM/DD/YYYY”. We use the directives %m for the month, %d for the day, and %Y for the year to parse the string correctly.
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Example 2: YYYY-MM-DD Format
from datetime import datetime
date_string = "2023-05-15"
datetime_object = datetime.strptime(date_string, "%Y-%m-%d")
timestamp = datetime_object.timestamp()
print(timestamp)Output:
1684089000.0I have executed the above example code and added the screenshot below.

Here, the date string is in the format “YYYY-MM-DD”. We adjust the directives accordingly, using %Y for the year, %m for the month, and %d for the day.
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Convert Timestamp to Date String
Sometimes, you may need to convert a timestamp back to a human-readable date string. Python’s datetime module provides the fromtimestamp() method for this purpose.
from datetime import datetime
timestamp = 1684144200.0
datetime_object = datetime.fromtimestamp(timestamp)
date_string = datetime_object.strftime("%Y-%m-%d %H:%M:%S")
print(date_string)In this example, we have a timestamp 1684144200.0. We use the fromtimestamp() method to convert it back to a datetime object. Then, we use the strftime() method to format the datetime object into a desired string representation. The directives used strftime() are similar to those used in strptime().
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Conclusion
In this tutorial, I have explained how to convert a string to a timestamp in Python. I explained strptime() method, we can easily parse date strings in various formats and convert them to timestamps. Additionally, you can use the fromtimestamp() method to convert timestamps back to human-readable date strings.
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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.