5 Best Ways to Convert Date String to Timestamp in Python

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πŸ’‘ Problem Formulation: In Python programming, developers often need to convert date strings into timestamp representations for consistent time-related calculations and data storage. For instance, transforming the date string ‘2023-03-15’ to a POSIX timestamp like 1678857600 is a common requirement. This article offers various methods to achieve this conversion efficiently.

Method 1: Using datetime.strptime and datetime.timestamp

The datetime module in Python provides a strptime function to parse a date string into a datetime object according to a specified format, and the timestamp method converts that object into a Unix timestamp. This method is straightforward and customizable to different date formats.

Here’s an example:

from datetime import datetime

date_str = '2023-03-15'
date_format = '%Y-%m-%d'
date_obj = datetime.strptime(date_str, date_format)
timestamp = date_obj.timestamp()
print(timestamp)

Output: 1678857600.0

This snippet first imports the datetime class. Then, it converts the string date_str into a datetime object with the pre-defined date_format. Finally, it uses the timestamp() method to get the Unix timestamp of the datetime object.

Method 2: Using time.mktime and time.strptime

The time module’s mktime function takes a time tuple and returns a Unix timestamp. Combined with time.strptime, which parses a date string into a time tuple, this method is an efficient alternative to the datetime module for conversion.

Here’s an example:

import time

date_str = '2023-03-15'
date_format = '%Y-%m-%d'
time_tuple = time.strptime(date_str, date_format)
timestamp = time.mktime(time_tuple)
print(timestamp)

Output: 1678857600.0

This code first imports the time module, parses the date_str to a time tuple with strptime, and then converts the time tuple to a Unix timestamp with mktime.

Method 3: Using pandas.to_datetime

For data science applications, Pandas provides a to_datetime function that converts a date string into a Timestamp object, which can then be converted to a Unix timestamp easily. This method is very efficient when dealing with data in dataframes.

Here’s an example:

import pandas as pd

date_str = '2023-03-15'
timestamp = pd.to_datetime(date_str).timestamp()
print(timestamp)

Output: 1678857600.0

The snippet uses Pandas’ to_datetime function to convert the date_str directly into a Timestamp object. Calling timestamp() on the resultant object gives us the Unix timestamp.

Method 4: Using calendar.timegm and time.strptime

For UTC timestamps, the calendar.timegm function can be used in conjunction with time.strptime to convert a date string into a UTC-based timestamp, avoiding local timezone issues that might arise with mktime.

Here’s an example:

import calendar
import time

date_str = '2023-03-15T00:00:00Z'
date_format = '%Y-%m-%dT%H:%M:%SZ'
time_tuple = time.strptime(date_str, date_format)
timestamp = calendar.timegm(time_tuple)
print(timestamp)

Output: 1678836000

In this example, time.strptime parses the date string to a time tuple and calendar.timegm then converts this UTC time tuple to a Unix timestamp.

Bonus One-Liner Method 5: Using dateutil.parser

The dateutil library simplifies parsing by automatically detecting the date format, which is useful when dealing with multiple date formats. The parsed date can then be converted to a timestamp.

Here’s an example:

from dateutil import parser

date_str = 'March 15, 2023 12:00AM'
timestamp = parser.parse(date_str).timestamp()
print(timestamp)

Output: 1678857600.0

This code snippet uses dateutil.parser to intelligently parse the human-readable date_str into a datetime object, negating the need to explicitly define the format. The timestamp() method is then used to obtain the Unix timestamp of the parsed date.

Summary/Discussion

  • Method 1: Using datetime.strptime and datetime.timestamp. It allows precise control over date formats. It’s part of standard Python, but requires specifying the date format.
  • Method 2: Using time.mktime and time.strptime. It’s useful for systems without the datetime module. It may involve local time considerations.
  • Method 3: Using pandas.to_datetime. This is ideal for large datasets and data analysis workflows. It requires the Pandas library, which is an additional dependency.
  • Method 4: Using calendar.timegm and time.strptime. Suited for UTC timestamps conversion, it avoids timezone issues but may not be as straightforward as Method 1.
  • Bonus Method 5: Using dateutil.parser. It’s highly flexible and convenient when working with multiple date formats. Nevertheless, it’s an external dependency and may be slightly slower than other methods.