5 Best Ways to Convert Python Time to Epoch Seconds

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💡 Problem Formulation: In Python programming, there are several ways to convert a given time into the number of seconds since the epoch (January 1, 1970, 00:00:00 UTC). This article aims to explore various methods to perform this conversion, which is commonly needed for timestamp manipulation and data storage. Suppose you have a datetime object representing February 13, 2021, 23:31:30 and you want to obtain the epoch timestamp of this time, which would be 1613256690 seconds.

Method 1: Using the time Module

This method involves utilizing the time module in Python. The function time.mktime() can be used to convert a time tuple in local time to seconds passed since the epoch. The function time.localtime() can be further applied if one wants to convert local time.

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Here’s an example:

import time
from datetime import datetime

# Assumption: d is a datetime object
d = datetime(2021, 2, 13, 23, 31, 30)
epoch_seconds = int(time.mktime(d.timetuple()))

print(epoch_seconds)

Output:

1613256690

This code snippet creates a datetime object for a specific date and time, converts it into a time tuple using timetuple(), and then uses time.mktime() to convert this tuple into epoch seconds.

Method 2: Using the datetime Module

Another popular method is to subtract the epoch date from your datetime object and then convert this difference to seconds. The datetime module’s timestamp() function provides a shortcut for this operation.

Here’s an example:

from datetime import datetime

# Assumption: d is a datetime object
d = datetime(2021, 2, 13, 23, 31, 30)
epoch_seconds = int(d.timestamp())

print(epoch_seconds)

Output:

1613256690

This snippet demonstrates obtaining the epoch seconds directly using the timestamp() method from the datetime object. It’s a straightforward and readable way to get the epoch time.

Method 3: Using Calendar and Time Modules

The calendar module offers a way to perform the conversion by combining it with the time module’s gmtime() function for UTC time. This is useful when you need to deal with UTC explicitly.

Here’s an example:

import calendar
import time
from datetime import datetime

d = datetime(2021, 2, 13, 23, 31, 30)
epoch_seconds = calendar.timegm(d.utctimetuple())

print(epoch_seconds)

Output:

1613256690

The code uses utctimetuple() to get a UTC-based time tuple from a datetime object, then passes it to calendar.timegm() to get the epoch time in seconds.

Method 4: Using Pandas Library

For those who are working with dataframes, the Pandas library offers convenient ways to convert times to epoch seconds. The pd.Timestamp() class can be leveraged to create a timestamp object which can then be converted to epoch seconds.

Here’s an example:

import pandas as pd

timestamp = pd.Timestamp('2021-02-13 23:31:30')
epoch_seconds = int(timestamp.timestamp())

print(epoch_seconds)

Output:

1613256690

Here we create a Pandas Timestamp object and use its timestamp() method, similar to the datetime library’s approach but useful within the context of Pandas dataframes operations.

Bonus One-Liner Method 5: Using datetime and time Modules

If you’re looking for a quick one-liner, you can combine methods from the datetime and time modules. This avoids the need to create an intermediate datetime object explicitly.

Here’s an example:

import time
from datetime import datetime

epoch_seconds = int(time.mktime(datetime(2021, 2, 13, 23, 31, 30).timetuple()))

print(epoch_seconds)

Output:

1613256690

The one-liner combines the datetime construction, timetuple() conversion, and time.mktime() to directly get the epoch seconds without intermediate variables, offering a concise solution.

Summary/Discussion

  • Method 1: time.mktime() method. Strengths: Part of the standard library, converts local time. Weaknesses: Not timezone aware.
  • Method 2: datetime.timestamp() method. Strengths: Simple and direct, timezone aware. Weaknesses: Only available in Python 3.3 and above.
  • Method 3: Calendar and Time Modules. Strengths: Explicitly handles UTC, part of the standard library. Weaknesses: Slightly more complex to use.
  • Method 4: Pandas Library. Strengths: Integrates with Pandas dataframe operations, useful for data science tasks. Weaknesses: Requires an external library.
  • Method 5: Combined One-Liner. Strengths: Compact code, no intermediate variables. Weaknesses: Less readable and maintainable.