SysTutorials

Getting Epoch Timestamps in Python

· Q A · updated April 12, 2026

Python offers several ways to get the current epoch timestamp, each suited to different use cases. The choice depends on your precision requirements and whether you’re working with timezone-aware operations.

Using time.time()

The simplest approach is time.time(), which returns the current time as a float representing seconds since the Unix epoch (January 1, 1970, 00:00:00 UTC):

import time

epoch_seconds = time.time()
print(epoch_seconds)  # Output: 1735689600.123456

If you need an integer, cast it:

epoch_seconds_int = int(time.time())

This works across all platforms and is the standard for compatibility. However, it has microsecond precision at best and the float representation can accumulate rounding errors in long-running processes.

Using datetime with UTC Timezone

For timezone-aware operations and clearer intent, use the datetime module:

from datetime import datetime, timezone

epoch_seconds = int(datetime.now(timezone.utc).timestamp())

This is more explicit about handling UTC and integrates better with code that manipulates dates and times. The timezone.utc ensures you’re always working in UTC regardless of your system’s local timezone.

High-Precision Timestamps with Nanoseconds

For performance monitoring, distributed tracing, and high-frequency logging, use time.time_ns() to get nanosecond precision:

import time

epoch_nanoseconds = time.time_ns()
epoch_milliseconds = epoch_nanoseconds // 1_000_000
epoch_microseconds = epoch_nanoseconds // 1_000

This avoids floating-point precision issues entirely and is essential when correlating events across microsservices or measuring sub-millisecond latencies.

Comparison

Method Precision Returns Use Case
time.time() Microseconds Float General purpose, compatibility
datetime.now(timezone.utc).timestamp() Microseconds Float Timezone-aware, date/time manipulation
time.time_ns() Nanoseconds Integer Performance monitoring, tracing

Practical Examples

Logging with millisecond precision:

import time
import logging

logging.basicConfig(
    format='%(asctime)s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)

timestamp_ms = int(time.time() * 1000)
logging.info(f"Event at {timestamp_ms}")

Measuring elapsed time:

import time

start = time.time_ns()
# ... do work ...
elapsed_ns = time.time_ns() - start
elapsed_ms = elapsed_ns / 1_000_000
print(f"Took {elapsed_ms:.2f}ms")

Converting to datetime for readability:

from datetime import datetime, timezone
import time

timestamp = time.time()
readable = datetime.fromtimestamp(timestamp, tz=timezone.utc)
print(readable)  # Output: 2026-01-01 12:30:45.123456+00:00

For most applications, time.time() remains the default choice. Use datetime methods when you need timezone awareness or plan to perform date arithmetic. Reserve time.time_ns() for scenarios where microsecond precision is insufficient—typically observability infrastructure, performance testing, or systems programming where you’re measuring very short durations.

Common Pitfalls and Best Practices

When working with Python on Linux systems, keep these considerations in mind. Always use virtual environments to avoid polluting the system Python installation. Python 2 reached end-of-life in 2020, so ensure you are using Python 3 for all new projects.

For system scripting, prefer the subprocess module over os.system for better control over process execution. Use pathlib instead of os.path for cleaner file path handling in modern Python.

These complementary Python tools and commands are useful for daily development workflows:

Quick Verification

After applying the changes described above, verify that everything works as expected. Run the relevant commands to confirm the new configuration is active. Check system logs for any errors or warnings that might indicate problems. If something does not work as expected, review the steps carefully and consult the official documentation for your specific version.

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