Python time Module

Python provides two primary modules for timestamp operations: time for low-level Unix timestamp work, and datetime for higher-level date manipulation. The function time.time() returns the current Unix timestamp as a floating-point number in seconds.

Getting the current timestamp:

import time
from datetime import datetime, timezone

# Seconds since epoch (float)
time.time()                    # 1700000000.123456

# Integer seconds
int(time.time())               # 1700000000

# Using datetime
datetime.now(timezone.utc).timestamp()  # 1700000000.123456

Converting timestamps to dates:

# Timestamp to datetime (UTC)
dt = datetime.fromtimestamp(1700000000, tz=timezone.utc)
# datetime(2023, 11, 14, 22, 13, 20, tzinfo=timezone.utc)

# WARNING: without tz, uses local timezone
datetime.fromtimestamp(1700000000)  # local time — avoid this

# Timestamp to formatted string
dt.strftime("%Y-%m-%d %H:%M:%S")   # "2023-11-14 22:13:20"
dt.isoformat()                      # "2023-11-14T22:13:20+00:00"

Converting dates to timestamps:

# String to timestamp
from datetime import datetime, timezone
dt = datetime(2024, 1, 15, 9, 30, 0, tzinfo=timezone.utc)
dt.timestamp()                 # 1705310600.0

# ISO format string to timestamp (Python 3.7+)
dt = datetime.fromisoformat("2024-01-15T09:30:00+00:00")
dt.timestamp()

Critical pitfall — naive vs. aware datetimes: A datetime object without timezone info is called "naive." Calling .timestamp() on a naive datetime assumes local time, which produces different results on different servers. Always use timezone-aware datetimes by passing tz=timezone.utc or using zoneinfo.ZoneInfo (Python 3.9+).

Performance measurement: For benchmarking, use time.perf_counter() or time.monotonic() instead of time.time(). These monotonic clocks are not affected by NTP adjustments or manual clock changes, making them reliable for measuring elapsed time.