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# Introduction

 
Most Python developers are familiar with the time module, for its handy functions such as time.sleep(). This makes the modiule the go-to for pausing execution, a simple but essential tool. However, the time module is far more versatile, offering a suite of functions for precise measurement, time conversion, and formatting that often go unnoticed. Exploring these capabilities can unlock more efficient ways to handle time-related tasks in your data science and other coding projects.

I’ve gotten some flack for the naming of previous “10 Surprising Things” articles, and I get it. “Yes, it is so very surprising that I can perform date and time tasks with the datetime module, thank you.” Valid criticism. However, the name is sticking because it’s catchy, so deal with it 🙂

In any case, here are 10 surprising and useful things you can do with Python’s time module.

# 1. Accurately Measure Elapsed Wall-Clock Time with time.monotonic()

 
While you might automatically go for time.time() to measure how long a function takes, it has a critical flaw: it’s based on the system clock, which can be changed manually or by network time protocols. This can lead to inaccurate or even negative time differences. A more robust solution is time.monotonic(). This function returns the value of a monotonic clock, which cannot go backward and is unaffected by system time updates. This really does make it the ideal choice for measuring durations reliably.

import time

start_time = time.monotonic()

# Simulate a task
time.sleep(2)

end_time = time.monotonic()
duration = end_time - start_time

print(f"The task took {duration:.2f} seconds.")

Output:

The task took 2.01 seconds.

# 2. Measure CPU Processing Time with time.process_time()

 
Sometimes, you don’t care about the total time passed (wall-clock time). Instead, you might want to know how much time the CPU actually spent executing your code. This is crucial for benchmarking algorithm efficiency, as it ignores time spent sleeping or waiting for I/O operations. The time.process_time() function returns the sum of the system and user CPU time of the current process, providing a pure measure of computational effort.

import time

start_cpu = time.process_time()

# A CPU-intensive task
total = 0
for i in range(10_000_000):
    total += i

end_cpu = time.process_time()
cpu_duration = end_cpu - start_cpu

print(f"The CPU-intensive task took {cpu_duration:.2f} CPU seconds.")

Output:

The CPU-intensive task took 0.44 CPU seconds.

# 3. Get High-Precision Timestamps with time.perf_counter()

 
For highly precise timing, especially for very short durations, time.perf_counter() is an essential tool. It returns the value of a high-resolution performance counter, which is the most accurate clock available on your system. This is a system-wide count, including time elapsed during sleep, which makes it perfect for benchmark scenarios where every nanosecond counts.

import time

start_perf = time.perf_counter()

# A very short operation
_ = [x*x for x in range(1000)]

end_perf = time.perf_counter()
perf_duration = end_perf - start_perf

print(f"The short operation took {perf_duration:.6f} seconds.")

Output:

The short operation took 0.000028 seconds.

# 4. Convert Timestamps to Readable Strings with time.ctime()

 
The output of time.time() is a float representing seconds since the “epoch” (January 1, 1970, for Unix systems). While useful for calculations, it’s not human-readable. The time.ctime() function takes this timestamp and converts it into a standard, easy-to-read string format, like ‘Thu Jul 31 16:32:30 2025’.

import time

current_timestamp = time.time()
readable_time = time.ctime(current_timestamp)

print(f"Timestamp: {current_timestamp}")
print(f"Readable Time: {readable_time}")

Output:

Timestamp: 1754044568.821037
Readable Time: Fri Aug  1 06:36:08 2025

# 5. Parse Time from a String with time.strptime()

 
Let’s say you have time information stored as a string and need to convert it into a structured time object for further processing. time.strptime() (string parse time) is your function. You provide the string and a format code that specifies how the date and time components are arranged. It returns a struct_time object, which is a tuple containing elements — like year, month, day, and so on — which can then be extracted.

import time

date_string = "31 July, 2025"
format_code = "%d %B, %Y"

time_struct = time.strptime(date_string, format_code)

print(f"Parsed time structure: {time_struct}")
print(f"Year: {time_struct.tm_year}, Month: {time_struct.tm_mon}")

Output:

Parsed time structure: time.struct_time(tm_year=2025, tm_mon=7, tm_mday=31, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=3, tm_yday=212, tm_isdst=-1)
Year: 2025, Month: 7

# 6. Format Time into Custom Strings with time.strftime()

 
The opposite of parsing is formatting. time.strftime() (string format time) takes a struct_time object (like the one returned by strptime or localtime) and formats it into a string according to your specified format codes. This gives you full control over the output, whether you prefer “2025-07-31” or “Thursday, July 31”.

import time

# Get current time as a struct_time object
current_time_struct = time.localtime()

# Format it in a custom way
formatted_string = time.strftime("%Y-%m-%d %H:%M:%S", current_time_struct)
print(f"Custom formatted time: {formatted_string}")

day_of_week = time.strftime("%A", current_time_struct)
print(f"Today is {day_of_week}.")

Output:

Custom formatted time: 2025-08-01 06:41:33
Today is Friday

# 7. Get Basic Timezone Information with time.timezone and time.tzname

 
While the datetime module (and libraries like pytz) are better for complex timezone handling, the time module offers some basic information. time.timezone provides the offset of the local non-DST (Daylight Savings Time) timezone in offset seconds west of UTC, while time.tzname is a tuple containing the names of the local non-DST and DST timezones.

import time

# Offset in seconds west of UTC
offset_seconds = time.timezone

# Timezone names (standard, daylight saving)
tz_names = time.tzname

print(f"Timezone offset: {offset_seconds / 3600} hours west of UTC")
print(f"Timezone names: {tz_names}")

Output:

Timezone offset: 5.0 hours west of UTC
Timezone names: ('EST', 'EDT')

# 8. Convert Between UTC and Local Time with time.gmtime() and time.localtime()

 
Working with different timezones can be tricky. A common practice is to store all time data in Coordinated Universal Time (UTC) and convert it to local time only for display. The time module facilitates this with time.gmtime() and time.localtime(). These functions take a timestamp in seconds and return a struct_time object — gmtime() returns it in UTC, while localtime() returns it for your system’s configured timezone.

import time

timestamp = time.time()

# Convert timestamp to struct_time in UTC
utc_time = time.gmtime(timestamp)

# Convert timestamp to struct_time in local time
local_time = time.localtime(timestamp)

print(f"UTC Time: {time.strftime('%Y-%m-%d %H:%M:%S', utc_time)}")
print(f"Local Time: {time.strftime('%Y-%m-%d %H:%M:%S', local_time)}")

Output:

UTC Time: 2025-08-01 10:47:58
Local Time: 2025-08-01 06:47:58

# 9. Perform the Inverse of time.time() with time.mktime()

 
time.localtime() converts a timestamp into a struct_time object, which is useful… but how do you go in the reverse direction? The time.mktime() function does exactly this. It takes a struct_time object (representing local time) and converts it back into a floating-point number representing seconds since the epoch. This is then useful for calculating future or past timestamps or performing date arithmetic.

import time

# Get current local time structure
now_struct = time.localtime()

# Create a modified time structure for one hour from now
future_struct_list = list(now_struct)
future_struct_list[3] += 1 # Add 1 to the hour (tm_hour)
future_struct = time.struct_time(future_struct_list)

# Convert back to a timestamp
future_timestamp = time.mktime(future_struct)

print(f"Current timestamp: {time.time():.0f}")
print(f"Timestamp in one hour: {future_timestamp:.0f}")

Output:

Current timestamp: 1754045415
Timestamp in one hour: 1754049015

# 10. Get Thread-Specific CPU Time with time.thread_time()

 
In multi-threaded applications, time.process_time() gives you the total CPU time for the entire process. But what if you want to profile the CPU usage of a specific thread? In this case, time.thread_time() is the function you are looking for. This function returns the sum of system and user CPU time for the current thread, allowing you to identify which threads are the most computationally expensive.

import time
import threading

def worker_task():
    start_thread_time = time.thread_time()

    # Simulate work
    _ = [i * i for i in range(10_000_000)]

    end_thread_time = time.thread_time()

    print(f"Worker thread CPU time: {end_thread_time - start_thread_time:.2f}s")

# Run the task in a separate thread
thread = threading.Thread(target=worker_task)
thread.start()
thread.join()

print(f"Total process CPU time: {time.process_time():.2f}s")

Output:

Worker thread CPU time: 0.23s
Total process CPU time: 0.32s

# Wrapping Up

 
The time module is an integral and powerful segment of Python’s standard library. While time.sleep() is undoubtedly its most famous function, its capabilities for timing, duration measurement, and time formatting make it a handy tool for all sorts of practically-useful tasks.

By moving beyond the basics, you can learn new tricks for writing more accurate and efficient code. For more advanced, object-oriented date and time manipulation, be sure to check out surprising things you can do with the datetime module next.
 
 

Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.