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# Introduction
Most Python builders are accustomed to the time module, for its useful capabilities akin to time.sleep(). This makes the modiule the go-to for pausing execution, a easy however important device. Nonetheless, the time module is much extra versatile, providing a collection of capabilities for exact measurement, time conversion, and formatting that usually go unnoticed. Exploring these capabilities can unlock extra environment friendly methods to deal with time-related duties in your knowledge science and different coding tasks.
I’ve gotten some flack for the naming of earlier “10 Stunning Issues” articles, and I get it. “Sure, it’s so very shocking that I can carry out date and time duties with the datetime module, thanks.” Legitimate criticism. Nonetheless, the title is sticking as a result of it is catchy, so take care of it 🙂
In any case, listed below are 10 shocking and helpful issues you are able to do with Python’s time module.
# 1. Precisely Measure Elapsed Wall-Clock Time with time.monotonic()
When you may mechanically go for time.time() to measure how lengthy a operate takes, it has a vital flaw: it’s primarily based on the system clock, which may be modified manually or by community time protocols. This may result in inaccurate and even unfavourable time variations. A extra sturdy resolution is time.monotonic(). This operate returns the worth of a monotonic clock, which can’t go backward and is unaffected by system time updates. This actually does make it the best selection for measuring durations reliably.
import time
start_time = time.monotonic()
# Simulate a process
time.sleep(2)
end_time = time.monotonic()
length = end_time - start_time
print(f"The duty took {length:.2f} seconds.")
Output:
The duty took 2.01 seconds.
# 2. Measure CPU Processing Time with time.process_time()
Typically, you do not care in regards to the whole time handed (wall-clock time). As a substitute, you may wish to understand how a lot time the CPU really spent executing your code. That is essential for benchmarking algorithm effectivity, because it ignores time spent sleeping or ready for I/O operations. The time.process_time() operate returns the sum of the system and consumer CPU time of the present course of, offering a pure measure of computational effort.
import time
start_cpu = time.process_time()
# A CPU-intensive process
whole = 0
for i in vary(10_000_000):
whole += i
end_cpu = time.process_time()
cpu_duration = end_cpu - start_cpu
print(f"The CPU-intensive process took {cpu_duration:.2f} CPU seconds.")
Output:
The CPU-intensive process took 0.44 CPU seconds.
# 3. Get Excessive-Precision Timestamps with time.perf_counter()
For extremely exact timing, particularly for very brief durations, time.perf_counter() is a necessary device. It returns the worth of a high-resolution efficiency counter, which is essentially the most correct clock obtainable in your system. It is a system-wide rely, together with time elapsed throughout sleep, which makes it good for benchmark situations the place each nanosecond counts.
import time
start_perf = time.perf_counter()
# A really brief operation
_ = [x*x for x in range(1000)]
end_perf = time.perf_counter()
perf_duration = end_perf - start_perf
print(f"The brief operation took {perf_duration:.6f} seconds.")
Output:
The brief operation took 0.000028 seconds.
# 4. Convert Timestamps to Readable Strings with time.ctime()
The output of time.time() is a float representing seconds for the reason that “epoch” (January 1, 1970, for Unix techniques). Whereas helpful for calculations, it’s not human-readable. The time.ctime() operate takes this timestamp and converts it into a normal, 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()
To illustrate you could have time data saved as a string and have to convert it right into a structured time object for additional processing. time.strptime() (string parse time) is your operate. You present the string and a format code that specifies how the date and time elements are organized. It returns a struct_time object, which is a tuple containing parts — like 12 months, month, day, and so forth — which may 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 construction: {time_struct}")
print(f"Yr: {time_struct.tm_year}, Month: {time_struct.tm_mon}")
Output:
Parsed time construction: 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)
Yr: 2025, Month: 7
# 6. Format Time into Customized Strings with time.strftime()
The alternative of parsing is formatting. time.strftime() (string format time) takes a struct_time object (just like the one returned by strptime or localtime) and codecs it right into a string based on your specified format codes. This offers you full management over the output, whether or not you favor “2025-07-31” or “Thursday, July 31”.
import time
# Get present time as a struct_time object
current_time_struct = time.localtime()
# Format it in a customized manner
formatted_string = time.strftime("%Y-%m-%d %H:%M:%S", current_time_struct)
print(f"Customized formatted time: {formatted_string}")
day_of_week = time.strftime("%A", current_time_struct)
print(f"Right now is {day_of_week}.")
Output:
Customized formatted time: 2025-08-01 06:41:33
Right now is Friday
# 7. Get Fundamental Timezone Info with time.timezone and time.tzname
Whereas the datetime module (and libraries like pytz) are higher for complicated timezone dealing with, the time module provides some fundamental data. time.timezone offers the offset of the native non-DST (Daylight Financial savings Time) timezone in offset seconds west of UTC, whereas time.tzname is a tuple containing the names of the native non-DST and DST timezones.
import time
# Offset in seconds west of UTC
offset_seconds = time.timezone
# Timezone names (commonplace, 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 Native Time with time.gmtime() and time.localtime()
Working with completely different timezones may be tough. A typical observe is to retailer all time knowledge in Coordinated Common Time (UTC) and convert it to native time just for show. The time module facilitates this with time.gmtime() and time.localtime(). These capabilities take a timestamp in seconds and return a struct_time object — gmtime() returns it in UTC, whereas localtime() returns it on 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 native time
local_time = time.localtime(timestamp)
print(f"UTC Time: {time.strftime('%Y-%m-%d %H:%M:%S', utc_time)}")
print(f"Native Time: {time.strftime('%Y-%m-%d %H:%M:%S', local_time)}")
Output:
UTC Time: 2025-08-01 10:47:58
Native Time: 2025-08-01 06:47:58
# 9. Carry out the Inverse of time.time() with time.mktime()
time.localtime() converts a timestamp right into a struct_time object, which is beneficial… however how do you go within the reverse course? The time.mktime() operate does precisely this. It takes a struct_time object (representing native time) and converts it again right into a floating-point quantity representing seconds for the reason that epoch. That is then helpful for calculating future or previous timestamps or performing date arithmetic.
import time
# Get present native time construction
now_struct = time.localtime()
# Create a modified time construction for one hour from now
future_struct_list = listing(now_struct)
future_struct_list[3] += 1 # Add 1 to the hour (tm_hour)
future_struct = time.struct_time(future_struct_list)
# Convert again to a timestamp
future_timestamp = time.mktime(future_struct)
print(f"Present timestamp: {time.time():.0f}")
print(f"Timestamp in a single hour: {future_timestamp:.0f}")
Output:
Present timestamp: 1754045415
Timestamp in a single hour: 1754049015
# 10. Get Thread-Particular CPU Time with time.thread_time()
In multi-threaded functions, time.process_time() offers you the whole CPU time for your complete course of. However what if you wish to profile the CPU utilization of a particular thread? On this case, time.thread_time() is the operate you might be on the lookout for. This operate returns the sum of system and consumer CPU time for the present thread, permitting you to establish which threads are essentially the most computationally costly.
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"Employee thread CPU time: {end_thread_time - start_thread_time:.2f}s")
# Run the duty in a separate thread
thread = threading.Thread(goal=worker_task)
thread.begin()
thread.be a part of()
print(f"Complete course of CPU time: {time.process_time():.2f}s")
Output:
Employee thread CPU time: 0.23s
Complete course of CPU time: 0.32s
# Wrapping Up
The time module is an integral and highly effective section of Python’s commonplace library. Whereas time.sleep() is undoubtedly its most well-known operate, its capabilities for timing, length measurement, and time formatting make it a useful device for all kinds of practically-useful duties.
By transferring past the fundamentals, you possibly can study new tips for writing extra correct and environment friendly code. For extra superior, object-oriented date and time manipulation, make sure you try shocking issues you are able to do with the datetime module subsequent.
Matthew Mayo (@mattmayo13) holds a grasp’s diploma in laptop science and a graduate diploma in knowledge mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Studying Mastery, Matthew goals to make complicated knowledge science ideas accessible. His skilled pursuits embody pure language processing, language fashions, machine studying algorithms, and exploring rising AI. He’s pushed by a mission to democratize data within the knowledge science neighborhood. Matthew has been coding since he was 6 years outdated.
