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# Introduction
Most Python builders are accustomed to the time module, for its helpful capabilities equivalent to time.sleep(). This makes the modiule the go-to for pausing execution, a easy however important instrument. Nonetheless, the time module is way 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 identify is sticking as a result of it is catchy, so take care of it 🙂
In any case, listed here 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()
Whilst you may robotically go for time.time() to measure how lengthy a operate takes, it has a important flaw: it’s based mostly on the system clock, which will be modified manually or by community time protocols. This will result in inaccurate and even adverse 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 alternative for measuring durations reliably.
import time
start_time = time.monotonic()
# Simulate a job
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()
Generally, you do not care in regards to the complete time handed (wall-clock time). As an alternative, you may wish to understand how a lot time the CPU truly 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 person CPU time of the present course of, offering a pure measure of computational effort.
import time
start_cpu = time.process_time()
# A CPU-intensive job
complete = 0
for i in vary(10_000_000):
complete += i
end_cpu = time.process_time()
cpu_duration = end_cpu - start_cpu
print(f"The CPU-intensive job took {cpu_duration:.2f} CPU seconds.")
Output:
The CPU-intensive job 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 vital instrument. It returns the worth of a high-resolution efficiency counter, which is essentially the most correct clock obtainable in your system. This can be a system-wide depend, together with time elapsed throughout sleep, which makes it excellent 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 methods). 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()
For example you have got time info 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 parts 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"12 months: {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)
12 months: 2025, Month: 7
# 6. Format Time into Customized Strings with time.strftime()
The other 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 in accordance with your specified format codes. This provides you full management over the output, whether or not you like “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 method
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"In the present day is {day_of_week}.")
Output:
Customized formatted time: 2025-08-01 06:41:33
In the present day is Friday
# 7. Get Fundamental Timezone Data with time.timezone and time.tzname
Whereas the datetime module (and libraries like pytz) are higher for complicated timezone dealing with, the time module affords some primary info. time.timezone gives 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 totally different timezones will be tough. A standard 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 to 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 helpful… however how do you go within the reverse path? 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 = record(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 purposes, time.process_time() provides you the full CPU time for all the course of. However what if you wish to profile the CPU utilization of a selected thread? On this case, time.thread_time() is the operate you’re in search of. This operate returns the sum of system and person 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 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 phase 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 helpful instrument for all types of practically-useful duties.
By shifting past the fundamentals, you may study new methods for writing extra correct and environment friendly code. For extra superior, object-oriented date and time manipulation, you should definitely take a look at 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 embrace 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.
