Basic DateTime Operations in Python
Last Updated :
20 Oct, 2021
Python has an in-built module named DateTime to deal with dates and times in numerous ways. In this article, we are going to see basic DateTime operations in Python.
There are six main object classes with their respective components in the datetime module mentioned below:
- datetime.date
- datetime.time
- datetime.datetime
- datetime.tzinfo
- datetime.timedelta
- datetime.timezone
Now we will see the program for each of the functions under datetime module mentioned above.
datetime.date():
We can generate date objects from the date class. A date object represents a date having a year, month, and day.
Syntax:datetime.date( year, month, day)
strftime to print day, month, and year in various formats. Here are some of them are:
- current.strftime("%m/%d/%y") that prints in month(Numeric)/date/year format
- current.strftime("%b-%d-%Y") that prints in month(abbreviation)-date-year format
- current.strftime("%d/%m/%Y") that prints in date/month/year format
- current.strftime("%B %d, %Y") that prints in month(words) date, year format
Python3
from datetime import date
# You can create a date object containing
# the current date
# by using a classmethod named today()
current = date.today()
# print current year, month, and year individually
print("Current Day is :", current.day)
print("Current Month is :", current.month)
print("Current Year is :", current.year)
# strftime() creates string representing date in
# various formats
print("\n")
print("Let's print date, month and year in different-different ways")
format1 = current.strftime("%m/%d/%y")
# prints in month/date/year format
print("format1 =", format1)
format2 = current.strftime("%b-%d-%Y")
# prints in month(abbreviation)-date-year format
print("format2 =", format2)
format3 = current.strftime("%d/%m/%Y")
# prints in date/month/year format
print("format3 =", format3)
format4 = current.strftime("%B %d, %Y")
# prints in month(words) date, year format
print("format4 =", format4)
Output:
Current Day is : 23
Current Month is : 3
Current Year is : 2021
Let's print date, month and year in different-different ways
format1 = 03/23/21
format2 = Mar-23-2021
format3 = 23/03/2021
format4 = March 23, 2021
datetime.time():
A time object generated from the time class represents the local time.
Components:
- hour
- minute
- second
- microsecond
- tzinfo
Syntax: datetime.time(hour, minute, second, microsecond)
Code:
Python3
from datetime import time
# time() takes hour, minutes, second,
# microsecond respectively in order
# if no parameter is passed in time() by default
# it takes 0
defaultTime = time()
print("default_hour =", defaultTime.hour)
print("default_minute =", defaultTime.minute)
print("default_second =", defaultTime.second)
print("default_microsecond =", defaultTime.microsecond)
# passing parameter in different-different ways
# hour, minute and second respectively is a default
# order
time1= time(10, 5, 25)
print("time_1 =", time1)
# assigning hour, minute and second to respective
# variables
time2= time(hour = 10, minute = 5, second = 25)
print("time_2 =", time2)
# assigning hour, minute, second and microsecond to
# respective variables
time3= time(hour=10, minute= 5, second=25, microsecond=55)
print("time_3 =", time3)
Output:
default_hour = 0
default_minute = 0
default_second = 0
default_microsecond = 0
time_1 = 10:05:25
time_2 = 10:05:25
time_3 = 10:05:25.000055
datetime.datetime():
datetime.datetime() module shows the combination of a date and a time.
Components:
- year
- month
- day
- hour
- minute
- second,
- microsecond
- tzinfo
Syntax: datetime.datetime( year, month, day )
or
datetime.datetime(year, month, day, hour, minute, second, microsecond)
Current date and time using the strftime() method in different ways:
- strftime("%d") gives current day
- strftime("%m") gives current month
- strftime("%Y") gives current year
- strftime("%H:%M:%S") gives current time in an hour, minute, and second format
- strftime("%m/%d/%Y, %H:%M:%S") gives date and time together
Code:
Python3
from datetime import datetime
# now() gives current date and time
current = datetime.now()
# print combinedly
print(current)
print("\n")
print("print each term individually")
day = current.strftime("%d")
# print day
print("day:", day)
month = current.strftime("%m")
# print month
print("month:", month)
year = current.strftime("%Y")
# print year
print("year:", year)
time = current.strftime("%H:%M:%S")
# time in hour, minute and second
print("time:", time)
print("\n")
print("printing date and time together")
date_time = current.strftime("%m/%d/%Y, %H:%M:%S")
print("date and time:", date_time)
print("\n")
# fetching details from timestamp
timestamp = 1615797322
date_time = datetime.fromtimestamp(timestamp)
# %c, %x and %X are used for locale's proper date and time representation
time_1 = date_time.strftime("%c")
print("first_output:", time_1)
time_2 = date_time.strftime("%x")
print("second_output:", time_2)
time_3 = date_time.strftime("%X")
print("third_output:", time_3)
print("\n")
# assigning each term manually
manual = datetime(2021, 3, 28, 23, 55, 59, 342380)
print("year =", manual.year)
print("month =", manual.month)
print("hour =", manual.hour)
print("minute =", manual.minute)
print("timestamp =", manual.timestamp())
Output:
2021-03-23 19:00:20.726833
print each term individually
day: 23
month: 03
year: 2021
time: 19:00:20
printing date and time together
date and time: 03/23/2021, 19:00:20
first_output: Mon Mar 15 14:05:22 2021
second_output: 03/15/21
third_output: 14:05:22
year = 2021
month = 3
hour = 23
minute = 55
timestamp = 1616955959.34238
datetime.timedelta():
It shows a duration that expresses the difference between two date, time, or datetime instances to microsecond resolution.
Here we implemented some basic functions and printed past and future days. Also, we will print some other attributes of timedelta max, min, and resolution that show maximum days and time, minimum date and time, and the smallest possible difference between non-equal timedelta objects respectively. Here we will also apply some arithmetic operations on two different dates and times.
Python3
from datetime import timedelta, datetime
present_date_with_time = datetime.now()
print("Present Date :", present_date_with_time)
# coming date after 10 days
ten_days_after= present_date_with_time + timedelta(days = 10)
print('Date after 10 days :',ten_days_after)
# date before 10 days
ten_days_before= present_date_with_time - timedelta(days = 10)
print('Date before 10 days :',ten_days_before)
# date before one year ago
one_year_before_today= present_date_with_time + timedelta(days = 365)
print('One year before present Date :', one_year_before_today)
#date before one year ago
one_year_after_today= present_date_with_time - timedelta(days = 365)
print('One year before present Date :', one_year_after_today)
print("\n")
print("print some other attributes of timedelta\n")
# maximum days and time
print("Max : ",timedelta.max)
# minimum days and time
print("Min : ",timedelta.min)
# The smallest possible difference between non-equal
# timedelta objects, timedelta(microseconds=1)
print("Resolution: ",timedelta.resolution)
print('Total number of seconds in an year :',
timedelta(days = 365).total_seconds())
print("\nApply some operations on timedelta function\n")
time_after_one_min = present_date_with_time + timedelta(seconds=10) * 6
print('Time after one minute :', time_after_one_min)
print('Timedelta absolute value :', abs(timedelta(days = +20)))
print('Timedelta string representation :', str(timedelta(days = 5,
seconds = 40, hours = 20, milliseconds = 355)))
print('Timedelta object representation :', repr(timedelta(days = 5,
seconds = 40, hours = 20, milliseconds = 355)))
Output:
Present Date : 2021-03-25 22:34:27.651128
Date after 10 days : 2021-04-04 22:34:27.651128
Date before 10 days : 2021-03-15 22:34:27.651128
One year before present Date : 2022-03-25 22:34:27.651128
One year before present Date : 2020-03-25 22:34:27.651128
print some other attributes of timedelta
Max : 999999999 days, 23:59:59.999999
Min : -999999999 days, 0:00:00
Resolution: 0:00:00.000001
Total number of seconds in an year : 31536000.0
Apply some operations on timedelta function
Time after one minute : 2021-03-25 22:35:27.651128
Timedelta absolute value : 20 days, 0:00:00
Timedelta string representation : 5 days, 20:00:40.355000
Timedelta object representation : datetime.timedelta(days=5, seconds=72040, microseconds=355000)
datetime.tzinfo():
It is an abstract base class for time zone information objects. They are used by the datetime and time classes to provide a customizable notion of time adjustment.
There are the following four methods available for tzinfo base class:
- utcoffset(self, dt): returns the offset of the datetime instance passed as an argument
- dst(self, dt): dst stands for Daylight Saving Time. dst denotes advancing the clock 1 hour in summer so that darkness falls later according to the clock. It is set to on or off. It is checked on the basis of the following elements:
(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.weekday(), 0, 0)
- tzname(self, dt): It returns a Python String object. It is used to find the time zone name of the datetime object passed.
- fromutc(self, dt) : This function returns the equivalent local time and takes up the date and time of the object in UTC. It is mostly used to adjust the date and time. It is called from default datetime.astimezone() implementation. The dt.tzinfo will be passed as self, dst date and time data will be returned as an equivalent local time.
Note: It raises ValueError if dt.tzinfo is not self or/and dst() is None.
Python3
# code
from datetime import datetime, timedelta
from pytz import timezone
import pytz
time_zone = timezone('Asia/Calcutta')
normal = datetime(2021, 3, 16)
ambiguous = datetime(2021, 4, 16, 23, 30)
# is_dst parameter is ignored for most of the
# timstamps.It is only used during DST
# transition ambiguous periods to resolve that
# ambiguity
print("Operations on normal datetime")
print(time_zone.utcoffset(normal, is_dst=True))
print(time_zone.dst(normal, is_dst=True))
print(time_zone.tzname(normal, is_dst=True))
# put is_dst=False
print(time_zone.utcoffset(normal, is_dst=False))
print(time_zone.dst(normal, is_dst=False))
print(time_zone.tzname(normal, is_dst=False))
print("\n")
print("Operations on ambiguous datetime")
print(time_zone.utcoffset(ambiguous, is_dst=True))
print(time_zone.dst(ambiguous, is_dst=True))
print(time_zone.tzname(ambiguous, is_dst=True))
# is_dst=False
print(time_zone.utcoffset(ambiguous, is_dst=False))
print(time_zone.dst(ambiguous, is_dst=False))
print(time_zone.tzname(ambiguous, is_dst=False))
OutputOperations on normal datetime
5:30:00
0:00:00
IST
5:30:00
0:00:00
IST
Operations on ambiguous datetime
5:30:00
0:00:00
IST
5:30:00
0:00:00
IST
Output:
Operations on normal datetime
5:30:00
0:00:00
IST
5:30:00
0:00:00
IST
Operations on ambiguous datetime
5:30:00
0:00:00
IST
5:30:00
0:00:00
IST
datetime.timezone():
Description: It is a class that implements the tzinfo abstract base class as a fixed offset from the UTC.
Syntax: datetime.timezone()
Python3
from datetime import datetime, timedelta
from pytz import timezone
import pytz
utc = pytz.utc
print(utc.zone)
india = timezone('Asia/Calcutta')
print(india.zone)
eastern = timezone('US/Eastern')
print(eastern.zone)
time_format = '%Y-%m-%d %H:%M:%S %Z%z'
# localize() is used to localize
# datetime with no timezone information
loc_dt = india.localize(datetime(2021, 3, 16, 6, 0, 0))
loc_dt = india.localize(datetime(2021, 3, 16, 6, 0, 0))
print(loc_dt.strftime(time_format))
# another way of building a localized time is by converting
# an existing localized time
# using the standard astimezone() method
eastern_dt = loc_dt.astimezone(eastern)
print(eastern_dt.strftime(time_format))
print(datetime(2021, 3, 16, 12, 0, 0, tzinfo=pytz.utc).strftime(time_format))
# 10 minutes before
before_dt = loc_dt - timedelta(minutes=10)
print(before_dt.strftime(time_format))
print(india.normalize(before_dt).strftime(time_format))
# 20 mins later
after_dt = india.normalize(before_dt + timedelta(minutes=20))
print(after_dt.strftime(time_format))
Output:
UTC
Asia/Calcutta
US/Eastern
2021-03-16 06:00:00 IST+0530
2021-03-15 20:30:00 EDT-0400
2021-03-16 12:00:00 UTC+0000
2021-03-16 05:50:00 IST+0530
2021-03-16 05:50:00 IST+0530
2021-03-16 06:10:00 IST+0530
Let's see different Functions with description under time module :-
Function | Description |
---|
time( ) | Returns the time in floating point number in seconds |
ctime( ) | Returns the current date and time |
sleep( ) | Stops execution of a thread for the given duration |
localtime( ) | Returns the date and time in time.struct_time format |
gmtime( ) | Returns time.struct_time in UTC format |
mktime( ) | Returns the seconds passed since epochs are output |
asctime( ) | Returns a string representing the same |
Now we will see the program and output for each of the above-mentioned functions in the table.
1: time( ) method: The time() method returns the time as a floating-point number expressed in seconds since the epoch, in UTC.
Syntax: time.time([ ])
NOTE: It does not have any parameter
Python3
# import time
import time
#prints total number of seconds passed since epoch
print(time.time())
Output:
1616692391.3081982
2: ctime( ) method
ctime() method converts a time expressed in seconds since the epoch to a string representing local time. The current time as returned by time() is used If secs is not provided or None. This method is equivalent to asctime(localtime(secs)). Locale information is not used by ctime() method.
Syntax: time.ctime([ sec ])
Where sec passed as an argument is the number of seconds to be converted Into string representation.
Python3
import time
number_of_seconds=1625925769.9618232
# function takes seconds passed since epoch as an argument and returns
# a string representing local time
print(time.ctime(number_of_seconds))
Output
Sat Jul 10 14:02:49 2021
3: sleep( ) method
Python time method sleep() stops execution for the given number of seconds. The floating-point the number can be passed as an argument to get more precise sleep time.
Syntax: time.sleep([ sec ])
where sec passed as an argument is the number of seconds for which
the process is to be stopped.
Python3
import time
# prints GEEKSFORGEEKS immediately
print("GEEKSFORGEEKS")
time.sleep(1.23)
# prints GEEKSFORGEEKS after 1.23 seconds
# as it stops execution for that time interval
print("GEEKSFORGEEKS")
Output
GEEKSFORGEEKS
GEEKSFORGEEKS
4: localtime( ) method
localtime() method converts number of seconds to local time. If secs is not provided or None, the current time as returned by time() is used. The dst flag is set to 1 when DST applies to the given time.
Syntax: time.localtime([ sec ])
Where sec passed as an argument is the number of seconds to be converted into struct_time representation.
Python3
import time
# returns a time.struct_time
# object with a named tuple interface
print(time.localtime())
Output
time.struct_time(tm_year=2021, tm_mon=3, tm_mday=30, tm_hour=8, tm_min=48, tm_sec=58, tm_wday=1, tm_yday=89, tm_isdst=0)
5: gmtime( ) method.
gmtime() method converts a time expressed in seconds since the Epoch to a struct_time in UTC in which the dst flag is always zero. If secs is not provided or None, the current time as returned by time() is used.
Syntax: time.gmtime([ sec ])
Where sec passed as an argument is the number of seconds to be converted into structure struct_time representation.
Python3
# code
import time
# returns a time.struct_time object with a named tuple interface
# If secs is not provided or None,
# the current time as returned by time() is used
print(time.gmtime())
Output:
time.struct_time(tm_year=2021, tm_mon=3, tm_mday=30, tm_hour=8, tm_min=49, tm_sec=18, tm_wday=1, tm_yday=89, tm_isdst=0)
6: mktime( ) method
It is the inverse function of localtime() method. It takes an argument as struct_time or full 9-tuple and it returns a floating-point number. If the input value is not represented as a valid time, then either OverflowError or ValueError is raised.
Syntax: time.mktime([t])
Where t passed as an argument is a time.struct_time object or a tuple containing 9 elements corresponding to time.struct_time object
Python3
# code
import time
# method mktime() is the inverse function of localtime()
# Its argument is the struct_time or full 9-tuple and
# it returns a floating point number, for compatibility with time().
t = (2016, 2, 15, 10, 13, 38, 1, 48, 0)
d = time.mktime(t)
print ("time.mktime(t) : %f" % d)
print ("asctime(localtime(secs)): %s" % time.asctime(time.localtime(d)))
Output
time.mktime(t) : 1455531218.000000
asctime(localtime(secs)): Mon Feb 15 10:13:38 2016
7: asctime( ) method
Python time method asctime() converts a struct_time representing a time as returned by gmtime() or localtime() to a 24-character string of the following form: 'Tue Mar 23 23:21:05 2021'.
Syntax: time.asctime([t])
Where t passed as an argument is a tuple of 9 elements or struct_time representing a time as returned by gmtime() or localtime() function.
Python3
import time
# method returns 24-character string of
# the following form − 'Mon March 15 23:21:05 2021'
local_time = time.localtime()
print ("asctime : ",time.asctime(local_time))
Output
asctime : Tue Mar 16 06:02:42 2021
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The Exponential Distribution is a continuous probability distribution that models the time between independent events occurring at a constant average rate. It is widely used in fields like reliability analysis, queuing theory, and survival analysis. The exponential distribution is a special case of
5 min read
Mathematics | Probability Distributions Set 4 (Binomial Distribution)
The previous articles talked about some of the Continuous Probability Distributions. This article covers one of the distributions which are not continuous but discrete, namely the Binomial Distribution.Introduction -To understand the Binomial distribution, we must first understand what a Bernoulli T
5 min read
Poisson Distribution | Definition, Formula, Table and Examples
The Poisson distribution is a discrete probability distribution that calculates the likelihood of a certain number of events happening in a fixed time or space, assuming the events occur independently and at a constant rate.It is characterized by a single parameter, λ (lambda), which represents the
11 min read
P-Value: Comprehensive Guide to Understand, Apply, and Interpret
A p-value is a statistical metric used to assess a hypothesis by comparing it with observed data. This article delves into the concept of p-value, its calculation, interpretation, and significance. It also explores the factors that influence p-value and highlights its limitations. Table of Content W
12 min read
Z-Score in Statistics | Definition, Formula, Calculation and Uses
Z-Score in statistics is a measurement of how many standard deviations away a data point is from the mean of a distribution. A z-score of 0 indicates that the data point's score is the same as the mean score. A positive z-score indicates that the data point is above average, while a negative z-score
15+ min read
How to Calculate Point Estimates in R?
Point estimation is a technique used to find the estimate or approximate value of population parameters from a given data sample of the population. The point estimate is calculated for the following two measuring parameters:Measuring parameterPopulation ParameterPoint EstimateProportionÏp MeanμxÌ Th
3 min read
Confidence Interval
Confidence Interval (CI) is a range of values that estimates where the true population value is likely to fall. Instead of just saying The average height of students is 165 cm a confidence interval allow us to say We are 95% confident that the true average height is between 160 cm and 170 cm.Before
9 min read
Chi-square test in Machine Learning
Chi-Square test helps us determine if there is a significant relationship between two categorical variables and the target variable. It is a non-parametric statistical test meaning it doesnât follow normal distribution. It checks whether thereâs a significant difference between expected and observed
9 min read
Understanding Hypothesis Testing
Hypothesis method compares two opposite statements about a population and uses sample data to decide which one is more likely to be correct.To test this assumption we first take a sample from the population and analyze it and use the results of the analysis to decide if the claim is valid or not. Su
13 min read
Time Series Data Analysis
Data Mining - Time-Series, Symbolic and Biological Sequences Data
Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns. Theoreticians and practitioners are continually seeking improved tech
3 min read
Basic DateTime Operations in Python
Python has an in-built module named DateTime to deal with dates and times in numerous ways. In this article, we are going to see basic DateTime operations in Python. There are six main object classes with their respective components in the datetime module mentioned below: datetime.datedatetime.timed
12 min read
Time Series Analysis & Visualization in Python
Every dataset has distinct qualities that function as essential aspects in the field of data analytics, providing insightful information about the underlying data. Time series data is one kind of dataset that is especially important. This article delves into the complexities of time series datasets,
11 min read
How to deal with missing values in a Timeseries in Python?
It is common to come across missing values when working with real-world data. Time series data is different from traditional machine learning datasets because it is collected under varying conditions over time. As a result, different mechanisms can be responsible for missing records at different tim
9 min read
How to calculate MOVING AVERAGE in a Pandas DataFrame?
Calculating the moving average in a Pandas DataFrame is used for smoothing time series data and identifying trends. The moving average, also known as the rolling mean, helps reduce noise and highlight significant patterns by averaging data points over a specific window. In Pandas, this can be achiev
7 min read
What is a trend in time series?
Time series data is a sequence of data points that measure some variable over ordered period of time. It is the fastest-growing category of databases as it is widely used in a variety of industries to understand and forecast data patterns. So while preparing this time series data for modeling it's i
3 min read
How to Perform an Augmented Dickey-Fuller Test in R
Augmented Dickey-Fuller Test: It is a common test in statistics and is used to check whether a given time series is at rest. A given time series can be called stationary or at rest if it doesn't have any trend and depicts a constant variance over time and follows autocorrelation structure over a per
3 min read
AutoCorrelation
Autocorrelation is a fundamental concept in time series analysis. Autocorrelation is a statistical concept that assesses the degree of correlation between the values of variable at different time points. The article aims to discuss the fundamentals and working of Autocorrelation. Table of Content Wh
10 min read
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