A Python program can handle date and time in several ways. Converting between date formats is a common chore for computers. Python’s time and calendar modules help track dates and times.

Table of Contents

What is Tick?

Time intervals are floating-point numbers in units of seconds. Particular instants in time are expressed in seconds since 12:00am, January 1, 1970(epoch).
There is a popular time module available in Python which provides functions for working with times, and for converting between representations. The function time.time() returns the current system time in ticks since 12:00am, January 1, 1970(epoch).

Example

#!/usr/bin/python
import time; # This is required to include time module.

ticks = time.time()
print “Number of ticks since 12:00am, January 1, 1970:”, ticks
This would produce a result something as follows −
Number of ticks since 12:00am, January 1, 1970: 7186862.73399
Date arithmetic is easy to do with ticks. However, dates before the epoch cannot be represented in this form. Dates in the far future also cannot be represented this way – the cutoff point is sometime in 2038 for UNIX and Windows.

What is TimeTuple?

Many of Python’s time functions handle time as a tuple of 9 numbers, as shown below −

Index	Field	Values
0	4-digit year	2008
1	Month	1 to 12
2	Day	1 to 31
3	Hour	0 to 23
4	Minute	0 to 59
5	Second	0 to 61 (60 or 61 are leap-seconds)
6	Day of Week	0 to 6 (0 is Monday)
7	Day of year	1 to 366 (Julian day)
8	Daylight savings	-1, 0, 1, -1 means library determines DST

The above tuple is equivalent to struct_time structure. This structure has following attributes −

Index	Attributes	Values
0	tm_year	2008
1	tm_mon	1 to 12
2	tm_mday	1 to 31
3	tm_hour	0 to 23
4	tm_min	0 to 59
5	tm_sec	0 to 61 (60 or 61 are leap-seconds)
6	tm_wday	0 to 6 (0 is Monday)
7	tm_yday	1 to 366 (Julian day)
8	tm_isdst	-1, 0, 1, -1 means library determines DST

Getting current time

To translate a time instant from a seconds since the epoch floating-point value into a time-tuple, pass the floating-point value to a function (e.g., localtime) that returns a time-tuple with all nine items valid.

#!/usr/bin/python
import time;

localtime = time.localtime(time.time())
print “Local current time :”, localtime
This would produce the following result, which could be formatted in any other presentable form −
Local current time : time.struct_time(tm_year=2013, tm_mon=7,
tm_mday=17, tm_hour=21, tm_min=26, tm_sec=3, tm_wday=2, tm_yday=198, tm_isdst=0)

Getting formatted time

You can format any time as per your requirement, but simple method to get time in readable format is asctime() −

#!/usr/bin/python
import time;

localtime = time.asctime( time.localtime(time.time()) )
print “Local current time :”, localtime
This would produce the following result −
Local current time : Tue Jan 13 10:17:09 2009

Getting calendar for a month

The calendar module gives a wide range of methods to play with yearly and monthly calendars. Here, we print a calendar for a given month ( Jan 2008 ) −

#!/usr/bin/python
import calendar

cal = calendar.month(2008, 1)
print “Here is the calendar:”
print cal
This would produce the following result −
Here is the calendar:
January 2008
Mo Tu We Th Fr Sa Su
1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19 20
21 22 23 24 25 26 27
28 29 30 31

The time Module

There is a popular time module available in Python which provides functions for working with times and for converting between representations. Here is the list of all available methods −

Sr.No.	Function with Description
1	time.altzone The offset of the local DST timezone, in seconds west of UTC, if one is defined. This is negative if the local DST timezone is east of UTC (as in Western Europe, including the UK). Only use this if daylight is nonzero.
2	time.asctime([tupletime]) Accepts a time-tuple and returns a readable 24-character string such as ‘Tue Dec 11 18:07:14 2008’.
3	time.clock( ) Returns the current CPU time as a floating-point number of seconds. To measure computational costs of different approaches, the value of time.clock is more useful than that of time.time().
4	time.ctime([secs]) Like asctime(localtime(secs)) and without arguments is like asctime( )
5	time.gmtime([secs]) Accepts an instant expressed in seconds since the epoch and returns a time-tuple t with the UTC time. Note : t.tm_isdst is always 0
6	time.localtime([secs]) Accepts an instant expressed in seconds since the epoch and returns a time-tuple t with the local time (t.tm_isdst is 0 or 1, depending on whether DST applies to instant secs by local rules).
7	time.mktime(tupletime) Accepts an instant expressed as a time-tuple in local time and returns a floating-point value with the instant expressed in seconds since the epoch.
8	time.sleep(secs) Suspends the calling thread for secs seconds.
9	time.strftime(fmt[,tupletime]) Accepts an instant expressed as a time-tuple in local time and returns a string representing the instant as specified by string fmt.
10	time.strptime(str,fmt=’%a %b %d %H:%M:%S %Y’) Parses str according to format string fmt and returns the instant in time-tuple format.
11	time.time( ) Returns the current time instant, a floating-point number of seconds since the epoch.
12	time.tzset() Resets the time conversion rules used by the library routines. The environment variable TZ specifies how this is done.

Let us go through the functions briefly −
There are following two important attributes available with time module −

Sr.No.

Attribute with Description

time.timezone

Attribute time.timezone is the offset in seconds of the local time zone (without DST) from UTC (>0 in the Americas; <=0 in most of Europe, Asia, Africa).

time.tzname

Attribute time.tzname is a pair of locale-dependent strings, which are the names of the local time zone without and with DST, respectively.

The calendar Module

The calendar module supplies calendar-related functions, including functions to print a text calendar for a given month or year.
By default, calendar takes Monday as the first day of the week and Sunday as the last one. To change this, call calendar.setfirstweekday() function.
Here is a list of functions available with the calendar module −

Sr.No.	Function with Description
1	calendar.calendar(year,w=2,l=1,c=6) Returns a multiline string with a calendar for year year formatted into three columns separated by c spaces. w is the width in characters of each date; each line has length 21w+18+2c. l is the number of lines for each week.
2	calendar.firstweekday( ) Returns the current setting for the weekday that starts each week. By default, when calendar is first imported, this is 0, meaning Monday.
3	calendar.isleap(year) Returns True if year is a leap year; otherwise, False.
4	calendar.leapdays(y1,y2) Returns the total number of leap days in the years within range(y1,y2).
5	calendar.month(year,month,w=2,l=1) Returns a multiline string with a calendar for month month of year year, one line per week plus two header lines. w is the width in characters of each date; each line has length 7*w+6. l is the number of lines for each week.
6	calendar.monthcalendar(year,month) Returns a list of lists of ints. Each sublist denotes a week. Days outside month month of year year are set to 0; days within the month are set to their day-of-month, 1 and up.
7	calendar.monthrange(year,month) Returns two integers. The first one is the code of the weekday for the first day of the month month in year year; the second one is the number of days in the month. Weekday codes are 0 (Monday) to 6 (Sunday); month numbers are 1 to 12.
8	calendar.prcal(year,w=2,l=1,c=6) Like print calendar.calendar(year,w,l,c).
9	calendar.prmonth(year,month,w=2,l=1) Like print calendar.month(year,month,w,l).
10	calendar.setfirstweekday(weekday) Sets the first day of each week to weekday code weekday. Weekday codes are 0 (Monday) to 6 (Sunday).
11	calendar.timegm(tupletime) The inverse of time.gmtime: accepts a time instant in time-tuple form and returns the same instant as a floating-point number of seconds since the epoch.
12	calendar.weekday(year,month,day) Returns the weekday code for the given date. Weekday codes are 0 (Monday) to 6 (Sunday); month numbers are 1 (January) to 12 (December).

Other Modules & Functions

If you are interested, then here you would find a list of other important modules and functions to play with date & time in Python −
• The datetime Module
• The pytz Module
• The dateutil Module
So, this brings us to the end of blog. This Tecklearn ‘Basic Syntax in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Python Programming domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period.

Python with Data Science Training

About the Course

Python with Data Science training lets you master the concepts of the widely used and powerful programming language, Python. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, NumPy, Matplotlib which are essential for Data Science. You will work on real-world projects in the domain of Python and apply it for various domains of Big Data, Data Science and Machine Learning.

Why Should you take Python with Data Science Training?

• Python is the preferred language for new technologies such as Data Science and Machine Learning.
• Average salary of Python Certified Developer is $123,656 per annum – Indeed.com
• Python is by far the most popular language for data science. Python held 65.6% of the data science market.

What you will Learn in this Course?

Introduction to Python

• Define Python
• Understand the need for Programming
• Know why to choose Python over other languages
• Setup Python environment
• Understand Various Python concepts – Variables, Data Types Operators, Conditional Statements and Loops
• Illustrate String formatting
• Understand Command Line Parameters and Flow control

Python Environment Setup and Essentials

• Python installation
• Windows, Mac & Linux distribution for Anaconda Python
• Deploying Python IDE
• Basic Python commands, data types, variables, keywords and more

Python language Basic Constructs

• Looping in Python
• Data Structures: List, Tuple, Dictionary, Set
• First Python program
• Write a Python Function (with and without parameters)
• Create a member function and a variable
• Tuple
• Dictionary
• Set and Frozen Set
• Lambda function

OOP (Object Oriented Programming) in Python

• Object-Oriented Concepts

Working with Modules, Handling Exceptions and File Handling

• Standard Libraries
• Modules Used in Python (OS, Sys, Date and Time etc.)
• The Import statements
• Module search path
• Package installation ways
• Errors and Exception Handling
• Handling multiple exceptions

Introduction to NumPy

• Introduction to arrays and matrices
• Indexing of array, datatypes, broadcasting of array math
• Standard deviation, Conditional probability
• Correlation and covariance
• NumPy Exercise Solution

Introduction to Pandas

• Pandas for data analysis and machine learning
• Pandas for data analysis and machine learning Continued
• Time series analysis
• Linear regression
• Logistic Regression
• ROC Curve
• Neural Network Implementation
• K Means Clustering Method

Data Visualisation

• Matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots – bar graphs, pie charts, histograms
• Contour plots

Data Manipulation

• Perform function manipulations on Data objects
• Perform Concatenation, Merging and Joining on DataFrames
• Iterate through DataFrames
• Explore Datasets and extract insights from it

Scikit-Learn for Natural Language Processing

• What is natural language processing, working with NLP on text data
• Scikit-Learn for Natural Language Processing
• The Scikit-Learn machine learning algorithms
• Sentimental Analysis – Twitter

Introduction to Python for Hadoop

• Deploying Python coding for MapReduce jobs on Hadoop framework.
• Python for Apache Spark coding
• Deploying Spark code with Python
• Machine learning library of Spark MLlib
• Deploying Spark MLlib for Classification, Clustering and Regression

Got a question for us? Please mention it in the comments section and we will get back to you.

230