A Python closure is a nested function which has access to a variable from an enclosing function that has finished its execution. Such a variable is not bound in the local scope. To use immutable variables (number or string), we have to use the non-local keyword.
The main advantage of Python closures is that we can help avoid the using global values and provide some form of data hiding. They are used in Python decorators.
Closures are closely related to nested functions and allow inner functions to capture and retain the enclosing function’s local state, even after the outer function has finished execution. Understanding closures requires familiarity with nested functions, variable scope and how Python handles function objects.
Nested Functions: In Python functions can be defined inside other functions. These are called nested functions or inner functions.
Accessing Enclosing Scope: Inner functions can access variables from the enclosing i.e. outer scope. This is where closures come into play.
Retention of State: When an inner function i.e. closure captures and retains variables from its enclosing scope, even if the outer function has completed execution or the scope is no longer available.
Nested Functions
Nested functions in Python refer to the practice of defining one function inside another function. This concept allows us to organize code more effectively, encapsulate functionality and manage variable scope.
Following is the example of nested functions where functionB is defined inside functionA. Inner function is then called from inside the outer function’s scope.
When a closure is created i.e. an inner function that captures variables from its enclosing scope, it retains access to those variables even after the outer function has finished executing. This behavior allows closures to “remember” and manipulate the values of variables from the enclosing scope.
Example
Following is the example of the closure with the variable scope −
defouter_function(x):
y =10definner_function(z):return x + y + z # x and y are captured from the enclosing scopereturn inner_function
closure = outer_function(5)
result = closure(3)print(result)
Output
18
Creating a closure
Creating a closure in Python involves defining a nested function within an outer function and returning the inner function. Closures are useful for capturing and retaining the state of variables from the enclosing scope.
Example
In the below example, we have a functionA function which creates and returns another function functionB. The nested functionB function is the closure.
The outer functionA function returns a functionB function and assigns it to the myfunction variable. Even if it has finished its execution. However, the printer closure still has access to the name variable.
Following is the example of creating the closure in python −
deffunctionA(name):
name ="New name"deffunctionB():print(name)return functionB
myfunction = functionA("My name")
myfunction()
Output
New name
nonlocal Keyword
In Python, nonlocal keyword allows a variable outside the local scope to be accessed. This is used in a closure to modify an immutable variable present in the scope of outer variable. Here is the example of the closure with the nonlocal keyword.
Generators in Python are a convenient way to create iterators. They allow us to iterate through a sequence of values which means, values are generated on the fly and not stored in memory, which is especially useful for large datasets or infinite sequences.
The generator in Python is a special type of function that returns an iterator object. It appears similar to a normal Python function in that its definition also starts with def keyword. However, instead of return statement at the end, generator uses the yield keyword.
Syntax
The following is the syntax of the generator() function −
defgenerator():......yield obj it = generator()next(it)... Creating Generators
There are two primary ways to create generators in python −
Using Generator Functions Using Generator Expressions Using Generator Functions
The generator function uses 'yield' statement for returning the values all at a time. Each time the generators __next__() method is called the generator resumes where it left off i.e. from right after the last yield statement. Here's the example of creating the generator function.
def count_up_to(max_value): current = 1 while current <= max_value: yield current current += 1
# Using the generator counter = count_up_to(5) for number in counter: print(number) Output
1 2 3 4 5 Using Generator Expressions
Generator expressions provide a compact way to create generators. They use a syntax similar to list comprehensions but used parentheses i.e. "{}" instead of square brackets i.e. "[]"
gen_expr = (x * x for x in range(1, 6))
for value in gen_expr: print(value) Output
1 4 9 16 25 Exception Handling in Generators
We can create a generator and iterate it using a 'while' loop with exception handling for 'StopIteration' exception. The function in the below code is a generator that successively yield integers from 1 to 5.
When this function is called, it returns an iterator. Every call to next() method transfers the control back to the generator and fetches next integer.
def generator(num): for x in range(1, num+1): yield x return
it = generator(5) while True: try: print (next(it)) except StopIteration: break Output
1 2 3 4 5 Normal function vs Generator function
Normal functions and generator functions in Python serve different purposes and exhibit distinct behaviors. Understanding their differences is essential for leveraging them effectively in our code.
A normal function computes and returns a single value or a set of values whether in a list or tuple, when called. Once it returns, the function's execution is complete and all local variables are discarded where as a generator function yields values one at a time by suspending and resuming its state between each yield. It uses the yield statement instead of return.
Example
In this example we are creating a normal function and build a list of Fibonacci numbers and then iterate the list using a loop −
def fibonacci(n): fibo = [] a, b = 0, 1 while True: c=a+b if c>=n: break fibo.append(c) a, b = b, c return fibo f = fibonacci(10) for i in f: print (i) Output
1 2 3 5 8 Example
In the above example we created a fibonacci series using the normal function and When we want to collect all Fibonacci series numbers in a list and then the list is traversed using a loop. Imagine that we want Fibonacci series going upto a large number.
In such cases, all the numbers must be collected in a list requiring huge memory. This is where generator is useful as it generates a single number in the list and gives it for consumption. Following code is the generator-based solution for list of Fibonacci numbers −
def fibonacci(n): a, b = 0, 1 while True: c=a+b if c>=n: break yield c a, b = b, c return
f = fibonacci(10) while True: try: print (next(f)) except StopIteration: break Output
1 2 3 5 8 Asynchronous Generator
An asynchronous generator is a co-routine that returns an asynchronous iterator. A co-routine is a Python function defined with async keyword, and it can schedule and await other co-routines and tasks.
Just like a normal generator, the asynchronous generator yields incremental item in the iterator for every call to anext() function, instead of next() function.
Syntax
The following is the syntax of the Asynchronous Generator −
async def generator(): . . . . . . yield obj it = generator() anext(it) . . . Example
Following code demonstrates a coroutine generator that yields incrementing integers on every iteration of an async for loop.
import asyncio
async def async_generator(x): for i in range(1, x+1): await asyncio.sleep(1) yield i
async def main(): async for item in async_generator(5): print(item)
asyncio.run(main()) Output
1 2 3 4 5 Example
Let us now write an asynchronous generator for Fibonacci numbers. To simulate some asynchronous task inside the co-routine, the program calls sleep() method for a duration of 1 second before yielding the next number. As a result, we will get the numbers printed on the screen after a delay of one second.
import asyncio
async def fibonacci(n): a, b = 0, 1 while True: c=a+b if c>=n: break await asyncio.sleep(1) yield c a, b = b, c return
async def main(): f = fibonacci(10) async for num in f: print (num)
An iterator in Python is an object that enables traversal through a collection such as a list or a tuple, one element at a time. It follows the iterator protocol by using the implementation of two methods __iter__() and __next__().
The __iter__() method returns the iterator object itself and the __next__()method returns the next element in the sequence by raising a StopIterationexception when no more elements are available.
Iterators provide a memory-efficient way to iterate over data, especially useful for large datasets. They can be created from iterable objects using the iter()function or implemented using custom classes and generators.
Iterables vs Iterators
Before going deep into the iterator working, we should know the difference between the Iterables and Iterators.
Iterable: An object capable of returning its members one at a time (e.g., lists, tuples).
Iterator: An object representing a stream of data, returned one element at a time.
We normally use for loop to iterate through an iterable as follows −
for element in sequence:print(element)
Python’s built-in method iter() implements __iter__() method. It receives an iterable and returns iterator object.
Example of Python Iterator
Following code obtains iterator object from sequence types such as list, string and tuple. The iter() function also returns keyiterator from dictionary.
<str_iterator object at 0x7fd0416b42e0>
<list_iterator object at 0x7fd0416b42e0>
<tuple_iterator object at 0x7fd0416b42e0>
<dict_keyiterator object at 0x7fd041707560>
However, int id not iterable, hence it produces TypeError.
iterator =iter(100)print(iterator)
It will produce the following output −
Traceback (most recent call last):
File "C:\Users\user\example.py", line 5, in <module>
print (iter(100))
^^^^^^^^^
TypeError: 'int' object is not iterable
Error Handling in Iterators
Iterator object has a method named __next__(). Every time it is called, it returns next element in iterator stream. Call to next() function is equivalent to calling __next__() method of iterator object.
This method which raises a StopIteration exception when there are no more items to return.
Example
In the following is an example the iterator object we have created have only 3 elements and we are iterating through it more than thrice −
it =iter([1,2,3])print(next(it))print(it.__next__())print(it.__next__())print(next(it))
It will produce the following output −
1
2
3
Traceback (most recent call last):
File "C:\Users\user\example.py", line 5, in <module>
print (next(it))
^^^^^^^^
StopIteration
This exception can be caught in the code that consumes the iterator using try and except blocks, though it’s more common to handle it implicitly by using constructs like for loops which manage the StopIteration exception internally.
it =iter([1,2,3,4,5])print(next(it))whileTrue:try:
no =next(it)print(no)except StopIteration:break</pre>
It will produce the following output −
1
2
3
4
5
Custom Iterator
A custom iterator in Python is a user-defined class that implements the iterator protocol which consists of two methods __iter__() and __next__(). This allows the class to behave like an iterator, enabling traversal through its elements one at a time.
To define a custom iterator class in Python, the class must define these methods.
Example
In the following example, the Oddnumbers is a class implementing __iter__() and __next__() methods. On every call to __next__(), the number increments by 2 thereby streaming odd numbers in the range 1 to 10.
# Using the Fibonacci iterator
fib_iterator = Fibonacci(10)for number in fib_iterator:print(number)
It will produce the following output −
0
1
1
2
3
5
8
13
21
34
Asynchronous Iterator
Asynchronous iterators in Python allow us to iterate over asynchronous sequences, enabling the handling of async operations within a loop.
They follow the asynchronous iterator protocol which consists of the methods __aiter__() and __anext__() (added in Python 3.10 version onwards.). These methods are used in conjunction with the async for loop to iterate over asynchronous data sources.
The aiter() function returns an asynchronous iterator object. It is an asynchronous counter part of the classical iterator. Any asynchronous iterator must support ___aiter()__ and __anext__() methods. These methods are internally called by the two built-in functions.
Asynchronous functions are called co-routines and are executed with asyncio.run() method. The main() co-routine contains a while loop that successively obtains odd numbers and raises StopAsyncIteration if the number exceeds 9.
Like the classical iterator the asynchronous iterator gives a stream of objects. When the stream is exhausted, the StopAsyncIteration exception is raised.
Example
In the example give below, an asynchronous iterator class Oddnumbers is declared. It implements __aiter__() and __anext__() method. On each iteration, a next odd number is returned and the program waits for one second, so that it can perform any other process asynchronously.
The math module is a built-in module in Python that is used for performing mathematical operations. This module provides various built-in methods for performing different mathematical tasks.
Note: The math module’s methods do not work with complex numbers. For that, you can use the cmath module.
Importing math Module
Before using the methods of the math module, you need to import the mathmodule into your code. The following is the syntax:
import math
Methods of Python math Module
The following is the list of math module methods that we have categorized based on their functionality and usage.
Math Module – Theoretic and Representation Methods
Python includes following theoretic and representation Functions in the mathmodule −
Sr.No.
Function & Description
1
math.ceil(x)The ceiling of x: the smallest integer not less than x
2
math.comb(n,k)This function is used to find the returns the number of ways to choose “x” items from “y” items without repetition and without order.
3
math.copysign(x, y)This function returns a float with the magnitude (absolute value) of x but the sign of y.
4
math.cmp(x, y)This function is used to compare the values of to objects. This function is deprecated in Python3.
5
math.fabs(x)This function is used to calculate the absolute value of a given integer.
6
math.factorial(n)This function is used to find the factorial of a given integer.
7
math.floor(x)This function calculates the floor value of a given integer.
8
math.fmod(x, y)The fmod() function in math module returns same result as the “%”operator. However fmod() gives more accurate result of modulo division than modulo operator.
9
math.frexp(x)This function is used to calculate the mantissa and exponent of a given number.
10
math.fsum(iterable)This function returns the floating point sum of all numeric items in an iterable i.e. list, tuple, array.
11
math.gcd(*integers)This function is used to calculate the greatest common divisor of all the given integers.
12
math.isclose()This function is used to determine whether two given numeric values are close to each other.
13
math.isfinite(x)This function is used to determine whether the given number is a finite number.
14
math.isinf(x)This function is used to determine whether the given value is infinity (+ve or, -ve).
15
math.isnan(x)This function is used to determine whether the given number is “NaN”.
16
math.isqrt(n)This function calculates the integer square-root of the given non negative integer.
17
math.lcm(*integers)This function is used to calculate the least common factor of the given integer arguments.
18
math.ldexp(x, i)This function returns product of first number with exponent of second number. So, ldexp(x,y) returns x*2**y. This is inverse of frexp() function.
19
math.modf(x)This returns the fractional and integer parts of x in a two-item tuple.
math.perm(n, k)This function is used to calculate the permutation. It returns the number of ways to choose x items from y items without repetition and with order.
22
math.prod(iterable, *, start)This function is used to calculate the product of all numeric items in the iterable (list, tuple) given as argument.
23
math.remainder(x,y)This function returns the remainder of x with respect to y. This is the difference x − n*y, where n is the integer closest to the quotient x / y.
24
math.trunc(x)This function returns integral part of the number, removing the fractional part. trunc() is equivalent to floor() for positive x, and equivalent to ceil() for negative x.
25
math.ulp(x)This function returns the value of the least significant bit of the float x. trunc() is equivalent to floor() for positive x, and equivalent to ceil() for negative x.
Math Module – Power and Logarithmic Methods
Sr.No.
Function & Description
1
math.cbrt(x)This function is used to calculate the cube root of a number.
2
math.exp(x)This function calculate the exponential of x: ex
3
math.exp2(x)This function returns 2 raised to power x. It is equivalent to 2**x.
4
math.expm1(x)This function returns e raised to the power x, minus 1. Here e is the base of natural logarithms.
5
math.log(x)This function calculates the natural logarithm of x, for x> 0.
6
math.log1p(x)This function returns the natural logarithm of 1+x (base e). The result is calculated in a way which is accurate for x near zero.
7
math.log2(x)This function returns the base-2 logarithm of x. This is usually more accurate than log(x, 2).
math.cos(x)This function returns the cosine of x radians.
6
math.sin(x)This function returns the sine of x radians.
7
math.tan(x)This function returns the tangent of x radians.
8
math.hypot(x, y)This function returns the Euclidean norm, sqrt(x*x + y*y).
Math Module – Angular conversion Methods
Following are the angular conversion function provided by Python math module −
Sr.No.
Function & Description
1
math.degrees(x)This function converts the given angle from radians to degrees.
2
math.radians(x)This function converts the given angle from degrees to radians.
Math Module – Mathematical Constants
The Python math module defines the following mathematical constants −
Sr.No.
Constants & Description
1
math.piThis represents the mathematical constant pi, which equals to “3.141592…” to available precision.
2
math.eThis represents the mathematical constant e, which is equal to “2.718281…” to available precision.
3
math.tauThis represents the mathematical constant Tau (denoted by ). It is equivalent to the ratio of circumference to radius, and is equal to 2.
4
math.infThis represents positive infinity. For negative infinity use “−math.inf”.
5
math.nanThis constant is a floating-point “not a number” (NaN) value. Its value is equivalent to the output of float(‘nan’).
Math Module – Hyperbolic Methods
Hyperbolic functions are analogs of trigonometric functions that are based on hyperbolas instead of circles. Following are the hyperbolic functions of the Python math module −
Sr.No.
Function & Description
1
math.acosh(x)This function is used to calculate the inverse hyperbolic cosine of the given value.
2
math.asinh(x)This function is used to calculate the inverse hyperbolic sine of a given number.
3
math.atanh(x)This function is used to calculate the inverse hyperbolic tangent of a number.
4
math.cosh(x)This function is used to calculate the hyperbolic cosine of the given value.
5
math.sinh(x)This function is used to calculate the hyperbolic sine of a given number.
6
math.tanh(x)This function is used to calculate the hyperbolic tangent of a number.
Math Module – Special Methods
Following are the special functions provided by the Python math module −
Sr.No.
Function & Description
1
math.erf(x)This function returns the value of the Gauss error function for the given parameter.
2
math.erfc(x)This function is the complementary for the error function. Value of erf(x) is equivalent to 1-erf(x).
3
math.gamma(x)This is used to calculate the factorial of the complex numbers. It is defined for all the complex numbers except the non-positive integers.
4
math.lgamma(x)This function is used to calculate the natural logarithm of the absolute value of the Gamma function at x.
Example Usage
The following example demonstrates the use of math module and its methods:
# Importing math Moduleimport math
# Using methods of math moduleprint(math.sqrt(9))print(math.pow(3,3))print(math.exp(1))print(math.log(100,10))print(math.factorial(4))print(math.gcd(12,3))
A Python program can handle date and time in several ways. Converting between date formats is a common chore for computers. Following modules in Python’s standard library handle date and time related processing −
DateTime module
Time module
Calendar module
What are Tick Intervals
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
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: 1681928297.5316436
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 the Python’s time functions handle time as a tuple of 9 numbers, as shown below −
The above tuple is equivalent to struct_time structure. This structure has the following attributes −
Index
Attributes
Values
0
tm_year
2016
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 the Current Time
To translate a time instant from 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 valid nine items.
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=2023, tm_mon=4, tm_mday=19, tm_hour=23, tm_min=42, tm_sec=41, tm_wday=2, tm_yday=109, tm_isdst=0)
Getting the Formatted Time
You can format any time as per your requirement, but a simple method to get time in a readable format is asctime() −
import time
localtime = time.asctime( time.localtime(time.time()))print("Local current time :", localtime)
This would produce the following output −
Local current time : Wed Apr 19 23:45:27 2023
Getting the 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).
import calendar
cal = calendar.month(2023,4)print("Here is the calendar:")print(cal)
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.altzoneThe 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.
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.
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 two important attributes available with time module. They are −
Sr.No.
Attribute with Description
1
time.timezoneAttribute 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).
2
time.tznameAttribute 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 the calendar.setfirstweekday() function.
Here is a list of functions available with the calendar module −
Sr.No.
Function with Description
1
calendar.calendar()Returns a multi-line 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 21*w+18+2*c. 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()Returns True if year is a leap year; otherwise, False.
4
calendar.leapdays()Returns the total number of leap days in the years within range(y1,y2).
5
calendar.month()Returns a multi-line 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()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()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.
calendar.setfirstweekday()Sets the first day of each week to weekday code weekday. Weekday codes are 0 (Monday) to 6 (Sunday).
11
calendar.timegm()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()Returns the weekday code for the given date. Weekday codes are 0 (Monday) to 6 (Sunday); month numbers are 1 (January) to 12 (December).
Python datetime Module
Python’s datetime module is included in the standard library. It consists of classes that help manipulate data and time data and perform date time arithmetic.
Objects of datetime classes are either aware or nave. If the object includes timezone information it is aware, and if not it is classified as nave. An object of date class is nave, whereas time and datetime objects are aware.
Python date Object
A date object represents a date with year, month, and day. The current Gregorian calendar is indefinitely extended in both directions.
Syntax
datetime.date(year, month, day)
Arguments must be integers, in the following ranges −
year − MINYEAR <= year <= MAXYEAR
month − 1 <= month <= 12
day − 1 <= day <= number of days in the given month and year
If the value of any argument outside those ranges is given, ValueError is raised.
Example
from datetime import date
date1 = date(2023,4,19)print("Date:", date1)
date2 = date(2023,4,31)
It will produce the following output −
Date: 2023-04-19
Traceback (most recent call last):
File "C:\Python311\hello.py", line 8, in <module>
date2 = date(2023, 4, 31)
ValueError: day is out of range for month
date class attributes
date.min − The earliest representable date, date(MINYEAR, 1, 1).
date.max − The latest representable date, date(MAXYEAR, 12, 31).
date.resolution − The smallest possible difference between non-equal date objects.
date.year − Between MINYEAR and MAXYEAR inclusive.
date.month − Between 1 and 12 inclusive.
date.day − Between 1 and the number of days in the given month of the given year.
Example
from datetime import date
# Getting min date
mindate = date.minprint("Minimum Date:", mindate)# Getting max date
maxdate = date.maxprint("Maximum Date:", maxdate)
Date1 = date(2023,4,20)print("Year:", Date1.year)print("Month:", Date1.month)print("Day:", Date1.day)
fromtimestamp(timestamp) − Return the local date corresponding to the POSIX timestamp, such as is returned by time.time().
fromordinal(ordinal) − Return the date corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1.
fromisoformat(date_string) − Return a date corresponding to a date_string given in any valid ISO 8601 format, except ordinal dates
Example
from datetime import date
print(date.today())
d1=date.fromisoformat('2023-04-20')print(d1)
d2=date.fromisoformat('20230420')print(d2)
d3=date.fromisoformat('2023-W16-4')print(d3)
It will produce the following output −
2023-04-20
2023-04-20
2023-04-20
2023-04-20
Instance Methods in Date Class
replace() − Return a date by replacing specified attributes with new values by keyword arguments are specified.
timetuple() − Return a time.struct_time such as returned by time.localtime().
toordinal() − Return the proleptic Gregorian ordinal of the date, where January 1 of year 1 has ordinal 1. For any date object d, date.fromordinal(d.toordinal()) == d.
weekday() − Return the day of the week as an integer, where Monday is 0 and Sunday is 6.
isoweekday() − Return the day of the week as an integer, where Monday is 1 and Sunday is 7.
isocalendar() − Return a named tuple object with three components: year, week and weekday.
isoformat() − Return a string representing the date in ISO 8601 format, YYYY-MM-DD:
__str__() − For a date d, str(d) is equivalent to d.isoformat()
ctime() − Return a string representing the date:
strftime(format) − Return a string representing the date, controlled by an explicit format string.
__format__(format) − Same as date.strftime().
Example
from datetime import date
d = date.fromordinal(738630)# 738630th day after 1. 1. 0001print(d)print(d.timetuple())# Methods related to formatting string outputprint(d.isoformat())print(d.strftime("%d/%m/%y"))print(d.strftime("%A %d. %B %Y"))print(d.ctime())print('The {1} is {0:%d}, the {2} is {0:%B}.'.format(d,"day","month"))# Methods for to extracting 'components' under different calendars
t = d.timetuple()for i in t:print(i)
ic = d.isocalendar()for i in ic:print(i)# A date object is immutable; all operations produce a new objectprint(d.replace(month=5))
It will produce the following output −
2023-04-20
time.struct_time(tm_year=2023, tm_mon=4, tm_mday=20, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=3, tm_yday=110, tm_isdst=-1)
2023-04-20
20/04/23
Thursday 20. April 2023
Thu Apr 20 00:00:00 2023
The day is 20, the month is April.
2023
4
20
0
0
0
3
110
-1
2023
16
4
2023-05-20
Python time Module
An object time class represents the local time of the day. It is independent of any particular day. It the object contains the tzinfo details, it is the aware object. If it is None then the time object is the naive object.
All arguments are optional. tzinfo may be None, or an instance of a tzinfo subclass. The remaining arguments must be integers in the following ranges −
hour − 0 <= hour < 24,
minute − 0 <= minute < 60,
second − 0 <= second < 60,
microsecond − 0 <= microsecond < 1000000
If any of the arguments are outside those ranges is given, ValueError is raised.
Time: 08:14:36
time 00:12:00
time 00:00:00
Traceback (most recent call last):
File "/home/cg/root/64b912f27faef/main.py", line 12, in
time4 = time(hour = 26)
ValueError: hour must be in 0..23
Class attributes
time.min − The earliest representable time, time(0, 0, 0, 0).
time.max − The latest representable time, time(23, 59, 59, 999999).
time.resolution − The smallest possible difference between non-equal time objects.
Example
from datetime import time
print(time.min)print(time.max)print(time.resolution)
It will produce the following output −
00:00:00
23:59:59.999999
0:00:00.000001
Instance attributes
time.hour − In range(24)
time.minute − In range(60)
time.second − In range(60)
time.microsecond − In range(1000000)
time.tzinfo − the tzinfo argument to the time constructor, or None.
Example
from datetime import time
t = time(8,23,45,5000)print(t.hour)print(t.minute)print(t.second)print(t.microsecond)
It will produce the following output −
8
23
455000
Instance Methods of time Object
replace() − Return a time with the same value, except for those attributes given new values by whichever keyword arguments are specified.
isoformat() − Return a string representing the time in ISO 8601 format
__str__() − For a time t, str(t) is equivalent to t.isoformat().
strftime(format) − Return a string representing the time, controlled by an explicit format string.
__format__(format) − Same as time.strftime().
utcoffset() − If tzinfo is None, returns None, else returns self.tzinfo.utcoffset(None),
dst() − If tzinfo is None, returns None, else returns self.tzinfo.dst(None),
tzname() − If tzinfo is None, returns None, else returns self.tzinfo.tzname(None), or raises an exception
Python datetime object
An object of datetime class contains the information of date and time together. It assumes the current Gregorian calendar extended in both directions; like a time object, and there are exactly 3600*24 seconds in every day.
today() − Return the current local datetime, with tzinfo None.
now(tz=None) − Return the current local date and time.
utcnow() − Return the current UTC date and time, with tzinfo None.
utcfromtimestamp(timestamp) − Return the UTC datetime corresponding to the POSIX timestamp, with tzinfo None
fromtimestamp(timestamp, timezone.utc) − On the POSIX compliant platforms, it is equivalent todatetime(1970, 1, 1, tzinfo=timezone.utc) + timedelta(seconds=timestamp)
fromordinal(ordinal) − Return the datetime corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1.
fromisoformat(date_string) − Return a datetime corresponding to a date_string in any valid ISO 8601 format.
Instance Methods of datetime Object
date() − Return date object with same year, month and day.
time() − Return time object with same hour, minute, second, microsecond and fold.
timetz() − Return time object with same hour, minute, second, microsecond, fold, and tzinfo attributes. See also method time().
replace() − Return a datetime with the same attributes, except for those attributes given new values by whichever keyword arguments are specified.
astimezone(tz=None) − Return a datetime object with new tzinfo attribute tz
utcoffset() − If tzinfo is None, returns None, else returns self.tzinfo.utcoffset(self)
dst() − If tzinfo is None, returns None, else returns self.tzinfo.dst(self)
tzname() − If tzinfo is None, returns None, else returns self.tzinfo.tzname(self)
timetuple() − Return a time.struct_time such as returned by time.localtime().
atetime.toordinal() − Return the proleptic Gregorian ordinal of the date.
timestamp() − Return POSIX timestamp corresponding to the datetime instance.
isoweekday() − Return day of the week as an integer, where Monday is 1, Sunday is 7.
isocalendar() − Return a named tuple with three components: year, week and weekday.
isoformat(sep=’T’, timespec=’auto’) − Return a string representing the date and time in ISO 8601 format
__str__() − For a datetime instance d, str(d) is equivalent to d.isoformat(‘ ‘).
ctime() − Return a string representing the date and time:
strftime(format) − Return a string representing the date and time, controlled by an explicit format string.
__format__(format) − Same as strftime().
Example
from datetime import datetime, date, time, timezone
# Using datetime.combine()
d = date(2022,4,20)
t = time(12,30)
datetime.combine(d, t)# Using datetime.now()
d = datetime.now()print(d)# Using datetime.strptime()
dt = datetime.strptime("23/04/20 16:30","%d/%m/%y %H:%M")# Using datetime.timetuple() to get tuple of all attributes
tt = dt.timetuple()for it in tt:print(it)# Date in ISO format
ic = dt.isocalendar()for it in ic:print(it)
In Python, generics is a mechanism with which you to define functions, classes, or methods that can operate on multiple types while maintaining type safety. With the implementation of Generics enable it is possible to write reusable code that can be used with different data types. It ensures promoting code flexibility and type correctness.
Normally, in Python programming, you don’t need to declare a variable type. The type is determined dynamically by the value assigned to it. Python’s interpreter doesn’t perform type checks and hence it may raise runtime exceptions.
Python introduced generics with type hints in version 3.5, allowing you to specify the expected types of variables, function arguments, and return values. This feature helps in reducing runtime errors and improving code readability.
Generics extend the concept of type hints by introducing type variables, which represent generic types that can be replaced with specific types when using the generic function or class.
Defining a Generic Function
Let us have a look at the following example that defines a generic function −
from typing import List, TypeVar, Generic
T = TypeVar('T')defreverse(items: List[T])-> List[T]:return items[::-1]
Here, we define a generic function called ‘reverse’. The function takes a list (‘List[T]’) as an argument and returns a list of the same type. The type variable ‘T’ represents the generic type, which will be replaced with a specific type when the function is used.
Calling the Generic Function with Different Data Types
The function reverse() function is called with different data types −
A generic type is typically declared by adding a list of type parameters after the class name. The following example uses generics with a generic class −
from typing import List, TypeVar, Generic
T = TypeVar('T')classBox(Generic[T]):def__init__(self, item: T):
self.item = item
defget_item(self)-> T:return self.item
Let us create objects of the above generic classwithintandstrtype
box1 = Box(42)print(box1.get_item())
box2 = Box('Hello')print(box2.get_item())
In the world of Internet, different resources are identified by URLs (Uniform Resource Locators). Python’s standard library includes the urllib package, which has modules for working with URLs. It helps you parse URLs, fetch web content, and manage errors.
This tutorial introduces urllib basics to help you start using it. Improve your skills in web scraping, fetching data, and managing URLs with Python using urllib.
The urllib package contains the following modules for processing URLs −
urllib.parse module is used for parsing a URL into its parts.
urllib.request module contains functions for opening and reading URLs
urllib.error module carries definitions of the exceptions raised by urllib.request
urllib.robotparser module parses the robots.txt files
The urllib.parse Module
This module serves as a standard interface to obtain various parts from a URL string. The module contains following functions −
urlparse(urlstring)
Parse a URL into six components, returning a 6-item named tuple. Each tuple item is a string corresponding to following attributes −
This function Parse a query string given as a string argument. Data is returned as a dictionary. The dictionary keys are the unique query variable names and the values are lists of values for each name.
To further fetch the query parameters from the query string into a dictionary, use parse_qs() function of the query attribute of ParseResult object as follows −
This is similar to urlparse(), but does not split the params from the URL. This should generally be used instead of urlparse() if the more recent URL syntax allowing parameters to be applied to each segment of the path portion of the URL is wanted.
urlunparse(parts)
This function is the opposite of urlparse() function. It constructs a URL from a tuple as returned by urlparse(). The parts argument can be any six-item iterable. This returns an equivalent URL.
Example
from urllib.parse import urlunparse
lst =['https','example.com','/employees/name/','','salary>=25000','']
new_url = urlunparse(lst)print("URL:", new_url)
Combine the elements of a tuple as returned by urlsplit() into a complete URL as a string. The parts argument can be any five-item iterable.
The urllib.request Module
This module offers the functions and classes for handling the URL’s opening and reading operations by using the urlopen() function.
urlopen() function
This function opens the given URL, which can be either a string or a Request object. The optional timeout parameter specifies a timeout in seconds for blocking operations This actually only works for HTTP, HTTPS and FTP connections.
This function always returns an object which can work as a context manager and has the properties url, headers, and status. For HTTP and HTTPS URLs, this function returns a http.client.HTTPResponse object slightly modified.
Example
The following code uses urlopen() function to read the binary data from an image file, and writes it to local file. You can open the image file on your computer using any image viewer.
from urllib.request import urlopen
obj = urlopen("https://www.tutorialspoint.com/images/logo.png")
data = obj.read()
img =open("img.jpg","wb")
img.write(data)
img.close()
It will produce the following output −
The Request Object
The urllib.request module includes Request class. This class is an abstraction of a URL request. The constructor requires a mandatory string argument a valid URL.
data − An object specifying additional data to send to the server. This parameter can only be used with HTTP requests. Data may be bytes, file-like objects, and iterables of bytes-like objects.
headers − Should be a dictionary of headers and their associated values.
origin_req_host − Should be the request-host of the origin transaction
method − should be a string that indicates the HTTP request method. One of GET, POST, PUT, DELETE and other HTTP verbs. Default is GET.
Example
from urllib.request import Request
obj = Request("https://www.tutorialspoint.com/")
This Request object can now be used as an argument to urlopen() method.
Following exceptions are defined in urllib.error module −
URLError
URLError is raised because there is no network connection (no route to the specified server), or the specified server doesn’t exist. In this case, the exception raised will have a ‘reason’ attribute.
Example
from urllib.request import Request, urlopen
import urllib.error as err
obj = Request("http://www.nosuchserver.com")try:
urlopen(obj)except err.URLError as e:print(e)
It will produce the following output −
HTTP Error 403: Forbidden
HTTPError
Every time the server sends a HTTP response it is associated with a numeric “status code”. It code indicates why the server is unable to fulfill the request. The default handlers will handle some of these responses for you. For those it can’t handle, urlopen() function raises an HTTPError. Typical examples of HTTPErrors are ‘404’ (page not found), ‘403’ (request forbidden), and ‘401’ (authentication required).
Example
from urllib.request import Request, urlopen
import urllib.error as err
obj = Request("http://www.python.org/fish.html")try:
urlopen(obj)except err.HTTPError as e:print(e.code)
Socket programming is a technique in which we communicate between two nodes connected in a network where the server node listens to the incoming requests from the client nodes.
In Python, the socket module is used for socket programming. The socketmodule in the standard library included functionality required for communication between server and client at hardware level.
This module provides access to the BSD socket interface. It is available on all operating systems such as Linux, Windows, MacOS.
What are Sockets?
Sockets are the endpoints of a bidirectional communications channel. Sockets may communicate within a process, between processes on the same machine, or between processes on different continents.
A socket is identified by the combination of IP address and the port number. It should be properly configured at both ends to begin communication.
Sockets may be implemented over a number of different channel types: Unix domain sockets, TCP, UDP, and so on. The socket library provides specific classes for handling the common transports as well as a generic interface for handling the rest.
The term socket programming implies programmatically setting up sockets to be able to send and receive data.
There are two types of communication protocols −
connection-oriented protocol
connection-less protocol
TCP or Transmission Control Protocol is a connection-oriented protocol. The data is transmitted in packets by the server, and assembled in the same order of transmission by the receiver. Since the sockets at either end of the communication need to be set before starting, this method is more reliable.
UDP or User Datagram Protocol is connectionless. The method is not reliable because the sockets does not require establishing any connection and termination process for transferring the data.
Python socket Module
The socket module is used for creating and managing socket programming for the connected nodes in a network. The socket module provides a socket class. You need to create a socket using the socket.socket() constructor.
An object of the socket class represents the pair of host name and the port numbers.
Syntax
The following is the syntax of socket.socket() constructor –
family − AF_INET by default. Other values – AF_INET6 (eight groups of four hexadecimal digits), AF_UNIX, AF_CAN (Controller Area Network) or AF_RDS (Reliable Datagram Sockets).
socket_type − should be SOCK_STREAM (the default), SOCK_DGRAM, SOCK_RAW or perhaps one of the other SOCK_ constants.
protocol − number is usually zero and may be omitted.
Return Type
This method returns a socket object.
Once you have the socket object, then you can use the required methods to create your client or server program.
Server Socket Methods
The socket instantiated on server is called server socket. Following methods are available to the socket object on the server −
bind() method − This method binds the socket to specified IP address and port number.
listen() method − This method starts server and runs into a listen loop looking for connection request from client.
accept() method − When connection request is intercepted by server, this method accepts it and identifies the client socket with its address.
To create a socket on a server, use the following snippet −
By default, the server is bound to local machine’s IP address ‘localhost’ listening at arbitrary empty port number.
Client Socket Methods
Similar socket is set up on the client end. It mainly sends connection request to server socket listening at its IP address and port number
connect() method
This method takes a two-item tuple object as argument. The two items are IP address and port number of the server.
obj=socket.socket()
obj.connect((host,port))
Once the connection is accepted by the server, both the socket objects can send and/or receive data.
send() method
The server sends data to client by using the address it has intercepted.
client.send(bytes)
Client socket sends data to socket it has established connection with.
sendall() method
similar to send(). However, unlike send(),this method continues to send data from bytes until either all data has been sent or an error occurs. None is returned on success.
sendto() method
This method is to be used in case of UDP protocol only.
recv() method
This method is used to retrieve data sent to the client. In case of server, it uses the remote socket whose request has been accepted.
client.recv(bytes)
recvfrom() method
This method is used in case of UDP protocol.
Python – Socket Server
To write Internet servers, we use the socket function available in socket module to create a socket object. A socket object is then used to call other functions to setup a socket server.
Now call the bind(hostname, port) function to specify a port for your service on the given host.
Next, call the accept method of the returned object. This method waits until a client connects to the port you specified, and then returns a connection object that represents the connection to that client.
Example of Server Socket
import socket
host ="127.0.0.1"
port =5001
server = socket.socket()
server.bind((host,port))
server.listen()
conn, addr = server.accept()print("Connection from: "+str(addr))whileTrue:
data = conn.recv(1024).decode()ifnot data:break
data =str(data).upper()print(" from client: "+str(data))
data =input("type message: ")
conn.send(data.encode())
conn.close()
Python – Socket Client
Let us write a very simple client program, which opens a connection to a given port 5001 and a given localhost. It is very simple to create a socket client using the Python’s socket module function.
The socket.connect(hosname, port) opens a TCP connection to hostname on the port. Once you have a socket open, you can read from it like any IO object. When done, remember to close it, as you would close a file.
Example of Client Socket
The following code is a very simple client that connects to a given host and port, reads any available data from the socket, and then exits when ‘q’ is entered.
import socket
host ='127.0.0.1'
port =5001
obj = socket.socket()
obj.connect((host,port))
message =input("type message: ")while message !='q':
obj.send(message.encode())
data = obj.recv(1024).decode()print('Received from server: '+ data)
message =input("type message: ")
obj.close()
Run Server code first. It starts listening.
Then start client code. It sends request.
Request accepted. Client address identified
Type some text and press Enter.
Data received is printed. Send data to client.
Data from server is received.
Loop terminates when ‘q’ is input.
Server-client interaction is shown below −
We have implemented client-server communication with socket module on the local machine. To put server and client codes on two different machines on a network, we need to find the IP address of the server machine.
On Windows, you can find the IP address by running ipconfig command. The ifconfig command is the equivalent command on Ubuntu.
Change host string in both the server and client codes with IPv4 Address value and run them as before.
Python File Transfer with Socket Module
The following program demonstrates how socket communication can be used to transfer a file from server to the client
Server Code
The code for establishing connection is same as before. After the connection request is accepted, a file on server is opened in binary mode for reading, and bytes are successively read and sent to the client stream till end of file is reached.
import socket
host ="127.0.0.1"
port =5001
server = socket.socket()
server.bind((host, port))
server.listen()
conn, addr = server.accept()
data = conn.recv(1024).decode()
filename='test.txt'
f =open(filename,'rb')whileTrue:
l = f.read(1024)ifnot l:break
conn.send(l)print('Sent ',repr(l))
f.close()print('File transferred')
conn.close()
Client Code
On the client side, a new file is opened in wb mode. The stream of data received from server is written to the file. As the stream ends, the output file is closed. A new file will be created on the client machine.
import socket
s = socket.socket()
host ="127.0.0.1"
port =5001
s.connect((host, port))
s.send("Hello server!".encode())withopen('recv.txt','wb')as f:whileTrue:print('receiving data...')
The socketserver module in Python’s standard library is a framework for simplifying task of writing network servers. There are following classes in module, which represent synchronous servers −
These classes work with corresponding RequestHandler classes for implementing the service. BaseServer is the superclass of all Server objects in the module.
TCPServer class uses the internet TCP protocol, to provide continuous streams of data between the client and server. The constructor automatically attempts to invoke server_bind() and server_activate(). The other parameters are passed to the BaseServer base class.
You must also create a subclass of StreamRequestHandler class. IT provides self.rfile and self.wfile attributes to read or write to get the request data or return data to the client.
UDPServer and DatagramRequestHandler − These classes are meant to be used for UDP protocol.
DatagramRequestHandler and UnixDatagramServer − These classes use Unix domain sockets; they’re not available on non-Unix platforms.
Server Code
You must write a RequestHandler class. It is instantiated once per connection to the server, and must override the handle() method to implement communication to the client.
On the server's assigned port number, an object of TCPServer class calls the forever() method to put the server in the listening mode and accepts incoming requests from clients.
if __name__ =="__main__":
HOST, PORT ="localhost",9999with socketserver.TCPServer((HOST, PORT), MyTCPHandler)as server:
server.serve_forever()</pre>
Client Code
When working with socketserver, the client code is more or less similar with the socket client application.
import socket
import sys
HOST, PORT ="localhost",9999whileTrue:with socket.socket(socket.AF_INET, socket.SOCK_STREAM)as sock:# Connect to server and send data
sock.connect((HOST, PORT))
data =input("enter text .. .")
sock.sendall(bytes(data +"\n","utf-8"))# Receive data from the server and shut down
received =str(sock.recv(1024),"utf-8")print("Sent: {}".format(data))print("Received: {}".format(received))</pre>
Run the server code in one command prompt terminal. Open multiple terminals for client instances. You can simulate a concurrent communication between the server and more than one clients.
Server
Client-1
Client-2
D:\socketsrvr>python myserver.py127.0.0.1:54518 wrote:from client-1enter text ..hello127.0.0.1:54522 wrote:how are youenter text ..fine127.0.0.1:54523 wrote:from client-2enter text ..hi client-2127.0.0.1:54526 wrote:good byeenter text ..bye bye127.0.0.1:54530 wrote:thanksenter text ..bye client-2
The threading module in Python’s standard library is capable of handling multiple threads and their interaction within a single process. Communication between two processes running on the same machine is handled by Unix domain sockets, whereas for the processes running on different machines connected with TCP (Transmission control protocol), Internet domain sockets are used.
Python’s standard library consists of various built-in modules that support interprocess communication and networking. Python provides two levels of access to the network services. At a low level, you can access the basic socket support in the underlying operating system, which allows you to implement clients and servers for both connection-oriented and connectionless protocols.
Python also has libraries that provide higher-level access to specific application-level network protocols, such as FTP, HTTP, and so on.
Interrupting a thread in Python is a common requirement in multi-threaded programming, where a thread’s execution needs to be terminated under certain conditions. In a multi-threaded program, a task in a new thread, may be required to be stopped. This may be for many reasons, such as − task completion, application shutdown, or other external conditions.
In Python, interrupting threads can be achieved using threading.Event or by setting a termination flag within the thread itself. These methods allow you to interrupt the threads effectively, ensuring that resources are properly released and threads exit cleanly.
Thread Interruption using Event Object
One of the straightforward ways to interrupt a thread is by using the threading.Event class. This class allows one thread to signal to another that a particular event has occurred. Here’s how you can implement thread interruption using threading.Event
Example
In this example, we have a MyThread class. Its object starts executing the run() method. The main thread sleeps for a certain period and then sets an event. Till the event is detected, loop in the run() method continues. As soon as the event is detected, the loop terminates.
from time import sleep
from threading import Thread
from threading import Event
classMyThread(Thread):def__init__(self, event):super(MyThread, self).__init__()
Another approach to interrupting threads is by using a flag that the thread checks at regular intervals. This method involves setting a flag attribute in the thread object and regularly checking its value in the thread's execution loop.
Example
This example demonstrates how to use a flag to control and stop a running thread in Python multithreaded program.
import threading import time
def foo(): t = threading.current_thread() while getattr(t, "do_run", True): print("working on a task") time.sleep(1) print("Stopping the Thread after some time.")
# Create a thread t = threading.Thread(target=foo) t.start()
# Allow the thread to run for 5 seconds time.sleep(5)
# Set the termination flag to stop the thread t.do_run = False When you execute this code, it will produce the following output −
working on a task working on a task working on a task working on a task working on a task Stopping the Thread after some time.
