Python for loop increment by float

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Python for loop increment by float

Python has its limits when it comes to coil increments. Import numpy and use arange numpy.arange (start, stop, increment) start means the starting point of the coil, stop means the end point of the coil, and increment is the increment of the floating point. Here is your code: import numpy k=1 for i in numpy.arange(0,3,0.2): for j in numpy.arange(k,k+3,1): print (I= %i J=%i%(i,j)) k=k+0.2 Get help with your ResearchGate researchjoin to ask questions, get information and input your work. National Institute of Technology National University of Singapore, Durgapur John GoerzenOne the most interesting way to use Python today is the Apache mod_python module. The mod_python module actually immerses a fully functional Python interpreter inside the Apache web server. It's most commonly used as a powerful and efficient way to generate dynamic websites, but it also has other uses. That's fine if you have a good increment like 0.1, otherwise, take note that for a coil only special case instantly coils. r = 0.0 while r < 6.4:= print= r= r= +=0.1 print= ##= ##= and= if= you= have= an= unusual= series= ...= jump= to= post= you= actually= picked= a= strange= example= where= the= floating= point= representation= error= makes= a= difference= ...= r=6.0 while= r=> < 6.4:= print= r= r= +=0.1 my= result= --=> 6.0 6.1 6.2 6.3 6.4 <--- oops!= (float= rep= of= 6.4= --=>6.399999999986) It works ... Jump to Post woooee 814 Almost Post Maven 11 Years Ago That's fine if you have a good hike like 0.1, otherwise, note that for coils only special case instantly coil. r = 0.0 while r < 6.4:= print= r= r= +=0.1 print= ##= ##= and= if= you= have= an= unusual= series= of= numbers,= use= a= list= iterate_list=[0.0, 0.2,= 0.3,= 0.5,= 0.6]= for= r= in= iterate_list:= print= r= dmcquinn= 0= newbie= poster= 11= years= ago= thanks,= i= looked= a= long= time= for= a= floating= point= equivalent= of= range.= e.g.= range(0,= 6.= 4,= 0.1)= i= guess= there= is= nothing= like= this?= vegaseat= 1,735= daniweb's= hypocrite= team= colleague= 11= years= ago= you= actually= picked= a= strange= where= where= where= floating= point= representation= error= makes= a= difference= ...= r=6.0 while= r=> < 6.4:= print= r= r= +=0.1 my= result= --=> 6.0 6.1 6.2 6.3 6.6.0 4 <--- oops!= (float= rep= of= 6.4= --=>6.3999999999986) It works correctly ... r = 1.0 manakala kod r < 1.4:= print= r= r= +=0.1 my= result= --=> 1.0 1.1 1.2 <--- correct= (float= rep= of= 1.3= --=>1.3 1.300000000000000000003 Sebenarnya kod C atau C++ akan memberi anda masalah yang sama ... #include <stdio.h>int main() { double r; untuk(r = 6.0; r < 6.4;= r=r += 0.1)= printf(%0.1f,= r);= return= 0;= }= my= result= --=> 6.0 6.1 6.2 6.3 <--- oops!= (float= rep= of= 6.4= --=>6.4 6.39999999999986) */ jice 53 Posting in Training 11 Years Ago ## and if you had an incredible series of numbers, use the iterate_list list = [0.0, 0.2, 0.3, 0.5, 0.6] to r in iterate_list: print r To proceed in this direction, you can use a list that you built the same time (called list understanding)... This is not so different to your solution, it just shortens the code. for y in &lt;/--> </stdio.h> </---> </---> </---> & lt;/---> for i in the range (63)]: print y, Edited 11 Years ago by jice because: n/a vegaseat 1,735 DaniWeb's Hypocrite Team Colleagues 11 Years ago Being part of our DaniWeb community is a friendly community, Industry-focused 1.20 million developers, IT pros, digital marketers, and technology enthusiasts Get Python Cookbook now with O'Reilly's online learning. O'Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers. Credit: Dinu C. Gherman, Paul M. WinklerYou needs arithmetic development, similar to the built-in functional range, but with a floating value (various functions only on the integer). Although this function is not available as built-in, it is not difficult to code with loops: defange (start, end=No, inc=1.0): Functions such as ranges that receive floating increases ... if the end == None: end = start + 0.0 # Make sure the float value for the 'end' start = 0.0 guarantee inc # sanity check L = [] while 1: next = start + len(L) * inc if inc > 0 onwards >= final: breaking elif inc < 0 onwards & lt;= final: breaking L.append (next) returns LSadly to disappear in the standard library of Python, the functionality in this recipe allows you to use the range, just like the built-in function range, but with a floating argument. Many theoretical restrictions apply, but this function is more useful in practice than in theory. People working with floating point numbers all the time have plenty of war stories about billion-dollar projects that fail because one doesn't take into account the strange things that modern hardware does when comparing floating point numbers. But for pedestrian cases, a simple approach like this recipe generally works. You can get a huge speed boost by ahead of the list instead of calling the attachment over and over again. This also allows you to eliminate the situation in the internal loop. For one element, this version is almost not faster, but with more than 10 elements it is consistently about 5 times faster--the type of performance ratio should be leveled. I get the same output for each test case that I can think of: def frange2 (start, end=No, inc=1.0): Functions like faster ranges that receive floating increases ... If the end == None: end = start + 0.0 start = 0.0 else: start += 0.0 # force it to be a floating count = int(end - start) / inc) if starting + count * inc != final: # Need to adjust the count. AFAICT, it always appears one short. count += 1 L = [start] * count for i in xrange(1, count): L[i] = start + i* inc return version of LBoth depending on a single multiplier and one supplement to calculate each item, to prevent accumulated errors by repeated supplements. This is why, for example, Loop in frange2 does: L[i] = L[i-1] + incIn Python 2.2, if all you need to do is loop at frange results, you can save some memory by turning this function into a simple generator, producing iterator when you call it: from _ _future_ _ frangei import generator (start, end, final= No, Simple generators like xrange that receive floating rise... if the end == None: end = start + 0.0 start = 0.0 assign inc # sanity check i = 0 while 1: next = start + i * inc if inc > 0 onwards >= final: break elif inc < 0 onwards <= final: break the next result i+= 1If you may need to see Numeric Python and other third-party packages taking computing with serious floating point numbers. This recipe, for example, will not scale well to a very large range, while those defined in numeric Python will. Get Python Cookbook now with O'Reilly online learning. O'Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers. The Python Range() is a built-in function available with Python from Python (3.x), and it provides a sequence of numbers based on the start and stop index given. If the startup index is not given, the index is considered to be 0, and it will increase in value by 1 until the index stops. For example the range (5) will remove you the value of 0,1,2,3,4 . The Python Range()is a very useful command and mostly used when you need iterate using for loops. In this tutorial, you will learn: The Range of Syntax (start, stop, step) Parametersstart: (optional) The startup index is integer, and if not given, the default value is 0. Stop: The index stops disconnecting the value at which the range function needs to stop. It is mandatory input for various functions. The final value will always be 1 less than the stop value.step: (optional). The step value is the number at which the next number is the range needs to be disputed, by default, it is 1.The return value:The return value is the sequence of numbers from the given start to stop the index. The Python Range() Functions and historical rangeSPython() was introduced from the python version 3, before the xrange() is functional. The range and xrange() are used to produce a sequence of numbers. Here's the difference between the range and xrange(): the range() xrange() Range() gives the number sequence and returns a list of numbers. The functionality of xrange() gives the generator objects that need to be redirected in loops to gain values. Range() returns the list. xrange() returns the generator object. The range() method uses more memory because the return list should be saved compared to xrange(). As xrange() returns the generator object, it does not give immediate value and should be used inside for loops to gain values. Memory usage is more therefore a slow code execution when working on a large set of data. Faster code execution using xrange(). Using range()This example shows how to print value from 0-9 using range(). Values used The range is 10, so the output is 0 1 2 2 3 4 5 6 7 8 9 Since the starter was not given the starter considered to be 0 and the last value is given up to 9. The last value is always 1 less than the given value of stop-1. to i in range(10): print (i, end = ) Output: 0 1 2 3 4 5 6 7 8 9 Using the start and stop in code, the initial value is 3, and the stop value is 10. Here the starting index is 3, so the sequence of numbers will start from 3 to the stop value. The final value in sequence will be 1 less than the stop value of 10-1 = 9. for i in range (3, 10): print(i, end = ) Output: 3 5 6 6 7 8 9 Using the starting value, stop and stepThe start is 3, so the sequence of numbers will start at 3. The stop value is 10, so the sequence of numbers will stop at (10-1) which is 9. The step is 2, so each value in the sequence will be increased by 2. If the step value isn't provided, the value for the default step to 1. to i in range (3, 10, 2): print (i, end = ) Output: 3 5 7 9 So far, we have seen how the range() function provides additional value for the given stop value. Let us try an example to get the decided value within the given range. Increase value in range using positive steps. Parameter steps in range() can be used for incremental/devaluation. By default, it is a positive value 1. So it will always provide additional values. The value of the step needs to be a positive sluge you want to want additional values as a ouput. to i in range (1, 30, 5): print (i, end = ) Output: 1 6 11 16 21 26 Countdown Range: Reduces the value using negative measures. Parameter steps with negative values in range() can be used to obtain a decided value. In the example below the step value is negative so that the output will be decompassed from the given range value. for i in the range (15, 5, -1): print(i, end = ) Output: 15 14 13 12 11 10 8 7 6 The initial value is 15, the stop value is 5 and the step value is the negative number i.e. negative number -1. With the input range above() the function will reduce the value from 15 onwards until reaching the stop value , but here the difference is the last value will stop + 1. Using the floating numbers in the Python range()Let us now work on the range() using the floating point number. Example: for i in range (10.5): print(i, end = ) In the example above, we have used the stop value as 10.5. Output is : Traceback (last newest call): file python_range.py, line 1, <module > in range (10.5): Taiperror: 'floating' object cannot be interpreted as Python integer gives an error because the range() function does not support floating point numbers to start, stop, and steps. Using for loops with the Python range()In this example we will use various numbers and, let us see how to use array iterate inside for loops using the range() Example: arr_list = ['Mysql', 'Mongodb', 'PostgreSQL', 'Firebase'] for i in range (len(arr_list)): print (arr_list[i], end = ) Output: MysqlMongodb PostgreSQL Firebase In the example above we have len (arr_list) as the stop value. For the loop will be twelve until the value stops i.e. the length of the charity and that will be 4, because we have four items in the arr_list. The initial value is 0 and the step will 1.So then the value will start from 0 and will stop at 3 which is the length of the array -1 which means 4 -1 = 3. Using the Python range() as a listIn this example will be </module> </module> how to use output from the range as a list. Example: print (list(range(10))) Output: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] You can see the output is the list format. It is not necessary to loop the range() and use the list() method we can directly convert output from range to list format. Using characters in the python range()So far, we've used integers within the python range(). We've also seen that floating point numbers aren't supported within the python range. Let us try and see output on what happens when we use characters. Example: for c in range ('z'): print (c, end = ) Output: Traceback (last recent call): File python_range.py, line 1, & lt;module > in range c ('z'): TypeError: 'str' object cannot be interpreted as integer It throws an error saying the string cannot be interpreted as an integer. To get an alphabetical list you can customize the code and get the desired outputa shown below: Example: def get_alphabets (startletter, stopletter, step): for c in range (ord(startletter.lower()), ord (stopletter.lower()), steps): chr(c) print results (list(get_alphabets(a, h, 1))) Output: ['a', 'b', 'c', 'd', 'e', 'f', 'g'] How to Access the ElementsYou Range can use loops to obtain values from the range or use the index to access elements from the range(). Using for-loopExample: for i in range (6): print(i) Output: 0 1 2 3 4 5 Using the indexThe is used with the range to get the value available in that position. If the range value is 5, to get a startvalue, you can use the range (5)[0] and the next value range (5)[1] and so on. Example: startvalue = range (5)[0] print (First element in range is = , startvalue) secondvalue = range(5)[1] prints (Second element in range is = secondvalue) lastvalue = range(5)[-1] prints (First element in range are = lastvaluee) Output: startvalue = range(5)[0] print (First element in range is = , startvalue) secondvalue = range(5)[1] print (Second element in range is = secondvalue) lastvalue = range(5)[-1] prints (First element in range is = , lastvalue) Using the list()This method will print all elements from the range(). Using the list() it will return the element from the range() in the list format. Example: print (list (range(10))) Output: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] It gives output of the list for the given range. Example: Get even a number using the range()Using the range() will get a list of numbers even within the range given as input. The parameters for the range() are, starting are 2, the stop is 20, and the step is 2, so the value will be increased by 2 and will give the number even up to stop-2. Example: for i in range (2, 20, 2): print(i, end = ) Output: 2 4 6 8 10 14 16 18 Merging two-range() outputIn example will combine 2 ranges() functions with the help of an itertools() module chain example: from print chain import itertools (Combining two ranges into one) frange =chain (range(10), range (10, 20, 1)) to i in frange: print (i, end= ) Output: Combine two ranges into one 0 1 2 3 4 </module> </module > 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Using the range() With NumPy Module NumPy having arange function() that works and provides the same output as the range(). Arrangement() takes in the same parameters as the range(). Syntax: arange (start, stop, step) To work with NumPy following the steps given below. Step 1: Import the NumPy import module numpy Incase during execution, it gives an error saying the numpy module cannot be found, you need to install the module as shown in step 2. Step 2: Install NumPy pipe install numpy Step 3: Examples of arange work() use NumPy import numpy as np to i in np.arange(10): print(i, end = ) Output: 0 1 2 3 4 5 6 7 8 9 Floating point number using Arange NumPy()It is not possible to get a floating point sequence using the range(), but may use Arange NumPy(). Example: The range we want is from 0.5 to 1.5. The value will be an increase of 0.2. import numpy as np for i in np.arange(0.5, 1.5, 0.2): prints (i, end = ) Output: 0.5 0.7 0.899999999999999999 1.099999999999999999 1.2999999998 Output we got a little weird, some floating numbers showed with 16 decimals. This happens because the hassle of storing decimal floating numbers into binary format. You can also back up the value if needed and limit it to the decimal place you need. Summary:Python Range() is a built-in function available with Python from Python(3.x), and it provides a sequence of numbers based on the start and stop index given. If the index starts not given, the index is considered to be 0, and it will increase in value until the index stops. The Python Range() was introduced from the python version 3, before the xrange() was a function. Range() provides a sequence of numbers and returns a list of numbers. The functionality of xrange() gives the generator objects that need to be redirected in loops to gain values. Parameter steps in range() can be used for incremental/devaluation. By default, it is a positive value 1. So it will always provide additional values. Python gives errors for floating point numbers because the range() function supports only integer values. The value of the range() can be accessed using a for-loop, using the index and list()the NumPy module has arange function() that works and provides the same output as the range(). Arange() takes in the same parameters as the range(). It is possible to get a sequence of NumPy arange() floating points that are not supported using the range(). Page Map 2Python() uses functionality on all table items assigned as input. Iterator, for example, can be a list, a tuple, a set, a dictionary, a string, and it restores a heatable map object. Python Map() is a built-in function. In this tutorial, you will learn: map (function, iterator1,iterator2 ... iteratorN) ParametersHere are two important functions: The mandatory function that will be given to the map, which will be used for all items contained in iterator.iterator: The mandatory object that can be replaced. It can be your list, tuple, etc. pass multiple iterator objects for maps() functions. Returning ValueThe map() function will use the functionality assigned to all items in the table and restore assurable map objects i.e. tuple, list, etc. How does the map function() work? The function of the map() takes two inputs as functions and objects that can be replaced. The functionality assigned to the map () is a common function, and it will continue all the values found in the replaceable object provided. For example, consider you have a list of numbers, and you want to find squares per number. Getting output, we need functions that will return square numbers given. Its functions are as follows: def square(n): back n*n List of items we want to find the square is as follows: my_list = [2,3,4,5,6,7,8.9] Now let us use the map() built-in python in the function to get square per item on my_list. The final code is as follows: def square(n): return n*n my_list = [2,3,4,5,6,7,8,9] updated_list = map (square, my_list) print (updated_list) print (list(updated_list)) Output: <map object= 0x0000002c59601748=>[4, 9, 16, 25, 36, 49, 64, 81] Output map() works, as seen in the output, is a map object displayed on the screen & lt;map object= at= 0x0000002c59601748=>as . You need to continue output from the map using the method to loop or use the list() to get the final output. I have used the list() in the code that displays the values in the given list. So using the map() function, we are able to square per number. The list given to the map is [2,3,4,5,6,7,8,9] and uses square functions () output from the map() we got were [4, 9, 16, 25, 36, 49, 64, 81] . Functional maps() apply square functionality() to all items in the list. For example, my_list and update the list squarely per number. Exit saved in a updated_list variable. Using a map() with Python's built-in functions is a built-in function and can also be used with other built-in functions available in Python. In the example, we will use the Round Python() built-in function that rounds up the given values. Example: The list I have is my_list = [2.6743,3.63526,4.2325,5.9687967,6.3265,7.6988,8.232,9.6907] . I need a round value for each item in the list. We will use round() as a function to map(). my_list = [2.6743,3.63526,4.2325,5.9687967,6.3265,7.6988,8.232,9.6907] updated_list = map my_list) print (updated_list) print (list(updated_list)) Output: <map object= at= 0x000000e65f901748=>[3, 4, 6, 6, 8, 8, 10] Round function() applies to all items in the list, and it returns the list with all as shown in the output. Using map() with string as iteratorWe can also use map() on the string. In Python, bursts act like a variety so we can easily use them inside the map(). In the example, we have a myMapFunc() function that keeps changing the string given to </map> </map> </map> </map> The function of myMapFunc () is assigned to the map() function. The function of the map will take care of changing the strings assigned to the hurla by passing the string to myMapFunc(). def myMapFunc(s): back s.upper() my_str = welcome to the guru99 tutorial! updated_list = map (myMapFunc, my_str) prints (updated_list) for i in updated_list: print (i, end=) Output: <map object= at= 0x000000df2e711748=>WELCOME TO GURU99 TUTORIAL! Using a map() with listof NumbersTo works with a list in the map() will take a list of numbers and breed each number in the list by 10. The list we will use is : [2,3,4,5,6,7,8,9]. The function of myMapFunc () takes care of breeding the numbers assigned by 10. Functions are assigned to the map along with the list. Example: def myMapFunc(n): return n*10 my_list = [2,3,4,5,6,7,8,9] updated_list = map (myMapFunc, my_list) print (updated_list) print (list(updated_list)) Output: & lt;map object= at= 0x000000ee2c061898=>[20, 30, 40, 50, 60, 70, 80, 90] The output we see is that each number in the list is multiplied by 10. Using a map() with tuple Tuplea is an object in Python that has items separated by commas and included in round brackets. In an example, we will take a tuple with a string value. The functions we will use will convert the values assigned to large processes. Example: def myMapFunc(n): return of n.upper() my_tuple = ('php','java','python','c+','c') updated_list = map (myMapFunc, my_tuple) print print (updated_list) print (list(updated_list)) Output: <map object= at= 0x0000009c3c3a16a0=>['PHP', 'JAVA', 'PYTHON', 'C++', 'C'] The output we get is the salute back with all the values in it converted to uppercase. Using a map() with the DictionaryA dictionary in Python was created using curly brackets ({}). Since the dictionary is a itinerary, you can use it in the map() function. Let us use the dictionary as a table in the map() function. The following example shows the auction works in the map() def myMapFunc(n): return n*10 my_dict = {2,3,4,5,6,7,8,9} finalitems = map (myMapFunc, my_dict) print (finalitems) print (list (finalitems)) Output: <map object= at= 0x0000007eb451def0=>[20, 30, 40, 50, 60, 70, 80, 90] Using the map() with sets in Python is a collection of items not stacked in curly brackets(()). Since set() is also a table, you can use it in the map() function. Here's an example of work using the set as a table in the map() def myMapFunc(n): return n*10 my_set = {2,3,4,5,6,7,8,9} finalitems = map (myMapFunc, my_set) print print (finalitems) print (list (finalitems)) Output: & lt;map object= at= 0x000000ac8f05def0=>[20, 30, 40, 50, 60, 70, 80, 90] Using map() with Lambda functionIn Python, slow express is used to build a function You need to use words lambda as you use to define normal functions. So in the example, we'll use the lambda function inside the map(). The lambda function will bias each value in the list by 10. Example: my_list = [2,3,4,5,6,7,8,9] updated_list = map (lambda x:</map> </map> </map> </map> </map> </map> * 10, my_list) printout (updated_list) printout (list(updated_list)) Output: <map object= at= 0x000000bd18b11898=>[20, 30, 40, 50, 60, 70, 80, 90] Using Multiple Iterators in the map() functionExample 1: Pass two list coolers to the map ()You can send more than one schedule i.e. lists, tuples, etc. all at the same time for the map() function. For example, if you want to add two lists. The same can be done using the map() function. We will use two lists and my_list1 and my_list2. In the example below, the first item in the my_list1 is added to the first item my_list2. The myMapFunc() function retrieves my_list1 my_list2 items and returns both amounts. Here is an example of the work of adding two lists given using the map() function. def myMapFunc (list1, list2): return list1+list2 my_list1 = [2,3,4,5,6,7,8,9] my_list2 = [4,8,12,16,20,24,28] updated_list = map (myMapFunc, my_list1,my_list2) print (updated_list) print (list(updated_list)) Output: <map object= at= 0x0000004d5f751860=>[6, 11, 16, 21, 26, 31, 36] Example 2: Pass one Tuple and cool down the list for the map()We will use the list and the tuple cooling tool in the function map(). The function given to the map ? myMapFunc() will get items from the list and Tuple. The item will be accompanied by an underscore(_). The example work is as shown below: def myMapFunc(list1, tuple1): return list1+_+tuple1 my_list = ['a','b', 'b', 'd', 'e'] my_tuple = ('PHP','Java','Python','C+','C') updated_list = (myFunc','Java','Python','C+','C') updated_list = (myFunc','Java','Python','C+','C') updated_list =(myFupnc','Java','Python','C+','C') updated_list =(myFupnc','Java','Python','C+','C') updated_list =(myFupnc','Java' ,'Python','C+','C') updated_list =(myFunc', map(myFunc','Java','Python','C+','C') updated_list = (myFupnc','Java','Python'my_list,my_tuple) print (updated_list) print(list(updated_list)) Output: <map object= at= 0x00000059f37bb4e0=>['a_PHP', 'b_Java', 'b_Python', 'd_C+', 'e_C'] SummaryPython() map is an internal built-in function that uses a function on all completed items given as input. An iterator, for example, can be a list, a tuple, a string, etc. and it returns a staged map object. The map() function will use the function given to all items in the table and return the expandable map object i.e. tuple, list, etc. The Python() map function is a built-in function and can also be used with other built-in functions found in Python. Tuple is an object in Python that has items separated by commas and enclosed in rounded cages. In the example will take a tuple with a string value. The function we will use will convert the values given to the large hurus. Dictionaries in Python are created using kerinting cages ({}). By dictionary is a travel schedule, you can use it in the map() function. Located in Python is a collection of items that are not arranged in kerinting(()) confinement. Since set() is also a schedule, you can use it inside the map() function. In Python, the expression lambda (or lambda form) is used to construct unnamed functions. So the lambda keyword needs to be used if you want to use lambda in the map(). You can send more than one schedule i.e. list, tuple to map() function. 3Python page() time is a method in Python libraries to measure the execution time of </map> </map> </map> </map> snippets of the given code. The Python Library conducts a code statement 1 million times and provides the minimum time taken from the set of given code snippets. Python Time() is a useful method that helps in checking code performance. timeit.timeit (stmt, setup, timer, number) stmt: This will take the code you want to measure the execution time. The default value is pass. Preparation: This will have the details of the setup that needs to be implemented before the stmt. The default value is pass. Timer: This will have a timer value, time() already has a set default value, and we can ignore it. Numbers: Stmt will perform according to the numbers given here. The default value of the 1000000.To works with the time(), we need to import the module, as shown below: import timeit First ExampleHere is a simple example of timeit() functional Example Code 1: # testing timeit() import timeit print (timeit.timeit('output = 10*5'))) Output: 0.061278803999999999999 we have seen a simple example that gives us the time of implementation of the simple code statement = 10 and the time taken to implement it is 0.0612788039999999999. Multi-line time in the pythonThere code are two you can execute multiple lines of code in timeit.timeit(), using a verified comma or by storing code that is included as a string with a three-time quote. Here are the examples that show the work. Example 1: Using semicolon import time print (Time taken is ,timeit.timeit(stmt='a=10;b=10;sum=a+b')) Output: The time taken is 0.1370314 Example 2: Using triple time import quotes import_module = random import testcode = ''' test def(): random return.randint(10, 100) print '') (timeit.repeat(stmt=testcode, setup=import_module)) Output: C:\pythontest>python testtimeit.py Time taken is 0.182619178 time - Method:Here, are 2 important time methods timeit.default_timer() : This will restore the default time when implemented. timeit.repeat (stmt, setup, timer, repeat, number) : just like timeit() , but with recurring time() is called the number of repeated times given. Example 1 Program: #testing timeit() import timeit import_module = import random testcode = ''' def test(): return random.randint(10, 100) print (timeit.timeit(stmt=testcode, setup=import_module)) Output: 0.4671591240000004 Example 2: default_timer() Example # testing timeit() import timeit import random def test(): return random.randint(10, 100) starttime = timeit.default_timer() print (The start time is :,starttime) test() print(The time difference is :, timeit.default_timer() - starttime) Output: The start time is : 0.220261875 The time difference is : 0.000473732000000045 Example 3: timeit.repeat() # testing timeit() import timeit import_module = import random testcode = ''' def test(): return random.randint(10, 100) print(timeit.repeat(stmt=testcode, repeat=5)) Output: [0.43638873, 0.5040939680000001, 0.5069179909999999, 0.3943449330000002, 0.3546886979999999] timeit.repeat() works similar to to functionality, with the only difference required in repeated arguments and gives back the execution time in array format with values as in repeat numbers. Performing timetime function.timeit() in the command line interfaceThe synx to perform your functions in time() on the command line is as follows: python -m timeit [-n N] [-r N] [-t] [-t] [-h] [code statement ...] Command line parameters: -n N: the number of times you want the code to perform.-r N: the number of times you want time() function to repeat S: this will have the setup details to be performed before the execution of the code.-t: for this, you can use time.time()-c: for this, you can use time.clock()-h: for help code statements: Details of the code. Example: C:\pythontest>python -m timeit -s 'text=hello world' 20000000 loop, the best of 5: 13.1 nsec per loop the other Way you can perform in the command line is as shown below: Example : >>>import time >> printing (The time taken is ,timeit.timeit(stmt='a='a=10;b=10;sum=a+b')) The time taken is 0.1504853630000137 >> Why is it the best time to measure the implementation time of the Python code? Here are some of the reasons why we consider timeit() is the best way to measure execution time. It runs a 1 million-time code statement which is the default value, and from that, it will return you the minimum time it takes. You can also increase/decrease 1 million by setting the argument number within () the function. When performing the test, garbage collection is disabled every time () function.timeit() internally takes the right time according to your operating system used. For example, it will use time.clock() for Windows and time.time() operating systems for mac and Linux.SummaryTimeit()used to get execution time taken for small codes given parameters used with time () stmtlamt: This will take the code you want to measure the execution time: This will have the set of sets of settings and we can ignore it.number: Stmt will perform according to the numbers given here. Page 4The produces keywords in the python works like a return with the only difference is that instead of returning value, it gives the caller back objects. When a function is called and the execution thread finds the result keyword in function, the functionality stops on the line itself and it returns the generator object back to the caller. In this Python tutorial, you will learn: Syntax produces descriptionPython expression results of restoring generator objects. Generators are special functions that need to be encouraged to obtain The resulting keyword converts the expression given to the generating function that returns the generating object. To get the values of an object, it needs to be transerated to read the values given to the result. Example: Yield MethodHere is an easy example of results. The Testyield function() has the result with a string of Welcome to Teacher99 Python Tutorial. When a function is called, the output is printed and it gives the generator object instead of the actual value. def testyield(): Welcome results to Teacher99 Python Tutorials output = testield() print (output) Output: &generator object= testyield= at= 0x00000028265eb9a8=>Output given is the generator object, which has the value we have given to the results. But we don't get the message we need to give to produce output! To print messages given to the results should continue the generator object as shown in the example below: def testyield(): Welcome result to Guru99 Python Tutorial output = testield() for i in output: print(i) Welcome Output to Guru99 Python Tutorial What is Generator in Python? A generator is a function that restores generator objects that can be on it. Values from generator objects are connected one at a time instead of a full list together and thus to get the actual value you can use the method for loops, using the next() or list(). Using the GeneratorYou function can create generators using generator functions and use generator expressions. The function of the generator is like a normal function, rather than having a return value it will have a keyword revenue. To create a generator function, you need to add a result keyword. The following example shows how to create a generator function. def generator(): results H results E results L results L results O test = generator() for i in test: print(i) Output: H E L O Difference between Normal function v/s Function Generator. Let us understand how generator functions differ from normal functions. There are 2 main normal_test() and generator_test(). Both functions were supposed to restore Hello World's strings. The normal_test() uses returns and generator_test() using the results. # Normal function def normal_test(): returns Hello World #Generator def generator_test(): results Hello World prints (normal_test()) #call for normal print function (generator_test()) # call to function Output generator: Hello World Output shows that when you & lt;generator object= generator_test= at= 0x00000012f2f5ba20=>call normal functionality normal_test() it restores the hello of the string world. For the generator function with the keyword results it 'lt;generator object= generator_test= at= 0x00000012f2f5ba20=>back instead of string. This is the main difference between the functions of the generator and the usual functions. Now to get value from the generator object we need to either use the object inside to loop or use the next() method or use the list(). print (next (generator_test()) # will output Hello World One more differences to add normal functionality v/s functional generator is that when you call normal function execution will begin and stop it can return and the value is returned to the caller. Therefore, when the execution starts, you cannot stop the normal functioning in between and it will only stop when it comes across keyword returns. But in case</generator> & lt;/generator> </generator> </generator> function once the execution begins when it gets the first result it stops execution and gives back the generator object. You can use generator objects to get value as well, pause and reconnect according to your needs. How to read the values from the generator? You can read the values from the generator object using the list(), to loop and use the next method(). Using : list()List is a table object that has elements in bracke. Using the list() on the generator object will give all the value of the generator to hold. def even_numbers(n): for x in range(n): if (x%2==0): results x num = even_numbers(10) print (list(num)) Output: [0, 2, 4, 6, 8] Using : for-inIn for example, there are functions defined even_numbers() that will give you all the numbers for example, there are functions defined even_numbers() that will give you all the numbers for example, there are functions defined even_numbers() that will give you all numbers for example, there are functions defined even_numbers() that will give you all the numbers for example. Calls to the even_numbers() function will return the generator object, which is used inside for loops. Example: def even_numbers(n): for x in range(n): if (x%2==0): results x num = even_numbers(10) to i in num: print(i) Output: 0 2 4 6 8 Using the next ()Next() method will give you the next item in the list, settings, or objects. Once the list is empty, and if next() is called, it will provide back an error with a stopIteration signal. This error, from the next() indicates that there are no more items in the list. def even_numbers(n): for x range(n): if (x%2==0): results x num = even_numbers(10) prints (next (num)) prints (next (num)) prints (next (nu print (next (num)) print (next (num)) prints (next (num)) Output: 0 2 4 6 8 Traceback (last newest call): main.py, line 11, in &module>print (next(num)) StopIteration Generators are once UseIncase generators they can use only once. If you try to reuse it, it will be empty. For example: def even_numbers(n): for x in range(n): if (x%2==0): results x num = even_numbers(10) to i in num: print(i) print (Calling the generator again: [num)) Output: [] you need to make a call to function again. Examples: Generators and results for Fibonacci SeriesThe following examples show how to use generators and produce in Python. An example will generate a Fibonacci series. def getFibonnaciSeries (num): c1, c2 = 0, 1 count = 0 while & lt; num:= yield= c1= c3=c1 += c2= c1=c2=c2=c3 count= +=1 fin=getFibonnaciSeries(7) print(fin)= for= i= in= fin:= print(i)= output:=> & lt;generator object= getfibonnaciseries= at= 0x0000007f39c8ba20=>count 0 1 1 2 3 5 8 Examples: Call Function with YieldIn this example will see how to call functions with results. The example below has a function called test() that restores number given. There is another function called getSquare() that uses tests() with keyword results. Output provides a square value for the given number range. def test(n): return n*n def getSquare(n): for i in range(n): results test(i) sq = getSquare(10) to i in sq: print(i) Output: 0 1 4 9 16 25 36 49 64 81 When using Results From & lt;/generator > & lt;/module> </module> in the keyword PythonPython3 Revenue returns the generator to the caller and the execution of the code begins only when the generator is dilenced. The return in function is the end of functional execution, and a value is given back to the caller. Here, is the situation when you need to use Results instead of returnUse results instead of returns when bigYield data size is the best option when you need your execution to be faster on big data set results when you want to return a large set of values to call functionYield is an efficient way of producing large or infinitive data. The result vs ReturnHere, is the difference between Revenue and Return On Return returns returning the generator object to the caller, and the execution of the code begins only when the generator is dilencered. The return in function is the end of functional execution, and a value is given back to the caller. When the function is called and it faces a keyword revenue, the functionality execution stops. It returns the generator object back to the caller. Functional execution will only begin when the generator object is performed. When the function is called, the execution begins and the value is given back to the caller if there is a return keyword. The returns in the function indicate the end of functional execution. Revenue return expression No memory is used when the result keyword is used. Memory is allocated for the returned value. Very useful if you need to deal with large data sizes because memory is not used. Easy to size very small data. Performance is better if the revenue keywords are used for large data sizes. Many memory is used if the data size is large that will hinder performance. Faster execution time if the result is for large data size. The execution time used is more because there is additional processing done in case your data size is large, it will work well for small data sizes. Summary:The result keywords in the python work like a return with the only difference is that instead of returning value, it gives back the generator functionality to the caller. Generators are a special type of iterator that, once used, will no longer be available. The value is not stored in memory and is only available when called. Values from the generator can be read using the for-in, list() and so on() methods. The main difference between revenue and returns is that the return of the generater's function returns to the caller and the return provides a single value to the caller. The result does not store any value in memory, and the advantage is that it helps when the size of the data is large, since no value is stored in memory. Performance is better if the revenue keywords are used compared to returning for large data sizes. Page 5A lineup is a container that Data. The data entered first will be removed first, and thus the row is also called First in First Out (FIFO). The line has two front and rear ends. The item is inserted from behind and and from the front. In this Python tutorial, you'll learn: How does Python Queue Work? Queues can be easily compared to real-world examples of lines of people waiting in line at ticket counters, people standing in advance will get tickets in advance, followed by the next person and so on. The same logic is also for line data structures. Here are queue figure delegates: The back represents the point at which items are put in the queue. In this example, 7 is the value for that. The row represents the point at which items from the row will be removed. If you remove an item from the line, the first element you'll gamble for is 1, as shown in the digits. Article 1 was the first to be put in the lineup, and while removing it was the first to get out. Therefore, the queue is called FIRST IN THE FIRST EXIT (FIFO) In the lineup, the goods are manufactured in order and cannot be removed from among them. You can't randomly remove 5 items from the line, to do so, you'll need to remove all items before 5. Items in the queue will be removed in the order they are included. The Type of Queue at PythonThere is mainly two types of queues in Python: First in the First Row: For this, the element that goes first will be the first to come out. To work with FIFO, you need to call a Queue class() from the queue module. Last in the First Outing Queue: Here, the inserted element last will be the first out. To work with LIFO, you need to call the LifoQueue class() from the line module. Python queues Installation It is very easy to work by queuing up in python. Here are the steps to follow to use the queue in your code. Step 1) You only need to import the line module, as shown below: import lines of module are available by default with the python, and you do not require additional installation to start working with the line. There are 2 types of queue fifo (first at first) and LIFO (last initially). Step 2) To work with the FIFO line, contact the Queue class using the imported line module as shown below: import line q1 = queue. Line() Step 3) To work with queue queues calling lifoQueue classes() as shown below: import line q1 = queue. LifoQueue() Rules found in the line and lifoQueue classFollowing are important methods in the Quent and LifoQueue classes: put (items): This will put the item in the queue.get(): This will return the item from the line.empty(): It will return true if the queue is empty and false if the item is present.qsize(): restore the full queue size(): First In First Out Queue ExampleIn cases initially out, the element that went first would be the first out. Add and items in the We work on examples to add items in the queue. To start working with a queue, import the first line of the module, as shown in the example below. To add an item, you can use the put() method as shown in the example: import q1 = line. Queue() q1.put(10) #this an additional 10 to the line. By default, row size is insane and you can add any number of items to it. If you want to specify the same line size can be done as follows queue import q1 = queue lines. Queue(5) of #The maximum number is 5. q1.put(1) q1.put(2) q1.put(3) q1.put(4) q1.put(5) print(q1.full()) # will return true. Output: True Now the size of the line is 5, and it won't take more than 5 items, and the q1.full() method will return true. Adding more items won't perform code again. Remove items from the Remove items from the line, you can use a method called a get(). This method allows items from queuing lines when called. The following example shows how to remove an item from the queue. import line active q1 = queue queue. Queue() q1.put(10) item1 = q1.get() print ('Items removed from the line are ', item1) Output: Items removed from the line are the Last 10 In The First Queue Exit Last Example in the first outlet, the last inserted element will be the first out. To work with LIFO, that is, last in the first line of exit, we need to import the line module and use the LifoQueue method(). Add and items in the line, we'll understand how to add items to the LIFO queue. import line active q1 = queue queue. LifoQueue() q1.put(10) You need to use the put method() on LifoQueue, as shown in the example above. Remove items from the queue Remove items from an elevatorOqueue you can use the get() method. import line active q1 = queue queue. LifoQueue() q1.put(10) item1 = q1.get() print ('Items removed from the LIFO line are ', item1) Output: Items removed from the lympho line are 10 Add more than 1 item in the Line In the Instance above, we've seen how to add a single item and remove items for FIFO and LIFOqueue. Now we'll see how to add more than one item and also remove it. Add and items in the FIFOqueue import line q1 = queue. Queue() to i in range(20): q1.put(i) # this will be additional from 0 to 20 to line Remove items from the IMPORT LINE FIFOqueue q1 = queue line. Queue() to i in range(20): q1.put(i) # this will be additional from 0 to 20 to the line while not q1.empty(): prints (Value , q1.get()) # get() will remove the item from the line. Output: Value is 0 Value 1 Value is 2 Value is 3 Value is 4 Value is 5 Value is 6 Value is 7 Value is 8 Value is 9 Value is 10 Value is 11 Value 12 Value is 13 Value is 14 Value is 15 Value is 16 Value is 17 Value is 18 Value is 19 Value is 19 Added and items in the LINE of LIOF LifoQueue() to i in range(20): q1.put(i) # this will be additional from 0 to 20 to line Remove items from the import line OF LIFOqueue q1 = queue. LifoQueue() to i in range(20): q1.put(i) # this will be additional from 0 to 20 to the line while not q1.empty(): print (Value, q1.get()) # get() will remove the item from the line. Output: Value is Value 18 Value is 17

Values is 16 Values are 15 Values are 14 Values are 13 Values are 11 Values are 10 Values are 9 Values 8 Values are 7 Values are 5 Values are 4 Values are 3 Values are 2 Values are 0 Arranging examples QueueFollowing shows the sorting line shows the filling line. The algorithm used to sort is a type of bubble. import line active q1 = queue queue. Queue() #Addingitems queue q1.put(11) q1.put(5) q1.put(4) q1.put(21) q1.put(3) q1.put(10) #using bubble sort on line n = q1.qsize() to i in range(n): x = q1.get() to j in range (n-1): y = q1.get() # element removed if x > y : q 1.put(y) a smaller #the is placed at the beginning of another lineup: q1.put(x) # smaller placed at the beginning of the line x = y # bigger replaced with x and compared again with nextelement q. put(x) while (q1.empty() == False): print (q1.queue[0], end = ) q1.get() Output: 3 4 5 10 11 21 Reverse Queue, you can use queues and other recurrences. The following example shows how to get the reversed queue. Example: import line q1 = queue row. Queue() q1.put(11) q1.put(5) q1.put(4) q1.put(21) q1.put(3) q1.put(10) def reverseQueue (q1src, q2dest) : buffer = q1src.get() if (q1src.empty() =false= : ReverseQueue(q1src, q2dest) #using recursion q2dest.put(buffer) return q2dest q2dest q2dest = queue. Queue() qReversed = ReverseQueue(q1,q2dest) while (qReversed.empty() == False): prints (qReversed.queue[0], end = ) qReversed.get() Output: 10 3 21 4 5 11 Summary:A queue is a container holding the data. There are two types of Lines, FIFO, and LIFO. For FIFO (First out Queue), the element that went first would be the first out. For LIFO (Last in the First Line), the inserted element last will be the first out. Items in the row are added using the put method (item). To remove an item, get() the method used. The 6Python Page counter is a container that will hold the count of every element found in the container. This counter is a sub-class found in the dictionary class. This counter is a sub-class found in the dictionary class. Using the Python Counter tool, you can calculate the key value pair in an object, also called a hash table object. Why use Python Counter? Here, is the main reason to use Python Counter 3: Counter holds data in an unfolding collection, just like an object that can be noticed. The elements here represent the keys and count as values. It allows you to calculate items in the list can be on it. Arithmetic operations such as add-ons, rejections, intersections, and unions can be done easily at the Counter.A Counter can also count the elements from other countersIn tutorials this you will learn: The introduction to the CounterPython Python Counter takes the list input, tuple, dictionary, string, which are all replaceable objects, and it will give you the output that is Counter(list) Consider you have the following list : list1 = ['x','y','z','x','x','x','x','y', 'z'] This list has x, y and z.When you use Counters in this list, it will calculate how many times x, y and z attend. Output if the counter is used in the list1 should be something like : Counter ({'x': 4, 'y': 2, 'z': 2}) So we have count x as 4, y as 2 and z as 2. To use The Counter we need to import it first as shown in the example given below: from the Counter import collection Here is a simple example, which shows the work of the Counter module. from the collection import counter list1 = ['x','y','z','x','x','x','x','y', 'z'] print (Counter (list1)) Output: Counter ({'x': 4, 'y': 2, 'z': 2}) Counter with Python StringIn, all of which are objects and strings are objects and a string of python strings can be created simply by symbolising the characters in double quotes. Python does not support character type. This is considered a long string of one, also considered substring. In the example below, a string is handed over to the Counter. It restores the dictionary format, with the key pair/value where the key is an element and the value is count. It also considers space as an element and provides a space count in a string. Example: from online import collection of my_str = Welcome to Guru99 Tutorial! print (Counter (my_str))Output: Counter ({'o': 3, ': 3, 'u': 3, 'e': 2, 'l': 2, 't': 2, 'r': 2, '9': 2, 'W': 1, 'c': 1, 'm': 1, 'G': 1, 'T': 1, 'i': 1, 'a': 1, 's': 1, '': 1}) Counter with listA is a replaceable object that has elements in a square bracket The elements in the list when assigned to the Counter will be converted to inevitable objects where the elements will be key and the values will be counting the elements from the given list. For example ['x','y','z','x','x','x','y','z']. Once you give a Counter list, it will give you a count of each element in the list. from the import collection of list1 counters = ['x','y','z','x','x','x','y','z'] prints (Counter (list1)) Output: Counter ({'x': 4, 'y': 2, 'z': 2}) Counters with dictionaries have elements as key pairs/ values, and they are written in curly brackets. Once the dictionary is given to the Counter, it will be converted to a simple object where the elements will be key, and the values will be the count of the elements of the given dictionary. For example : {'x': 4, 'y': 2, 'z': 2, 'z': 2}. Counter Function will try to find the count of each key in the given dictionary. from the collection of importing counters atct1 = {'x': 4, 'y': 2, 'z': 2, 'z': 2} print (Counter(dict1)) Output: Counter({'x': 4, 'y': 2, 'z': 2}) Counter with TupleTuple is a collection of objects separated by the column in kurung The counter will give you an element count given tuple. Once the tuple is given to the Counter, it will be converted to an inevitable object where the elements will be key and the values will be the count of elements from the given tuple. Given. tuple1 counter import collection = ('x','y','z','x','x','x','x','y','z') prints (Counter (tuple1)) Output: Counter ({'x': 4, 'y': 2, 'z': 2}) Accessing, Controlling and updating Counter CountersInitializing CounterA can be started by surpassing the string value, list, dictionary, Dictionary, or tuple as shown below: from the import collection of print counters (Counter(Welcome to Teacher Tutorial 99!)) #using print strings (Counter(['x','y','z','x','x','y', 'z'])) #using print list ((Counter'x': 4, 'y': 'z'])) #using print list (Counter({'x': 4, 'y': 2', 'z'])) #using print list (Counter({'x': 4, 'y': 2', 'z'])) #using print list (Counter({') x': 4, 'y': 2', 'z'])) #using print list (Counter({'x': 4, 'y': 2', 'z'])) #using print list (Counter({'x'x': 4, 'y': 2' , 'z'])) list of #using prints (Counter({'x'x': 4, 'y': 2', 'z'])) 2})) #using dictionary prints (Counters('x','y','z','x','x','x','y', 'z'))) #using tuple You can also start the blank counter as shown below: from the _count import counter collection = Counter() Update CounterYou can add value to the Counter by using (update) _count.update ('Welcome to Teacher Tutorial99!') The final code is : from the import collection of _count Counter = Counter() _count.update ('Welcome to Tutorial Guru99!') prints (_count) Output are: Counter ({'o': 3, ' ': 3, 'u': 3, 'e': 2, 'l': 2, 't': 2, 'r': 2, '9': 2, 'W': 1, 'c': 1, 'm': 1, 'G': 1, 'T': 1, 'i': 1, 'a': 1, 's': 1, '!': 1}) AccessIng CounterTo gets values from the Counter, you can do as follows: from the collection of _count import counter = Counter() _count.update ('Welcome to Teacher Tutorial99!') prints ('%s : %d' % ('u', _count['u'])) prints ('') for char in 'Teacher': print('%s : %d' % (char, _count (char])) Output: u : 3 G : 1 u : 3 r : 2 u : 3 Delete Elements from CounterTo delete elements from the Counter you can use the del , as shown in the example below : Example: from the collection importing counter at dict1 = {'x': 4, 'y': 2, 'z': 2} del dict1[x] print (Counter(dict1)) Output: Counter ({'y': 2, 'z': 2}) Arithmetic operations of Python Arithon operations such as add-ons, rejections, intersections and unions can be done at the Counter as shown in the example below: Examples: from the collection of imported counters : Counter 1 = Counter ({x4': 'y': 2, 'z': -2}) counter2 = Counter ({'x1': -12, 'y': 5, 'z':4 }) #Addition counter = counter1 + counter2 # only positive values will be returned. print(counter3) #Subtraction counter4 = counter1 - counter2 # all ve numbers are excluded. For example z will z = -2-4=-6, because it is -ve value it is not shown in output print (counter4) #Intersection counter5 = counter1 & counter2 # it will give all the normal minimum positive value from the counter1 and counter2 prints (counter5) #Union counter6 = counter | counter 2 # it will provide maximum positive value from the counter1 and print (counter6) Output: Counter ({'y': 7, 'x': 4, 'z': 2}) Counter ({'x1': 12, 'x': 4}) Counter ({'y': Counter 2}) ({'y': 5, 'x': 4, 'z': 4}) The methods available at the Python Counter Are some of the important methods available with the Counter, here is the same list: elements() : This method will return you all elements with count >0. Elements with a count of 0 or -1 will not returned.most_common (value): This method will restore the most common elements from the Counter.minus list(): This is used to cut elements from counter.other updates(): This method is used to update elements from other counters. Example : elements() from the collection of import1 counters = Counters ({'x': 5, 'y': 2, 'z': -2, 'x1':0}) _elements = counter1.elements() # will give you all elements with a positive value and count>0 for a in _elements: print(a) Output: x x x x x y Example: most_common(value) from the Counter 1 import collection = Counter ({x'5: 5 'y': 12, 'z': -2, 'x1':0}) common_element = counter1.most_common(2) # Dictionary will be sorted according to the most common elements followed next. print(common_element) common_element1 = counter1.most_common() # if the value is not given to most_common , it will sort the dictionary and give the most common elements from scratch. The final element will be the least common element. print(common_element1) Output: [('y', 12), ('x', 5)] [('y', 12), ('x', 5), ('x1', 0), ('z', -2)] Example:subtract() from collections import Counter counter1 = Counter({'x': 5, 'y': 12, 'z': -2, 'x1':0}) counter2 = Counter({'x': 2, 'y':5}) counter1.subtract(counter2) print(counter1) Output: Counter({'y': 7, 'x': 3, 'x1': 0, 'z': -2}) Example:update() from collections import Counter counter1 = Counter({'x': 5, 'y': 12, 'z': -2, 'x1':0}) counter2 = Counter({'x': 2, 'y':5}) counter1.update(counter2) print(counter1) Output: Counter({'y': 17, 'x': 7, 'x1': 0, 'z': -2}) Reassigning Counts in PythonYou can re-assign counts of Counter as shown below: Consider you have a dictionary as : {'x': 5, 'y': 12, 'z': -2, 'x1':0} You can change the count of the element as shown below : from the collection of importing counter1 = Counter ({'x': 5, 'y': 12, 'z': -2, 'x1':0}) counter1['y'] = 20 prints (counter1) Output: After performing you will see that the y count is changed from 12 to 20 Counter({'y': 20, 'x': 5, 'x1': 0, 'z': -2}) Get and set the Element count using CounterTo get the element count using the Counter that you can do as follows: from the import1 counter collection = Counter ({'x': 5, 'y': 12, 'z': -2, 'x1':0}) print (counter1['y']) # this will give you a count of the element 'y' Output: 12 To set the count of elements you can do as follows: from the collection of counter import counter 1 = Counter ({'x': 5, 'y': 12, 'z': -2, 'x1':0}) print (counter1['y']) counter1['y'] = 20 counter1['y1'] = 10 prints (counter1) Output : 12 Counters ({'y' : 20, 'y1': 10, 'x': 5, 'x1': 0, 'z': -2}) Summary:The counter is a container that will hold the count of each element found in the container. Counters are sub-classes found in dictionary classes. Using the Python Counter tool, you can calculate the key value pair in an object, also called an object that's easy to notice. Counters hold data in unsynced collections, similar to a shortenable object. The elements here represent the keys and count as values. It allows you to calculate items the list can be on it. Arithmetic operations such as add-ons, rejections, intersections, and unions can counter.A counters can also calculate elements from other counters. The important methods contained in the Counter are elements(), most_common (value), reject() and update(). Counters can be used on strings, lists, dictionaries, and tuples. Page 7Python Enumerate() is a buit-in function available with the Python library. It takes the given input as a collection or tuples and returns it as an enumerate object. The Python Enumerate() command adds a counter to each curved object item and returns the enumerate object as an output string. In this Python Enumerate tutorial, you will learn: Syntax Python enumerate() enumerate (mobile, startIndex) ParametersThree parameter is: Table: objects that can be loosened. StartIndex: (optional) The calculated will start with the value given in startIndex for the first item in the coil and increment it for so on until it reaches the end of the coil. However, if startIndex is not specified, the number will start at 0. ReturnValue: It will return an iterableobject, with a countvalue to each item to the iteratorobject given as input. Enumerate() in the Python ExampleEnumerate method comes with an automatic counter/index to each item contained in the Enumerate list in Python. The value of firstindex will start at 0. You can also specify startindex by using the startIndex option parameter in enumerate. ExampleIn the code below, my list is the list given to enumerate functions in Python. The list() function is used to display Python Enumerate output. Note: No startIndex is used therefore the index for the firstitem will start at 0. Output from enumerate will be in the following ways: (0, item_1), (1, item_2), (2, item_3), ... (n, item_n) Fail: python_enumerate.py mylist = ['A', 'B' ,'C', 'D'] e_list = enumerate(mylist) print (list(e_list)) Output: [(0, 'A'), (1, 'B'), (2, 'C'), (3, 'D')] UsingEnumerate() in the list with startIndex In the example below, the startindex given as 2.Index firstitem will start from the given startindex. Example: In the example below, my list is the list given to be supplied. The list() function is used to display the enumerate output. mylist = ['A', 'B' ,'C', 'D'] e_list = enumerate(mylist,2) print (list(e_list)) Output: [(2, 'A'), (3, 'B'), (4, 'C'), (5, 'D')] Coil More Enumerate objects Example shows enumerating over objects with and without startInd First coil has no startIndex, so index starts from 0. The second coil startsIndex as 10, so the index starts at 10. Example: mylist = ['A', 'B' ,'C', 'D'] for i in enumerate(mylist): print(i) print() print() Print() Using startIndex as 10) for i in enumerate (mylist, 10): print(i) print() Output: (0, 'A') (1, 'B') (2, 'C') (3, 'D') Using startIndex 10 (10, 'A') (11, 'B') (12, 'C') (13, 'D') Enriching the TupleIn example below, you can use the tuple inside the enumerate. You can also use startIndex, and the keys for each item will start from startIndexgiven. By default, startIndex is 0. There, therefore you see the key as 0 for item A and 1 for B and so on. Example: my_tuple = (A, B, C, D) for i in enumerate(my_tuple): print(i) Output: (0, 'A') (1, 'B') (2, 'C') (3, 'D') (4, 'E') Lure Python StringIn, the string is various, and therefore you can solve it. If you pass a string to duplicate(), the output will show you the index and value for each string script. Example: my_str = Guru99 for i in enumerate(my_str): print(i) Output: (0, 'G') (1, 'u') (2, 'r') (3, 'u') (4, '9') (5, '9') Enrich the Python Dictionaryin, the dictionary is listed in a kerinting cage, in this cage, the values are declared. Each element is a key/value pair and is separated by commas. You can use the dictionary inside enumerate() and view the output. my_dict = {a: PHP, b:JAVA, cPYTHON, d:NODEJS} for i in enumerate(my_dict): print(i) Output: (0, 'a') (1, 'b') (2, 'c') (3, 'd') The advantage of using EnumerateHere, is the goodness/benefit of using Enumerate in Python: Enumerate allows you to coil through lists, tuples, dictionaries, strings, and provide values together with indexes. To get index values using a for-loop, you can use list.index(n). However, list.index(n) is very expensive as it will affect to coil twice. Enumerate is helpful in such a way because it gives indexes and items on one go. The SummaryEnumerate() function is an internal built-in function available with python. The Enumerate() function adds a counter to each object item it can have on it and returns an enumerate object. In Enumeration in Python, you can specify startIndex, that is, the counter you want the value to start. Enumerate can be used for list coils, tuples, dictionaries, and sequences. Enumerate is very helpful when you want indexes and values when coiling up lists, tuples, dictionaries, etc. Enumerate comes with an automatic counter/index for each item listed. The value of the first index will start at 0. You can also specify startindex by using the startIndex option parameter in enumerate. If you pass a string to duplicate(), the output will show you the index and value for each string script. Page 8Python sleep() is a function used to slow down the execution of a code for a given current number as an input for sleep(). The sleep() command is part of the time module. You can use the sleep() function to temporarily stop the execution of your code. For example, you are waiting for the process to complete or upload the file. In this tutorial, you will learn: time.sleep() Syntax time import time.sleep (when) Parameters:when: The number of times you want your code to be terminated. Using the sleep() function in Python Follow the steps given below to add sleep() in your python script. Step 1: import time Step 2: Add time.sleep() Number 5 given as input for sleep(), is the current number you want the execution of the code to be stopped when it is time.sleep(5) Here is the code of working together with the message in the print(), to indicate the delay in the display of messages in the terminal when implemented. Printout time import (Welcome to Guru99 Python Tutorial) time.sleep(5) printout (This message will be printed after waiting 5 seconds) Output: Welcome to Guru99 Python Tutorial This message will be printed after waiting 5 seconds How to slow down the implementation of functions using sleep()? The example shown below has a defined function called exposure(). Display() function prints the Welcome to Tutorial Guru99 message. When the function is called, it will execute and display messages inside the terminal. To increase the delay in implementing functions, let's add time.sleep in Python before making a call to the function. During execution, Python time.sleep will stop there for the current number given, and then the display function() will be called. Example: import print time ('Implementation Code Starts') display def(): print ('Welcome to Guru99 Tutorial') time.sleep(5) print display('Implementation of Expired Function') Output: Implementation of Code Starts Welcome to Teacher99 Tutorial Implementation Function DelayWhat is a different way to add delay in Python Scripts? Using the Sleep() functionWe have seen some early examples of how to use time.sleep(). Let's try another example here using time.sleep(). Example: The code has a coil that will retrieve the string change and print each character with a delay of 1 second. import period my_message = Guru99 for i in my_message: print(i) time.sleep(1) Output: G you 9 9 Using the asyncio.sleep function available from (Python 3.4 or higher)You can use asyncio.sleep with python version 3.4 and higher. To use the asyncio sleep method, you need to add an asylum and wait for the function, as shown in the example below: Example: The script has a display function call() that prints the Welcome message to the Guru99 tutorial. There are two keywords used in the async function and await. The async keyword is added at the beginning of the function definition, and waits for it to be added shortly before asyncio.sleep(). Both async/waiting keywords aim to control asynchronous tasks. When the display function() is called, and it faces waiting for asyncio.sleep(5), the code will sleep or stop at that point for 5 moments and, once completed, will print the message. import asyncio print ('Implementation Code Starts') display def movement(): waiting for asyncio.sleep(5) printout ('Welcome to Tutorial Guru99') asyncio.run(display()) Output: Implementation of Code Starts Welcome to Guru99 Tutorial Using Event ().waitThe Event().wait for method to come from threading module. The Event.wait() method will stop the execution of any process for the current number as needed as Event Work is shown in the example below:Example: Code using Events().wait(5). Number 5 is the number of seconds the code will slow down to go to the next row calling the functional view(). Once 5 seconds display function() will be called, and the message will be printed inside the terminal. from threading import Event print ('Implementation Code Starts') display def(): print ('Welcome to Guru99 Tutorial') Event ().wait(5) display() Output:Implementation of The Code Commences Welcome to Guru99 Tutorial Using Pemasa Pemasa is another method available with Threading, and it helps to get the same functionality as Python bedtime. The Pemasa work is shown in the example below: Example: The predator takes input as a time of delay in Python in a while, together with the tasks that need to be started. To make the driver work, you need to call the start() method. In the code, the Predator is given 5 moments, and the display function needs to be called when 5 is performed. The installer will start working when the Timer.start() method is called. from threading import Timer print ('Implementation Code Starts') display def(): the printout ('Welcome to Tutorial Guru99') does not = Predator(5, display) t.start() Implementation of the Start Code Welcome to the Tutorial Summary Guru99:Python sleep() will pause the Python code or slow down the implementation of the program for the current number given The Sleep() function is part of the Python time module. You can use the Python sleep function if you want to temporarily stop the execution of your code. For example, if you are waiting for another process to complete, or upload files, etc. There are many ways to add Python overdue functionality to code other than sleep, and they use the asyncio.sleep, Event().wait and Timer.Similar to sleep() methods, there is asyncio.sleep() with python versions 3.4 and higher. To use the asyncio sleep method, you need to add it immediately and wait for the event functionThe Event().wait for the method to come from the thread module. The Event.wait() method will stop the execution of any process for the current number as an argument. Pemasa is another method available with Threading, and it helps to get the same function as sleepPage 9Python has a built-in function called type() that helps you find the type of variable class given as input. For example, if the input is a string, you will get output <class 'str'=>as , for the list, it will be , and <class 'list'=>others. Using the type() directive, you can pass one argument, and the return value will be the type of argument class given, e.g. type (object). It is also possible to pass three arguments to type(), i.e., type (name, base, dict), in such a case, it will return a new type object. In this tutorial, you'll learn: Syntax for type():type() can be used in two ways as shown below: type (object) type (nominee, base, channel) Parameter: object type: This is a mandatory parameter. this is just a parameter submitted to type(), from it will return you the type of parameter. Parameters: type (name, base, dict) name:class.bases name: (optional). This is the preferred parameter, and it is a basic class dict:>/class> </class> </class> This is the preferred parameter, and it is a namespace that has a class definition. Return Value:If the object is the only parameter passed to type() it will return the object type. If the param approved for typing is a type (object, base, dict), in such case, it will return a new object type. Example type()In this case, for example we have a string value, number, floating value, complex number, list, tuple, dict and set. We'll use a variable getr with type to see the output for each of them. str_list = Welcome To Guru99 = 50 pi = 3.14 c_num = 3j+10 my_list = [A, B, C, D] my_tuple = (A, B, C) my_dict = {A:a, B:bb, C:c, D:d} my_set = {'A', 'B', 'C', 'D'} print(Type is: ,type(str_list)) printout(That type is: ,type(age)) print (Type)). c_num)) mold(Type is: ,type(my_list)) mold() Type is: ,type(my_tuple)) mold(Type is: ,type(my_dict)) mold(Type is: ,type(my_set)) Output: Type is: Type is: Type<class 'str'=> <class 'int'=> <class 'float'=> <class 'complex'=> <class 'list'=> is :<class 'tuple'=> <class 'dict'=> Type is :<class 'set'=> For example: For example. : Uses type() for the object class. When you check an object created from a class using type(), it returns the class type together with the class name. In this example, we'll create a class and check the type of object created from the class test. test class: s = 'test' t = print test(type(t)) Output: <class '__main__.test'=>Example: Using names, bases, and dirt in this type()This type can also be called using syntax: type (name, base, dict). The three parameters approved for typing() name, base and channel are the components that make up the class definition. Names represent class names, principles are principle classes, and dicts are dictionary attributes of the base class. In this example, we will use all three params i.e. name, basis, and planning in type(). Example: class MyClass: x = 'Hello World' y = 50 t1 = type ('NewClass', (MyClass,), dict (x='Hello World', y=50)) print (type(t1)) print (vars(t1)) Output: <class 'type'=>{'x': 'Hello World', 'y': 50, '__module__': '__main__', '__doc__': None} When you pass all three arguments to type() , it helps you to start a new class with basic class attributes. What is isinstance() in Python? Python isinstance is part of the built-in functions in python. Python isinstance() takes two arguments, and it returns true if the first argument is an example of classinfo given as the second argument. Isinstance Syntax() isinstance (object, style) Parametersobject: An object for example you compare to a style. It will return true if the type is commensurate with the fake .class type: Type or class or tuple and/or classes. Return value: It will return true if the object is a style instance and is false otherwise. Example isinstance()In</class> </class> </class> </class> </class> </class> </class> </class> </class> </class> we will study various examples to study isinstance() Examples : isinstance() Integer checkThe code below compares the integer values of 51 with int type. It will return true of type 51 games with int instead of fake. age = isinstance (51,int) prints (age is integer:, age) Output: Age is integer: True Example : isinstance() This example floating inspection we will compare the value of the float with a floating type of 3.14 values will be compared to the float of this type. pi = isinstance (3.14,float) prints (pi is float:, pi) Output: pi is float: True example: isinstance() String check message = isinstance (Hello World,str) prints (messages are strings:, Message) Output: The message is a string: True example : isinstance() Tuple checkThe code checks for tuple (1,2,3,4,5) with tuple It will return true if the input given is tuple and false type otherwise. my_tuple = isinstance((1,2,3,4,5),tuple) prints (my_tuple are tuple:, my_tuple) Output: my_tuple is a tuple: True example : isinstance() Set code review for a set ({1,2,3,4,5}, with the specified type. It will return true if the given input is a prescribed and false type otherwise. my_set = isinstance({1,2,3,4,5},set) prints (my_set are set:, my_set) Output: my_set is a set: Real Example: isinstance() checklist Checklist For list [1,2,3,4,5],with a list of types. It will return true if the input given is a list of types and false otherwise. my_list = isinstance ([1,2,3,4,5],list) prints (my_list are lists:, my_list) Output: my_list is a list: True example: isinstance() check the Code check code for dictation ({A:aa, B:bb, C:cc, D:d},with dict type. It will return true if the input given is a dict and false type otherwise my_dict = isinstance({A:aa, B:bb, C: c , D:d},dict) prints (my_dict are dictation:, my_dict) Output: my_dict is dictation: The real example: isinstance() test on the class code Shows class type by is a class object compared to the class name inside the isinstance(). It goes back to true if the object belongs to class and is false instead. MyClass class: _message = Hello World _class = MyClass() prints (_class is an example of MyClass() : , isinstance (_class,MyClass)) Output: _class is an example of MyClassClass() The Actual Differences Between types() and isinstance() in pythontype() of Python have a built-in function called type() that helps you find the type of variable class given as input. Python has a built-in function called isinstance() that compares values with the given type. If the value and given corresponding type it will return to true otherwise false. The return value is the object of the return value type is Boolean which is true or false. class A: my_listA = [1,2,3] class B(A): my_listB = [1,2,3] print (type(A()) == A) print ==A) Output: False True In case the subclass review type gives a false return. class A: my_listA = [1,2,3] class B(A): my_listB = [1,2,3] print (isinstance(A(), A)) print (isinstance(B(), A)) Output: True provides true value when checked subclasses. Summary:Python has a built-in function called type() that helps you find the type of variable class given as input. For example, if input is a string, you will get output <class 'str'=>as, for the list, it will be , and <class 'list'=>others. For this type(), you can pass one argument, and the return value will be the type of argument class given, for example, type (object). It is also possible to pass three arguments for typing(), that is, type (name, base, dict), in such cases, it will return a new type object. Python has a built-in function called example() that compares values with the given type. It's value and the type given corresponding it will return true otherwise false. Using isinstance(), you can test strings, floating, int, lists, tuple, dict, set, class, etc. Using the isinstance method(), you can test strings, floating, int, list, tuple, dict, set, class, etc.</class> </class>

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