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Last week, Chinese ride-hailing giant Didi said it will move its listing to Hong Kong from the US.

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Gary Bowser admitted to making hacking tools that allowed people to play pirated games.

Game maker Ubisoft is launching its in-game NFT system this week.

Platform boss unveils new safety features a day before being quizzed by the US Senate.

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Lawmakers tossed out some bipartisan provisions as they rushed to advance the bill, which would increase the Pentagon’s budget by more than what President Biden had requested.

The bill would provide a one-time pathway for the Senate to raise the debt ceiling on a simple majority vote, skirting Republican obstruction.

Under the agreement, commanders’ powers would be clipped after years of complaints about unfairness and retaliation.

President Biden said a Russian invasion of Ukraine would result in heavy economic penalties for Mr. Putin, in a tense meeting.

The former White House chief of staff told the House panel scrutinizing the Capitol attack that he was no longer willing to be deposed, reversing a commitment he made last week.

As legislators across the country draw House maps to protect incumbents, a nonpartisan commission of California citizens is drafting one that will scramble political fortunes for both parties.

ItsMyCode | We always use print statements in Python to display the output in the console or command line terminal. However, sometimes we want to change this behavior to print to a text file instead of the console. Print to file using file argument The print() method accepts the file parameter as one of the arguments, and using this, we can print the standard output to a file. The default value of the file argument is sys.stdout, which prints the output on the screen. Example 1: Print to the text file The below program opens the existing text file in the write mode using the open() function (if file is not available it will create a new file) and writes all the text specified inside the print statement. Once the content is written to a file, we close the file using the close() method. # Print to the text file file = open('log.txt', 'w') print('This is a sample print statement', file = file) print('Hello World', file = file) file.close() Output This is a sample print statement Hello World Example 2: Print to the text file using inline file argument If you want to control only a few print statements to output to a text file, you could inline the file argument inside the print statement, as shown below. # Appends the print statement into a log.txt print("Welcome to ItsMyCode", file=open('log.txt','a')) Output Welcome to ItsMyCode Redirecting the Standard Output Stream to a file Specifying the file argument helps debug the smaller programs but is not desirable in some situations.  In this case, we can redirect all the standard output stream to a file. Setting the standard output to a file ensures that the text specified inside the print() function will be written to a file instead of displaying in the console.  The standard output can be set in Python by importing the sys module and setting the stdout to a file or file-like object. import sys # Prints in a console/terminal print('Default mode: Prints on a console.') # Store the reference of original standard output into variable original_stdout = sys.stdout # Create or open an existing file in write mode with open('log.txt', 'w') as file: # Set the stdout to file object sys.stdout = file print('File Mode: Print text to a file.') # Set the stdout back to the original or default mode sys.stdout = original_stdout print('Default Mode: Prints again on a console.') Output Hello Welcome to Python Tutorials !!! File Mode: Print text to a file. Default mode: Prints on a console. Default Mode: Prints again on a console. Redirect the Python Script Output to File The easiest and dirty way is to redirect the Python script’s output directly from the command line window while executing the script.  For example, if you have a Python script file named main.py with the following code. We can redirect the output of the Python script using the right angle bracket, as shown below. python3 main.py > output.txt   print("Hello World !!!") If you open the output.txt file, you can see the below content written into the text file. Hello World !!! The post Python Print to File appeared first on ItsMyCode.

Python provides a great module for creating your own iterators. The module I am referring to is itertools. The tools provided by itertools are fast and memory efficient. You will be able to take these building blocks to create your own specialized iterators that can be used for efficient looping. In this article, you will be looking at examples of each building block so that by the end you will understand how to use them for your own codebases. Let’s get started by looking at some infinite iterators! The Infinite Iterators The itertools package comes with three iterators that can iterate infinitely. What this means is that when you use them, you need to understand that you will need to break out of these iterators eventually or you'll have an infinite loop. These can be useful for generating numbers or cycling over iterables of unknown length, for example. Let's get started learning about these interesting iterables! count(start=0, step=1) The count iterator will return evenly spaced values starting with the number you pass in as its start parameter. Count also accepts a step parameter. Let’s take a look at a simple example: >>> from itertools import count >>> for i in count(10): ... if i > 20: ... break ... else: ... print(i) ... 10 11 12 13 14 15 16 17 18 19 20 Here you import count from itertools and you create a for loop. You add a conditional check that will break out of the loop should the iterator exceed 20, otherwise, it prints out where you are in the iterator. You will note that the output starts at 10 as that was what you passed to count as our start value. Another way to limit the output of this infinite iterator is to use another sub-module from itertools named islice. Here’s how: >>> from itertools import islice >>> for i in islice(count(10), 5): ... print(i) ... 10 11 12 13 14 In this example you import islice and loop over count starting at 10 and ending after 5 items. As you may have guessed, the second argument to islice is when to stop iterating. But it doesn’t mean "stop when I reach the number 5". Instead, it means “stop when you’ve reached five iterations”. cycle(iterable) The cycle iterator from itertools allows you to create an iterator that will cycle through a series of values infinitely. Let’s pass it a 3 letter string and see what happens: >>> from itertools import cycle >>> count = 0 >>> for item in cycle('XYZ'): ... if count > 7: ... break ... print(item) ... count += 1 ... X Y Z X Y Z X Here you create a for loop to loop over the infinite cycle of the three letters: XYZ. Of course, you don’t want to actually cycle forever, so you add a simple counter to break out of the loop with. You can also use Python’s next built-in to iterate over the iterators you create with itertools: >>> polys = ['triangle', 'square', 'pentagon', 'rectangle'] >>> iterator = cycle(polys) >>> next(iterator) 'triangle' >>> next(iterator) 'square' >>> next(iterator) 'pentagon' >>> next(iterator) 'rectangle' >>> next(iterator) 'triangle' >>> next(iterator) 'square' In the code above, you create a simple list of polygons and pass them to cycle. You save our new iterator to a variable and then you pass that variable to the next function. Every time you call next, it returns the next value in the iterator. Since this iterator is infinite, you can call next all day long and never run out of items. repeat(object) The repeat iterators will return an object over and over again forever unless you set its times argument. It is quite similar to cycle except that it doesn’t cycle over a set of values repeatedly. Let’s take a look at a simple example: >>> from itertools import repeat >>> repeat(5, 5) repeat(5, 5) >>> iterator = repeat(5, 5) >>> next(iterator) 5 >>> next(iterator) 5 >>> next(iterator) 5 >>> next(iterator) 5 >>> next(iterator) 5 >>> next(iterator) Traceback (most recent call last): Python Shell, prompt 21, line 1 builtins.StopIteration: Here you import repeat and tell it to repeat the number 5 five times. Then you call next on our new iterator six times to see if it works correctly. When you run this code, you will see that StopIteration gets raised because you have run out of values in our iterator. Iterators That Terminate Most of the iterators that you create with itertools are not infinite. In this section, you will be studying the finite iterators of itertools. To get an output that is readable, you will be using Python’s built-in list type. If you do not use list, then you will only get an itertools object printed out. accumulate(iterable) The accumulate iterator will return accumulated sums or the accumulated results of a two-argument function that you can pass to accumulate. The default of accumulate is addition, so let’s give that a quick try: >>> from itertools import accumulate >>> list(accumulate(range(10))) [0, 1, 3, 6, 10, 15, 21, 28, 36, 45] Here we import accumulate and pass it a range of 10 numbers, 0-9. It adds each of them in turn, so the first is 0, the second is 0+1, the 3rd is 1+2, etc. Now let’s import the operator module and add it into the mix: >>> import operator >>> list(accumulate(range(1, 5), operator.mul)) [1, 2, 6, 24] This function accepts two arguments to be multiplied. So for each iteration, it multiplies instead of adds (1x1=1, 1x2=2, 2x3=6, etc). The documentation for accumulate shows some other interesting examples such as the amortization of a loan or the chaotic recurrence relation. You should definitely give those examples a look as they are well worth your time. chain(*iterables) The chain iterator will take a series of iterables and basically flatten them down into one long iterable. I actually recently needed its assistance in a project I was helping with. Basically, you had a list with some items already in it and two other lists that you wanted to add to the original list, but you only wanted to append the items in each list to the original list instead of creating a list of lists. Originally I tried something like this: >>> my_list = ['foo', 'bar'] >>> numbers = list(range(5)) >>> cmd = ['ls', '/some/dir'] >>> my_list.extend(cmd, numbers) >>> my_list ['foo', 'bar', ['ls', '/some/dir'], [0, 1, 2, 3, 4]] Well, that didn’t work quite the way I wanted it to. The itertools module provides a much more elegant way of flattening these lists into one using chain: >>> from itertools import chain >>> my_list = list(chain(['foo', 'bar'], cmd, numbers)) >>> my_list ['foo', 'bar', 'ls', '/some/dir', 0, 1, 2, 3, 4] My more astute readers might notice that there’s actually another way you could have accomplished the same thing without using itertools. You could do this to get the same effect: >>> my_list = ['foo', 'bar'] >>> my_list += cmd + numbers >>> my_list ['foo', 'bar', 'ls', '/some/dir', 0, 1, 2, 3, 4] Both of these methods are certainly valid and before I knew about chain I would have probably gone this route, but I think chain is a more elegant and easier to understand solution in this particular case. chain.from_iterable(iterable) You can also use a method of chain called from_iterable. This method works slightly differently than using chain directly. Instead of passing in a series of iterables, you have to pass in a nested list. Let’s take a look: >>> from itertools import chain >>> numbers = list(range(5)) >>> cmd = ['ls', '/some/dir'] >>> chain.from_iterable(cmd, numbers) Traceback (most recent call last): Python Shell, prompt 66, line 1 builtins.TypeError: from_iterable() takes exactly one argument (2 given) >>> list(chain.from_iterable([cmd, numbers])) ['ls', '/some/dir', 0, 1, 2, 3, 4] Here you import chain as you did previously. You try passing in our two lists but you end up getting a TypeError! To fix this, you change your call slightly such that you put cmd and numbers inside a list and then pass that nested list to from_iterable. It’s a subtle difference but still easy to use! compress(data, selectors) The compress sub-module is useful for filtering the first iterable with the second. This works by making the second iterable a list of Booleans (or ones and zeroes which amounts to the same thing). Here’s how it works: >>> from itertools import compress >>> letters = 'ABCDEFG' >>> bools = [True, False, True, True, False] >>> list(compress(letters, bools)) ['A', 'C', 'D'] In this example, you have a group of seven letters and a list of five Bools. Then you pass them into the compress function. the compress function will go through each respective iterable and check the first against the second. If the second has a matching True, then it will be kept. If it’s a False, then that item will be dropped. Thus if you study the example above, you will notice that you have a True in the first, third and fourth positions which correspond with A, C, and D. dropwhile(predicate, iterable) There is a neat little iterator contained in itertools called dropwhile. This fun little iterator will drop elements as long as the filter criteria is True. Because of this, you will not see any output from this iterator until the predicate becomes False. This can make the start-up time lengthy, so it’s something to be aware of. Let’s look at an example from Python’s documentation: >>> from itertools import dropwhile >>> list(dropwhile(lambda x: x<5, [1,4,6,4,1])) [6, 4, 1] Here you import dropwhile and then you pass it a simple lambda statement. This function will return True if x is less than 5. Otherwise it will return False. The dropwhile function will loop over the list and pass each element into the lambda. If the lambda returns True, then that value gets dropped. Once you reach the number 6, the lambda returns False and you retain the number 6 and all the values that follow it. I find it useful to use a regular function over a lambda when I’m learning something new. So let’s flip this on its head and create a function that returns True if the number is greater than 5. >>> from itertools import dropwhile >>> def greater_than_five(x): ... return x > 5 ... >>> list(dropwhile(greater_than_five, [6, 7, 8, 9, 1, 2, 3, 10])) [1, 2, 3, 10] Here you create a simple function in Python’s interpreter. This function is your predicate or filter. If the values you pass to it are True, then they will get dropped. Once you hit a value that is less than 5, then ALL the values after and including that value will be kept, which you can see in the example above. filterfalse(predicate, iterable) The filterfalse function from itertools is very similar to dropwhile. However, instead of dropping values that match True, filterfalse will only return those values that are evaluated to False. Let’s use our function from the previous section to illustrate: >>> from itertools import filterfalse >>> def greater_than_five(x): ... return x > 5 ... >>> list(filterfalse(greater_than_five, [6, 7, 8, 9, 1, 2, 3, 10])) [1, 2, 3] Here you pass filterfalse your function and a list of integers. If the integer is less than 5, it is kept. Otherwise, it is thrown away. You will notice that our result is only 1, 2 and 3. Unlike dropwhile, filterfalse will check each and every value against our predicate. groupby(iterable, key=None) The groupby iterator will return consecutive keys and groups from your iterable. This one is kind of hard to wrap your head around without seeing an example. So let’s take a look at one! Put the following code into your interpreter or save it in a file: from itertools import groupby vehicles = [('Ford', 'Taurus'), ('Dodge', 'Durango'), ('Chevrolet', 'Cobalt'), ('Ford', 'F150'), ('Dodge', 'Charger'), ('Ford', 'GT')] sorted_vehicles = sorted(vehicles) for key, group in groupby(sorted_vehicles, lambda make: make[0]): for make, model in group: print('{model} is made by {make}'.format(model=model, make=make)) print ("**** END OF GROUP ***\n") Here you import groupby and then create a list of tuples. Then you sort the data so it makes more sense when you output it and it also lets groupby actually group items correctly. Next, you actually loop over the iterator returned by groupby which gives you the key and the group. Finally, you loop over the group and print out what’s in it. If you run this code, you should see something like this: Cobalt is made by Chevrolet **** END OF GROUP *** Charger is made by Dodge Durango is made by Dodge **** END OF GROUP *** F150 is made by Ford GT is made by Ford Taurus is made by Ford **** END OF GROUP *** Just for fun, try changing the code such that you pass in vehicles instead of sorted_vehicles. You will quickly learn why you should sort the data before running it through groupby if you do. islice(iterable, start, stop) You actually learned about islice way back in the count section. But here you’ll look at it a little more in depth. islice is an iterator that returns selected elements from the iterable. That’s kind of an opaque statement. Basically what islice does is take a slice by index of your iterable (the thing you iterate over) and returns the selected items as an iterator. There are actually two implementations of islice. There are itertools.islice(iterable, stop) and then there’s the version of islice that more closely matches regular Python slicing: islice(iterable, start, stop[, step]). Let’s look at the first version to see how it works: >>> from itertools import islice >>> iterator = islice('123456', 4) >>> next(iterator) '1' >>> next(iterator) '2' >>> next(iterator) '3' >>> next(iterator) '4' >>> next(iterator) Traceback (most recent call last): Python Shell, prompt 15, line 1 builtins.StopIteration: In the code above, you pass a string of six characters to islice along with the number 4 which is the stop argument. What this means is that the iterator that islice returns will have the first 4 items from the string in it. You can verify this by calling next on our iterator four times, which is what you do above. Python is smart enough to know that if there are only two arguments passed to islice, then the second argument is the stop argument. Let’s try giving it three arguments to demonstrate that you can pass it a start and a stop argument. The count tool from itertools can help us illustrate this concept: >>> from itertools import islice >>> from itertools import count >>> for i in islice(count(), 3, 15): ... print(i) ... 3 4 5 6 7 8 9 10 11 12 13 14 Here you just call count and tell islice to start at the number 3 and stop when you reach 15. It’s just like doing a slice except that you are doing it to an iterator and returning a new iterator! starmap(function, iterable) The starmap tool will create an iterator that can compute using the function and iterable provided. As the documentation mentions, "the difference between map() and starmap() parallels the distinction between function(a,b) and function(*c)." Let’s look at a simple example: >>> from itertools import starmap >>> def add(a, b): ... return a+b ... >>> for item in starmap(add, [(2,3), (4,5)]): ... print(item) ... 5 9 Here you create a simple adding function that accepts two arguments. Next, you create a for loop and call starmap with the function as its first argument and a list of tuples for the iterable. The starmap function will then pass each tuple element into the function and return an iterator of the results, which you print out. takewhile(predicate, iterable) The takewhile function is basically the opposite of the dropwhile iterator that you looked at earlier. takewhile will create an iterator that returns elements from the iterable only as long as our predicate or filter is True. Let’s try a simple example to see how it works: >>> from itertools import takewhile >>> list(takewhile(lambda x: x<5, [1,4,6,4,1])) [1, 4] Here you run takewhile using a lambda function and a list. The output is only the first two integers from our iterable. The reason is that 1 and 4 are both less than 5, but 6 is greater. So once takewhile sees the 6, the condition becomes False and it will ignore the rest of the items in the iterable. tee(iterable, n=2) The tee function will create n iterators from a single iterable. What this means is that you can create multiple iterators from one iterable. Let’s look at some explanatory code to how it works: >>> from itertools import tee >>> data = 'ABCDE' >>> iter1, iter2 = tee(data) >>> for item in iter1: ... print(item) ... A B C D E >>> for item in iter2: ... print(item) ... A B C D E Here you create a 5-letter string and pass it to tee. Because tee defaults to 2, you use multiple assignments to acquire the two iterators that are returned from tee. Finally, you loop over each of the iterators and print out their contents. As you can see, their content is the same. zip_longest(*iterables, fillvalue=None) The zip_longest iterator can be used to zip two iterables together. If the iterables don’t happen to be the same length, then you can also pass in a fillvalue. Let’s look at a silly example based on the documentation for this function: >>> from itertools import zip_longest >>> for item in zip_longest('ABCD', 'xy', fillvalue='BLANK'): ... print (item) ... ('A', 'x') ('B', 'y') ('C', 'BLANK') ('D', 'BLANK')   In this code, you import zip_longest and then pass it two strings to zip together. You can see that the first string is 4-characters long while the second is only 2-characters in length. You also set a fill value of “BLANK”. When you loop over this and print it out, you will see that you get tuples returned. The first two tuples are combinations of the first and second letters from each string respectively. The last two have your fill value inserted. It should be noted that if the iterable(s) passed to zip_longest have the potential to be infinite, then you should wrap the function with something like islice to limit the number of calls. The Combinatoric Generators The itertools library contains four iterators that can be used for creating combinations and permutations of data. You will be covering these fun iterators in this section. combinations(iterable, r) If you have the need to create combinations, Python has you covered with itertools.combinations. What combinations allow you to do is create an iterator from an iterable that is some length long. Let’s take a look: >>> from itertools import combinations >>> list(combinations('WXYZ', 2)) [('W', 'X'), ('W', 'Y'), ('W', 'Z'), ('X', 'Y'), ('X', 'Z'), ('Y', 'Z')] When you run this, you may notice that combinations returns tuples. To make this output a bit more readable, let’s loop over our iterator and join the tuples into a single string: >>> for item in combinations('WXYZ', 2): ... print(''.join(item)) ... WX WY WZ XY XZ YZ Now it’s a little easier to see all the various combinations. Note that the combinations function does its combination in lexicographic sort order, so if your iterable is sorted, then your combination tuples will also be sorted. Also worth noting is that combinations will not produce repeat values in the combinations if all the input elements are unique. combinations_with_replacement(iterable, r) The combinations_with_replacement iterator is very similar to combinations. The only difference is that it will actually create combinations where elements do repeat. Let’s try an example from the previous section to illustrate: >>> from itertools import combinations_with_replacement >>> for item in combinations_with_replacement('WXYZ', 2): ... print(''.join(item)) ... WW WX WY WZ XX XY XZ YY YZ ZZ As you can see, you now have four new items in our output: WW, XX, YY and ZZ. product(*iterables, repeat=1) The itertools package has a neat little function for creating Cartesian products from a series of input iterables. Yes, that function is product. Let’s see how it works! >>> from itertools import product >>> arrays = [(-1,1), (-3,3), (-5,5)] >>> cp = list(product(*arrays)) >>> cp [(-1, -3, -5), (-1, -3, 5), (-1, 3, -5), (-1, 3, 5), (1, -3, -5), (1, -3, 5), (1, 3, -5), (1, 3, 5)] Here you import product and then set up a list of tuples which you assign to the variable arrays. Next, you call product with those arrays. You will notice that you call it using *arrays. This will cause the list to be "exploded" or applied to the product function in sequence. It means that you are passing in 3 arguments instead of one. If you want, try calling it with the asterisk pre-pended to arrays and see what happens. permutations The permutations sub-module of itertools will return successive r length permutations of elements from the iterable you give it. Much like the combinations function, permutations are emitted in lexicographic sort order. Let’s take a look: >>> from itertools import permutations >>> for item in permutations('WXYZ', 2): ... print(''.join(item)) ... WX WY WZ XW XY XZ YW YX YZ ZW ZX ZY You will notice that the output is quite a bit longer than the output from combinations. When you use permutations, it will go through all the permutatations of the string, but it won’t do repeat values if the input elements are unique. Wrapping Up The itertools is a very versatile set of tools for creating iterators. You can use them to create your own iterators all by themselves or in combination with each other. The Python documentation has a lot of great examples that you can study to give you ideas of what can be done with this valuable library. The post A Tour of Python's itertools Library appeared first on Mouse Vs Python.

With the release of Visual Studio 2022, we are making changes to how we support installing Python runtimes from Visual Studio Setup, to help reconcile the fact that the lifetime of Python versions available as binary installers from the Python Software Foundation is different to that of Visual Studio. Each version of Visual Studio targets a specific Python version. For example, Visual Studio 2019 targeted Python 3.7, while Visual Studio 2022 targets Python 3.9 (Python 3.10 has just come out, but there are still many popular packages that are not fully supported, so Python 3.9 is a better choice). The life cycle of Python versions is very different to that of Visual Studio releases. New versions of Python are released annually, and binary installers are available for Windows for 1.5 years after release. That means that the last binary installer update for Python 3.9 will be made available by the Python Software Foundation (PSF) around March in 2022, and earlier versions are not getting installer updates at all. Going forward, the most recent release of Visual Studio will support the most recent version of Python that has broad adoption. That means at some point in the future you can expect newer releases of Visual Studio 2022 to add support for Python 3.10, and then 3.11. However, older versions of the Python workload for Visual Studio will not be updated to support newer versions of Python. This poses a problem if security issues are found in older versions of Python. In that case, our approach will be to mark versions of Python that are not getting updated installers built by PSF and that have known security issues being marked as “Out of Support” in Visual Studio setup. We will not be removing these versions, nor adding newer versions. The reasons for doing this are: We want to mark old versions as “out of support” and not select them by default to make it clear that these versions of Python are effectively past their end of life and may have security vulnerabilities; At the same time, we don’t want to remove them, as customers may still need them; code is often updated to support new versions slowly if at all, and in many cases the security issues that may exist are edge cases that may not be relevant; customers should use their own judgement about the risks involved in continuing to use old versions of Python; With the exception of adding Python 3.9 installer to Visual Studio 2019, we don’t want to add installers for new versions of Python that may not be fully supported by the Python workload; you can use newer versions of Python to run your older code in most cases with no issues, but we want to avoid giving the impression that changes to the Python language are fully supported by older versions of Visual Studio when they are not; In most cases, you can simply install a newer version of Python from https://python.org or the Windows store and use your existing Python code unchanged; the number of breaking changes between Python versions is small and typically in edge cases; To help aid this transition we made an exception in Visual Studio 2019 where we added an install option for Python 3.9 (which will in turn be marked as “out of support” sometime in 2022). We’re making this change now as the language server in Visual Studio 2019 has been deprecated, while Visual Studio 2022 ships with Pylance, our modern and performant language server. The language server is the component most affected by changes in Python versions, so we will have more flexibility in the future as to how we reconcile the difference between the lifetime of Python versions and that of Visual Studio versions. We continue to evaluate how best to serve our users and may make further changes to our approach in the future. The post Changes to How We Bundle Python in Visual Studio appeared first on Python.

#502 – DECEMBER 7, 2021 View in Browser » Advent of Code: Solving Your Puzzles With Python Advent of Code is an online advent calendar that shares new programming puzzles each day from December 1st to the 25th. In this tutorial, you’ll learn why solving programming puzzles can be beneficial and how you can get started with Advent of Code using Python. REAL PYTHON Error Message Style Guides of Various Languages “PyPy has been trying to produce good SyntaxErrors and other errors for a long time. CPython has also made an enormous push to improve its SyntaxErrors in the last few releases. These improvements are great, but the process feels somewhat arbitrary sometimes. 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HACKER NEWS Python Jobs Python Django Consultant (United States) Credentive Security Senior Python Developer (Anywhere) Six Feet Up Senior Backend Engineer (Anywhere) BMAT Senior Backend Software Engineer (Anywhere) Clay Senior Python Engineer (Anywhere) Flashpoint Senior Software Engineer (United States) Tomo Python Backend Engineer (Hasselt, Belgium) madewithlove More Python Jobs >>> Articles & Tutorials Prettify Your Data Structures With Pretty Print in Python The pprint module, Python’s data pretty printer, is a useful part of the standard library. You can work with it for debugging data structures and increasing the readability of your output. In this tutorial, you’ll find that pprint is both straightforward to use and also highly customizable. REAL PYTHON Story of Jython [2002] “The story of Jython begins one summer in Ashland, Oregon. I was juggling in a park behind a theater when I met Pavel Curtis, a scientist at Xerox PARC, who wanted to pass clubs. While we were juggling together he told me about a wonderful new programming language called Python. Writing code in Python felt like writing the sort of natural informal code that developers would use when they wanted to quickly share ideas. It was executable pseudo-code.” JIM HUGUNIN Shortcut Provides Speedy Task Management, Reporting, and Collaboration for Software Teams Shortcut is project management built for developers. Whether you’re a startup that iterates quickly by providing every engineer with a free pallet of Red Bull, or a large org that has strict ship dates to hit, give us a try for free → SHORTCUT sponsor Discussing Type Hints, Protocols, and Ducks in Python With Luciano Ramalho There seem to be three kinds of Python developers: those unaware of type hints or have no opinion, ones that embrace them, and others who have an allergic reaction at the mention of them. Python is famously a dynamically typed language, but there are advantages to adding type hints to your code. This week on the show, it’s Luciano Ramalho discussing his recent talk titled, “Type hints, protocols, and good sense.” REAL PYTHON podcast How to Create a Computer Virus in Python “I was relaxing on a beach during my summer leave when I received a mail from a reader that asked me if it is technically possible to write a virus using Python. The short answer: YES. The longer answer: yes, BUT…” DAVIDE MASTROMATTEO • Shared by Davide Get Your Next Python Job through Hired Hired is home to thousands of companies from startups to Fortune 500s actively hiring developers, data scientists, mobile engineers & more. Create a profile & let hiring managers reach out to you. It’s completely free for job-seekers, so don’t wait! HIRED sponsor Writing Simple Python GUIs for Your Command-Line-Phobe Coworkers “These are some of the lessons I’ve learned on how to package rudimentary Python scripts into simple Windows GUIs for your […] coworkers to use.” NASH REILLY Creating a Better Flamegraph Visualization Flamegraphs are a great way to visualize performance and memory bottlenecks, but with a little tweaking, you can make them even more useful. ITAMAR TURNER-TRAURING Python’s None Problem, and How Mypy Helps You Deal With It JR HEARD Projects & Code emacs-python-coverage: Show Python Coverage Information Directly in Emacs GITHUB.COM/WBOLSTER pip-audit: Audits Python Environments and Dependency Trees for Known Vulnerabilities GITHUB.COM/TRAILOFBITS numpy-financial: Financial Functions in NumPy NUMPY.ORG PyXIRR: Collection of Financial Functions GITHUB.COM/ANEXEN • Shared by Alexander latexrun: A 21st Century LaTeX Wrapper GITHUB.COM/ACLEMENTS pkgversions: Update Python Package Versions During Development GITHUB.COM/HASII2011 • Shared by Humberto Sanchez II Events Weekly Real Python Office Hours Q&A (Virtual) December 8, 2021 REALPYTHON.COM Python Atlanta December 9, 2021 MEETUP.COM Python Miami December 11 to December 12, 2021 PYTHONDEVELOPERSMIAMI.COM DFW Pythoneers 2nd Saturday Teaching Meeting December 11, 2021 MEETUP.COM Edmonton Python User Group December 13, 2021 EDMONTONPY.COM IndyPy Monthly Meetup December 14, 2021 MEETUP.COM TuPLE (Tucson Python Language Enthusiasts) December 14, 2021 MEETUP.COM Edmonton.py: The Edmonton Python User Group December 14, 2021 EDMONTONPY.COM Happy Pythoning!This was PyCoder’s Weekly Issue #502.View in Browser » [ Subscribe to ? PyCoder’s Weekly ? – Get the best Python news, articles, and tutorials delivered to your inbox once a week >> Click here to learn more ]

Computers store all kinds of information as a stream of binary digits called bits. Whether you’re working with text, images, or videos, they all boil down to ones and zeros. Python’s bitwise operators let you manipulate those individual bits of data at the most granular level. Python isolates you from the underlying bits with high-level abstractions. You’re more likely to find the overloaded flavors of bitwise operators in practice. But when you work with them in their original form, you’ll be surprised by their quirks! In this course, you’ll learn how to: Read binary numbers Perform bitwise math and read truth tables Represent fixed and arbitrary precision itegers in Python Perform bitwise operations in Python Use bitmasks to pack information on a single byte Differentiate Big-Endian and Little-Endian byte orders Overload Python bitwise operators in custom data types [ Improve Your Python With ? Python Tricks ? – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Today is my last day at the PSF. It's been an honor and a pleasure serving the Python community for over a decade. It's a very bittersweet feeling for me. Although I recognized that I needed change, this community has always been much more than just a job. I like to say that I helped the PSF grow to what it is today, and in turn, the PSF certainly has helped me grow into who I am today. I've met people from our community who have forever impacted my life professionally and personally. I've contributed to what has grown to be the most popular programming language on the planet. I've led a small non-profit to financial sustainability. We've been through a few crises that nearly spelled the end of our foundation, including this ongoing pandemic. I've gotten a chance to work with some of the most brilliant and caring folks every day. I'm incredibly grateful to all of you for that experience.The PSF staff meeting in November of 2021The PSF Board Executive Director Transition Committee is working diligently to find a replacement. In the meantime, Thomas Wouters will be the interim General Manager, supporting the staff through this transition. I look forward to seeing how the PSF will continue supporting the Python community and who'll be leading it into this new era.I wish all my Python friends health, happiness, and success in all you do!https://twitter.com/ewa_jodlowskahttps://www.linkedin.com/in/ewa-jodlowska-39810611/

The Django team is happy to announce the release of Django 4.0. The release notes cover the abundance of new features in detail, but a few highlights are: The new RedisCache backend provides built-in support for caching with Redis. To ease customization of Forms, Formsets, and ErrorList they are now rendered using the template engine. The Python standard library’s zoneinfo is now the default timezone implementation in Django. You can get Django 4.0 from our downloads page or from the Python Package Index. The PGP key ID used for this release is Mariusz Felisiak: 2EF56372BA48CD1B. With the release of Django 4.0, Django 3.2 has reached the end of mainstream support. The final minor bug fix release, 3.2.10, was issued today. Django 3.2 is an LTS release and will receive security and data loss fixes until April 2024. All users are encouraged to upgrade before then to continue receiving fixes for security issues. Django 3.1 has reached the end of extended support. The final security release (3.1.14) was issued today. All Django 3.1 users are encouraged to upgrade to Django 3.2 or later. See the downloads page for a table of supported versions and the future release schedule.

Enterprise Firewall Hardware Market Breaks Out to New High | Cisco Systems, Fortinet, Juniper Networks – UK Parents Lounge  UK Parents Lounge

ACA hoping to create new Louisiana Music Museum in Lafayette  KATC Lafayette News

FreeBSD 12.3 Released With Updated AMD & Networking Hardware, Password Protected ZIPs  Phoronix

Union Craft Brewing becomes latest business to be employee-owned  WBAL TV Baltimore

New Filing Hints at a Memory Upgrade for RTX 3080 and RTX 3070 Ti  Tom's Hardware

VIZIO’s Well-timed Entry Into The Ad Business Is Just The Latest Iteration For The Brand  TVRev

OEMs Target Miners with RTX 2060 12GB, But Gamers Need It More  Tom's Hardware

The PHP Foundation has been announced as an entity for funding the work of developing the PHP language. For more information regarding the structure and purpose of the foundation, please check out the blog post at: jetbrains.com.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 6. This is the sixth and final release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 6 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the production-ready, general availability release, planned for 25 November 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 5. This is the fifth release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 5 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the sixth and last release candidate (RC 6), planned for 11 November 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 4. This is the fourth release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 4 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the fifth release candidate (RC 5), planned for 28 October 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 3. This is the third release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 3 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the fourth release candidate (RC 4), planned for 14 October 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 2. This is the second release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 2 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the third release candidate (RC 3), planned for 30 September 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP team is pleased to announce the release of PHP 8.1.0, RC 1. This is the first release candidate, continuing the PHP 8.1 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 8.1.0, RC 1 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release will be the second release candidate (RC 2), planned for 16 September 2021. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

PSH highlights and indents source code using highlight configuration files which are similar to Ultraedit highlighting files As such it currently supports C C Java Perl PHP Pascal Lisp Mumps The post PHP Syntax Highlighter appeared first on PHPBuilder.

404 redirect not found The post Redirecting on Page Not Found (404) appeared first on PHPBuilder.

This page will validate the structure of a string to your specifications This script will verify a password to make sure it contains a lower and upper case alpha character The post Password Structure Validation appeared first on PHPBuilder.

The Zend Framework's Zend_Cache component is an easy way to implement PHP caching, a sure site performance The post Boost PHP Site Performance with Zend Framework Data Caching appeared first on PHPBuilder.

By Ying Zhang on July 30, 2000 This document describes how to display safely formatted output from user input. We will discuss the dangers of displaying unfiltered output and then provide a safe means of displaying formatted output. Download the attachment and extract it into your web documents directory. Dangers... The post Displaying Formatted User Input appeared first on PHPBuilder.

This script will display a progress bar. It's beneficial when you are performing many tasks on the back end that take a while to complete. In these The post Displaying Dynamic Progress Bars appeared first on PHPBuilder.

All web developers will at some point need to program their own form. Whether it is for database submission, The post PHP Form Validation System: An Object-Oriented Approach appeared first on PHPBuilder.

Netcraft recently confirmed that a Bangladesh Army site was hosting an Outlook Web Access (OWA) web shell. Additionally, an OWA web shell was found on the Department of Arts and Culture site for the South-African Kwazulu-Natal province and an Iraqi government site was found to be hosting a PHP shell. Web shells are a common tool used by attackers to maintain control of a compromised web server, providing a web interface from which arbitrary commands can be executed on the server hosting the shell. OWA provides remote access to Microsoft Exchange mailboxes; since the disclosure of the ProxyLogon vulnerabilities in March, Microsoft Exchange has become a popular target for cyberattacks.

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 Rackspace Linux 0:00:00 0.000 0.463 0.008 0.020 0.020 2 Bigstep Linux 0:00:00 0.000 0.190 0.063 0.126 0.126 3 CWCS Managed Hosting Linux 0:00:00 0.000 0.291 0.064 0.127 0.127 4 Hyve Managed Hosting Linux 0:00:00 0.000 0.128 0.068 0.137 0.137 5 www.dinahosting.com Linux 0:00:00 0.000 0.189 0.072 0.144 0.144 6 Swishmail Linux 0:00:00 0.000 0.218 0.098 0.196 0.196 7 ServerStack Linux 0:00:00 0.000 0.201 0.104 0.205 0.205 8 Aruba Linux 0:00:00 0.007 0.340 0.006 0.030 0.030 9 New York Internet (NYI) FreeBSD 0:00:00 0.007 0.536 0.063 0.127 0.127 10 krystal.uk Linux 0:00:00 0.007 0.176 0.084 0.159 0.159 See full table Rackspace had the most reliable hosting company site in November 2021, with an average connection time of just 8ms across the month and no failed requests. Rackspace has appeared in the top 10 most reliable hosting company sites every month of the past 12 months, and has taken the number one spot in five of those. Rackspace offers a wide variety of cloud hosting solutions from over 40 data centres across the Americas, Europe, Asia and Australia. Places two, three and four in November 2021 go to Bigstep, CWCS Managed Hosting and Hyve Managed Hosting respectively. All three of these hosting company sites had no failed requests and were competitive on average connection times, coming in at 63ms, 64ms and 68ms respectively. Bigstep’s bare metal cloud hosting provides the flexibility of cloud hosting without the associated overhead and performance reductions of virtualization. The bare metal offerings are available in data centres in the UK and Romania. CWCS Managed Hosting offer dedicated servers, cloud and VPS hosting, colocation services, domain names and email services from their seven data centres across the UK, USA and Canada. Hyve Managed Hosting offer cloud hosting, dedicated servers and managed services from data centres in 34 locations around the world. Nine of the top 10 hosting company sites used Linux in October, continuing the dominance of Linux. In ninth place, New York Internet (NYI) used FreeBSD.

Netcraft has to date identified nearly 10,000 websites used in the distribution of the FluBot family of Android malware. As detailed in our previous articles on FluBot, these sites are unwittingly hosting a PHP script that acts as a proxy to a further backend server, allowing otherwise legitimate sites to deliver Android malware to victims. When visited by the intended victim, a “lure” is displayed that implores them to download and install the FluBot malware. The most common lure themes are parcel delivery and voicemail messages, where the user is told to install the malicious app to track a parcel or listen to a voicemail message. One particularly interesting lure took advantage of FluBot’s infamy, by offering a fake “Android security update” that claimed to protect against the malware family. Users installing this “security update” would instead be infected with FluBot. Most sites distributing FluBot malware also host legitimate content, suggesting they were compromised by the operators of this malware distribution network, without the knowledge of the site operator. While the use of unrelated domains makes the lures less convincing, as compared to domains specifically registered for fraud, it allows the malware distribution network to operate at a much larger scale. These affected sites all have one factor in common: they run self-hosted WordPress instances. Netcraft believes the operators of this malware distribution network are actively exploiting well-known vulnerabilities in WordPress plugins and themes to upload malicious content onto insecure sites, joining a growing list of threat actors doing the same. A collection of lures used by the FluBot distribution network

In the November 2021 survey we received responses from 1,175,392,792 sites across 267,027,794 unique domains and 11,525,855 web-facing computers. This reflects a loss of 4.06 million sites, but a gain of 1.60 million domains and 137,000 computers. nginx gained the largest number of domains (+741,000) and web-facing computers (+81,300) this month and continues to lead in both metrics with market shares of 30.1% and 37.3%. Further down in the market, there was also a noticeable increase in the total number of web-facing computers running LiteSpeed, which went up by 11,200 to 101,000 (+12.5%), although this resulted in only a 1.44% increase in domains. These counts include sites that run on LiteSpeed Web Server and its open source variant, OpenLiteSpeed, both of which exhibit the same “LiteSpeed” server banner. Both nginx and Apache lost nearly 4 million hostnames each, reducing their sites market shares to 34.7% and 24.4%. Meanwhile, Cloudflare gained 1.15 million sites, which has taken its total up to 58.6 million (+2.00%) and increased its sites share to 4.99%. nginx and Apache also suffered losses amongst the top million websites, paving the way for Microsoft to increase its presence by 2,369 sites (+3.75%). Microsoft web server software is now used by 65,600 of the top million sites, but Apache is still the most commonly used web server in this sector, with 240,000 of the top million sites using it, and nginx is not far behind with 224,000. Apache 2.4.49 vulnerability Following last month’s news of a path traversal vulnerability in Apache 2.4.49 being actively exploited in the wild, this month’s survey shows that more than 11 million websites had server banners containing “Apache/2.4.49” before a fix was released. The only other version vulnerable to attack was Apache 2.4.50, which failed to fix the vulnerability properly – but this version was released after the survey ran and was promptly replaced with Apache 2.4.51, where the vulnerability was resolved properly. The true number of websites that were vulnerable during the survey period is likely to have been much greater than the 11 million websites that openly reported themselves to be running Apache 2.4.49, as nearly two-thirds of all Apache-powered websites do not reveal a version number in their server banners. This configuration is often a deliberate act towards security through obscurity, although attackers can often deduce precise version numbers by carrying out additional tests. There may also have been additional vulnerable instances of Apache 2.4.49 hidden behind frontend load balancers or content delivery networks such as Cloudflare. Conversely, some websites running on Apache 2.4.49 may not have been vulnerable if they used an appropriately configured web application firewall that prevents path traversal attacks. More generally, the true number of web servers that contain a version-specific vulnerability can also be masked by future backported security patches, which typically fix vulnerabilities without changing the apparent version number of the software. From an external perspective, a server might appear to be running a vulnerable software version but may not actually be vulnerable to the issues affecting that version. Vendor news LiteSpeed Web Server 6.0.11 was released on 10 November. This is the latest version in the LSWS 6.0 stream and includes improvements in HTTP/2 and HTTP/3 throughput, new support for WebSocket proxy targets in rewrite rules, and several bugfixes. Microsoft has announced new Azure Bounty Program rewards of up to $60,000 to encourage and reward research into vulnerabilities that would have the highest potential impact on the security of its customers. nginx 1.21.4 mainline was released on 2 November. This version includes some new features and changes relating to TLS and HTTP/2. Lighttpd 1.4.61 was released on 28 October to address a number of bugs. Lighttpd is used by 245,000 unique domains in this month’s survey. njs 0.7.0 was released on 19 October to add HTTPS support for its Fetch API, along with a few other new features and bugfixes. Apache Tomcat 9.0.54, 10.0.12 and 10.1.0-M6 (alpha) were released on 1 October, followed by Tomcat 8.5.72 on 6 October. Cloudflare Pages now supports custom headers natively, without having to use Cloudflare Workers. This makes it easier for developers to add best-practice security headers and others to their JAMstack applications. Cloudflare for SaaS is now generally available to all, following a beta launch earlier in the year. DeveloperOctober 2021PercentNovember 2021PercentChange nginx412,222,22134.95%408,226,31934.73%-0.22 Apache290,462,41024.63%286,494,60024.37%-0.25 OpenResty76,038,5766.45%76,480,9276.51%0.06 Cloudflare57,482,1034.87%58,629,3654.99%0.11

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 Rackspace Linux 0:00:00 0.000 0.490 0.008 0.017 0.017 2 CWCS Managed Hosting Linux 0:00:00 0.000 0.246 0.062 0.125 0.125 3 www.choopa.com Linux 0:00:00 0.007 0.256 0.003 0.020 0.020 4 Bigstep Linux 0:00:00 0.007 0.203 0.063 0.127 0.127 5 Swishmail Linux 0:00:00 0.007 0.229 0.098 0.194 0.194 6 ServerStack Linux 0:00:00 0.007 0.211 0.102 0.203 0.203 7 New York Internet (NYI) FreeBSD 0:00:00 0.027 0.551 0.062 0.124 0.124 8 Hivelocity Linux 0:00:00 0.034 0.354 0.003 0.035 0.035 9 Hyve Managed Hosting Linux 0:00:00 0.034 0.136 0.072 0.147 0.147 10 Multacom Linux 0:00:00 0.047 0.409 0.147 0.296 0.296 See full table Rackspace had the most reliable hosting company site in October 2021, with an average connection time of 8ms and no failed requests. So far in 2021, Rackspace has appeared in the top 10 every month, with four appearances in first place. Rackspace provides a wide variety of cloud services from its global network of over 50 locations across five continents. In second place, CWCS Managed Hosting also responded to each of Netcraft’s requests in October, with an average connection time of 62ms. CWCS provides dedicated servers and cloud services with data centres across the UK and North America. The top five is completed by Choopa.com, Bigstep and Swishmail whose sites each responded to the same number of requests and were separated by average connection time. Choopa.com had the fastest average connection time of just 3ms. Choopa.com offers a range of services including cloud hosting, dedicated hosting and colocation with its own primary facility in Piscataway, New Jersey and other facilities in Los Angeles, Amsterdam, and Tokyo. Colocation from Choopa.com is available in the US, Amsterdam and London. Bigstep provides bare metal hosting solutions from data centres in the UK and Romania with additional facilities in North America and Europe for project delivery. Swishmail provides business email services with hosting services from three data centre facilities in New York. Nine of the top 10 hosting company sites used Linux in October, continuing the dominance of Linux. In seventh place, New York Internet (NYI) used FreeBSD.

The Government of Eswatini’s website, www.gov.sz, is running a cryptojacker. Cryptojackers use website visitors' CPU power to mine cryptocurrency, most often without their knowledge or permission. Data from archive.org suggests the JavaScript snippet was added to the site’s HTML source between 28th September and 6th October. WebMinePool cryptojacker injection on www.gov[.]sz. While sites that are kept open for long periods of time are often the most lucrative – the longer the victim’s browser tab is open, the more cryptocurrency can be mined — criminals are typically not fussy when deploying cryptojackers. Criminals can target large swathes of sites at once, including those using vulnerable or out-of-date software, compromised third-party JavaScript, or with easily guessable administrator credentials.

In the October 2021 survey we received responses from 1,179,448,021 sites across 265,426,928 unique domains and 11,388,826 web-facing computers. This reflects a loss of 8.59 million sites, but a gain of 1.07 million domains and 20,800 computers. The number of unique domains powered by the nginx web server grew by 789,000 this month, which has increased its total to 79.5 million domains and its leading market share to 29.9%. Conversely, Apache lost 753,000 domains and saw its second-place share fall to 24.7%. Meanwhile, Cloudflare gained 746,000 domains – almost as many as nginx – but it stays in fourth place with an 8.15% share while OpenResty's shrank slightly to 14.5%. Cloudflare also made strong progress amongst the top million websites, where it increased its share by 0.24 percentage points to 18.2%. nginx is in second place with a 22.5% (+0.12pp) share but has closed the gap on Apache which still leads with 24.0% after losing 0.21pp. Apache also continues to lead in terms of active sites, where it has a total of 48.0 million. However, it was the only major vendor to suffer a drop in this metric, with a loss of 277,000 active sites reducing its share down to 23.9% (-0.29pp). In terms of all sites, nginx lost the most (-9.99 million) but remains far in the lead with a total of 412 million. Apache vulnerability being actively exploited in the wild Apache 2.4.51 was released on 7 October. This is the latest release in the 2.4.x stable branch, which the developers consider to be the best available version of the Apache HTTP Server; but more importantly, this release fixes a path traversal vulnerability present in Apache 2.4.49 and 2.4.50. Apache 2.4.50 was itself released a day earlier in an attempt to fix the vulnerability present in 2.4.49, but the fix was found to be insufficient. The vulnerability is being actively exploited in the wild, so anyone still running an unpatched Apache 2.4.49 or 2.4.50 installation should upgrade immediately. In some cases, the path traversal vulnerability could facilitate remote code execution on the web server. Due to the nature of this vulnerability, some otherwise vulnerable installations may be immune to attack if a web application firewall (WAF) is in place, or if a frontend proxy or load balancer modifies malicious requests in a way that makes them safe. For instance, all vulnerable Apache installations served via the Cloudflare content delivery network would have been protected from the outset if Normalize URLS to origin were enabled, and the Cloudflare WAF has rules that would have stopped many exploit attempts. Other vendor and hosting news During September, Microsoft released fixes for three elevation of privilege and one remote code execution vulnerabilities in the Open Management Infrastructure (OMI) framework, which is used by several Azure Virtual Machine management extensions. The remote code execution vulnerability can only affect customers using a Linux management solution with remote OMI enabled. A full list of the vulnerable extensions and update availability is being maintained on the Microsoft Security Response Center blog. Microsoft announced the general availability its Azure Purview data governance solution on 28 September. On 5 October, Microsoft removed the waiting list for its Azure NetApp Files bare-metal cloud file storage and data management service. lighttpd 1.4.60 was released on 3 October. This version includes a large number of changes, including several bugfixes and improved handling of HTTP/2 connections. LiteSpeed Web Server 6.0.9 was released on 20 September to address several bugs and add a new log rotation feature. OpenLiteSpeed 1.7.14 – the open source edition of LiteSpeed Web Server Enterprise – was released on 7 September. DeveloperSeptember 2021PercentOctober 2021PercentChange nginx422,211,70335.54%412,222,22134.95%-0.59 Apache295,667,36124.89%290,462,41024.63%-0.26 OpenResty77,052,3706.49%76,038,5766.45%-0.04 Cloudflare56,362,3634.74%57,482,1034.87%0.13

Samsung's Galaxy Z Fold 3 and Z Flip 3 have been massively discounted today on Amazon AU.

Hyundai India aims to have 6 BEVs (battery electric vehicles) in its India lineup by 2028. This includes a facelifted Kona EV.

Employees suffering from burnout aren't following security guidelines and are much more likely to engage in shadow IT.

Techy gifts don't have to cost an arm and a leg, you can find plenty of affordable options on Amazon Australia.

AWS says it is recovering, but can't say when it'll be back to full strength.

Nvidia CFO Colette Kress said this week that the company expects graphics card supply to improve in the second half of 2022.

Twitter's acquisition of Quill gives the company everything it needs to launch a business messaging service.