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In python, we use lists in almost every program. We have already discussed ways to compare two lists and to reverse a list in python. In this article, we will discuss different ways to delete all the elements of a list in python. Delete All Elements Of A List In Python Using The clear() Method The pop() method is used to delete the last element of a list. When invoked on a list, the pop() method returns the last element of the list and deletes it from the list. We can use the while loop and the pop() method to remove all the elements of the list.  For this, we can keep invoking the pop() method on the list inside the while loop until the list becomes empty.  Once the list becomes empty, the while loop will stop its execution and we will get a list that has no elements. You can observe this in the following program. myList = [1, 2, 3, 4, 5, 6, 7, 8, 9] print("The original list is:") print(myList) # deleting elements using the pop() method while myList: myList.pop() print("List after deleting all the elements:",myList) Output: The original list is: [1, 2, 3, 4, 5, 6, 7, 8, 9] List after deleting all the elements: [] Instead of using the pop() method several times, we can use the clear() method to delete all the elements from a list. The clear() method, when invoked on a list, deletes all the elements of the list.  myList = [1, 2, 3, 4, 5, 6, 7, 8, 9] print("The original list is:") print(myList) # deleting elements using the clear() method myList.clear() print("List after deleting all the elements:",myList) Output: The original list is: [1, 2, 3, 4, 5, 6, 7, 8, 9] List after deleting all the elements: [] Delete All Elements Of A List In Python Using The del Statement The del statement is used to delete an object in python. We can also use the del statement to remove the elements of a list. For this, we will create a slice of the list that contains all the elements. After that, we will delete the slice. As the slice contains references to the elements in the original list, all the elements in the original list will be deleted and we will get an empty list. You can do it as follows. myList = [1, 2, 3, 4, 5, 6, 7, 8, 9] print("The original list is:") print(myList) # deleting elements using the del method del myList[:] print("List after deleting all the elements:", myList) Output: The original list is: [1, 2, 3, 4, 5, 6, 7, 8, 9] List after deleting all the elements: [] Delete All Elements Of A List In Python Using The * Operator This is one of the least used ways to remove elements from a list in Python. You might be knowing that multiplying a list to any number N repeats the elements of the list N times. In the same way, when we multiply a list to 0, all the elements of the list are deleted. So, we can multiply the given list with 0 to remove all of its elements as follows. myList = [1, 2, 3, 4, 5, 6, 7, 8, 9] print("The original list is:") print(myList) # deleting elements using * myList = myList * 0 print("List after deleting all the elements:", myList) Output: The original list is: [1, 2, 3, 4, 5, 6, 7, 8, 9] List after deleting all the elements: [] Conclusion In this article, we have discussed four different ways to delete all the elements of a list in python. To know more about lists in python, you can read this article on list comprehension. You might also like this article on how to get the last element of a list in python. The post Delete All Elements Of A List In Python appeared first on PythonForBeginners.com.

... in which we use PyYAML to safely read and write YAML with any tags, in a way that's as straightforward as interacting with built-in types. If you're in a hurry, you can find the code at the end. Contents Why is this useful? A note on PyYAML extensibility Preserving tags Constructing unknown objects A better wrapper Representing tagged objects Unhashable keys Constructing pairs Representing pairs Conclusion Bonus: hashable wrapper Bonus: broken YAML Why is this useful? # People mostly use YAML as a friendlier alternative to JSON1, but it can do way more. Among others, it can represent user-defined and native data structures. Say you need to read (or write) an AWS Cloud Formation template: EC2Instance: Type: AWS::EC2::Instance Properties: ImageId: !FindInMap [ AWSRegionArch2AMI, !Ref 'AWS::Region', !FindInMap [AWSInstanceType2Arch, !Ref InstanceType, Arch], ] InstanceType: !Ref InstanceType >>> yaml.safe_load(text) Traceback (most recent call last): ... yaml.constructor.ConstructorError: could not determine a constructor for the tag '!FindInMap' in "<unicode string>", line 4, column 14: ImageId: !FindInMap [ ^ ... or, you need to safely read untrusted YAML that represents Python objects: !!python/object/new:module.Class { attribute: value } >>> yaml.safe_load(text) Traceback (most recent call last): ... yaml.constructor.ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/object/new:module.Class' in "<unicode string>", line 1, column 1: !!python/object/new:module.Class ... ^ Warning Historically, yaml.load(thing) was unsafe for untrusted data, because it allowed running arbitrary code. Consider using use safe_load() instead. Details. For example, you could do this: >>> yaml.load("!!python/object/new:os.system [echo WOOSH. YOU HAVE been compromised]") WOOSH. YOU HAVE been compromised 0 There were a bunch of CVEs about it. To address the issue, load() requires an explicit Loader since PyYAML 6. Also, version 5 added two new functions and corresponding loaders: full_load() resolves all tags except those known to be unsafe (note that this was broken before 5.4, and thus vulnerable) unsafe_load() resolves all tags, even those known to be unsafe (the old load() behavior) safe_load() resolves only basic tags, remaining the safest. Can't I just get the data, without it being turned into objects? You can! The YAML spec says: In a given processing environment, there need not be an available native type corresponding to a given tag. If a node’s tag is unavailable, a YAML processor will not be able to construct a native data structure for it. In this case, a complete representation may still be composed and an application may wish to use this representation directly. And PyYAML obliges: >>> text = """\ ... one: !myscalar string ... two: !mysequence [1, 2] ... """ >>> yaml.compose(text) MappingNode( tag='tag:yaml.org,2002:map', value=[ ( ScalarNode(tag='tag:yaml.org,2002:str', value='one'), ScalarNode(tag='!myscalar', value='string'), ), ( ScalarNode(tag='tag:yaml.org,2002:str', value='two'), SequenceNode( tag='!mysequence', value=[ ScalarNode(tag='tag:yaml.org,2002:int', value='1'), ScalarNode(tag='tag:yaml.org,2002:int', value='2'), ], ), ), ], ) >>> print(yaml.serialize(_)) one: !myscalar 'string' two: !mysequence [1, 2] ... the spec didn't say the representation has to be concise. ¯\_(ツ)_/¯ Here's how YAML processing works, to give you an idea what we're looking at: YAML Processing Overview The output of compose() above is the representation (node graph). From that, safe_load() does its best to construct objects, but it can't do anything for tags it doesn't know about. There must be a better way! Thankfully, the spec also says: That said, tag resolution is specific to the application. YAML processors should therefore provide a mechanism allowing the application to override and expand these default tag resolution rules. We'll use this mechanism to convert tagged nodes to almost-native types, while preserving the tags. A note on PyYAML extensibility # PyYAML is a bit unusual. For each processing direction, you have a corresponding Loader/Dumper class. For each processing step, you can add callbacks, stored in class-level registries. The callbacks are method-like – they receive the Loader/Dumper as the first argument: Dice = namedtuple('Dice', 'a b') def dice_representer(dumper, data): return dumper.represent_scalar(u'!dice', u'%sd%s' % data) yaml.Dumper.add_representer(Dice, dice_representer) You may notice the add_...() methods modify the class in-place, for everyone, which isn't necessarily great; imagine getting a Dice from safe_load(), when you were expecting only built-in types. We can avoid this by subclassing, since the registry is copied from the parent. Note that because of how copying is implemented, registries from two direct parents are not merged – you only get the registry of the first parent in the MRO. So, we'll start by subclassing SafeLoader/Dumper: 4 5 6 7 8class Loader(yaml.SafeLoader): pass class Dumper(yaml.SafeDumper): pass Preserving tags # Constructing unknown objects # For now, we can use named tuples for objects with unknown tags, since they are naturally tag/value pairs: 12 13 14class Tagged(typing.NamedTuple): tag: str value: object Tag or no tag, all YAML nodes are either a scalar, a sequence, or a mapping. For unknown tags, we delegate construction to the loader's default constructors, and wrap the resulting value: 17 18 19 20 21 22 23 24 25 26 27 28def construct_undefined(self, node): if isinstance(node, yaml.nodes.ScalarNode): value = self.construct_scalar(node) elif isinstance(node, yaml.nodes.SequenceNode): value = self.construct_sequence(node) elif isinstance(node, yaml.nodes.MappingNode): value = self.construct_mapping(node) else: assert False, f"unexpected node: {node!r}" return Tagged(node.tag, value) Loader.add_constructor(None, construct_undefined) Constructors are registered by tag, with None meaning "unknown". Things look much better already: >>> yaml.load(text, Loader=Loader) { 'one': Tagged(tag='!myscalar', value='string'), 'two': Tagged(tag='!mysequence', value=[1, 2]), } A better wrapper # That's nice, but every time we use any value, we have to check if it's tagged, and then go through value if is: >>> one = _['one'] >>> one.tag '!myscalar' >>> one.value.upper() 'STRING' We could subclass the Python types corresponding to core YAML tags (str, list, and so on), and add a tag attribute to each. We could subclass most of them, anyway – neither bool nor NoneType can be subclassed. Or, we could wrap tagged objects in a class with the same interface, that delegates method calls and attribute access to the wrapee, with a tag attribute on top. Tip This is known as the decorator pattern design pattern (not to be confused with Python decorators). Doing this naively entails writing one wrapper per type, with one wrapper method per method and one property per attribute. That's even worse than subclassing! There must be a better way! Of course, this is Python, so there is. We can use an object proxy instead (also known as "dynamic wrapper"). While they're not perfect in general, the one wrapt provides is damn near perfect enough2: 12 13 14 15 16 17 18 19 20 21 22class Tagged(wrapt.ObjectProxy): # tell wrapt to set the attribute on the proxy, not the wrapped object tag = None def __init__(self, tag, wrapped): super().__init__(wrapped) self.tag = tag def __repr__(self): return f"{type(self).__name__}({self.tag!r}, {self.__wrapped__!r})" >>> yaml.load(text, Loader=Loader) { 'one': Tagged('!myscalar', 'string'), 'two': Tagged('!mysequence', [1, 2]), } The proxy behaves identically to the proxied object: >>> one = _['one'] >>> one.tag '!myscalar' >>> one.upper() 'STRING' >>> one[:3] 'str' ...up to and including fancy things like isinstance(): >>> isinstance(one, str) True >>> isinstance(one, Tagged) True And now you don't have to care about tags if you don't want to. Representing tagged objects # The trip back is exactly the same, but much shorter: 39 40 41 42 43 44 45def represent_tagged(self, data): assert isinstance(data, Tagged), data node = self.represent_data(data.__wrapped__) node.tag = data.tag return node Dumper.add_representer(Tagged, represent_tagged) Representers are registered by type. >>> print(yaml.dump(Tagged('!hello', 'world'), Dumper=Dumper)) !hello 'world' Let's mark the occasion with some tests. Since we still have stuff to do, we parametrize the tests from the start. 7 8 9 10 11 12 13 14 15 16 17 18 19 20BASIC_TEXT = """\ one: !myscalar string two: !mymapping three: !mysequence [1, 2] """ BASIC_DATA = { 'one': Tagged('!myscalar', 'string'), 'two': Tagged('!mymapping', {'three': Tagged('!mysequence', [1, 2])}), } DATA = [ (BASIC_TEXT, BASIC_DATA), ] Loading works: 23 24 25@pytest.mark.parametrize('text, data', DATA) def test_load(text, data): assert yaml.load(text, Loader=Loader) == data And dumping works: 28 29 30 31@pytest.mark.parametrize('text', [t[0] for t in DATA]) def test_roundtrip(text): data = yaml.load(text, Loader=Loader) assert data == yaml.load(yaml.dump(data, Dumper=Dumper), Loader=Loader) ... but only for known types: 34 35 36def test_dump_error(): with pytest.raises(yaml.representer.RepresenterError): yaml.dump(object(), Dumper=Dumper) Unhashable keys # Let's try an example from the PyYAML documentation: >>> text = """\ ... ? !!python/tuple [0,0] ... : The Hero ... ? !!python/tuple [1,0] ... : Treasure ... ? !!python/tuple [1,1] ... : The Dragon ... """ This is supposed to result in something like: >>> yaml.unsafe_load(text) {(0, 0): 'The Hero', (1, 0): 'Treasure', (1, 1): 'The Dragon'} Instead, we get: >>> yaml.load(text, Loader=Loader) Traceback (most recent call last): ... TypeError: unhashable type: 'list' That's because the keys are tagged lists, and neither type is hashable: >>> yaml.load("!!python/tuple [0,0]", Loader=Loader) Tagged('tag:yaml.org,2002:python/tuple', [0, 0]) This limitation comes from how Python dicts are implemented,3 not from YAML; quoting from the spec again: The content of a mapping node is an unordered set of key/value node pairs, with the restriction that each of the keys is unique. YAML places no further restrictions on the nodes. In particular, keys may be arbitrary nodes, the same node may be used as the value of several key/value pairs and a mapping could even contain itself as a key or a value. Constructing pairs # What now? Same strategy as before: wrap the things we can't handle. Specifically, whenever we have a mapping with unhashable keys, we return a list of pairs instead. To tell it apart from plain lists, we use a subclass: 48 49 50class Pairs(list): def __repr__(self): return f"{type(self).__name__}({super().__repr__()})" Again, we let the loader do most of the work: 53 54 55 56 57 58 59 60 61def construct_mapping(self, node): value = self.construct_pairs(node) try: return dict(value) except TypeError: return Pairs(value) Loader.construct_mapping = construct_mapping Loader.add_constructor('tag:yaml.org,2002:map', Loader.construct_mapping) We set construct_mapping so that any other Loader constructor wanting to make a mapping gets to use it (like our own construct_undefined() above). Don't be fooled by the assignment, it's a method like any other.4 But we're changing the class from outside anyway, it's best to stay consistent. Note that overriding construct_mapping() is not enough: we have to register the constructor explictly, otherwise SafeDumper's construct_mapping() will be used (since that's what was in the registry before). Note In case you're wondering, this feature is orthogonal from handling unknown tags; we could have used different classes for them. However, as mentioned before, the constructor registry breaks multiple inheritance, so we couldn't use the two features together. Anyway, it works: >>> yaml.load(text, Loader=Loader) Pairs( [ (Tagged('tag:yaml.org,2002:python/tuple', [0, 0]), 'The Hero'), (Tagged('tag:yaml.org,2002:python/tuple', [1, 0]), 'Treasure'), (Tagged('tag:yaml.org,2002:python/tuple', [1, 1]), 'The Dragon'), ] ) Representing pairs # Like before, the trip back is short and uneventful: 64 65 66 67 68 69def represent_pairs(self, data): assert isinstance(data, Pairs), data node = self.represent_dict(data) return node Dumper.add_representer(Pairs, represent_pairs) >>> print(yaml.dump(Pairs([([], 'one')]), Dumper=Dumper)) []: one Let's test this more thoroughly. Because the tests are parametrized, we just need to add more data: 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33UNHASHABLE_TEXT = """\ [0,0]: one !key {0: 1}: {[]: !value three} """ UNHASHABLE_DATA = Pairs( [ ([0, 0], 'one'), (Tagged('!key', {0: 1}), Pairs([([], Tagged('!value', 'three'))])), ] ) DATA = [ (BASIC_TEXT, BASIC_DATA), (UNHASHABLE_TEXT, UNHASHABLE_DATA), ] Conclusion # YAML is extensible by design. I hope that besides what it says on the tin, this article shed some light on how to customize PyYAML for your own purposes, and that you've learned at least one new Python thing. You can get the code here, and the tests here. Learned something new today? Share this with others, it really helps! Want more? Get updates via email or Atom feed. Bonus: hashable wrapper # You may be asking, why not make the wrapper hashable? Most unhashable (data) objects are that for a reason: because they're mutable. We have two options: Make the wrapper hash change with the content. This this will break dictionaries in strange and unexpected ways (and other things too) – the language requires mutable objects to be unhashable. Make the wrapper hash not change with the content, and wrappers equal only to themselves – that's what user-defined classes do by default anyway. This works, but it's not very useful, because equal values don't compare equal anymore (data != load(dump(data))). Also, it means you can only get things from a dict if you already have the object used as key: >>> data = {Hashable([1]): 'one'} >>> data[Hashable([1])] Traceback (most recent call last): ... KeyError: Hashable([1]) >>> key = list(data)[0] >>> data[key] 'one' I'd file this under "strange and unexpected" too. (You can find the code for the example above here.) Bonus: broken YAML # We can venture even farther, into arguably broken YAML. Let's look at some examples. First, there are undefined tag prefixes: >>> yaml.load("!m!xyz x", Loader=Loader) Traceback (most recent call last): ... yaml.parser.ParserError: while parsing a node found undefined tag handle '!m!' in "<unicode string>", line 1, column 1: !m!xyz x ^ A valid version: >>> yaml.load("""\ ... %TAG !m! !my- ... --- ... !m!xyz x ... """, Loader=Loader) Tagged('!my-xyz', 'x') Second, there are undefined aliases: >>> yaml.load("two: *anchor", Loader=Loader) Traceback (most recent call last): ... yaml.composer.ComposerError: found undefined alias 'anchor' in "<unicode string>", line 1, column 6: two: *anchor ^ A valid version: >>> yaml.load("""\ ... one: &anchor [1] ... two: *anchor ... """, Loader=Loader) {'one': [1], 'two': [1]} It's likely possible to handle these in a way similar to how we handled undefined tags, but we'd have to go deeper – the exceptions hint to which processing step to look at. Since I haven't actually encountered them in real life, we'll "save them for later" :) Of which YAML is actually a superset. [return] Timothy 20:9. [return] Using a hash table. For nice explanation of how it all works, complete with a pure-Python implementation, check out Raymond Hettinger's talk Modern Python Dictionaries: A confluence of a dozen great ideas (code). [return] Almost. The zero argument form of super() won't work for methods defined outside of a class definition, but we're not using it here. [return]

Programming screencasts are a popular way to teach programming and demo tools. Typically people will open up their favorite editor and record themselves tapping away. But this has a few problems. A good setup for coding isn't necessarily a good setup for video -- with text too small, a window too big, or too many distractions. Typing code in live means mistakes, which means more time editing or confusion for the people watching. DiffCast eliminates that, automatically generating screencasts from Python source files you prepare ahead of time. The following video was created with no user input. The editor view is configured to be easily readable in video, without messing with your IDE settings. Edits happen at a regular speed, without mistakes, making them easy to follow. Each diffcast is completely reproducible, with the same files producing the same output every time. Designed for creating reproducible tutoring examples, library demos or demonstrating code to a class. You can also step forwards and backwards through each file manually, using the control panel. Finally, you can write out edits to a another file, and show the file location in a file listing for context. Run the intermediate files to demonstrate the effect of the changes. DiffCast is open source (GPL licensed) & free to use. For bug reports, feature requests see the Github project.

In this article, we will be using the NumPy and SciPy modules to Calculate Euclidean Distance in Python. In mathematics, the Euclidean Distance refers to the distance between two points in the plane or 3-dimensional space. In short, we can say that it is the shortest distance between 2 points irrespective of dimensions. How to Calculate Euclidean Distance in Python? The formula to calculate the distance between two points (x1 1 , y1 1 ) and (x2 2 , y2 2 ) is d = √[(x2 – x1)2 + (y2 – y1)2]. Euclidean Distance Formula There are 4 different approaches for finding the Euclidean distance in Python using the NumPy and SciPy libraries. Using linalg.norm()Using dot() and sqrt()Using square() and sum() Using distance.euclidean() from SciPy Module Method 1: Using linalg.norm() Method in NumPy The NumPy module has a norm() method, which can be used to find the required distance when the data is provided in the form of an array. The norm() method returns the vector norm of an array. You can learn more about the linalg.norm() method here. Example # Python code to find Euclidean distance # using linalg.norm() # Import NumPy Library import numpy as np # initializing points in # numpy arrays point1 = np.array((4, 4, 2)) point2 = np.array((1, 2, 1)) # calculate Euclidean distance # using linalg.norm() method dist = np.linalg.norm(point1 - point2) # printing Euclidean distance print(dist) Output 3.7416573867739413 Method 2: Using dot() and sqrt() methods We can leverage the NumPy dot() method for finding the dot product of the difference of points, and by doing the square root of the output returned by the dot() method, we will be getting the Euclidean distance.  # Python code to find Euclidean distance # using dot() and sqrt() methods # Import NumPy Library import numpy as np # initializing points in # numpy arrays point1 = np.array((4, 4, 2)) point2 = np.array((1, 2, 1)) # subtracting both the vectors temp = point1 - point2 # Perform dot product # and do the square root dist = np.sqrt(np.dot(temp.T, temp)) # printing Euclidean distance print(dist) Output 3.7416573867739413 Method 3: Using square() and sum() methods Another alternate way is to apply the mathematical formula (d = √[(x2 – x1)2 + (y2 – y1)2]) using the NumPy Module to Calculate Euclidean Distance in Python The sum() function will return the sum of elements, and we will apply the square root to the returned element to get the Euclidean distance. # Python code to find Euclidean distance # using square() and sum() methods # Import NumPy Library import numpy as np # initializing points in # numpy arrays point1 = np.array((4, 4, 2)) point2 = np.array((1, 2, 1)) # finding sum of squares sum_vectors = np.sum(np.square(point1 - point2)) # perform the squareroot and # print Euclidean distance print(np.sqrt(sum_vectors)) Output 3.7416573867739413 Method 4: Using distance.euclidean() from SciPy Module We discussed several methods to Calculate Euclidean distance in Python using the NumPy module. These methods can be slower when it comes to performance, and hence we can use the SciPy library, which is much more performance efficient. The SciPy module is mainly used for mathematical and scientific calculations. It has a built-in distance.euclidean() method that returns the Euclidean Distance between two points. # Python code to find Euclidean distance # using distance.euclidean() method # Import SciPi Library from scipy.spatial import distance # initializing points in # numpy arrays point1 = (4, 4, 2) point2 = (1, 2, 1) # print Euclidean distance print(distance.euclidean(point1,point2)) Output 3.7416573867739413

Python is one of the most versatile programming languages, and we can use the print statement in several ways to print a variable in Python. In this article, let us look at different approaches available in Python to print a variable with examples. How to use the print() function in Python? In Python, we can use the print statement to print any variable, strings, and other data types provided in Python, or we can use it for debugging the code. The syntax of the print statement differs from Python 2 to Python 3.  In Python 2, the print statement is pretty straightforward. You need to use the print keyword and input the message, as shown in the example. Example – Print statement in Python 2  # print statement in Python 2 print "Welcome to Python Tutorials" Output Welcome to Python Tutorials In Python 3, you need to use the print statement as a method, which means the message you need to print should be wrapped in the parenthesis, as shown in the example. Example – Print statement in Python 3 # print statement in Python 3 print("Welcome to Python Tutorials") Output Welcome to Python Tutorials Note: In Python 2, we can run the print statement with and without parenthesis. However, in Python 3, we can run the print statement as a method; otherwise, you will get an error. How to Print variable in Python? There are 5 different approaches to print variables in Python. Let us explore each of the methods in depth with examples. Using comma , character Using string Formatting with the help of %Using string Formatting with the help of {}Using the + operatorUsing the f-string  Method 1: Using comma , character to separate the variables in a print statement The simplest and naive way to print the variable in Python is to separate the variable using the comma character in the print statement. The following example will print the variable and string in the same line in Python code. Example # printing string with print statement using comma "," first_name = "Bing" last_name = "Chandler" print("The full name of the Employee is ", last_name, first_name) Output The full name of the Employee is Chandler Bing Method 2: Using the String Formatting with the help of % character The string formatting is popular in other languages, and Python also gives multiple ways to string formatting and print them. String Formatting gives the user more options to customize, substitute, and place the variable in the right place to make the output of the print message more meaningful. For example, we can use %s to substitute the string variable and %d to substitute the numerical value. # print statement using string formatting first_name = "Bing" last_name = "Chandler" age =25 print("The full name of the Employee is %s %s and the age is %d " %(last_name, first_name,age)) Output The full name of the Employee is Chandler Bing and age is 25 Method 3: Using the string formatting with positional arguments {} We can use Python format specifiers as replacement fields within the string format. Starting from Python 3.1, the positional argument specifiers can be omitted. For example, until Python 3.1, we had to pass the positional specifiers as arguments to the print function as shown below. Example # string formatting without positional specifiers first_name = "Bing" last_name = "Chandler" age =25 print("The full name of the Employee is {0} {1} and the age is {2} ".format(last_name, first_name,age)) print("The full name of the Employee is {1} {0} and the age is {2} ".format(last_name, first_name,age)) Output The full name of the Employee is Chandler Bing and the age is 25 The full name of the Employee is Bing Chandler and the age is 25 From Python 3.1 onwards, the positional specifiers can be omitted, and the same can be written as shown in the below example. Example # string formatting without positional specifiers first_name = "Bing" last_name = "Chandler" age =25 print("The full name of the Employee is {} {} and the age is {} ".format(last_name, first_name,age)) Output The full name of the Employee is Chandler Bing and the age is 25 Note: We can use the positional specifiers in Python 3.1 and above as it has its own advantage. In fact, it works in all the Python versions. Method 4: Using the f-string in the print statement Another simplest way is to use f-string denoting with the letter f, which indicates Python, to format the string. The f-string is another method of achieving string formatting, and it is comparatively faster when compared to other string formatting methods. Example # print statement using f-string first_name = "Bing" last_name = "Chandler" age =25 print(f"The full name of the Employee is {last_name} {first_name} and the age is {age} ") Output The full name of the Employee is Chandler Bing and the age is 25 Method 5: Using the + operator to join variables The + operator is a mathematical operator, but you can use it in the print statement to join the variables. This basically is a string concatenation that concatenates the variables and string in Python. Example # print statement using the + character first_name = "Bing " last_name = "Chandler " age =25 print("The full name of the Employee is " +last_name + first_name +" and the age is "+str(age)) Output The full name of the Employee is Chandler Bing and the age is 25

I am a senior software engineer at Google Singapore and very often I am asked about which programming language to learn.

#509 – JANUARY 25, 2022 View in Browser » PEP 646: Variadic Generics Was Accepted This PEP introduces TypeVarTuple, enabling parameterisation with an arbitrary number of types. For example, it allows the type of array-like structures in numerical computing libraries such as NumPy and TensorFlow to be parameterised with the array shape, enabling static type checkers to catch shape-related bugs. The PEP was recently accepted by the Steering Council. PYTHON.ORG Build a Dice-Rolling Application With Python In this step-by-step project, you’ll build a dice-rolling simulator app with a minimal text-based user interface using Python. The app will simulate the rolling of up to six dice. Each individual die will have six sides. REAL PYTHON How to Quickly Label Data for Machine Learning With Toloka, you can control the accuracy and speed of data labeling to develop high performing ML models. Our platform supports annotation for image classification, semantic segmentation, object detection, named entity recognition, sentiment analysis, speech recognition, text classification → TOLOKA AI sponsor PEP 679: Allow Parentheses in assert Statements “It is a common user mistake when using the form of the assert statement that includes the error message to surround it with parentheses. Unfortunately, this mistake passes undetected as the assert will always pass, because it is interpreted as an assert statement where the expression is a two-tuple, which always has truth-y value.” PYTHON.ORG Strict Python Function Parameters Learn about keyword-only and positional-only parameters and see how using strict function signatures can help you write more resilient code. SETH MICHAEL LARSON Guiding Design Principles for Scientific Python Tips on how to design and organize scientific Python code. NSLS-II.GITHUB.IO Python Jobs Senior Full-Stack Web Developer (Anywhere) MonetizeMore Python Engineer (Web Scraping) (Asia, TX, America) NewsCatcher PyCoder's Weekly Curator and Podcast Co-Host (Anywhere) Real Python Senior Software Engineer (Anywhere) Brew More Python Jobs >>> Articles & Tutorials Designing for Users and Building a Social Network With Django Are you looking for a project to practice your Django skills? Designing the fundamental interactions of a social network is an instructive way to explore models and relationships while learning advanced Django skills. This week on the show, previous guest Martin Breuss talks about his new four-part tutorial series, “Build a Social Network With Django.” REAL PYTHON podcast Solving Wordle With Python “Wordle seems to be trending and it got me thinking of what would be the best algorithm to solve it. I decided to do a naive implementation in Deepnote with Python that only uses NLTK’s list of all english words and base letter frequencies in english texts. This way, I was able to solve the January 18 challenge, but I used up all available attempts.” SIMON SOTAK Scrape Your Web Data From Any Target With Oxylabs Scraper APIs, extract public data from the most complex targets. Our Scraper APIs handle JavaScript-heavy websites and support large data requests using over 102 million global proxy infrastructure. Receive data in JSON or CSV format and pay per successful request only. Start free trial → OXYLABS sponsor Starting With Python IDLE In this course, you’ll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You’ll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow. REAL PYTHON course Modulo String Formatting in Python You can use the % modulo operator for string formatting in Python. It’s a commonly used technique in older Python versions, especially in Python 2. Therefore, you might see it when digging into existing code bases, and it can be helpful to understand how it works. REAL PYTHON Understand Django: Go Fast With Django How do you make your Django app fast? You measure what is slow, scale your system when necessary, and use a combination of fast database queries and strategic caching. This artcile explores those topics and more to help you get a performant Django app. MATT LAYMAN • Shared by Matt Layman Static Typing for Python Decorators Accurately static typing decorators in Python is tricky. The wrapper function obfuscates type information required to statically determine the types of the wrapped function parameters. Here’s how you can circumnavigate that. REDOWAN DELOWAR • Shared by Redowan Delowar Python Type Hints: How to Type a Descriptor “The descriptor protocol allow us to completely customize attribute access. Python’s documentation describes the protocol with types involved described with words. Let’s look at how we can write those as type hints.” ADAM JOHNSON The Power of Python Descriptors An introduction to the Python descriptor protocol, with some example use cases. The descriptor protocol allows you to implement custom logic when a variable is accessed, or assigned a new value. PICCOLO-ORM.COM The Developer / Product Team Notification Template Problem Notifications are hard. They require infrastructure that is reliable, scalable and observable as well as an end user experience that is helpful and respectful. That’s why we built Courier. COURIER sponsor Dealing With YAML With Arbitrary Tags in Python Using the PyYAML library to safely read and write YAML with any tags, in a way that’s as straightforward as interacting with built-in types. ANDGRAVITY.COM An Introduction to Mypy’s @overload “Sometimes the type of the returned value in a function depend on the type of the arguments in ways that can’t be captured with a Union.” PATRICK NSUKAMI • Shared by Patrick Nsukami How Vectorization Speeds Up Your Python Code Vectorization allows you to speed up processing of homogeneous data in Python. Learn what it means, when it applies, and how to do it. ITAMAR TURNER-TRAURING A Trick to Have Arbitrary Infix Operators In Python Discussing an interesting trick which adds support for infix operators in Python, e.g. 5 |add| 6 TOMER FILIBA DIY Self Driving: A Holiday Side Project With Python TRISTAN RICE Projects & Code pyccolo: Declarative Instrumentation for Python GITHUB.COM/SMACKE Qtile: Hackable Tiling Window Manager Written in Python QTILE.ORG sonora: A gRPC-Web Implementation for Python GITHUB.COM/PUBLIC EasyRPC: Sharing Python Functions Between Processes and Applications GITHUB.COM/CODEMATION • Shared by Joshua Jamison Events Weekly Real Python Office Hours Q&A (Virtual) January 25, 2022 REALPYTHON.COM PyCamp Leipzig 2022 January 29 to January 31, 2022 BARCAMPS.EU PyDelhi User Group Meetup January 29, 2022 MEETUP.COM PythOnRio Meetup January 29, 2022 PYTHON.ORG.BR Introduction to the Python Programming Language (In Persian) February 1, 2022 INSTAGRAM.COM PyCon Iran Conference (Virtual) February 1 to February 2, 2022 PYCON.ORG • Shared by PyCon Iran Team DjangoCon Europe 2022 September 21 to 25, 2022 in Porto, Portugal DJANGO SOFTWARE FOUNDATION Happy Pythoning!This was PyCoder’s Weekly Issue #509.View in Browser » [ Subscribe to ? 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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.

In the January 2022 survey we received responses from 1,167,715,133 sites across 269,835,071 unique domains and 11,700,892 web-facing computers. This reflects a loss of 1.15 million sites, but a gain of 1.51 million domains and 31,100 computers. nginx lost 7.33 million sites this month (-1.91%) but continues to be the most commonly used web server with 32.3% of all sites using it. Although nginx’s share has fallen, Apache is still more than eight percentage points behind after losing 3.70 million sites (-1.31%), which has taken its own market share down to 23.9%. nginx also leads in the domains metric, where it has a share of 26.6% compared with Apache’s 23.9%. This reflects a small reduction in nginx’s share – despite a modest gain of 25,400 domains – while Apache suffered the largest loss of 287,000 domains. The largest site and domain growth was seen by Pepyaka, which is a web server that has primarily been used by the Wix web development platform since it switched from using nginx in 2018. The number of sites using Pepyaka grew by 4.02 million to 7.30 million this month, while its domain count went up by 1.80 million to 3.30 million. The next largest domain growth was seen by OpenResty, which gained 686,000 domains this month, and 1.34 million sites in total. The second largest site growth was seen by Microsoft, which gained 2.46 million sites and now accounts for 4.86% of all sites and 5.00% of all domains. Constraining the view to active sites, Apache is still the most commonly used web server, but its market share has fallen slightly to 23.4% after losing more than half a million active sites this month. Meanwhile, nginx gained 230,000 active sites and has increased its share to 20.2%. Apache also maintains a slight lead in the top million websites, where it is used by 235,000 sites compared with 222,000 for nginx. However, Cloudflare has increased its presence by a further 4,959 sites and is now not too far behind with a total of 191,000. If this trend continues, Cloudflare could soon overtake both nginx and Apache to become the most commonly used top-million web server. Looking at web-facing computers, nginx’s strong growth continues unabated. This month it is being used by an additional 32,700 web-facing computers and its market share has increased to 37.7%. Its lead over Apache was further extended by Apache’s loss of 29,100 computers, which sent Apache’s share down to 29.9%. Vendor news Apache 2.4.52 was released on 20 December 2021. This is the latest release from the 2.4.x stable branch and includes two security fixes amongst a host of other changes. Apache Tomcat 9.0.56, 10.0.14 and 10.1.0-M8 (alpha) were released on 8 December 2021. Each of these versions include a fix for a known operating system bug that could cause incoming connections to be reported more than once. nginx 1.21.5 was released on 28 December 2021. This is the latest release in the mainline branch of nginx and is now built with the PCRE2 library by default. njs 0.7.1 was also released on 28 December 2021. This release includes several bugfixes and some other changes to ensure that njs scripts use the same regular expression library as nginx. Microsoft has mitigated an insecure default behaviour in the Azure App Service that inadvertently exposed hundreds of source code repositories. The team that found the vulnerability noted that it had existed since September 2017 and has probably been exploited in the wild. The problem could have impacted PHP, Node, Ruby, Python and Java applications that serve static content, as well as some Azure App Service Linux applications that were deployed using Local Git after files were created or modified in the content root. Cloudflare has introduced a new product called Bulk Redirects, which lets website administrators upload and enable large numbers of URL redirects. These were typically implemented with Page Rules before, which are limited to a maximum of 125 redirects. OpenResty 1.21.4.1 RC1 was released on 16 December 2021. This version is based on nginx 1.21.4 and adds several new features including support for BoringSSL. DeveloperDecember 2021PercentJanuary 2022PercentChange nginx384,347,39432.88%377,019,05432.29%-0.60 Apache283,409,49124.25%279,709,81523.95%-0.29 OpenResty78,902,1386.75%80,238,4706.87%0.12 Cloudflare59,904,4505.13%60,881,0285.21%0.09

Examples of bank-themed survey scams seen by Netcraft Netcraft has seen a large increase in survey scams impersonating well-known banks as a lure. These are often run under the guise of a prize in celebration of the bank’s anniversary, though in some cases a reward is promised just for participating. These scams first came to Netcraft’s attention around 16 months ago, when businesses that were particularly useful during lockdown such as supermarkets, mobile phone networks, and delivery companies were targeted. The expansion of these attacks to use banks as a lure started in October 2021. To date we have seen over 75 distinct banks used as lures for these survey scams, with a global spread including banks from US, UK, Asia, and the Middle East.

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 Aruba Linux 0:00:00 0.000 0.310 0.005 0.030 0.030 2 Rackspace Linux 0:00:00 0.000 0.456 0.008 0.017 0.017 3 Hyve Managed Hosting Linux 0:00:00 0.000 0.122 0.062 0.124 0.124 4 ServerStack Linux 0:00:00 0.000 0.194 0.102 0.202 0.202 5 Pair Networks Linux 0:00:00 0.000 0.342 0.113 0.227 0.227 6 CWCS Managed Hosting Linux 0:00:00 0.007 0.284 0.062 0.123 0.124 7 Bigstep Linux 0:00:00 0.007 0.170 0.064 0.128 0.128 8 www.dinahosting.com Linux 0:00:00 0.007 0.184 0.072 0.145 0.145 9 HN Datacenter Linux 0:00:00 0.007 0.559 0.224 1.080 1.089 10 krystal.uk Linux 0:00:00 0.013 0.178 0.079 0.154 0.154 See full table Aruba finished 2021 as the most reliable hosting company site in December, with no failed requests and the second fastest average connection time. Aruba provides hosting, cloud and digital signature services, fibre optic internet, digital preservation, and much more, with data centres across Europe in the UK, Germany, Czechia, Poland, Italy and France. The top five hosting company sites each responded to all of Netcraft’s requests in December and so are ranked on their average connection times. The top five is completed by Rackspace, Hyve Managed Hosting, ServerStack and Pair Networks. In second place, Rackspace appeared in the top 10 every month in 2021 and offers a variety of cloud hosting solutions from 19 data centres across five different continents in the Americas, Europe, Asia and Australia. Hyve Managed Hosting also appeared in the top 10 every month in 2021. Hyve offers cloud hosting, dedicated servers and managed services from data centres in 34 locations around the world. ServerStack appeared 10 times in the top 10 in 2021 and provides managed and dedicated solutions from its three data centres in North America and Amsterdam. Pair Networks made seven top 10 appearances in 2021 and provides a range of managed and dedicated hosting solutions from its data centre in the US, as well as domain registration. Linux continues to dominate the top 10, with all of the top 10 using Linux in December. Across 2021 Linux appeared 109 times in the top 10, with FreeBSD making 9 appearances and SmartOS appearing once in January 2021.

In the December 2021 survey we received responses from 1,168,864,866 sites across 268,328,184 unique domains and 11,669,818 web-facing computers. This represents a loss of 6.53 million sites, but a gain of 1.30 million domains and 144,000 computers. nginx lost a significant number of sites (-23.88 million) and domains (-8.54 million) this month, though it continues to hold the highest market share in both categories with 32.9% of sites and 26.7% of domains. nginx’s domain market share lead over Apache dropped significantly, falling from a 5.6 percentage point lead to a 2.6 percentage point lead. nginx also gained 81,100 web-facing computers this month, giving it 37.5% of market share in this category. Apache also lost sites (-3.09 million) and domains (-446,000) this month, though it gained 5,700 web-facing computers. Apache continues to hold second place across all three key metrics. The largest increase in both domains and hostnames was seen for “awselb”, used by Amazon’s Elastic Load Balancing service, and accounts for the majority of the loss experienced by nginx. The change was as a result of GoDaddy’s URL redirector service, which allows domains registered with GoDaddy to be pointed at arbitrary URLs, being moved from their own hosting facilities to Amazon’s ELB service. Many other web servers also saw reasonable growth in the number of sites this month, with OpenResty and Microsoft gaining 2.42 million and 2.15 million respectively, followed by LiteSpeed and Cloudflare with 1.76 million and 1.28 million. Fewer servers gained domains this month, though OpenResty gained a respectable 850,500 (+2.19%). Cloudflare gained 2,431 sites in the million most popular sites, increasing its market share by 0.24 percentage points to 18.6%. Apache continues to maintain a slim lead over nginx, though both lost sites this month. Microsoft’s market share dropped, as it lost 4,119 sites this month taking it to 6.15% of the total and down from 6.89% at the start of the year. Log4Shell impact on web servers A critical vulnerability dubbed “Log4Shell” was identified in the Java log4j logging library, and was publicly disclosed on 9th December. The vulnerability has impacted a broad range of organizations as the log4j library is widely used, and the flaw can be easily exploited to break into systems, steal data, and infect networks with malicious software. Many widely-used web servers such as Tomcat and Jetty are written in Java but do not use the log4j library by default so are not directly affected by the issue. However, they can be configured to do so, and it is also possible that sites that use popular web servers written in other languages - Apache and nginx are written in C, for instance - may still use the vulnerable library at some level in their technology stack. Several less well-known servers integrate the log4j library directly, such as IBM WebSphere. Several WebSphere components such as the Admin Console use the library and so are vulnerable to the issue, while applications served using WebSphere may be vulnerable if they use the library. IBM WebSphere is not widely used: this month Netcraft identified 3,778 sites using the server, which were hosted on 830 IP addresses. Amongst these, Netcraft found government and banking websites, though it is unknown whether these sites are vulnerable. Vendor news Apache 2.4.52 was released on 20 December. This release fixes several security issues, including a possible buffer overflow in mod_lua and server-side request forgery vulnerability in forward proxy configurations. nginx unit 1.26.1 was made available on 2 December and fixes several bugs introduced in the 1.26.0 release. Lighttpd 1.4.62 and 1.4.63 were released in quick succession at the start of December and include many minor changes and bugfixes. Apache Tomcat 9.0.56, 10.0.14, and 10.1.0-M8 (alpha) were released on 2 December. DeveloperNovember 2021PercentDecember 2021PercentChange nginx408,226,31934.73%384,347,39432.88%-1.85 Apache286,494,60024.37%283,409,49124.25%-0.13 OpenResty76,480,9276.51%78,902,1386.75%0.24 Cloudflare58,629,3654.99%59,904,4505.13%0.14

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

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