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Tuples are an important data type in Python and are often used to store a fixed set of elements. In this article, we will be discussing how to pass a tuple as function arguments in Python. We will go over the syntax for passing tuple arguments and will provide examples of how to do so.

Let us first understand the basics that, we will be needing to begin approaching the problem. The problem asks us to pass a tuple as a function argument, for that we need to know what a function in python is, what are function arguments, and what are tuples in python.

What is a function in python?

A function in any programming language is a piece of code containing a block of statements that perform a specific function or task and may be required more than once, during the execution. It is important as they provide us with code reusability and are helpful in maintaining a code that is less prone to bugs, due to reduced redundancy.

Functions in python can be declared using the “def” keyword, all the code inside the function needs to be properly indented so that the compiler knows the proper definition of the function. A function in python can be defined as follows −

def functionName(): statement 1 statement 2 # End of function

A function is normally called using the name of the function followed by round braces. Given below is an example of a function call.

functionName()

The function defined above is the simplest form of function that does not take any input and does not return anything. But python provides us a way for us to do those things as well.

Arguments of a function

There are times when we need to have a function that takes some form of input along with it for processing. So, how can we achieve this? We can do so using function arguments.

A function argument is an input that a function needs to have whenever it is called. The function arguments are defined within the circular braces of a function call. For example −

# Function definition def functionWithArgs(argument1, argument2): statement 1 statement 2 functionWithArgs(arg1, arg2) # function call

We are not limited by any datatype or number of arguments we can pass to a function. We can pass any built-in datatypes as well as user-defined datatypes, and instances of the class. Along with passing input parameters, we can return values too.

Now we know what a function in python is and how we can use arguments to give input to a function for further processing. Let us now look at what a tuple is.

Tuple in Python

A tuple in python is a pre-defined datatype that acts as a container and can hold different types of, heterogenous, data within it. The point to note is that they are immutable, hence they cannot be modified after creation. All the elements of the tuple are stored in parentheses separated by a comma. The syntax for defining a tuple is given below.

A = (1, 2, 3)

Now we have all the basics that we need to know to solve the problem. Let us now discuss the ways to approach it.

Passing static tuples as arguments

As we know that we can use arguments to pass input to a function. To pass a tuple we just have to specify a tuple object within the round braces at the time of function definition.

Do note that we just have to specify a variable name, its value will be the tuple we define at the time of the function call.

Algorithm

Step 1 – Define a function with a variable as a parameter

Step 2 – Within the function, print the value the variable holds

Step 3 – Make a function value with a tuple inside the parentheses

Example def tupleArg(inputTuple): print("Tuple argument passed as input to the function is: ", inputTuple) tupleArg((1, 2, 3)) Output Tuple argument passed as input to the function is: (1, 2, 3) Using user input tuples

The program above discussed how we can pass a static tuple as an argument. But in many cases these tuples are dynamic in nature. This is why we will now be focusing on taking a tuple as input from user and passing it as an argument to a function.

Algorithm

Step 1 – Create a variable to store the tuple

Step 2 – Take a string of values from user as input

Step 3 – Split the input string, map every element to an integer and convert the whole map object to a tuple

Step 4 – Create a function that takes a variable as parameter

Step 5 – Within the function print the input tuple

Step 6 – Call the function with the user input tuple as argument

Example A = tuple(map(int, input("Enter the elements of tuple : ").split())) def tupleArg(inputTuple): print("Tuple argument passed as input to the function is: ", inputTuple) tupleArg(A) Output Enter the elements of tuple : 12 33 776 339 Tuple argument passed as input to the function is: (12, 33, 776, 339) Conclusion

In this article, we focused on three different ways to pass the tuple as a function argument. We learned what a function in python is, what is meant by a function argument and how can we pass both, static as well as dynamic, tuples

You're reading Python Program To Pass The Tuple As Function Arguments

How Does The Python Dump Function Work?

Definition of Python Dump

Python supports the json package, which allows for the execution of script files containing textual programming code. This package enables the transfer and storage of data by utilizing the functions provided by the json module. The dump function in json supports the code scripted in key-value pairs similar to the Python dictionary that is within curly brackets. The dumps function is mainly used when we want to store and transfer Python objects, and json package allows us to perform the operation efficiently.

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Syntax json.dump(object, skipkeys=False, ensure_ascii=True, indent=None, allow_nan=True, number_mode = None, datetime_mode = None, separators=None) pickle.dump(Object, Destination, pickle_protocol=None, )

json.dump represents the function that encodes and stores the Python object value into json value.

object is the filename of the input data or a value that we are passing through the dump function.

skipkeys is a parameter where we will declare Boolean values whether we want to skip the invalid dictionary keys.

ensure_ascii is a parameter where we will declare the Boolean values to ensure the output should contain ASCII values or not.

allow_nan is also a Boolean parameter that is used to allow null values.

number_mode & datetime_mode allow us to handle the type of behaviors we handle inside the function, and datetime mode allows us to handle to format of data and time instances.

The value we give first denotes the separation of a key-value pair from another key-value pair. 2nd value we give denotes the symbol which separates keys from their values.

For pickle package,

The object is the Python object we have created to be pickled

The destination is the file or data where the pickled python objected is written

Pickle_protocol refers to the version of the pickle protocol. By default, it assigns to the Python version.

How does Python Dump Function Work?

Let us discuss a basic example of how the json dump function works.

Example #1

Code:

import json # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Converting output to json format pets_data = open("pet_data.json", "w") json.dump(dict_pets, pets_data)

Output:

In this example, we have created a Python dictionary with three key-value pairs, and we have converted the Python dictionary to json file format using the json package. Then, we pass the dictionary variable to the chúng tôi function, which serializes the Python object and writes the JSON output to the pets_data file. The chúng tôi function requires two positional arguments: dict_pets represents the Python object to be serialized, and pets_data is the file where the JSON output is stored or written.

Example #2

In this example, we’ll discuss the package called Pickle in Python, which helps us to serialize the Python object.

Code:

import pickle # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Serializing output using pickle pets_data = open("pet_data.pickle", "wb") pickle.dump(dict_pets, pets_data)

Output:

Example #3

Let’s discuss another example where we use the json dumps() function, which is similar to the dump() function but the dumps() function allows us to convert the Python dictionary object to a string file in json format.

Code:

import json import json # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Converting output to json format json_dict = json.dumps(dict_pets) print(json_dict)

Output:

Similar to the 1st example, we have created the Python dictionary with the same three key-value pairs. Here, we pass only one positional argument to the dumps() function, unlike json.dump(), which requires two positional arguments.

Since we are converting the Python object to json string format, we only require the object variable.

Example #4

In this example, we utilize the allow_nan parameter, which we discussed earlier, to handle NaN (Not a Number) values in a Python dictionary.

import json import json # python dictionary dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom" }, "Cat": { "Species": "British Shorthair", "country": "United Kingdom" }, "Hamster": { "Species": "golden hamster ", "country": "Turkey" } } ## Converting output to json format json_dict = json.dumps(dict_pets) print(json_dict)

Output:

When we declare the allow_nan parameter as True

import json # python dictionary which should be dumped dict_pets ={ "Dog": { "Species": "cocker spaniel", "country": "United Kingdom", "life expectency": 20 }, "Hamster": { "Species": "golden hamster", "country": "Turkey", "life expectency": float("nan") } } ## Converting output to json format pets_data = open("pet_data.json", "w") json.dump(dict_pets, pets_data, allow_nan=True)

Output:

we can see from two codes that when we set the allow_nan parameter as True when our object has Nan values, we can dump the object to json output without any problem.

Conclusion

In this article, we have discussed the Python dump function in detail using various examples to get a clear understanding of the json dump function and its uses. We have also discussed the pickle package and dumps() function along with the examples, and we have discussed the usage of allow_nan parameters with an example. I hope this article helps.

Recommended Articles

This is a guide to Python Dump. Here we also discuss the definition and how the Python dump function works, along with different examples and its code implementation. You may also have a look at the following articles to learn more –

Python Program To Add Elements To A List

In this article, we will learn how to add elements in list. In python there many ways to add elements into a list by using different operators. “operators” are special symbols that carries out arithmetic or logical computation. The value that operator operates is known as “operand”. There are many types of operators used in python like” +” for add element, “-” to slicing element , ”*” for repeating the element and etc.

List in Python

A list is a data structure in python that is mutable or changeable, ordered sequence of elements. List are used to store multiple in single variable. List consists of four built in data type in python used to store collection of data. And the other three are Tuple, set, dictionary.

Example

The following example creates three lists – list1, list2 and list3. List1 contains numbers 1, 2, 3 and 4; List2 contains strings “keshav”, “mohan” and “govind”; List3 combines elements from both the lists containing a number followed by a string.

list1 = [1,2,3,4] print (list1) list2 = ["keshav","mohan","govind"] print (list2) list3 = [1,"keshav",2,"mohan"] print(list3) Output [1, 2, 3, 4] ['keshav', 'mohan', 'govind'] [1, 'keshav', 2, 'mohan']

Before learning how to add elements of a list let’s first learn how to create a list. This will give a quick revision of the basic concepts.

Creating a List

We can create a list, by placing elements inside square bracket [ ], separated by commas. Let’s learn it by giving an example.

Example

The following example shows how to craete a list. Here, we are taking three names: “alina”, “akash”, and “arjun”.

list= ["alina","akash", "arjun"] print(list) Output

Executing the above given program yields the following list of names.

['alina', 'akash', 'arjun'] Elements in List

Lists are mutable, which means this element can be changed, added and subtracted (sliced).

We use the operator = to change an item or element in list.

Example

The following example defines the names.

names=["ann","yash","maria"] print(names) Output

Upon executing the above program, we get the following output, it prints the elements present in the list as follows.

['ann', 'yash', 'maria'] Example

Here the operator = is used to change the element. In this example step 1 shows that names list is ann, yash , maria but in step 2 we had change the last name “maria “with “ mike” by using “=”operator.

names = ["ann","yash","maria"] print(names) names [2] = "mike" print (names) Output

Executing the above program gives the following output, thus changing the name “Maria” with “Mike” using = operator

['ann', 'yash', 'maria'] ['ann', 'yash', 'mike'] Adding Elements In List

We can add item to a list by using the append(), extend(), insert(), concatenation().

Python allows users to add elements to a list in various ways. The append() method adds an element to the end of the list, whereas extend() appends multiple items at once.

Insert() can be used to insert an item at any given index, while concatenation() combines two lists together into one. All four of these methods are useful for adding elements to a Python list.

Using Concatenation Operator (“+”)

Concatenation is the process of combining two or more strings together in Python. This can be done using the ‘+’ operator, or by using formatting functions such as str.format(), f-strings, and format specifiers. Concatenation allows us to create longer strings by joining shorter strings together.

Example

The example given below adds two lists together. The first list, names1, contains the names Ann, Yash and Maria. The second list, names2, contains the names John, Andrew and Robin.

names1=["ann","yash","maria"] names2=["john","andrew","robin"] print(names1+names2) Output ['ann', 'yash', 'maria', 'john', 'andrew', 'robin'] Using Append() Method

Append() is a built-in method in Python that adds an element to the end of a list. It takes one argument, which can be any data type such as an integer, string, or another list.

This method does not return anything; it simply modifies the original list by adding the new element. Append() is a useful method for adding items to lists in Python.

Example

This example adds the string “cherry” to a list of fruits called fruit. After adding it, the contents of the list are printed out which now includes “apple”, “mango”, “banana” and “cherry”.

fruit=["apple", "mango", "banana" ] a="cherry" fruit.append(a) print ("the updated list :",fruit) Output the updated list : ['apple', 'mango', 'banana', 'cherry']

In this program, we had a list which consisted of three fruit: [‘apple’, ‘mango’, ‘banana’] and we wanted to add fourth fruit name “cherry” at the end element of list . Here we have used append()as an operator and we got output as : [‘apple’, ‘mango’, ‘banana’, ‘cherry’].

Using Extend() Method

. Extend() is a function in Python that adds elements from one list to another. It appends all the items in an iterable to the end of the list, extending it. It takes an iterable as input and extends the list with its individual elements.

The original order of items is maintained when they are added to the existing list. This method does not return any value but updates existing lists in memory with new values.

Example

In this example, we are using extend() function to extend and add an element to the list fruits and output is [‘apple’, ‘mango’, ‘banana’, ‘c’, ‘h’, ‘e’, ‘r’, ‘r’, ‘y’]

fruit=["apple", "mango", "banana" ] a="cherry" fruit.extend(a) print("the updated list :",fruit) Output the updated list : ['apple', 'mango', 'banana', 'c', 'h', 'e', 'r', 'r', 'y'] Using Insert() Method

Insert() is a function in python that inserts an element at the specified position. It takes two parameters: the index of the element before which to insert, and the item to be inserted.

This can be used to add elements into a list or any other data structure such as tuples and dictionaries. It’s important to note that inserting an element shifts all existing elements after it one position ahead in memory.

Example

In the following example, we have a list which consistes of three fruits: [‘apple’, ‘mango’, ‘banana’] and since we want to add “orange” at a specific position, we used the operator insert(). By this insert() operator we can add any element at any index.

fruits=['apple','banana','cherry'] fruits.insert(1,'orange') print(fruits) Output ['apple', 'orange', 'banana', 'cherry'] Conclusion

In this article, we have briefly explained how to add the element in list using python. We have used four different methods insert(), concatenation(), append(), extend(). Each function has a different way of completing the task that is Concatenation() is used for combining two list . Append() is used for adding element at the end of list . extend() is used to add and extend element adding in list. insert() is used for add element at any index.

Python Program To Rotate Elements Of An Array

After the declaration of an array, the elements of the array until certain index are rotated such that the first elements until the desired index are placed in the last, next to the last element of the array. Let us discuss this with an Input output scenario.

Input Output Scenario

Consider an array arr = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ].

We can clearly say that the initial array consists of 10 elements and the index of the last element is 9.

Let us assume that the array is rotated by two elements.

In this case, the first two elements are placed after the last element “ 10 ”.

Firstly, the element “ 1 ” will be placed after 10 and after the element “ 1 ” is placed, then the next element “ 2 ” will be placed next to 1.

So, the resulting array will be arr = [ 3, 4, 5, 6, 7, 8, 9, 10, 1, 2 ].

Example

In this example, we are going to discuss about the process of rotating certain elements ( all at a time ) of an array by certain number of rotations. The steps that must be followed to construct a program are as follows:

Declare a function or a method that deals with rotating the elements of the array.

( Note that the parameters of the method must consist of the array, the maximum size of the array and the number of rotations that the user require )

Within the method, consider a new array with a variable name “ temp ” in order to store the array elements after rotation.

With the help of a variable “ i ” and a loop, iterate the elements ( up to the index which is equal to number of rotations ) of the array and append the elements one after another into the “ temp ” array.

Consider another loop and iterate the elements from the next index and store them accordingly.

Now, merge the array “ arr ” to the array “ temp ” and store the value into the array “ arr ”.

def rotate_elements(arr, max, no_of_elements): temp = [] i = 0 while (i < no_of_elements): temp.append(arr[i]) i += 1 i = 0 while (no_of_elements < max): arr[i] = arr[no_of_elements] i = i + 1 no_of_elements = no_of_elements + 1 arr[:] = arr[: i] + temp return arr arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] print("The array before rotation is: ", end =' ') print(arr) print("The array after rotation is: ", end=' ') max_size = len(arr) print(rotate_elements(arr, max_size, 2)) Output

The output for the above program is as follows −

The array before rotation is:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] The array after rotation is:  [3, 4, 5, 6, 7, 8, 9, 10, 1, 2] Example

In this example, we are going to discuss about the process of rotating certain elements ( one by one ) of an array by certain number of rotations. The steps that must be followed to construct a program are as follows −

Declare two functions. The first function will be used to iterate all the elements until the total number of rotations and simultaneously, it calls the second method such that after iterating the element, the element will be allowed to be rotated immediately.

( Note that the parameters of the method must consist of the array, the maximum size of the array and the number of rotations that the user require )

In the second method, consider an empty array with a variable name “ temp ” in order to store the array elements after rotation.

Within the help of a variable “ i ” and a loop, iterate all the elements from the index 0 to the last index of the last element and rotate the elements of the array one after another.

Print the elements present in the array “ arr ” after rotation.

def rotate_elements(arr, no_of_elements, max): for i in range(no_of_elements): rotate_one_by_one(arr, max) def rotate_one_by_one(arr, max): temp = arr[0] for i in range(max-1): arr[i] = arr[i+1] arr[max-1] = temp arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] print("The array before rotation: ") print(arr) rotate_elements(arr, 2, 10) print("The array after rotation: ") print(arr) Output

The output for the above program is as follows −

The array before rotation: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] The array after rotation: [3, 4, 5, 6, 7, 8, 9, 10, 1, 2] Conclusion

We can clearly observe that the output produced by both the programs discussed above are exactly equal. The only difference between the programs is the process followed within the program body and the methods used. In the first program, within a single method, the elements are rotated altogether by using an external array. In the second program, two different methods are used for rotating the elements one after another by calling a method. In this way, the elements of the arrays can be rotated.

The Map() Function In Swift

In Swift, the map() function can be used to apply a function for each element in a collection.

For example, let’s square an array of numbers:

let numbers = [1, 2, 3, 4, 5] let squared = chúng tôi { $0 * $0 } print(squared)

Output:

[1, 4, 9, 16, 25]

Here is an illustration of how the above code works:

In this guide, you learn how to use the map() function with closures on different collection types in Swift.

Before learning about the map() function it is important you understand what a closure is in Swift.

Closures in Swift – A Quick Primer

In Swift, a closure is a function that has no name. It is a block of code that can be passed around, for example as an argument to a function.

Closure Syntax

The syntax of closure looks like this:

statements }

Where:

params represents the function arguments for the closure.

return type is the type of the return value.

statements refers to any valid Swift expressions.

Calling a Closure

When you call a regular function, you call the name of the function followed by a set of parenthesis (with possible arguments):

f(arg)

Where f is a function and arg is an argument.

Calling a closure works the same way but it looks different. A closure has no name so is no way to call a closure by name. Thus you have to “use” a closure instantly as you define it.

Here is the syntax of calling a closure in general:

Here the closure is defined on the same line as it is called.

Closure Example

Let’s square a number using a closure function that takes a number as an argument and squares it:

print(result)

Output:

81

As you can see, we define the closure and call it in the making. After this line has been executed, there is no trace of that closure.

Simplifying a Closure in Swift

Usually, closures are written in a simpler form. This is because the Swift compiler can infer a lot of things about the closure without you having to specify them.

Here are the steps to reducing the above closure into a simpler form:

({ (num: Double) in return num * num })(9.0) ({ num in return num * num })(9.0) ({ num in num * num })(9.0) ({ $0 * $0 })(9.0) { $0 * $0 }(9.0) When Use Closures in Swift

A closure is useful when you only need the functionality once in your code.

Commonly, a closure is passed as an argument into another function. This is useful when you do not want to define a separate function for it.

A great example is when performing an operation on a collection of values using the map() function.

Now that you understand what a closure is it is time to learn how the map() function works.

The map() Function in Swift

In Swift, you can use the built-in map() function to modify each element in a collection.

The map() function loops through the collection, applying a function for each element. The map() function always returns an array where the transformed values are.

Before using the map() function, let’s use a for loop to transform an array of numbers.

A For Loop

Let’s create an array of numbers and square each number in the array using a for loop:

let numbers = [1, 2, 3, 4, 5] var squared: [Int] = [] for n in numbers { let sq = n * n squared.append(sq) } print(squared)

Output:

[1, 4, 9, 16, 25]

This approach is perfectly valid and it works as expected. However, this loop can be made significantly shorter by using the map() function.

Next, let’s use the map() function to solve the same problem.

Map with the map() Function

The map() function takes a function as an argument and applies it for each element in a collection.

For example, given a function that squares numbers, let’s use map() function to call it on each element in an array:

return num * num } let numbers = [1, 2, 3, 4, 5] let squared = numbers.map(square) print(squared)

Output:

[1, 4, 9, 16, 25]

If you have only called functions with parenthesis before, numbers.map(square) might look strange to you.

But it is easy to understand.

The map() function does not want you to call a function. Instead, it wants to know the name of the function to be called on each element.

Under the hood, the map() function operates like a for loop such that it:

Picks an element from the collection.

Calls the function on that element.

Stores the result in another collection.

Repeats 1-3 until there are no elements left after which it returns the result collection.

Now you understand how the map() function works. Next, let’s take a more conventional approach to using the map() function by calling it with closure arguments.

Map with Closures

The map() function calls a function for each element in a collection. This function can be a regular function defined separately or a closure function defined inline.

If you need the mapping functionality only once, it is better practice to declare it as a closure directly into the map() function. This prevents you from leaving unused functions in your codebase.

For example, let’s define the squaring functionality directly into the map() function in a form of a closure:

let numbers = [1, 2, 3, 4, 5] return num * num })

Now there is no need for a separate function that defines how to square a number. Instead, it is a closure that is defined in the map() call.

As you learned before, you can clean up the closure quite a bit. This is because you do not need to be explicit about:

The parameter type.

The return type.

The return statement.

The parameter name.

With this in mind, the above map() function call becomes significantly shorter and more concise:

let squared = numbers.map({ $0 * $0 })

Now you know how to use the map() function with closures. Let’s take a look at calling map() on different data types in Swift.

Use map() with Other Data Types

So far you have seen how to use the map() function with arrays. As it turns out, you can also call map() on other collection types in Swift. However, the result is always an array! This means for example that mapping a dictionary to another dictionary is not directly possible.

Dictionaries And the map() Function

In Swift, you can call the map() function on a dictionary. Just remember that the map() function always returns an array.

For instance, let’s convert all values in a dictionary to upper case:

let data = [ "name": "alice", "address": "imaginary street", "college": "oxford university" ] let upperCased = chúng tôi { $1.uppercased() } print(upperCased)

Output:

["IMAGINARY STREET", "OXFORD UNIVERSITY", "ALICE"]

Notice that a dictionary is an unordered collection. This means the order of the result varies.

To map a dictionary to another dictionary in Swift:

Call map() function on a dictionary to create an array of tuples of key-value pairs.

Convert the array of tuples to a dictionary using Dictionary(uniqueKeysWithValues:).

For example, let’s convert a dictionary to a dictionary with uppercased values.

let data = [ "name": "alice", "address": "imaginary street", "college": "oxford university" ] let upperCased = chúng tôi { ($0, $1.uppercased()) } let formattedDict = Dictionary(uniqueKeysWithValues: upperCased) print(formattedDict)

Output:

["college": "OXFORD UNIVERSITY", "address": "IMAGINARY STREET", "name": "ALICE"] Range And the map() Function

In Swift, a range is a collection of values between a starting value and the ending value.

In Swift, you can call map() function range to transform the range to an array of transformed values.

For example, let’s create a range of numbers from 1 to 10 and square each number:

let squaredRange = (1...10).map { $0 * $0 } print(squaredRange)

Output:

[1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Set And the map() Function

In Swift, a set is an unordered collection of unique values.

You can call map() on a set to transform it to an array of transformed values.

For example, let’s square a set of numbers:

let numbers: Set = [1, 2, 3, 4, 5] let squared = chúng tôi { $0 * $0 } print(squared)

Output:

[1, 9, 16, 25, 4] Conclusion

Today you learned how to use the map() function in Swift.

To recap, the map() function calls a function for each element in the collection. It returns a new array with the updated values.

You can call the map() function on an array, dictionary, string, or set. However, you always get an array back.

Using the map() function lets you replace the for loops with much more elegant expressions.

Thanks for reading.

Happy coding!

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As a programmer in the modern-day tech industry, you have to learn

Pandas_ml

Pandas is one of the most notable machine learning libraries that is written in Python. This library is meant for data analysis and manipulation. Moreover, it is handy as it combines some of the best and most reliable libraries of Python into one whole package. Thus, making it easy to use and implement.  

Argument Unpacking

List unpacking fails when used on a function; Python doesn’t natively unpack a list or a tuple when it is passed to a function. This is because it may cause ambiguity: it’s up to the developer to specify when this has to be done.  

Formatting of code using Black

What if you get a chance to review your code faster and in an efficient way? That’s when Black – an automatic code formatter for Python, steps in. One of the coolest Python unknown tricks is the formatters wherein you write code as per your style. And Black formats it, thus expediting the whole process of code review.  

Easy Value Swapping

Programmers make use of swapping of values of two variables in their everyday programming lives. Swapping of values of two variables is usually done with the assistance of a third variable, a temporary variable. This third variable allows the swapping of the other two variables. Meanwhile, Python enables programmers to conduct swapping without any temp variable.  

Python debugger

Several times, we get stuck on some errors and exert extensive time solving them. Further, turning all our efforts futile. The solution is unknown, but our efforts are all wasted. And that’s when you must use The Python debugger to make your task simple. The Python Debugger helps to review the code line by line without any hassle. Thus, making it one of the best features of Python.  

Launch Web Server

Hidden features of Python are as useful as the support of a website. For example, let’s imagine that you intend to launch your web server using which you will be able to share the files from your computer.  For this, using a simple command would be handy. It will launch a web server on any port. For everything to pass successfully, you should set the port from range 0 to 65353. There are other parameters that you can check out additionally.  

Negative indexing

One of the hidden Python features is that it supports negative indexing. We all are aware that indexes are used in arrays in all programming languages. And the elements in an array are accessed using these indexes. However, all programming languages deter from using negative indexes like ‘-2.’ But Python is an exception as it supports the negative indexing of arrays. So, -1 should be the last element, and -2 the second last one.  

Short Module Names

Are you tired of utilizing long library names over and over again? Then, the following hack will be helpful for you for sure. Python provides developers the opportunity to utilize the keyword “as” to create any library name according to them.  

Usage of emojis in code

Did you know that Python has a package wherein you can add emojis to your strings? Yes, you read it right! You may either use the emoji module or Unicode to include emojis.  

List of Easter Eggs

As a programmer in the modern-day tech industry, you have to learn programming languages that have excellent in-built features and can help develop your applications quickly. Not only that, the language has to be easy to learn and should also support the developer community. So, owing to these facilities most programmers, coders, and developers have started using the Python programming language. Python is one of the most widely used and sought-after programming languages in the industry. There are many Python features one needs to learn to become a good developer. Here are the top 10 hidden python features to learn as a beginner in 2023.Pandas is one of the most notable machine learning libraries that is written in Python. This library is meant for data analysis and manipulation. Moreover, it is handy as it combines some of the best and most reliable libraries of Python into one whole package. Thus, making it easy to use and chúng tôi unpacking fails when used on a function; Python doesn’t natively unpack a list or a tuple when it is passed to a function. This is because it may cause ambiguity: it’s up to the developer to specify when this has to be chúng tôi if you get a chance to review your code faster and in an efficient way? That’s when Black – an automatic code formatter for Python, steps in. One of the coolest Python unknown tricks is the formatters wherein you write code as per your style. And Black formats it, thus expediting the whole process of code review.Programmers make use of swapping of values of two variables in their everyday programming lives. Swapping of values of two variables is usually done with the assistance of a third variable, a temporary variable. This third variable allows the swapping of the other two variables. Meanwhile, Python enables programmers to conduct swapping without any temp variable.Several times, we get stuck on some errors and exert extensive time solving them. Further, turning all our efforts futile. The solution is unknown, but our efforts are all wasted. And that’s when you must use The Python debugger to make your task simple. The Python Debugger helps to review the code line by line without any hassle. Thus, making it one of the best features of Python.Hidden features of Python are as useful as the support of a website. For example, let’s imagine that you intend to launch your web server using which you will be able to share the files from your computer. For this, using a simple command would be handy. It will launch a web server on any port. For everything to pass successfully, you should set the port from range 0 to 65353. There are other parameters that you can check out chúng tôi of the hidden Python features is that it supports negative indexing. We all are aware that indexes are used in arrays in all programming languages. And the elements in an array are accessed using these indexes. However, all programming languages deter from using negative indexes like ‘-2.’ But Python is an exception as it supports the negative indexing of arrays. So, -1 should be the last element, and -2 the second last chúng tôi you tired of utilizing long library names over and over again? Then, the following hack will be helpful for you for sure. Python provides developers the opportunity to utilize the keyword “as” to create any library name according to chúng tôi you know that Python has a package wherein you can add emojis to your strings? Yes, you read it right! You may either use the emoji module or Unicode to include emojis.Programming may appear boring as writing argumentative essays and different academic assignments for some people. But what if we say that there are Easter eggs in Python? This may sound surprising because Easter eggs, as a rule, can be found in video games, movies, cartoons, etc.

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