The Top 10 High-Paying IT Jobs in 2020

The Top 10 High-Paying IT Jobs in 2020

"The Top 10 High-Paying IT Jobs in 2020" will introduce you to the most trending jobs in the IT domain which will help your career to flourish in 2020. Looking for a lucrative career? Here are some of the highest-paid IT jobs, in some of the most in-demand fields, to work toward for 2020.

This video on "The Top 10 High-Paying IT Jobs in 2020" will introduce you to the most trending jobs in the IT domain which will help your career to flourish in 2020. This will also talk about the salary package, job description, required skillset and companies hiring to land onto the best jobs in the market. Let us know your list of Top 10 Highest Paying IT Jobs in 2020 in the comment section.

Python For DevOps Tutorial - How to use DevOps with Python

Python For DevOps Tutorial - How to use DevOps with Python

Python For DevOps Tutorial | How to use DevOps with Python | Python Training will help you understand the effective reasons to choose Python for DevOps and various python modules that can be used for DevOps. You'll learn: Introduction To DevOps Life Cycle, Reasons To Use Python For DevOps, How To Use Python For DevOps?

This Edureka video on 'Python for DevOps' will help you understand the effective reasons to choose Python for DevOps and various python modules that can be used for DevOps. Following are the topics discussed in this session:

  • Introduction To DevOps Life Cycle
  • Reasons To Use Python For DevOps
  • How To Use Python For DevOps?

Dictionaries in Python - Learn how to work with Python Dictionaries

Dictionaries in Python - Learn how to work with Python Dictionaries

In this Python Dictionaries tutorial, you will learn how to work with Python Dictionaries, an incredibly helpful built-in data type that you will definitely use during your projects. In this Python dictionaries tutorial you'll cover the basic characteristics and learn how to access and manage dictionary data. Learn everything about Python dictionary; how they are created, accessing, adding and removing elements from them and, various built-in methods.

Welcome

In this article, you will learn how to work with Python Dictionaries, an incredibly helpful built-in data type that you will definitely use during your projects.

In particular, you will learn:

  • What dictionaries are used for and their main characteristics.
  • Why they are important for your programming projects.
  • The "anatomy" of a dictionary: keys, values, and key-value pairs.
  • The specific rules that determine if a value can be a key.
  • How to access, add, modify, and delete key-value pairs.
  • How to check if a key is in a dictionary.
  • What the length of a dictionary represents.
  • How to iterate over dictionaries using for loops.
  • What built-in dictionary methods you can use to leverage the power of this data type.

At the end of this article, we will dive into a simple project to apply your knowledge: we will write a function that creates and returns a dictionary with a particular purpose.

Let's begin! 🔅

🔸 Dictionaries in Context

Let's start by discussing the importance of dictionaries. To illustrate this, let me do a quick comparison with another data type that you are probably familiar with: lists.

When you work with lists in Python, you can access an element using a index, an integer that describes the position of the element in the list. Indices start from zero for the first element and increase by one for every subsequent element in the list. You can see an example right here:

But what if we need to store two related values and keep this "connection" in our code? Right now, we only have single, independent values stored in a list.

Let's say that we want to store names of students and "connect" each name with the grades of each particular student. We want to keep the "connection" between them. How would you do that in Python?

If you use nested lists, things would get very complex and inefficient after adding only a few items because you would need to use two or more indices two access each value, depending on the final list. This is where Python Dictionaries come to the rescue.

Meet Dictionaries

A Python dictionary looks like this (see below). With a dictionary, you can "connect" a value to another value to represent the relationship between them in your code. In this example,"Gino" is "connected" to the integer 15 and the string "Nora" is "connected" to the integer 30.

Let's see the different elements that make a dictionary.

🔹 The "Anatomy" of a Python Dictionary

Since a dictionary "connects" two values, it has two types of elements:

  • Keys: a key is a value used to access another value. Keys are the equivalent of "indices" in strings, lists, and tuples. In dictionaries, to access a value, you use the key, which is a value itself.
  • Values: these are the values that you can access with their corresponding key.

These two elements form what is called a key-value pair (a key with its corresponding value).

Syntax

This is an example of a Python Dictionary mapping the string "Gino" to the number 15 and the string "Nora" to the number 30:

>>> {"Gino": 15, "Nora": 30}

  • To create a dictionary, we use curly brackets { } .
  • Between these curly brackets, we write key-value pairs separated by a comma.
  • For the key-value pairs, we write the key followed by a colon, a space, and the value that corresponds to the key.

💡 Tips:

  • For readability and style purposes, it is recommended to add a space after each comma to separate the key-value pairs.
  • You can create an empty dictionary with an empty pair of curly brackets {}.

Important Rules for Keys

Not every value can be a key in a Python dictionary. Keys have to follow a set of rules:

According to the Python Documentation:

  • Keys have to be unique within one dictionary.

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary).

  • Keys have to be immutable.

Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys.

  • If the key is a tuple, it can only contain strings, numbers or tuples.

Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key.

  • Lists cannot be keys because they are mutable. This is a consequence of the previous rule.

You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().

💡 Note: Values have no specific rules, they can be either mutable or immutable values.

🔸 Dictionaries in Action

Now let's see how we can work with dictionaries in Python. We are going to access, add, modify, and delete key-value pairs.

We will start working with this dictionary, assigned to the ages variable:

>>> ages = {"Gino": 15, "Nora": 30}

Access Values using Keys

If we need to access the value associated with a specific key, we write the name of the variable that references the dictionary followed by square brackets [] and, within square brackets, the key that corresponds to the value:

<variable>[<key>]

This is an example of how we can access the value that corresponds to the string "Gino":

>>> ages = {"Gino": 15, "Nora": 30}
>>> ages["Gino"]
15

Notice that the syntax is very similar to indexing a string, tuple, or list, but now we are using the key as the index instead of an integer.

If we want to access the value that corresponds to "Nora", we would do this:

>>> ages = {"Gino": 15, "Nora": 30}
>>> ages["Nora"]
30

💡 Tip: If you try to access a key that does not exist in the dictionary, you will get a KeyError:

>>> ages = {"Gino": 15, "Nora": 30}
>>> ages["Talina"]
Traceback (most recent call last):
  File "<pyshell#10>", line 1, in <module>
    ages["Talina"]
KeyError: 'Talina'

Add Key-Value Pairs

If a key-value pair doesn't exist in the dictionary, we can add it. To do this, we write the variable that references the dictionary followed by the key within square brackets, an equal sign, and the new value:

This is an example in IDLE:

>>> ages = {"Gino": 15, "Nora": 30}

# Add the key-value pair "Talina": 24
>>> ages["Talina"] = 24

# The dictionary now has this key-value pair
>>> ages
{'Gino': 15, 'Nora': 30, 'Talina': 24}

Modify a Key-Value Pair

To modify the value associated to a specific key, we use the same syntax that we use to add a new key-value pair, but now we will be assigning the new value to an existing key:

>>> ages = {"Gino": 15, "Nora": 30}

# The key "Gino" already exists in the dictionary, so its associated value
# will be updated to 45.
>>> ages["Gino"] = 45

# The value was updated to 45.
>>> ages
{'Gino': 45, 'Nora': 30}

Deleting a Key-Value Pair

To delete a key-value pair, you would use the del keyword followed by the name of the variable that references the dictionary and, within square brackets [], the key of the key-value pair:

This is an example in IDLE:

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}

# Delete the key-value pair "Gino": 15.
>>> del ages["Gino"]

# The key-value pair was deleted.
>>> ages
{'Nora': 30, 'Talina': 45}
🔹 Check if a Key is in a Dictionary

Sometimes, it can be very helpful to check if a key already exists in a dictionary (remember that keys have to be unique).

According to the Python Documentation:

To check whether a single key is in the dictionary, use the in keyword.

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> "Talina" in ages
True
>>> "Gino" in ages
True
>>> "Lulu" in ages
False

The in operator checks the keys, not the values. If we write this:

>>> 15 in ages
False

We are checking if the key 15 is in the dictionary, not the value. This is why the expression evaluates to False.

💡 Tip: You can use the in operator to check if a value is in a dictionary with .values().

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> 30 in ages.values()
True
>>> 10 in ages.values()
False
🔸 Length of a Python Dictionary

The length of a dictionary is the number of key-value pairs it contains. You can check the length of a dictionary with the len() function that we commonly use, just like we check the length of lists, tuples, and strings:

# Two key-value pairs. Length 2.
>>> ages = {"Gino": 15, "Nora": 30}
>>> len(ages)
2

# Three key-value pairs. Length 3.
>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> len(ages)
3
🔹 Iterating over Dictionaries in Python

You can iterate over dictionaries using a for loop. There are various approaches to do this and they are all equally relevant. You should choose the approach that works best for you, depending on what you are trying to accomplish.

First Option - Iterate over the Keys

We can iterate over the keys of a dictionary like this:

for <key> in <dictionary>:
	# Do this

For example:

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> for student in ages:
	print(student)

Gino
Nora
Talina

Second Option - Iterate over the Key-Value Pairs

To do this, we need to use the built-in method .items(), which allows us to iterate over the key-value pairs as tuples of this format (key, value).

for <key-value-pair-as-tuple> in <dictionary>.items():
	# Do this

For example:

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}

>>> for pair in ages.items():
	print(pair)

('Gino', 15)
('Nora', 30)
('Talina', 45)

Third Option - Assign Keys and Values to Individual Variables

With .items() and for loops, you can use the power of a tuple assignment to directly assign the keys and values to individual variables that you can use within the loop:

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}

# Tuple assignment to assign the key to the variable key 
# and the value to the variable value.
>>> for key, value in ages.items():
	print("Key:", key, "; Value:", value)

Key: Gino ; Value: 15
Key: Nora ; Value: 30
Key: Talina ; Value: 45

Fourth Option - Iterate over the Values

You can iterate over the values of a dictionary using the .values() method.

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> for age in ages.values():
	print(age)

15
30
45
🔸 Dictionary Methods

Dictionaries include very helpful built-in methods that can save you time and work to perform common functionality:

.clear()

This method removes all the key-value pairs from the dictionary.

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> ages.clear()
>>> ages
{}

.get(, )

This method returns the value associated with the key. Otherwise, it returns the default value that was provided as the second argument (this second argument is optional).

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> ages.get("Nora")
30
>>> ages.get("Nor", "Not Found")
'Not Found'

If you don't add a second argument, this is equivalent to the previous syntax with square brackets []that you learned:

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> ages["Nora"]
30
>>> ages.get("Nora")
30

.pop(, )

This method removes the key-value pair from the dictionary and returns the value.

>>> ages = {"Gino": 15, "Nora": 30, "Talina": 45}
>>> ages.pop("Talina")
45
>>> ages
{'Gino': 15, 'Nora': 30}

.update()

This method replaces the values of a dictionary with the values of another dictionary only for those keys that exist in both dictionaries.

An example of this would be a dictionary with the original grades of three students (see code below). We only want to replace the grades of the students who took the make-up exam (in this case, only one student took the make-up exam, so the other grades should remain unchanged).

>>> grades = {"Gino": 0, "Nora": 98, "Talina": 99}
>>> new_grades = {"Gino": 67}
>>> grades.update(new_grades)
>>> grades
{'Gino': 67, 'Nora': 98, 'Talina': 99}

By using the .update() method, we could update the value associated with the string "Gino" in the original dictionary since this is the only common key in both dictionaries.

The original value would be replaced by the value associated with this key in the dictionary that was passed as argument to .update().

💡 Tips: To learn more about dictionary methods, I recommend reading this article in the Python Documentation.

🔹 Mini Project - A Frequencies Dictionary

Now you will apply your knowledge by writing a function freq_dict that creates and returns a dictionary with the frequency of each element of a list, string, or tuple (the number of times the element appears). The elements will be the keys and the frequencies will be the values.

Code

We will be writing the function step-by-step to see the logic behind each line of code.

  • Step 1: The first thing that we need to do is to write the function header. Notice that this function only takes one argument, the list, string or tuple, which we call data.
def freq_dict(data):
  • Step 2: Then, we need to create an empty dictionary that will map each element of the list, string, or tuple to its corresponding frequency.
def freq_dict(data):
	freq = {}
  • Step 3: Then, we need to iterate over the list, string, or tuple to determine what to do with each element.
def freq_dict(data):
	freq = {}
	for elem in data: 
  • Step 4: If the element has already been included in the dictionary, then the element appears more than once and we need to add 1 to its current frequency. Else, if the element is not in the dictionary already, it's the first time it appears and its initial value should be 1.
def freq_dict(data):
	freq = {}
	for elem in data:
		if elem in freq:
			freq[elem] += 1
		else:
			freq[elem] = 1
  • Step 5: Finally, we need to return the dictionary.
def freq_dict(data):
	freq = {}
	for elem in data:
		if elem in freq:
			freq[elem] += 1
		else:
			freq[elem] = 1
	return freq

🔔 Important: Since we are assigning the elements as the keys of the dictionary, they have to be of an immutable data type.

Examples

Here we have an example of the use of this function. Notice how the dictionary maps each character of the string to how many times it occurs.

>>> def freq_dict(data):
	freq = {}
	for elem in data:
		if elem in freq:
			freq[elem] += 1
		else:
			freq[elem] = 1
	return freq

>>> freq_dict("Hello, how are you?")
{'H': 1, 'e': 2, 'l': 2, 'o': 3, ',': 1, ' ': 3, 'h': 1, 'w': 1, 'a': 1, 'r': 1, 'y': 1, 'u': 1, '?': 1}

This is another example applied to a list of integers:

>>> def freq_dict(data):
	freq = {}
	for elem in data:
		if elem in freq:
			freq[elem] += 1
		else:
			freq[elem] = 1
	return freq

>>> freq_dict([5, 2, 6, 2, 6, 5, 2, 2, 2])
{5: 2, 2: 5, 6: 2}

Great Work! Now we have the final function.

🎓 In Summary
  • Dictionary are built-in data types in Python that associate (map) keys to values, forming key-value pairs.
  • You can access a value with its corresponding key.
  • Keys have to be of an immutable data type.
  • You can access, add, modify, and delete key-value pairs.
  • Dictionaries offer a wide variety of methods that can help you perform common functionality.

Originally published by Estefania Cassingena Navone at https://www.freecodecamp.org

JavaScript vs Python: Will Python Replace JavaScript popularity by 2020?

JavaScript vs Python: Will Python Replace JavaScript popularity by 2020?

JavaScript is currently the most commonly used programming language but now Python is dishing out some stiff competition. Python has been steadily increasing in popularity so much so that it is now the fastest-growing programming language. So will Python Replace JavaScript popularity by 2020?

This is the Clash of the Titans!!

And no…I am not talking about the Hollywood movie (don’t bother watching it…it’s horrible!). I am talking about JavaScript and Python, two of the most popular programming languages in existence today.

JavaScript is currently the most commonly used programming language (and has been for quite some time!) but now Python is dishing out some stiff competition. Python has been steadily increasing in popularity so much so that it is now the fastest-growing programming language. So now the question is…Will Python Replace JavaScript popularity by 2020?

To understand the above question correctly, it is important to know more about JavaScript and Python as well as the reasons for their popularity. So let’s start with JavaScript first!

Why is JavaScript so popular?

JavaScript is a high-level, interpreted programming language that is most popular as a scripting language for Web pages. This means that if a web page is not just sitting there and displaying static information, then JavaScript is probably behind that. And that’s not all, there are even advanced versions of the language such as Node.js which is used for server-side scripting.

JavaScript is an extremely popular language. And if my word doesn’t convince you, here are the facts!!!

According to StackOverflow Developer Survey Results 2019, JavaScript is the most commonly used programming language, used by 69.7 % of professional developers. And this is a title it has claimed the past seven years in a row.

In addition to that, the most commonly used Web Frameworks are jQuery, Angular.js and React.js (All of which incidentally use JavaScript). Now if that doesn’t demonstrate JavaScript’s popularity, what does?!

Image Source: Stackoverflow

So now the question arises…Why is JavaScript so popular?

Well, some of the reasons for that are:

  • JavaScript is used both on the client-side and the server-side. This means that it runs practically everywhere from browsers to powerful servers. This gives it an edge over other languages that are not so versatile.
  • JavaScript implements multiple paradigms ranging from OOP to procedural. This allows developers the freedom to experiment as they want.
  • JavaScript has a large community of enthusiasts that actively back the language. Without this, it would have been tough for JavaScript to establish the number one position it has.
Can Python Replace JavaScript in Popularity?

Python is an interpreted, general-purpose programming language that has multiple uses ranging from web applications to data analysis. This means that Python can be seen in complex websites such as YouTube or Instagram, in cloud computing projects such as OpenStack, in Machine Learning, etc. (basically everywhere!)

Python has been steadily increasing in popularity so much so that it is the fastest-growing major programming language today according to StackOverflow Developer Survey Results 2019.

This is further demonstrated by this Google Trends chart showing the growth of Python as compared to JavaScript over the last 5 years:

As shown in the above data, Python recorded increased search interest as compared to JavaScript for the first time around November 2017 and it has maintained its lead ever since. This shows remarkable growth in Python as compared to 5 years ago.

In fact, Stack Overflow created a model to forecast its future traffic based on a model called STL and guess what…the prediction is that Python could potentially stay in the lead against JavaScript till 2020 at the least.

Image Source : Stackoverflow

All these trends indicate that Python is extremely popular and getting even more popular with time. Some of the reasons for this incredible performance of Python are given as follows:

  • Python is Easy To Use
    No one likes excessively complicated things and that’s one of the reasons for the growing popularity of Python. It is simple with an easily readable syntax and that makes it well loved by both seasoned developers and experimental students. In addition to this, Python is also supremely efficient. It allows developers to complete more work using fewer lines of code. With all these advantages, what’s not to love?!!
  • Python has a Supportive Community
    Python has been around since 1990 and that is ample time to create a supportive community. Because of this support, Python learners can easily improve their knowledge, which only leads to increasing popularity. And that’s not all! There are many resources available online to promote Python, ranging from official documentation to YouTube tutorials that are a big help for learners.
  • Python has multiple Libraries and Frameworks
    Python is already quite popular and consequently, it has hundreds of different libraries and frameworks that can be used by developers. These libraries and frameworks are really useful in saving time which in turn makes Python even more popular. Some of the popular libraries of Python are NumPy and SciPy for scientific computing, Django for web development, BeautifulSoup for XML and HTML parsing, scikit-learn for machine learning applications, nltk for natural language processing, etc.
So What’s the Conclusion?

While JavaScript is currently the most popular programming language, Python could soon outstrip it of this title based on its incredible growth rate. So it is entirely possible that Python could be the most popular programming language by 2020.

However, this will merely impact the relative popularity of these two languages and not specify which among them is the better language. That choice is entirely subjective and may depend on multiple factors such as project requirements, scalability, ease of learning as well as the future growth prospects.