6 Free Python Programming Courses for Beginners in 2020

6 Free Python Programming Courses for Beginners in 2020

If you decide to learn Python online for FREE and looking for some awesome resources then you have come to the right place. Free Python courses online. Learn python programming from institutions like MIT, Microsoft and Georgia Tech: Introduction To Python Programming, Deep Learning Prerequisites: The Numpy Stack in Python, Python Core and Advanced, Master Python Complete Course, 2020 learning Python3.8 from beginner to the master, Programming with Python All in One

There is no doubt that Python is currently the world’s #1 programming language and the biggest advantage of that is it’s bringing more and more people into the programming world.

In recent years, I have seen more people learning Python than any other languages, yes, not even JavaScript. Many of them learning Python to explore some awesome Data Science and Machine learning libraries provided by Python.

Some people are also learning Python for Web Development and there are still many developers who are learning Python for scripting and automating trivial tasks.

It not just become one more tool in your arsenal but also allows you to explore areas like Data Science and Machine learning, which is available or easy with Java or any other mainstream programming language like C++ or JavaScript.

It’s always a good decision to learn Python, so don’t worry if you are a beginner programmer or C++/Java expert trying to learn Python. Any time and money invested in learning Python will go a long way and pay rich dividends much like learning UNIX, SQL, and Data Structure and Algorithms.

Some people like to start with free resources which are not bad because it encourages you to explore. Also free doesn’t mean garbage or bad, even though they are not as comprehensive as some of the paid resource they are still better with many others.

In this article, you will find free online courses in python programming, but not only will you find one, but you will also find 6 more free courses on Python! I am going to share some of the best online courses to learn Python in 2020

They are high quality courses with more than 4 star rating (from 0 to 5 stars), that means if you are starting your career with the python programming language, these are the best courses that will take you step-by-step , to start and learn from scratch the fundamentals about this language that so professional and useful has been in recent years.

6 Free Python Programming Courses For Beginners in 2020

1. Introduction To Python Programming

A Quick and Easy Intro into Python Programming

Description

Do you want to become a programmer?

Or is it that Python interests you?

If you need a quick brush-up, or learning Python for the first time, you've come to the right place!

Let's get started learning one of the most easiest coding languages out there right now. There's no need to fret if you haven't coded before. By the time you finish this course, you'll be a pro at Python!

Python is a great and friendly language to use and learn. It fun, and can be adapted to both small and large projects. Python will cut your development time greatly and overall, its much faster to write Python than other languages. This course will be a quick way to understand all the major concepts of Python programming. You'll be a whiz in no time.

This course is a one-stop-shop for everything you'll need to know to get started with Python, along with a few incentives. We'll begin with the basics of Python, learning about strings, variables, and getting to know the data types. We'll soon move on to the loops and conditions in Python. Afterwards, we'll discuss a bit of file manipulation and functions. By then, you'll know all the basics of Python.

What you'll learn

  • Program Python
  • Know the basics of Python
  • Write their own scripts, and functinos

I hope you're excited to dive into the World of Python with this course. Well, what are you waiting for? Let's get started!

2. Deep Learning Prerequisites: The Numpy Stack in Python

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence

Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.

One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.

Even if I write the code in full, if you don’t know Numpy, then it’s still very hard to read.

This course is designed to remove that obstacle - to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.

So what are those things?

Numpy. This forms the basis for everything else. The central object in Numpy is the Numpy array, on which you can do various operations.

The key is that a Numpy array isn’t just a regular array you’d see in a language like Java or C++, but instead is like a mathematical object like a vector or a matrix.

That means you can do vector and matrix operations like addition, subtraction, and multiplication.

The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list.

Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems.

Pandas. Pandas is great because it does a lot of things under the hood, which makes your life easier because you then don’t need to code those things manually.

Pandas makes working with datasets a lot like R, if you’re familiar with R.

The central object in R and Pandas is the DataFrame.

We’ll look at how much easier it is to load a dataset using Pandas vs. trying to do it manually.

Then we’ll look at some dataframe operations, like filtering by column, filtering by row, the apply function, and joins, which look a lot like SQL joins.

So if you have an SQL background and you like working with tables then Pandas will be a great next thing to learn about.

Since Pandas teaches us how to load data, the next step will be looking at the data. For that we will use Matplotlib.

In this section we’ll go over some common plots, namely the line chart, scatter plot, and histogram.

We’ll also look at how to show images using Matplotlib.

99% of the time, you’ll be using some form of the above plots.

Scipy.

I like to think of Scipy as an addon library to Numpy.

Whereas Numpy provides basic building blocks, like vectors, matrices, and operations on them, Scipy uses those general building blocks to do specific things.

For example, Scipy can do many common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

It has signal processing tools so it can do things like convolution and the Fourier transform.

In sum:

If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.

What you'll learn

  • Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
  • Understand and code using the Numpy stack
  • Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
  • Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!

3. Python Core and Advanced

Master the fundamentals of Python in easy steps

Description

Whether you are a College student learning the fundamentals of Python or a Data Science expert using python to analyze your data or a Web Developer using python frameworks like DJango or a Experienced python developer who wants to fill in the gaps , this course will help you accomplish your goals.

  • Master the Features of Python Language
  • Install Python Virtual Machine and the Eclipse IDE(PyDev)
  • Execute your first python program
  • Learn various simple types as well as collection types
  • Define logic using conditional statements ,looping constructs
  • Use the different types of operators
  • See the input and output functions in action
  • Pass Command line arguments
  • Create and use functions , Lambdas Decorators and Generators
  • Learn what Object Oriented Programming is the four OOPs principles
  • Implement inheritance, abstraction, polymorphism and encapsulation
  • Understand interfaces, their importance, and their uses
  • Use abstract classes and interfaces to implement abstraction
  • Spawn of multiple threads
  • Handle Exceptions
  • Read and Write files using the Files API
  • Do pattern matching using Regular expressions
  • Deal with data and time
  • All in simple steps

What are the requirements?

  • Python,Eclipse IDE(Installation is covered in easy setup section)

What you'll learn

  • Master the Features of Python Language
  • Install Python Virtual Machine and the Eclipse IDE(PyDev)
  • Execute your first python program
  • Learn various simple types as well as collection types
  • Define logic using conditional statements ,looping constructs
  • Use the different types of operators
  • See the input and output functions in action
  • Pass Command line arguments
  • Create and use functions , Lambdas Decorators and Generators
  • Learn what Object Oriented Programming is the four OOPs principles
  • Implement inheritance, abstraction, polymorphism and encapsulation
  • Understand interfaces, their importance, and their uses
  • Use abstract classes and interfaces to implement abstraction
  • Handle Exceptions
  • Read and Write files using the Files API
  • Do pattern matching using Regular expressions
  • Deal with data and time
  • All in simple steps

4. Master Python Complete Course

Python for Data Science and Machine Learning

Description

This course is part of Data science master course.

This course will teach you from Python basics to advanced concepts in a practical manner, with Hands on exercises covered as well.

This Python tutorial for data science will kick-start your learning of Python concepts needed for data science, as well as programming in general. Python is required for data science because, Python programming is a versatile language commonly preferred by data scientists and big tech giant companies around the world, from startups to behemoths.

Whether you are a newbie in data science or already know about basic python for data science, this course is for you. In this python certification course, you will Learn Python programming in a practical manner with hands on coding assignments at the end of each section.

What you'll learn

  • Get a complete understanding of Python from the beginning
  • Understand how to use the Jupyter Notebook
  • Master basics like variables, functions, tuples etc
  • Get hands-on with carefully designed coding assignments
  • Learn to use Object Oriented Programming with classes
  • Special Features and functions
  • Loops and condition formatting

5. 2020 learning python3.8 from beginner to the master

If you to become a Python 3 Developer , Learn Web Development, Machine Learning ,this course is right for you.

Description

If you to become a Python 3 Developer , Learn Web Development, Machine Learning ,this course is right for you.

This course will give you everything about python

Learn Python from scratch, get hired, and have fun along the way with the most modern, up-to-date Python course on Udemy. This course is focused on efficiency: never spend time on confusing, out of date, incomplete Python tutorials anymore.

What you'll learn

  • Become a professional Python Developer and get hired
  • Learn Object Oriented Programming
  • Learn Machine Learning with Python
  • Learn Data Science - Analyze and Visualize Data
  • Use Python to process: Images, CSVs, PDFs, and other Files
  • Build real world Python projects you can show off
  • Master modern Python 3 fundamentals as well as advanced topics
  • Learn Function Programming
  • Learn how to use Python in Web Development
  • Build a Machine Learning Model

6. Programming with Python All in One

Develop problem solving skills

Description

Programming is one aspect of computer science and software engineering. The primary goal of this course is to build a solid foundation of programming knowledge and skills. With what learned in this course, the students should find it is easier to learn more advanced concepts in computer science.

Not everyone will be or want to be a software engineer, however, this course can help them realize how a problem can be solved by using computer program; how Python can help scientists and engineers improve their productivity.

Believe or not, software developers usually join a product development from the very beginning to the very end. (while this is not true for mechanical engineers or electrical engineers). Most importantly, sometimes, updating software is the better solution to fix or improve a product.

The teaching can be viewed as a vehicle to help students develop problem solving skills. This course will use some mathematics or physics, but it is not a math or physics course, and we use them in programming to re-enforce the learning in those fields.

At the end of this course, It would be a great achievement for the students and me when they find they are able to learn so

What you'll learn

  • Basic programming skills
  • Computer science concept
  • Python programming language
  • Problem solving - put everything together with software
Conclusion

That’s on this list of free Python Programming courses for beginners. As I have said before, Python is an awesome, multipurpose programming language and every programmer should learn it.

You can automate things using Python by writing scripts, can do object-oriented programming and can even explore the world of web development, data science and machine learning using awesome Python libraries and modules.

Thanks for Reading

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

What's New In Python 3.8? Python 3.8 New Features

What's New In Python 3.8? Python 3.8 New Features

In this video 'What's New Features in Python 3.8?' covers the new features in Python 3.8 added to the new release of Python. What's New In Python 3.8? Python 3.8 New Features, Python 3.8 Tutorial

This video on 'What's New In Python 3.8?' covers the new features in python 3.8 added to the new release of Python and other language changes. Following are the topics discussed:
0:52 - New Features
08:33 - New Modules
09:05 - Other Language Changes

Learn Python from Zero - Full Fundamental Course for Beginners

Learn Python from Zero - Full Fundamental Course for Beginners

Learn Python from Zero - Full Fundamental Course for Beginners: This course will provides you a full introduction into all of the core concepts in python like data types, reserved words etc. View this Python tutorial for beginners to learn Python programming from zero. Every topic explained in detail to make this best Python tutorial for beginners.

Learn Python from Zero - Full Fundamental Course for Beginners

This course will provides you a full introduction into all of the core concepts in python like data types, reserved words etc. Follow along with the videos and you will become a python programmer in less time and you will entered into Python world.
View this Python tutorial for beginners to learn Python programming from zero. Every topic explained in detail to make this best Python tutorial for beginners.

Contents:

  1. (00:00:00) What is Python and Father of Python
  2. (00:19:30) Easiness of Python when compared with Other Languages
  3. (00:45:35) Why the name 'Python'
  4. (00:53:29) Python as All Rounder
  5. (01:04:28) Where we can use Python
  6. (01:11:26) Features of Python: Part-1
  7. (01:25:30) Features of Python: Part-2
  8. (01:44:47) Features of Python: Part-3
  9. (01:58:48) Features of Python: Part-4
  10. (02:11:58) Features of Python Summary
  11. (02:19:08) Limitations and Flavors of Python
  12. (02:37:13) Python Versions
  13. (02:51:05) Python Identifiers
  14. (03:13:26) Python Reserved Words
  15. (03:26:56) Data Types Introduction
  16. (03:42:00) Data Types: int data type
  17. (04:04:16) Data Types: Base Conversion Functions
  18. (04:12:59) Data Types: float data type
  19. (04:25:22) Data Types: complex data type
  20. (04:38:47) Data Types: bool data type
  21. (04:46:55) str data type representations by using single,double and triple quotes
  22. (05:07:02) Data Types: str data type - positive and negative index
  23. (05:14:16) Data Types: str data type - Slice Operator
  24. (05:30:45) Data Types: str data type - Slice Operator Applications
  25. (05:43:26) Data Types: + and * operators for str data type
  26. (05:56:29) Type Casting: introduction and int() function
  27. (06:10:00) Type Casting: float() and complex() functions
  28. (06:32:34) Type Casting: bool() and str() functions
  29. (06:44:57) Type Casting: Summary
  30. (06:53:39) Fundamental Data Types vs Immutability : Meaning Of Immutability
  31. (07:08:21) Fundamental Data Types vs Immutability : Need Of Immutability
  32. (07:29:06) Immutability vs Mutability
  33. (07:49:26) Python Data Types: List data type
  34. (08:14:00) Python Data Types: Tuple data type
  35. (08:35:53) Python Data Types: Set data type
  36. (08:56:58) Python Data Types: FrozenSet
  37. (09:07:39) Python Data Types: Dict
  38. (09:24:18) Python Data Types: range
  39. (09:48:35) Python Data Types: bytes and bytearray
  40. (10:05:25) Python Data Types Summary
  41. (10:25:13)None Data Type
  42. (10:37:46)Escape Characters,Comments and Constants