Python self-study tutorial | 30000 word explanation every important knowledge for you

Contents of this article:

What is Python?
What is Python used for?
How do I install Python?
Why Python?
R with Python
The best way to learn Python
What is a top-level Python IDE
Which is the best IDE for Python?
How do I power Jupyter Notebook?
Features in Python Notebook (Jupyter)
annotation
variable
The operator
Arithmetic operator
The assignment operator
Comparison operator
Logical operator
Identity operator
Member operator
Bitwise operators
lambda
An array of
The class
heritage
The iterator
The scope of
Module,
The date of
JSON format
Regular expression
Picture in picture
Try, except
User input
String format
Python data types
Process control Statement
File processing
Create functions in Python
Learn simple commands by using Python as a calculator

1. What is Python?

Python is a high-level, object-oriented programming language. Most beginners in the development world love Python because its simplicity and versatility make it one of the first languages to learn. It also enjoys strong community support, which is growing by the day. In this Python tutorial for beginners, we’ll learn the basics of Python as a programming language and learn how to get started using it. We’ll see how to download and install Python and how to start coding using a popular IDE. We will also discuss jupyter features in detail.

2. What is Python used for?

When you use Browsing, watching videos on YouTube every day, or listening to your favorite music, remember that all of them use Python for their programming needs. Python has many uses in applications, platforms, and services. Let’s talk about some of that here.

1 – Web development

A large number of pre-built Python libraries make Web development much easier. It takes less time to write Python code because of its concise syntax. This facilitates rapid prototyping and thus accelerates ROI on commercial products. The built-in testing framework helps deliver error-free code. A large number of well-supported frameworks can help speed up implementation without compromising solution performance.

2 – the Internet of things

For simplicity, let’s think of the Internet of Things as “the physical object that connects an embedded system to the Internet.” It plays a vital role in projects involving big data, machine learning, data analysis, wireless data networks and network physical systems. The iot project also involves real-time analytics. Given the above application areas, a programming language should be a bold choice. This is where Python checks all the checkboxes. In addition, Python is scalable, extensible, portable, and embeddable. This makes Python system independent and allows it to accommodate multiple single-board computers, regardless of the operating system or architecture.

Furthermore, Python is an excellent choice for managing and organizing complex data. It is particularly useful for internet-of-things systems with large amounts of data. Another reason Python is an ideal programming language for IoT applications is its close association with scientific computing.

3- Machine learning

Machine learning offers an entirely new approach to problem solving. Python is at the forefront of machine learning and data science for the following reasons:

Extensive open source library support
Efficient and accurate syntax
Easy integration with other programming languages
Python’s entry point is low
Extensible to different operating systems and architectures

3. How to install Python?

If you are a Windows user and have installed Python using the Anaconda distribution available at Anaconda.org, you need to go to “Download Anaconda” and Download the latest version for Python 3.6.

Once you download this file, it’s a straightforward process and will install Python for you. The next step is to start the IDE to start coding in Python.

Therefore, once Python is installed, you can have multiple ides or text editors at the top of your Python installation. For text editors, something Sublime or Notepad ++ can be used. If you prefer an integrated development environment, you can use Jupyter. In addition, there are other options such as Wingware, Komodo, Pycharm and Spyder.

There are multiple packages available in Python. Some libraries are numpy, PANDAS, seaborn for visualization, and calculations and statistics via SCIpy. The others are XLRB, OpenPyXL, Matplotlib and IO.

4. Why Python?

Python has become the programming language of choice for data science and machine learning applications. Of course, Python has its advantages. It is fast compared to other programming languages, even R.

It is easy to say that Python is a fast compiler. Because it is a Java-based programming language, you can extend its applications beyond analytical research, analytical modeling, and statistical modeling. You will be able to create Web applications using Python and integrate those Web applications directly into the analysis model in the background.

Python is also easy to integrate with other platforms and other programming languages. IT has a common object-oriented programming architecture in which existing IT developers, IT analysts, and IT programmers find IT easy to transition into the analytics space. Because the coding structure in Python is an object-oriented programming architecture, it has excellent documentation support.

7 reasons to Use Python

Readable and maintainable code
Multiple programming paradigms
Compatibility with major platforms and systems
A powerful standard library
Open source frameworks and tools
Simplify software development
Test-driven development

5. R vs Python?

R is developed for statistical analysis applications; On the other hand; Python was developed as a general-purpose programming language. Both are essential for anyone working with large data sets, solving machine learning problems, and creating complex data visualizations.

Let’s look at the differences between R and Python.

Python was developed to provide a way to write scripts to automate some of the routine tasks you encounter every day. Over time, however, Python has evolved and become very useful in many other areas, especially data analysis.

R is a programming language and open source software for graphics and data analysis. It has the advantage of running on any computer system and is used by data miners and statisticians to represent and analyze their data. Python and R for data analysis programming

Deciding whether to use Python or R for data analysis is a common challenge for data scientists. Although R was developed purely for statisticians, enabling it to describe analysis as a specific advantage of visualizing data, Python stands out for its general-purpose features and very regular syntax. Based on these differences, it is necessary to compare the two languages to determine which one best suits them. Python programming language

The Python programming language is inspired by Modula-3, ABC, and C
— Python focuses on code readability and productivity
– Easier to develop code and debug because of its ease of use and simple syntax
– Code indentation affects its meaning
– All functions are usually written in the same style
– Python is very flexible and can also be used for Web scripting.
— It has a relatively gradual and low learning curve because of its focus on simplicity and readability
— Suitable for people starting programming
– Its Package index is called PyPi. Its Python software repository with libraries. Although users can choose to contribute to Pypi. Difficult in practice.
– RPy2 is a library that can be used in Python to run R code. Used to provide lower levels from Python to R.
— In 2014, Dice Tech salary Survey found that experienced professionals made an average salary of $94,139
– Mainly used when the analyzed data needs to be integrated with Web applications or statistics are used for database production
— Despite the improvements, the ability to process data in the past remains a challenge due to the early days of its data processing package
— You must use tools like PANDAS and NumPy to use it for data analysis
Available ides include Spyder, IPython Notebook.

R programming

— S programming language fires R.
— Emphasis on user-friendly data analysis methods, graphical models and statistics.
The statistical model is a bit difficult to use because it is written in only a few lines.
— R style sheets exist, although they are rarely used
There are many ways to represent or write the same block of functionality.
— Makes it easy to use complex R formulas. Many statistical models and tests for it.
— When it comes to learning the basics, the learning curve starts off steep. But later it becomes much easier to learn advanced topics
— Not hard for professional programmers.
– Comprehensive R archive network (CRAN). CRAN is an R repository software package that is easily provided by the user.
– The rPython package in R is used to run Python code. Call a Python method or function and get the data.
— In 2014, Dice Tech’s salary survey found that experienced professionals made an average salary of $115,531
– Used for analysis that requires independent computing or an independent server.
— Easier for critical tasks for beginners. Write statistical methods with very few lines of code.
— Great for processing data in large packages. Use of available tests and formulas.
-R does not require other packages for basic analysis. For large data sets, only packages like DPlyr are needed.
— Use the R Studio IDE

Python advantages –

IPython Notebook facilitates and simplifies the use of Python and data. This is because you can share your laptop with others without having to tell them to install anything. This reduces the overhead of code organization, thus allowing people to focus on doing other useful work.
Given that it is a general-purpose language, it is intuitive and simple. It flattens the data scientist’s learning curve so he can improve his programming skills. Python also has a built-in testing framework that encourages improved testing coverage, which in turn ensures code reliability and reusability.
It is a versatile programming language that brings together people from different backgrounds (statisticians and programmers).

R advantages –

R provides clear data visualization so that data can be effectively designed and understood. Ggvis, GGploT2, rChart, and googleVis are examples of their visualization packages.
R has a vibrant community and a broad ecosystem of desirable software packages. The package is available from Github, BioConductor and CRAN.
It was developed by statisticians for statisticians. As a result, they can exchange concepts and ideas through R packages and code.

Both languages have their own advantages, and you can choose the one that can solve your problems according to your personal preference.

6. The best way to learn Python?

Ease of learning is a major reason for Python’s popularity. It is a simple and untyped programming language, and therefore easy to learn. The time it takes to learn the language depends on the level you want to reach with Python.

Similarly, the learning curve can get shorter or longer depending on an individual’s ability. It takes six to eight weeks for a person to learn the basics of Python. This will include learning syntax, keywords, functions and classes, data types, basic coding and exception handling

Not all Python professionals need advanced Python skills. Depending on the nature of your job, you can learn skills such as database programming, socket programming, multithreading, synchronization techniques, etc. Highly complex Python skills include concepts of data analysis, hands-on experience with required libraries, image processing, and more. Each specialized skill takes about a week to master.

7. What are the most popular Python ides?

There are seven top-level ides for Python

spader
YaoXiang
Tony
atomic
Peter zhu
komodo
Wing piece

8. Which is the best IDE for Python?

Jupyter is the best IDE for Python and one of the most widely used ides for Python. Let’s look at how to set up the Jupyter Notebook. Also, let’s take a look at what Jupyter Notebook does.

9. How to power the Jupyter Notebook

Here are the instructions for starting Jupyter laptops:

Open the Anaconda prompt. This option is available if you completed the installation through the Anaconda installer.
When you open the Anaconda command prompt, you will see the default path assigned to you. This is the username of the computer you are using.
Add the folder path to the default path where you want to open your notebook (for example, CD Desktop→ CD Python)
After setting the path, run the following commandjupyter notebookaddJupyterThe notebook
Press enter. This will open the laptop on the localhost (that is, the system)
The path described in the Anaconda tip will now appear on the Jupyter Notebook home page
The next step is to open a new Python Notebook. This is the environment in which you perform all your coding. You can rename the new notebook (without a title) to the name you want, and then click Rename.

Keep the Anaconda prompt active when using Jupyter locally. This is the power source you use to power up your Jupyter laptop. If the Anaconda prompt is turned off, Python is no longer running on the system and the kernel is disconnected.

10. Functions in Python Notebook (Jupyter)

There are multiple options on the toolbar, i.eFiles, editing, views, inserts, cells, kernels, widgets and help. Let’s take a look at some of these features and functions.

File options

Save and checkpoint – Setting checkpoints is an interesting concept. This file is automatically saved periodically, and by setting checkpoints, you can skip some of the automatic saving to set checkpoints.

If you made a mistake in the past few minutes or hours, this will help. You can always revert to a more stable checkpoint and continue executing code from there, rather than starting from scratch.

Download as – There are several ways to download Jupyter Notebook. The first is Classic Notebook, which is the IPynb extension. Before it was called jupyter Notebook, it was an Ipython notebook. That’s why the extension. Then you have the.py extension. Save a file with the.py extension that you can import into another IDE for easy use.

** Closes and pauses – ** This command closes all kernels running at this particular point in time and pauses all processes.

Editing options

It includes cutting cells, copying cells, pasting, deleting, splitting cells, moving up, moving down and so on. So, what is a cell? A cell is nothing more than code that you type in a dialog box that appears in a window. This is a cell where you can type code – at run time, each cell gives you output.

To run this particular code, you can click on a specific option, that is, run the cell or the shortcut is Shift + Enter. To browse the other shortcut options available, you can get them under Help in Keyboard Shortcuts. You can cut these cells and paste them later. You can merge, split, and so on. These are simple projects.

Check the options

You can also toggle title, toolbar, and line number.

Insert options

These are basic insert operations. You can insert a cell above or below as the code requires.

Unit options

If you click Run All, it runs all cells that exist in the entire workbook. When you click Run All, it runs all the cells above the selected cell. Similarly, if you click Run All files below, it will run all cells below the selected cell. Different types of cells, namely code, price reduction and raw conversion files.

One exciting feature that we’ll be using a lot in our code files is the Markdown file. The price reduction simply converts what you type into a text message in a cell. Cells that you convert to Markdown will not run or be treated as a line of code. When you run this cell, it is treated as a text field and the output is text. No calculations are performed on this cell.

The help option

Here, you can see the common libraries and packages available.

You can click on these options, which will open a guide or reference book where you can view the various methods available in your selected package. With Jupyter, you can try a variety of other options.

11. The annotation

Comments are useful for describing program logic and the purpose of individual modules. Other people besides the coder can understand the code by looking at meaningful comments.

When testing a program, comments can be used to disable portions of code and exclude them from execution.

Python comments begin with the # symbol. Comments can be their own, or they can start after the code in the same line. Both cases are illustrated below.

# comment in separate line  
x = x * 5


x = x * 5   # multiply by 5; comment in same line


# this function is going to divide two variables  
# the second value should not be zero  
# if it is zero, error is thrown  
def divide(first, second):
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There is no other way to mention multi-line comments. # can be added to different lines.

When # is in quotes, it is part of the string, not a comment.

For example,

str = "# hello world"
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12. The variable

Python variables are containers that hold data. These data values assigned to variables can be changed at a later stage.

The first value assignment to a variable creates the variable. There is no explicit variable declaration.

numOfBoxes = 7  
ownerName = "Karthik"  
print("numOfBoxes= ", numOfBoxes)  
print("ownerName= ", ownerName)
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In the example above, you create two variables with numeric and string data. The print statement is used to display these variables.

Variable names follow the following conventions.

Variable names begin with a letter or underscore character
Alphanumeric and underscore are allowed in the remaining names
Variable names are case sensitive

# valid names  
numOfBoxes = 7  
_num_of_boxes = 10           # this is a different variable than numOfBoxes  
_NUM_OF_BOXES = 15   # a different variable as names are case sensitive

ownerName = "Karthik"  
ownerName2 = "Charan" # different, valid variable



# invalid names  
2ownerName = "David"    # cannot start with number.    
                                          # Only letter or underscore in the beginning  
owner-name = "Ram"       # no hypen  
owner name = "Krish"      # no space allowed  
                                          # only alpha numeric and underscore
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Here we list some possible variable names and some invalid names.

In Python, you can assign multiple values to multiple variables. Assign the same value to multiple variables at the same time. Look at this example.

# different values assigned to many variables  
length, width, depth = 5.8.7  
print(length)  
print(width)  
print(depth)

# same value assigned to many variables  
length = width = depth = 5  
print(length)  
print(width)  
print(depth)
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13. The operator

Operators help with variables and values. For example, if we can use two numeric variables, we can add or subtract them, multiply or divide them. These operations change the value and give the new value.

Python supports the following types of operators.

Arithmetic operator
The assignment operator
Comparison operator
Logical operator
Identity operator
Member operator
Bitwise operators

14. Arithmetic operators

Use arithmetic operators to perform mathematical operations such as addition and subtraction. Let’s go through them.

a = 10  
b = 6  

print (a + b)    # addition  
print (a - b)     # subtraction  
print (a * b)     # multiplication  
print (a / b)      # division  
print (a % b)    # modulus  
print (a ** b)   # exponentiation  
print (a // b)    # floor division
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All operations are easy to understand. Modular operations return the remainder divided by two numbers (in our example, 4 is the hint). Similarly, base division is integer division, which returns the result of division as an integer (10 // 6 = 1).

15. Assignment operators

You can use the assignment operator to assign a value or variable content to another variable. The right-hand side can also be an expression (assign c in the following example). Here are some examples.

a = 7  # assign value to a  
b = a  # assign value of a into b  
c = a + b -2  # calculate an expression and place result into c  

b += 2   # equivalent to b = b + 2  
b -= 2    # equivalent to b = b - 2  
b *= 2   # equivalent to b = b * 2  
b /= 2    # equivalent to b = b / 2  
b %= 2   # equivalent to b = b % 2  
b //= 2   # equivalent to b = b // 2  
b **= 2  # equivalent to b = b ** 2  
b &= 2   # equivalent to b = b & 2  
b |= 2    # equivalent to b = b | 2  
b ^= 2    # equivalent to b = b ^ 2  
b >>= 2  # equivalent to b = b >> 2  
b <<= 2  # equivalent to b = b << 2
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The last five operators are explained later in the section below.

16. Comparison operators

Compare two values, resulting in a Boolean value True or False. Used in if and loop statements to make decisions.

a = 3  
b = 7  

print(a = = b)  # true if a and b are equal  
print(a ! = b)  # true if a and b are not equal  
print(a > b)  # true if a is greater than b  
print(a < b)  # true if a is less than b  
print(a > = b)  # true if a is greater than or equal to b  
print(a < = b)  # true if a is less than or equal to b
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Let’s look at an example of a practical comparison. In this case, we’re checking to see if A is less than B. If this condition is true, a statement is executed. Otherwise, other statements are executed.

a = 3  
b = 7  

if(a < b):  
    print("first number is less than second one");  
else:  
    print("first number is greater than or equal to second one")
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17. Logical operators

You can combine two or more comparison operations using logical operators. These logical operators return Boolean values.

a = 5  
b = 8  

# True if both conditions are true  
print(a > 3 and a < 7)  

# True if one condition is true  
print(a > 6 or b < 7)  

# True if given condition is false (inverse of given condition)  
print(not(a > 3))
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