What is the fastest way to learn Python with real-time examples?

In this article, we will discuss the fastest way to learn Python with real-time examples.

Python may be learned fast. How quickly you learn depends on what you want to do with it and how much time you have available to study and practice Python on a regular basis.

Below are the tips to learn python fastly.

Learn the Following Python Fundamentals

At the very least, you must cover the fundamentals. You will struggle to work with difficult challenges, projects, or use cases if you do not understand them. Python fundamentals include −

• Variables and types

• Basic operators

• Lists, dictionaries, and sets

• String formatting

• Basic string operations

• Conditions

• Loops

• Functions

• List comprehensions

• Classes and objects

After getting a basic knowledge about these fundamentals you can move further for more advanced concepts.

Set a Goal for your Study

Set a study goal before you begin studying Python. When you keep your goal in mind, the problems you meet when you begin learning will be easier to accomplish.

You'll also know what learning material to focus on or skim through in accordance with your goals. For example, if you want to learn Python for data analysis, you'll need to complete exercises, write functions, and understand Python libraries that help with data analysis.

Below are the typical Python goals that may apply to you −

• Data analysis

• Data science and machine learning

• Website development

• Mobile apps

• Work automation

Choose the Best Resource/Resources for Fast Learning of Python

Python resources are classified into three types −

• Interactive resources

• Non-interactive resources, and

• Video resources.

• In-person classes are also available but are not discussed in this post.

Interactive Resources

Through the popularity of interactive online courses that provide real coding problems and explanations, interactive resources have become more popular in recent years. If you feel like you're coding, it's because you are. Interactive resources are usually accessible for free or for a small price, and you may sign up for a free trial before purchasing.

Non-interactive Resources

Books (digital and paperback) and websites ("online tutorials") are the most classic and time-tested non-interactive resources. Because of their familiarity and convenience, these mediums are preferred by many first-time Python learners. As you can see, there are several non-interactive materials available to you, the majority of which are free.

MOOCs (massive online open courses) popularised video resources in the last ten years, and they resembled university lectures caught on video. In fact, they were frequently funded or promoted by top universities.

Video Resources

There are now a number of video resources available for a wide range of topics including Python programming. Some of these video materials are pre-recorded courses that are hosted on learning platforms, while others are live-streamed courses that are supplied by online education providers. In one week, General Assembly offers a live Python course that covers Python foundations.

Learn a Python Library

It is beneficial to learn one or two Python libraries in addition to Python. Libraries are specialized function collections that act as "accelerators." You'd have to build your own code to do specialized jobs without them.

Pandas, for example, is a well-known library for manipulating tabular data. Numpy assists in the performing of mathematical and logical operations on arrays.

Some of the resources to learn python libraries −

• Python.org page (for standard Python libraries)

• GitHub page(for additional Python libraries)

With Anaconda Speed up the Python Installation Process

You can either go through the trouble of downloading the Python installer from the Python Software Foundation website, and then sourcing and downloading additional libraries, or you can download the Anaconda installer, which already includes many of the packages you'll regularly use, especially if you plan on using Python for data analysis or data science.

Select and Install an IDE.

Install an integrated development environment (IDE), which is a program that allows you to script, test, and run Python code.

When it comes to IDEs, the best one is the one you love working with the most. The most popular Python IDEs/text editors, according to various sources, include PyCharm, Spyder, Jupyter Notebook, Visual Studio, Atom, and Sublime. First, the good news: they're all free, so test a few before deciding on one. The "bad" news is that each IDE/text editor has a slightly distinct user interface and set of functionality, so learning how to use each one will take some time.

For Python beginners, I recommend using Jupyter Notebook. It features a clean design and a reduced set of capabilities that will not distract and will make practicing and prototyping in Python simple. It also has a separate display for data frames and visualizations. Jupyter Notebook is pre-installed when you download Anaconda. You experiment with different IDEs that are more suited for programming (Pycharm) or data science (Rodeo) that allow integrations over time (Sublime).

Consider adding an error handler or autocomplete to complement your IDE, particularly if you find yourself working on large projects. It will highlight errors and help you create code faster. Kite is a nice alternative because it is free and works with most IDEs.

Use Google to Troubleshoot Code If In Doubt

Learning from other Python developers will be one of the simplest ways to resolve difficulties as you work on Python exercises, examples, and projects. Simply conduct a fast online search using keywords related to your mistake.

For example, "how to merge two lists in Python" or "Python how to convert to DateTime" are entirely appropriate searches that will take you to a few famous community-based websites like StackOverFlow, Stack Exchange, Quora, Tutorialspoint, Programiz, and GeeksforGeeks

Plan and Complete Your Python Learning

Most people neglect this step, which leads to problems or delays. All that remains is to create a schedule. I propose that you set aside at least two weeks to spread out your learning and ensure that you allow yourself enough time to review the Python fundamentals, practice coding in your IDE, and troubleshoot code.

Troubleshooting errors is part of the difficulty (and fun) of learning Python or any programming language. You'll be amazed at how far you've come after the first two weeks, and you'll have enough experience under your belt to continue learning the more difficult content supplied by your selected resource.

We've established a minimal learning timeframe by this stage, you know how to choose a learning goal for your study, you have a list of learning resources and learning methods to choose from, and you know what other coding considerations you'll need to make. We hope you take advantage of these suggestions to accelerate your Python learning!