Lucky Thirteen Python Articles of 2018 for Beginners

learn Python, python basics, eLearning
If you have just started learning Python, now is a great time to develop your skills further. Nowadays it is not problem to find resources about Python on the internet, however, it can be difficult to find good materials to read for beginners. Therefore I decided to gather and share with you my “Lucky thirteen” articles on Python written in 2018.

Top 3 Vertabelo Academy Python Articles for Beginners

The Vertabelo Academy is an online learning platform that delivers courses on SQL, data science, and Python. In 2018 the Academy released four new full Python courses and another four are being prepared! However, Vertabelo Academy also has a great selection of blog articles. I chose only three of the best ones among them. For more, please visit our blog.

1. "How to Create Python Heat Maps for Marketing Campaigns" - Kateryna Koidan

If you are already familiar with the basics of Python, you can build on your knowledge to learn about creating heat maps. The author of this post guides readers step by step in creating your own heat map in Python. You find out what a heat map is, what it can be used for, and what Python libraries can be used to create one. You also learn about how to collect the appropriate data, how to load the data, and how to create structures to build and draw a heat map.

2. "Developing Data Science Projects in Python: A Beginner’s Guide" - Kateryna Koidan

This guide is mainly for people who are just starting to learn to program with Python and are interested in building a portfolio of data science projects. Kateryna Koidan guides you through developing a real data science project in 5 steps: defining the project, preparing a real dataset, exploring the data, visualizing the data, creating a machine learning model used to predict housing prices, and presenting the results of your analysis. It is a very good resource for beginners in Python.

3. "Can Python Displace R for Data Science?" - Marija Ilic

Are you wondering whether Python will replace R in the near future? Currently both Python and R are very popular languages used by professionals in data science. In this article the author describes the history of Python and R and also the advantages of using each. At the end of this material you find out when it is better to use Python and when it is better to use R for data science projects.

Top 10 Python Articles for Beginners

The list below presents ten of the best articles for beginners that discuss general knowledge about Python or develop Python skills.

1. "Web Scraping, Regular Expressions, and Data Visualization: Doing it all in Python" - William Koehrsen

In this article you learn how to create a simple data science project based on a few special Python libraries. First, the author explains step by step how to get data from a website. Next, he shows how to use regular expressions in Python to extract the website data. You will learn something about the request and bs4 Python libraries. Finally, he presents ways of visualizing the dataset by using the matplotlib and seaborn libraries. This is a very good article for beginners in Python, especially for students who completed an introductory course and have basic data science skills with Python.

2. "A Complete Machine Learning Project Walk-Through in Python: Part One" - William Koehrsen

This is another interesting article for students with basic knowledge of Python. Part one of this series shows you the first steps to create a real-world machine learning project. First, the author presents a problem to resolve using publicly available data and describes how to prepare the data for analysis. In the next section you find out how to load data using the pandas library and how to operate on the data using the numpy library. The author teaches you about ways to explore data and how to use matplotlib to draw plots. Next, Koehrsen moves to feature engineering and variable selection in machine learning, and, finally, to establishing a baseline for comparison of your results. This article has great material for understanding the basics of coding a machine learning project.

3. "18 Python programming books for beginners and veterans" - Jen Wike Huger (Red Hat)

If you are looking for a good book for developing skills in Python, this article may help you. In this article you will find a list of 18 books that are worth a read. The books are not only for beginners; some of the books also cover more advanced material.

4. "Overview of Pandas Data Types" - Chris Moffitt

This article is a good guide to the data types in one of the most popular Python libraries. You will find how to check whether your data type is set correctly, how to convert data types, and where and when to use each type. This article is a great resource for students planning to get to know the Pandas library better.

5. "Asterisks in Python: what they are and how to use them" - Trey Hunner

Do you know what the * and ** (asterisks) operators are for in Python? These can be new not only for beginners in programming but also for experienced programmers coming from other languages. Trey Hunner helps you to understand this topic in detail. He discusses why you need to use these operators and how to use them, and he provides several interesting examples of how powerful these can be.

6. "Python Syntax Essentials and Best Practices" - Tomi Mester

This article is the collection of things to keep in mind to write correct code in Python. First, the author gives advice about Python syntax. Next, he provides guidelines for formatting code, with information about line breaks, identations, case sensitivity, using comments, blank lines and more. If you are interested in writing clear code in Python, this material is for you.

7. "Working with Excel worksheets using Python" - Rapture Godson

Do you have a project in mind in Python utilizing data from MS Excel or OpenOffice Calc files, but you don't know how to do it? In this article Rapture Godson describes a Python library which will help you get started. In this post you learn how to install the openpyxl module, import the module, and assign values to the cells in a worksheet, iterating by columns and rows. In the next part you learn how to use the openpyxl library to draw a chart. The end of this article provides a couple of practice tasks to code yourself.

8. "Python: 6 Different ways to create Dictionaries" - Varun

This material is very good for beginners who want to know more about creating dictionary structures in Python. Using examples the author explains how to create a dictionary with literals, with parameters, by a list of tuples, and by more than one list.

9. "Steps for Starting a New Flask Project using Python3" – patricksoftwareblog

This article shows how to create simple projects for web applications in Python using the Flask framework. First, you find out how to structure this type of application. Second, you learn about creating a virtual environment to use in this project. You next learn about how to install the necessary packages and about writing Python code. At the end, you learn how to run the web application.

10. "Get Started Quickly With Python Logging" - Erik Dietrich

The author discusses in this article how to get up and running quickly with logging in Python. First, you will see how to start a project in PyCharm IDE, then you will see how to set up a virtual environment. Next, you will learn about what logging is, why it is worthwhile to perform, and how to configure the logging module. Finally, the article covers material about formatting the output of logs.


I hope this list of the best articles for beginners from 2018 has inspired you to improve your Python skills. Of course, keep in mind that this represents only a small portion of the great articles written in 2018.

At Vertabelo Academy you will find Python courses for absolute beginners and for more advanced users, allowing you to continually develop your programming skills. You can also visit our blog with Python articles to learn about how to create new projects and how to clean existing code.

Dorota Wdzięczna

Data Science Writer @ Vertabelo

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