Make more use of Pandas. — Pandas is arguably the most popular data analysis and manipulation library in the data science ecosystem. First and foremost, it is easy to learn and offers an intuitive syntax. With that being said, we will focus on a different great feature of Pandas: Flexibility. The capabilities of Pandas functions can…

Explained with examples. SQL window functions are great tools for data analysis. What they basically do is to perform a calculation across a group of rows that are somehow related. Consider we have a table that contains a list of employees along with their department and salary. In order to…

Picked from Stackoverflow — Python has 4 built-in data structures: List, Tuple, Set, and Dictionary. These data structures are extremely important even if you are using Python for data science. All of them are essentially a collection of data. However, there are differences among them which make each data structure unique. For instance, lists…

Making the most out of Pandas — If you are reading this article, you must have heard of or used Pandas. Thus, I’m skipping the part where I talk about how great Pandas is. 😊 In this article, we will go over examples that demonstrate some of the not-so-commonly used Pandas features. …

Why is it a must-have? — My first data science project was a machine learning model that predicts used car prices. The main steps of the project were: Scraping used-car ads on a website Cleaning and preprocessing the scraped data Exploratory data analysis Model creation Model evaluation The end result was quite satisfactory with an R-squared…

Simplicity takes you to efficiency. Data visualization is basically a graphical representation of data. It is frequently used in different parts of data science workflows. In exploratory data analysis, we use data visualizations to understand the relationships between variables. …

A simple yet practical guide — Pandas is an extremely practical and functional library for data analysis and manipulation tasks. I have been using Pandas since 2019 and it has always been able to provide a solution for my tasks. What I realized after using Pandas for about 3 years is that I wasn’t using it…

Make your learning journey more efficient. — My data science journey started in 2019. Those who follow me on Medium would know that I like sharing my experience of learning data science. I write about the mistakes I made, the challenges I faced, the tools I frequently use, and so on. In this article, I would like…

Textual data carries more information than numbers — As much as I like working with numbers, textual data keeps in existence in my workflow. I haven’t measured or analyzed it but I feel like the amount of textual data I had exceeds that of numeric data. The most significant difference between textual data and numeric data is the…