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 — Pandas library is quite efficient with time series data. In fact, it was created by Wes McKinney to work with financial data which essentially consists of time series data. When working with time series data, a substantial amount of time is spent on date and time manipulation. …

Discover more about Pandas — When it comes to data analysis with Pandas, groupby is one of the most frequently used functions. It allows for grouping the data points (i.e. rows) based on the values in the given column or columns. Consider you have a car dataset and want to compare car brands based on…

These should not be overlooked. — A big portion of machine learning (ML) models never make it into production. It is one thing to create a model in a Jupyter notebook but deploying it into production and maintaining it as a continuous service is another world. …

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. …