Python and R are the programming languages that dominate the field of data science. What makes them so efficient and popular are packages or libraries that ease and expedite the typical tasks.

In this article, we will focus on Tidyverse, a collection of R packages for data science. Tidyverse contains several packages for data analysis, manipulation, and visualization.

The pipes we will implement actually come from the magritte package but we do not explicity install it. Tidyverse loads the pipe (%>%) automatically. We will go over several examples that demonstrate how pipes can combine data manipulation and analysis steps.

We…

Pandas is dominating the data analysis and manipulation tasks with small-to-medium sized data in tabular form. It is arguably the most popular library in the data science ecosystem.

I’m a big fan of Pandas and have been using it since I started my data science journey. I love it so far but my passion for Pandas should not and does not prevent me from trying different tools.

I like to try comparing different tools and libraries. My way of comparison is to do the same tasks with both. I usually compare what I already know with the new one I…

Data visualization is a fundamental piece of data science. If used in exploratory data analysis, data visualizations are highly effective at unveiling the underlying structure within a dataset or discovering relationships among variables.

Another common use case of data visualizations is to deliver results or findings. They carry much more informative power than plain numbers. Thus, we often use data visualization in storytelling, a critical part of the data science pipeline.

We can enhance the capabilities of data visualizations by adding interactivity. The Altair library for Python is highly efficient at creating interactive visualizations.

In this article, we will go…

Pandas is a highly popular data analysis and manipulation library. It provides numerous functions to perform efficient data analysis. Furthermore, its syntax is simple and easy-to-understand.

In this article, we focus on a particular function of Pandas, the groupby. It is used to group the data points (i.e. rows) based on the categories or distinct values in a column. We can then calculate a statistic or apply a function on a numerical column with regards to the grouped categories.

The process will be clear as we go through the examples. Let’s start by importing the libraries.

import numpy as np…

It is more than just plain numbers

Pandas is arguably the most popular data analysis and manipulation library. It makes it extremely easy to manipulate data in tabular form. The various functions of Pandas constitutes a powerful and versatile data analysis tool.

Data visualization is an essential part of exploratory data analysis. It is more effective than plain numbers at providing an overview or summary of data. Data visualizations help us understand the underlying structure within a dataset or explore the relationships among variables.

Pandas is not a data visualization library but it is capable of creating basic plots. If…

Data visualization is an essential building block of data science. Visualizations provide valuable insight into data. They are much more effective than plain numbers in many cases.

Data visualizations help to explore and understand the underlying structure within data and relationships between variables. We also use them to inform the stakeholders about our findings and to deliver results.

In this article, we will go over the top 5 Python data visualization libraries. We will create the same visualizations with all of them so that we get an overview of the differences and similarities between them.

We will create the plots…

Pandas is arguably the most popular data analysis and manipulation library. What I think makes Pandas widely-used is having a large number of powerful and versatile functions.

Pandas functions usually do a fine job with the default settings. However, they offer much more if you use the parameters efficiently. In this article, we will elaborate on the read_csv function to make the most of it.

The read_csv is one of the most commonly used Pandas functions. It creates a dataframe by reading data from a csv file. However, it is almost always executed with the default settings.

If you ever…