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Data Scientist | Machine Learning Engineer | Based in NYC | Writer |


Take the best out of Jupyter Notebook with these extensions

Python libraries are very popular for making our lives easier, and a popular way to use Python is with Jupyter Notebook. Even though Python libraries are great, Jupyter Notebook offers some great extensions that I highly recommend you trying out. Today, let’s explore 8 of my favorite extensions that will make your life easier.

If you are reading this, I believe that you are already familiar with Jupyter Notebook. In case you are not, here is what their website says about it.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain…


Discover 5 Python libraries that you should start using right now

One of the best things about using Python is its infinity of open-source libraries. There is a library for basically anything. If you have read some of my previous blogs, you may have noticed that I’m a big fan of low-code libraries. That’s not because I’m lazy to type code but because I prefer investing my time working on a project with what really matters. If a library can solve a problem, why not save your precious time and give it a try? Today, I will introduce you to 5 libraries that you probably have never heard about but you…


Discover three cool libraries and why you should try them right now

A few days ago, I published a blog on 5 Python Libraries That You Don’t Know About, But Should. That blog had dozens of thousands of views in less than a week. Thus, I decided to write a part two with other cool libraries that you should try out.

Python is one of the most popular languages in computation. One of the main reasons is that it contains hundreds of open source libraries that can make our life easier. People focus on more popular (and more reliable) libraries. However, other less-known libraries can save hours of your precious time. That…


Discover useful Python libraries that you should try out in your next project

Some of my most popular blogs are about Python libraries. I believe that they are so popular because Python libraries have the power to save us a lot of time and headaches. The problem is that most people focus on those most popular libraries but forget that multiple less-known Python libraries are just as good as their most famous cousins.

Finding new Python libraries can also be problematic. Sometimes we read about these great libraries, and when we try them, they don’t work as we expected. If this has ever happened to you, fear no more. I got your back!


Save hours on your next project with Pandas Profiling

Let’s imagine that you have to do an exploratory data analysis. Where do you start? You load the dataset, print the data, look for the number of observations, data types, missing values, duplicated values, and statistics summarization. Then, you perhaps need to create a few data visualizations to understand better what you have. All this will take multiple lines of code and maybe hours. What if I tell you that you can save your precious time, skip all these steps, and focus only on analyzing the data? Magic? No, that’s Pandas Profiling.

There are quite a few blogs about Pandas…


Which of these libraries perform better when compared to each other?

Data visualization is one of the most critical steps of any project. Data visualizations have the power to translate complex information into an easy-to-digest chart. The more you use charts in your projects, the better you can present insights to non-technical individuals.

Whether you are a beginner or have years of experience using Python, you know that Matplotlib is the most famous data visualization, but is it the best one? Today we are going to compare Matplotlib to Plotly Express. Who is going to be the winner?


This is obvious, but Matplotlib is way more popular than Plotly. The main…


Which of these is the best low-code library for machine learning?

Some of my most popular blogs on Medium are about libraries that I believe you should try. In this blog, I will focus on low-code machine learning libraries. The truth is that many data scientists believe that low-code libraries are shortcuts and should be avoided. I’m afraid I have to disagree! I think that low-code libraries should be included in our pipeline to help us make important decisions without wasting time.

For example, imagine that you just started a project and have no idea which model to start testing out. You can start with those models that you think would…


Learn how to use QuickDA to create data visualizations with one line of code

Everybody has heard of the sentence a picture is worth a thousand words. The reason why this sentence is so well known is that it’s true. There are things that only our eyes can catch. That’s why data visualization is an essential step for any project or report. A person who is not familiarized with the dataset can easily give us insights into it. However, creating data visualizations can be time-consuming, and sometimes, you want to take a quick look at the dataset. Luckily, we have QuickDA, a library that can solve this issue.

I have written about how you…


Learn how to do a use QuickDA for your next project and save hours of work

Whether you are a data analyst or data scientist, you have heard that professionals spend 80% of their time doing data cleaning and only 20% modeling. That is true that data cleaning and exploratory data analysis take multiple steps to be performed, and remember all the code requires a lot of practice. A simple data visualization, for example, requires at least three lines of code to be minimally presentable. However, what if I tell you that there is a way to reduce the time spent on EDA drastically? That’s when QuickDA can be handy.


Matplotlib, Seaborn, Plotly Express, and Altair. Which of these is the best library for data visualization?

Data visualization is a crucial step in any exploratory data analysis or report. It’s usually easy to read, and it can give us insight into the dataset in one look. There are dozens of great tools for business intelligence, such as Tableau, Google Data Studio, and PowerBI that allow us to create graphs easily. A data analyst or data scientist will often create visualizations on a Jupyter Notebook using Python. Luckily, there are dozens of great Python libraries that create great graphs. However, the million-dollar question is: which one is the best?

Whether you are a student or a professional…

Ismael Araujo

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