Support for the libraries provided by Python is one of the reasons this programming language is so popular among the data science community. To become a data scientist, you need to learn some of the Python libraries for data science. So if you’re one of them who doesn’t know which libraries you need to learn for data science, this article is for you. In this article, I will introduce you to all the Python libraries you need to learn for data science.
All Python Libraries for Data Science
Python provides more than one library for almost any data science task. So, do you need to learn all of these libraries? Actually no, you just need to learn all those libraries that are already in use by data science professionals working in the industry. So here are all the Python libraries you need to learn for data science.
NumPy is used to perform numerical and scientific calculations with Python. It is very popular among the data science community. Can’t remember what was the last data science project where I didn’t use NumPy. When you learn NumPy you will find many very important functions to learn, but most of the time in a data science task they are used to transform the data so that we can fit it into a machine learning model.
Pandas is used for working with data. From reading a dataset to exploring it, to analyzing the data, pandas are useful everywhere. When you want to make changes to a dataset, you won’t find anything more promising in Python than Pandas. Some many tools and technologies give a strong competition to the pandas library in Python for working with data, but nothing is easier or better than using pandas.
Matplotlib and Seaborn:
Matplotlib and Seaborn are two different libraries in Python for data visualization, but they are both used together most of the time. You can only use matplotlib in most visualizations, but in some visualizations, it is better to use seaborn than matplotlib, for example, when analyzing correlation using a heatmap. So just go through some of the fundamentals of matplotlib and seaborn and start experimenting with them in a dataset.
Without a doubt, Matplotlib and Seaborn are great Python libraries for data visualization, but sometimes when you want to create interactive visualizations, these libraries won’t help you. This is where the Plotly library in Python comes in. There are many other libraries in Python for interactive data visualization, but nothing is better than Plotly.
Scikit-learn is a very useful Python library for implementing machine learning algorithms. All the algorithms you might have heard of in machine learning theory are already available in the scikit-learn library in Python. I think it’s the only data science library in Python that doesn’t have a competition. Spotify is one of the biggest names among companies that use scikit-learn in their apps.
As a data scientist, you should also know how to build end-to-end applications for a machine learning model. Most of the Python frameworks used for end-to-end model deployment are not easy to learn. This is where the streamlit library comes in. Using the streamlit library in Python, you can easily build end-to-end applications for your machine learning model with just a few lines of code.
Deep Learning Libraries:
As a data scientist, you should also learn some deep learning libraries, as they help you use deep neural architectures to create powerful models. Not all data scientists need to implement deep neural architectures, but sometimes you will need to use the power of neural networks to solve a problem, regardless of the size of the company you work in. So you can explore all the deep learning Python libraries from here.
So these were all the Python libraries that you need to learn for data science. The support for libraries Python provides makes it one of the most popular programming languages for data science. I hope you liked this article on all the Python libraries that you need to learn for data science. Feel free to ask your valuable questions in the comments section below.