The Best Data Science Libraries in Python

The Best Data Science Libraries in Python

The Best Data Science Libraries in Python. You’ve been learning about data science and want to get rocking immediately on solving some problems. So, of course, you turned to Python

You’ve been learning about data science and want to get rocking immediately on solving some problems. So, of course, you turned to Python

This article will introduce you to the essential data science libraries so you can start flying today.

The Core

Python has three core data science libraries upon which many others have been built.

  • Numpy
  • Scipy
  • Matplotlib

For simplicity, you can think of Numpyas your go-to for arrays. Numpy arrays are different from standard Python lists in many ways, but a few to remember are they arefaster, take up less space, and have more functionality. It is important to note, though, that these arrays are of a fixed size and type, which you define at creation. No infinitely appending new values like you might with a list.

Scipy is built on top of Numpy and provides many of the optimization, statistics, and linear algebra functions you will need. While Numpy sometimes has similar functionality, I tend to prefer Scipy’s functionality. Want to calculate a correlation coefficient or create some normally distributed data? Scipy is the library for you.

Matplotlib is probably not winning any beauty awards, but it is the core library for plotting in Python. It has a ton of functionality and allows you to have significant control as well when needed.

2nd Generation

The core libraries are amazing and you will find yourself using them a lot. There are three 2nd generation libraries, though, which have significantly built on top of the core to give you more functionality with less code.

If you have been learning about data science and have not heard of Scikit-learn, then I’m not sure what to say. It is the library for machine learning in Python. It has incredible community support, amazing documentation, and a very easy to use and consistent API. The library focuses on “core” machine learning — regression, classification, and clustering on structured data. It is not the library you want for other things such as deep learning or bayesian machine learning.

Pandaswas created to make data analysis easier in Python. Pandas makes it very easy to load structured data, calculate statistics on it, and slice and dice the data in whichever way you want. It is an indispensable tool during the data exploration and analysis phase, but I would not recommend using it in production because it generally does not scale very well to large datasets. You can get significant speed boosts in production by converting your Pandas code to raw Numpy.

While Matplotlib is not the prettiest out of the box, Seaborn makes it easy to create beautiful visualizations. It is built upon Matplotlib, so you can still use Matplotlib functionality to augment or edit Seaborn charts. It also makes it a lot easier to create more complex chart types. Just check out the gallery for some inspiration:

This is image title

Deep Learning

With the incredible rise of deep learning, it would be wrong not to highlight the best Python packages in this area.

I am a huge fan of Pytorch. If you want to get started with deep learning while learning a library that makes it relatively easy to implement state-of-the-art deep learning algorithms, look no further than Pytorch. It is becoming the standard deep learning library for research and implementing a lot of functionality to make it more robust for production use cases. They provide a lot of great tutorials to get you started as well.

In my opinion, Keras was the first library to make deep learning truly accessible. You can implement and train a deep learning model in 10s of lines of code, which are very easy to read and understand. The downside of Keras is that the high-level abstraction can make it hard to implement newer research that is not currently supported (though they are improving in this area). It also supports multiple backends. Namely, Tensorflow and CNTK.

Tensorflow was built by Google and has the most support for putting deep learning into production. The original Tensorflow was pretty clunky in my opinion, but they have learned a lot, and TensorFlow 2.0 makes it a lot more accessible. While Pytorch is moving towards more production support, Tensorflow seems to be moving towards more usability.


I would like to end with two great statistical modeling libraries in Python.

If you are coming over from R, you will probably be confused about why scikit-learn doesn’t give you p-values for your regression coefficients. If so, you need to look at statsmodels. This library, in my opinion, has the best support for statistical models and tests and even supports a lot of syntax from R.

Probabilistic programming and modeling is a ton of fun. If you are not familiar with this area, I would check out Bayesian Methods for Hackers. And the library you will want to use is PyMC3. It makes it very intuitive to define your probabilistic models and has a lot of support for state-of-the-art methods.

Go Fly

I’ll be the first to admit that there are many other amazing libraries in Python for data science. The goal of this post, though, was to focus on the essential. Armed with Python and these amazing libraries, you will be astonished by how much you can achieve. I hope this article can be a great jumping-off point for your foray into data science and only the beginning of all the amazing libraries you will discover.

python Data Science programming

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Data Science Course in Dallas

Become a data analysis expert using the R programming language in this [data science]( "data science") certification training in Dallas, TX. You will master data...

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Python Programming & Data Handling

Python Programming & Data Handling

Basic Data Types in Python | Python Web Development For Beginners

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Data Science With Python | Python For Data Science | Data Science For Beginners

This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python.