Python for Data Science

Most important Python Libraries for Data Science

Most important Python Libraries for Data Science

According to the 2019 Stack Overflow Developer Survey. Python is the fastest-growing major programming language today second only to Javascript. It has become the most loved and the most wanted language in 2019 and a lot of the credit for that goes to the booming field of  Data Science and AI.

As today more than 60% of data scientist use Python as their primary language for Data Science all thanks to the wonderful Python libraries for Data Science and Machine Learning.

Below are the most important Python libraries for Data Science:

1. Numpy

Numpy in Python is the most fundamental package for scientific computing. It helps to work with large and multi-dimensional arrays with a wide variety of mathematical functions to operate on the matrices. Since in Data Science most of the time we are dealing with large datasets having so many features. Also, Numpy is about 20 times faster than plain python lists.

2. Pandas

Pandas library is a must to use while working with any kind of tabular data, its kind of Python’s version of Excel. Pandas offer data structure known as dataframe which allows for fast and flexible data loading, data preparation, data cleaning and data munging. Pandas is built on top of Numpy and its the fundamental building block for doing any type of data analysis using Python.

3. Matplotlib

Matplotlib is a plotting library for creating 2D plots and graphs with few lines of code. Exploratory Data Analysis and communicating results visually are the key steps in Data Science pipeline that's where matplotlib helps us. We can generate plots, histograms, power spectra, bar charts, error charts, scatterplots and a lot more along with that it's quite easy to use with Numpy dataframes.

4. Scikit-learn

Scikit-learn provides all set of tools required to build machine learning models. It features various classification, regression and clustering algorithms including support vector machines, decision trees, logistic regression, naive bayes, random forests, gradient boosting, k-means, DBSCAN, and many more machine learning algorithms. Along with the algorithms it comes with various methods which helps in cross-validation, building a basic data pipeline, and basic feature engineering. Also, it is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy since it is built on-top of Scipy, Numpy, and Pandas. Scipy is also one the most used libraries but the majority of the time it’s used as an abstraction to scikit learn models.  

5. Plotly

Plotly is used to make great modern looking, fully-interactive, and browser-based graphing library for Python. It ships with over 30 different chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and much more. It is used to create the next level of Data Visualization, we can play around interactively hovering for the values, dragging around, zoom in and zoom out which helps us in understanding the data with quite a depth. We can easily save the visualization as an image for easy sharing and embed directly on websites. It's built using plotly.js which in turn is built using D3.js.

These are the most popular and important Python libraries that every Data Scientist should have in their arsenal.

A little bonus video for all who made it till the end!

Good news is you don’t have to look any further we have got you all covered with all of the above libraries in depth in our Data Science Learning Path.

Written in collaboration with Aditya Gupta, Data Scientist at Board Infinity