Python has become one of the most popular programming languages of our time. It has numerous advantages over other general-purpose programming languages, making it a favourite among data scientists and software engineers alike. With that in mind, here are the 10 best Python libraries for data science you can use today to kick-start your development workflow or make it even more efficient than before.
NumPy is the fundamental package need to do scientific computing in Python. It includes highly optimized routines to handle numerical calculations like linear algebra, Fourier transforms, and fast summation of large arrays.
The NumPy library contains a multidimensional array object that can be use to efficiently manipulate the data, convert it between different formats, perform mathematical operations on it, etc.
Pandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools. It is available in the form of a standalone module that can be use in other programs and embedded within Python as a library or as functions. In addition to its primary goal of numeric computation and analysis, it has the broader goal of becoming a useful toolkit for scientists and engineers working with both small and large datasets.
SciPy is a collection of programs for mathematics, science, and engineering. It contains modules to solve ordinary differential equations; optimize linear, quadratic, and integer programs; use Fourier transforms; calculate probability distributions; analyze statistical data; and more. The SciPy library depends on NumPy which can be install in parallel with SciPy or independently. In the Data Science field, Python has become one of the dominant programming languages due to its robust handling of math and statistics operations.
Matplotlib is a library that was create to produce graphs of mathematical functions. It’s an easy-to-use tool and you can use it on Macs, Windows and Linux systems. If you’re looking for something with more features than the base graphics plotting in matplotlib, check out the documentation page, but most users will be happy with what matplotlib offers on its own.
Seaborn is a statistical graphics library that uses matplotlib. It provides a high-level interface for drawing statistical graphics such as histograms, scatterplots, and heatmaps. Seaborn also supports creating statistical plots from higher level functions, with minimal munging required from the user. This lets non-technical users tell more accurate stories about their data and it makes doing exploratory analysis with collected data more productive because the output tends to be cleaner and easier to read than other visualization packages.
6) Machine Learning in Python
Python has an abundance of ML modules available, which can be difficult to sift through. Below is a list of some great resources that I’ve found useful in my research:
ROS (Robotic Operating System) and OpenCV are also popular and well-supported packages for developing computer vision applications.
One of the most popular and powerful machine learning toolkits, scikit-learn, includes a wide range of supervised and unsupervised learning algorithms. Originally developed as a university research project in Japan, this library provides the statistical power to address many applications.
Statsmodels is an open-source library of statistical functions emphasising time series analysis, regression analysis, and probability models. Statsmodels has a really nice interface to build up models like linear regression and also has parametric uncertainty estimation. It provides functions for both frequentist inference as well as Bayesian inference.
InnoArchiLib is a unique software architecture and design library that the NASA Jet Propulsion Laboratory has developed. It includes the following 10 sub-packages:
Computer Animation and Movie Techniques
Composition Assistance Package (CAP)
Flowgraph Interaction Toolkit (FLIT) InnoArchiLib is not an open-source framework, but it is also not commercially licens.
Gensim is a python library which is well suit to perform topic modelling.
We provide an implementation of Latent Dirichlet Allocation (LDA), which is use as a part of many machine learning tasks such as information retrieval, document classification, clustering, indexing etc.
This plugin implements the basic LDA and assumes that all input documents are plain text with no particular structure on them.
Python is a very accessible language with many powerful libraries that can help you get start on your journey. Python Development Company can show you how to get the most out of these tools and how to best use them in order to be successful. This guide will introduce 10 Python libraries that are a staple in any Data Science career.