Software for Data Analysis

When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used as an efficient multi-dimensional container of generic data.

There is a long list of Python packages designed for working with complex data sets. But arguably, Pandas is the most important. Pandas helps you manipulate and analyze large sets of data without having to learn a specialized data-processing language like R.

One of the most popular general Python libraries is Requests that aims to make HTTP request simpler and more human-friendly. Licensed under the Apache2 license and written in Python, Requests is the de facto standard used by developers for making HTTP requests in Python.

Scrapy is a free and open-source Python framework that is widely used for web scraping and a number of other tasks, including automated testing and data mining.

An interactive visualization library for the Python programming language, Bokeh allows visualizing data in a beautiful and meaningful way inside contemporary web browsers. The data visualization library eases the creation of dashboards, data applications, and interactive plots.

Chaco is a Python plotting application toolkit that facilitates writing plotting applications at all levels of complexity, from simple scripts with hard-coded data to large plotting programs with complex data interrelationships and a multitude of interactive tools. While Chaco generates attractive static plots for publication and presentation, it also works well for interactive data visualization and exploration.

Gnuplot.py is a Python package that interfaces to gnuplot, the popular open-source plotting program. It allows you to use gnuplot from within Python to plot arrays of data from memory, data files, or mathematical functions. If you use Python to perform computations or as 'glue' for numerical programs, you can use this package to plot data on the fly as they are computed. And the combination with Python makes it is easy to automate things, including to create crude 'animations' by plotting different datasets one after another.

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

PyX is a Python package for the creation of PostScript, PDF, and SVG files. It combines an abstraction of the PostScript drawing model with a TeX/LaTeX interface. Complex tasks like 2d and 3d plots in publication-ready quality are built out of these primitives.

Simple and efficient tools for predictive data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license

TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

Topic modeling for humans. Train large-scale semantic NLP models, represent text as semantic vectors and find semantically related documents.

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.

spaCy is designed to help you do real work — to build real products, or gather real insights. spaCy excels at large-scale information extraction tasks.

Fast Artificial Neural Network Library, or FANN, implements artificial neural networks in C (which is what makes it up to 150 times faster than other libraries) while making them accessible in a number of different languages, including Python.

ffnet is a Python AI library for implementing feed-forward neural networks. It uses a graphical user interface to visualize training datasets. Another strong benefit is its automatic data normalization feature, saving a lot of time in the pre-processing stage of your workflow.

An open-source machine learning framework that accelerates the path from research prototyping to production deployment.

AirSim is a Unity/Unreal Engine based simulator built by Microsoft. While it’s not a Python AI library itself, AirSim allows developers to test and experiment with autonomous vehicle algorithms without actually needing to possess the physical hardware for it.

Bullet provides a physics sandbox where robotics/virtual reality can be created and tested. This allows the development of capital-intense development tasks like collision detection, locomotion skills and reinforcement learning (which requires a lot of failure) to be conducted in a harmless simulation scenario.

Carla caters to autonomous driving research. It has driver-specific features like flexible vehicle sensors, environmental conditions as well as a wide variety of buildings and vehicles already implemented.

ArcPy is a Python site package that provides a useful and productive way to perform geographic data analysis, data conversion, data management, and map automation with Python.