Python Data Analysis & Science

Python Data Analysis Library — pandas: Python Data Analysis Library
pandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

NumPy – fundamental package for scientific computing with Python
NumPy is the fundamental package for scientific computing with Python. It contains among other things, a powerful N-dimensional array object. sophisticated (broadcasting) functions. tools for integrating C/C++ and Fortran code. useful linear algebra, Fourier transform, and random number capabilities. NumPy can also be used as an efficient multi-dimensional container of generic data.

SciPy – Python ecosystem for mathematics, science, and engineering
SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. In particular, these are some of the core packages: NumPy. SciPy library. Matplotlib. IPython. SymPy. pandas.

Matplotlib: Python plotting — Matplotlib 3.1.2 documentation
Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Welcome to Data Analysis in Python! — Data Analysis in Python
Python is an increasingly popular tool for data analysis. In recent years, a number of libraries have reached maturity, allowing R and Stata users to take advantage of the beauty, flexibility, and performance of Python without sacrificing the functionality these older programs have accumulated over the years.

mpmath – Python library for arbitrary-precision floating-point arithmetic
mpmath is a free (BSD licensed) Python library for real and complex floating-point arithmetic with arbitrary precision. mpmath works with both Python 2 and Python 3, with no other required dependencies. It can be used as a library, interactively via the Python interpreter, or from within the SymPy or Sage computer algebra systems which include mpmath as standard component. CoCalc lets you use mpmath directly in the browser.

Welcome — Theano 0.7 documentation
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features include: tight integration with NumPy. transparent use of a GPU. efficient symbolic differentiation. speed and stability optimizations. dynamic C code generation. extensive unit-testing and self-verification to detect and diagnose many types of mistakes.

SymPy – Python library for symbolic mathematics
SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python.

Python Advanced: Graph Theory and Graphs in Python
Using Graphs in Python: Implementing Graphs and underlying theory. A “graph”1 in mathematics and computer science consists of “nodes”, also known as “vertices”. Nodes may or may not be connected with one another. Many practical problems can be represented by graphs. They are often used to model problems or situations in physics, biology, psychology and above all in computer science. In computer science, graphs are used to represent networks of communication, data organization, computational devices and the flow of computation.

A Complete Tutorial to Learn Python for Data Science from Scratch
This article is a complete tutorial to learn data science using python from scratch. It will also help you to learn basic data analysis methods using python. You will also be able to enhance your knowledge of machine learning algorithms.

Machine learning in Python — scikit-learn 0.22.1 documentation
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. Classification. Regression. Clustering. Dimensionality reduction. Model selection. Preprocessing.