Overview Scientific Packages for Python

Package can be seen as a container of variables and functions provided by others to help us accomplish our tasks

Import Packages into our enviroment

To use a package, the first step is to import them into our environment. There are several ways of doing so in Python.



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import math



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math.factorial(5)  # functions in math package



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math.e  # variables in math package

To reduce the name space, we can specify another name to refer to the original package



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import math as m



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m.factorial(5)



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m.e



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from math import factorial, e



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factorial(5)



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e



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from math import *



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factorial(10)



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e

Numpy stands for Numerical Python, is the foundational package for scientific computing in Python. It provides
- A fast and efficient multidimensional arrary object ndarray
- Functions for performing element-wise computation with arrays and mathematical operations between arrays
- Linear algebra operations, Fourier transform, and random number generation

matplotlib for producing plots and other 2D data visualizations. It is well-suited for creating plots suitable for publication.
SciPy is a collection of packages addressing a number of different standard problem domain in scientific computing. Here is some packages you may use:
- scipy.integrate: numerical integration routines and differential equation solvers
- scipy.linalg: linear algebra routines and matrix decompositions
- scipy.optimize: function optimizers and root finding algorithms
- scipy.stats: standard probability distributions (cdf, pdf, sampler and etc)

pandas provides rich operations and manipulations to make working with structured data fast, easy, and expressive. It is the critical ingredients enabling Python to be a powerfull and productive data analysis environment.
Scikit Learn is built on Numpy, SciPy and matplotlib for machine learning. This library contains a lot of effecient tools for machine learning and statistical modeling including classification, regression, clustering.

Statsmodels is a package for statistical modeling that allows users to explore data, estimate statistical models and perform statistical tests.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets.