Tutorial de Data Science da Udemy

1. numpy

   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

   
   

   Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

2. matplotlib.pyplot

   Provides a MATLAB-like plotting framework.

   pylab combines pyplot with numpy into a single namespace. This is convenient for interactive work, but for programming it is recommended that the namespaces be kept separate, e.g.:

   import numpy as np

   import matplotlib.pyplot as plt

   x = np.arange(0, 5, 0.1);

   y = np.sin(x)

   plt.plot(x, y)

3. pandas

   andas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.