Data Analysis with Python - Introduction to Important Python Libraries
1. NumPy
For performing element-level calculations on arrays and performing mathematical operations directly on arrays
Linear algebra operations, Fourier operations, random number generation
For integration of C/C++ code
2. pandas
Fast and convenient processing of structured data, DataFrame is a column-oriented two-dimensional table data that
combines NumPy's array computing capabilities and spreadsheet and relational database data processing capabilities
to quickly reshape, slice and dice, and select data subsections set
3. SciPy
The following packages are mainly introduced:
scipy.integrate numerical integration routines and differential equation solver
scipy.linalg extends the linear algebra routines and matrix factorization functions provided by
numpy.linalg scipy.optimize function optimizer (minimizer) and root finding algorithms
scipy.signal signal processing tools
scipy.sparse sparse matrix and sparse linear systems solver
scipy.weave tools to speed up array computation using inline C++
Fourth, matplotlib
visualization of data