Essential Python Libraries
NumPy
pandas
matplotlib
SciPy
NumPy
fundamental package for scientific computing
contains a powerful N-dimensional array object, also as the primary container for data to be passed between algorithms regards to data analysis
- reading/writing array-based data sets to disk
- element-wise computations with array
- mathematical operations between arrays
- linear algebra, Fourier transform, random number generation
- tools for integrating lower-level language can operate on the data stored in NumPy array without coping/modifying any data
pandas by Wes Mckinney
rich data structures (DataFrame, Index and Series etc.) and functions
primary data structure/object in pandas is DataFrame, which is 2-dimensional tabular and column oriented
well-suited for financial data: time series, which is exactly Wes Mckinney create pandas for
- NumPy: array computing features
- spreadsheet: flexible data manipulation
- rational database, such as SQL
- sophisticated indexing functionality: easy to reshape, slice, dice, aggregations, select subsets
matplotlib by John D. Hunter
producing plots and other 2D data visualizations
SciPy
collection of packages for scientific computation
- scipy.integrate: integrate/differential
- scipy.linalg: linear algebra, matrix decompositions beyond numpy.linalg
- scipy.optimize: function optimazers/minimizers, root finding
- scipy.signal: signal processing
- scipy.sparse: sparse matrices, sparse linear system
- scipy.special: wrapper around SPECFUN, a Fortran library implementing many common mathematical functions, such as the gamma function
- scipy.stats: probability distributions, statistical tests
- scipy.weave: tools for using inline C++ to accelerate array computations
