Python is suitable for research and prototyping as well as building the production systems. why maintain 2 development environments when one will suffice ?

- interpreted, so substantially slower than code written in a compiled language like Java/C++.
- tradeoff : programmer time is more valuable than CPU time. 
- highly concurrent, multithreaded apps particularly applications with many CPU bound threads.
- GIL : Global Interpreter Lock - mechanism preventing interpreter from executing more than one python instruction at a time. 
- Python C uses native multitasking(C/C++) to run code in parallel without impacted by GIL, as long as they do not need to regularly interact with python objects.

• NumPy : Numerical Python

  • data structures, algorithms and library glue needed for most scientific apps.

    • fast and efficient multidimentional array object ndarray
    • functions for performing element-wise computations with arrays or mathematical operations between arrays
    • tools for reading and writing array based data sets to disk.
    • linear algebra operations, Fourier transform and random number generation
    • mature C api to enable python extensions and native C/C++ code to access NumPy's data structures and computational facilities.

  • fast array processing capabilities

  • used as a container to pass around data between algorithms and libraries.

• Pandas : fast, easy, expressive data structures and functions.

  • panel data (Pandas) or Python data analysis (Pandas)

  • works with structured/tabulated data

  • DataFrame : tabular, column-oriented data structure with both row and column labels.

  • combines high performance array-computing ideas of NumPy with the flexible data manipulation capabilities of spreadsheets and relational databases.

  • sophisticated indexing functionality : reshape, slice and dice, aggregations and subset selection.

    • data structures with labelled axes
    • integrated time series functionality
    • handle both time series and non-time series data
    • arithmetic operations and reductions(like summing across axes) would pass on the metadata
    • Flexible handling of missing data
    • merge and other relations operations

• matplotlib : most popular library for producing plots and other 2D visualizations

  • safe choice as default visualization tool

• IPython and Jupyter : interactive python interpreter

  • execute explore workflow, rather than edit-compile-run workflow
  • easy access to OS shell and file system.
  • language agnostic interactive computing tool
  • support for over 40 programing languages
  • interactive and exploratory computing
  • data exploration and visualization

• SciPy : collection of packages addressing standard problem domains in scientific computing.

    scipy.integrate : numerical integration routines and differential equation solvers
    scipy.linalg : linear algebra routined and matrix decompositions extending beyond those provided by numpy.linalg
    scipy.optimize : function optimizers (minimizers) and root finding algorithms
    scipy.signal : signal processing tools
    scipy.sparse : sparse metrics and sparse linear systems solvers. 
    scipy.special : wrapper around SPECFUN, a fortran library implementing many common mathematical functions, such as the gamma function 
    scipy.stats : continuous and discrete probability distributions(density functions, samplers, continuous distribution functions), statistical tests and more descriptive statistics
    scipy.weave : tool for using inline C++ code to accelerate array computations. 
  

• scikit-learn : general purpose Machine Learning toolkit for python programmers.

    Classification : SVM, nearest neighbours, random forest etc
    Regression : Lasso, Ridge regression, Logistic etc.
    Clustering : k-mean, spectral clustering
    Dimensionality reduction : PCA, feature selection, matrix factorization 
    Model selection : grid search, cross-validation, metrics
    preprocessing : feature extraction, normalization
  

• statsmodels : statistical analysis package

  • regression models : linear regression, generalized linear models, robust linear models, linear mixed effects models etc.
  • ANOVA (analysis of variance)
  • Time series analysis : AR, ARMA, ARIMA, VAR and other models.
  • nonparametric methods : kernel density estimation, kernel regression.
  • visualization of statistical model results