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Essential Python Libraries for data analysis

NumPy

• NumPy, short for Numerical Python, is the foundational package for scientific computing in Python. provides, among other things:

  • A fast and efficient multidimensional 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
  • Tools for integrating connecting C, C++, and Fortran code to Python

• For numerical data, NumPy arrays are a much more efficient way of storing and manipulating data than the other built-in Python data structures.

pandas

• pandas provides rich data structures and functions designed to make working with structured data fast, easy, and expressive.
• The primary object in pandas are DataFrame,a twodimensional tabular, column-oriented data structure with both row and column labels
• pandas combines the high performance array-computing features of NumPy with the flexible data manipulation capabilities of spreadsheets and relational databases (such as SQL).
• For financial users, pandas features rich, high-performance time series functionality and tools well-suited for working with financial data.
• For users of the R language for statistical computing, the DataFrame name will be familiar, as the object was named after the similar R data.frame object. They are not the same, however; the functionality provided by data.frame in R is essentially a strict subset of that provided by the pandas DataFrame.

matplotlib

• matplotlib is the most popular Python library for producing plots and other 2D data visualizations.
• It is well-suited for creating plots suitable for publication.
• he plots are also interactive; you can zoom in on a section of the plot and pan around the plot using the toolbar in the plot window.

SciPy

• SciPy is a collection of packages addressing a number of different standard problem domains in scientific computing. Here is a sampling of the packages included:

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