Python Libraries for Data Science

Many popular Python toolboxes/libraries:
•NumPy
•SciPy
•Pandas
•SciKit-Learn
Visualization libraries
•matplotlib
•Seaborn
and many more …

NumPy:

• introduces objects for multidimensional arrays and matrices, as well as functions that allow to easily perform advanced mathematical and statistical operations on those objects
• provides vectorization of mathematical operations on arrays and matrices which significantly improves the performance
• many other python libraries are built on NumPy

SciPy:

• collection of algorithms for linear algebra, differential equations, numerical integration, optimization, statistics and more
• part of SciPyStack
• built on NumPy

Pandas:

• adds data structures and tools designed to work with table-like data (similar to Series and Data Frames in R)
• provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation etc.
• allows handling missing data

SciKit-Learn:

• provides machine learning algorithms: classification, regression, clustering, model validation etc.
• built on NumPy, SciPyand matplotlib

Matplotlib:

• python 2D plotting library which produces publication quality figures in a variety of hardcopy formats
• aset of functionalities similar to those of MATLAB
• line plots, scatter plots, barcharts, histograms, pie charts etc.
• relatively low-level; some effort needed to create advanced visualization

Seaborn:

• Based on matplotlib
• Provides high level interface for drawing attractive statistical graphics
• Similar (in style) to the popular ggplot2 library in R

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Download tutorial Notebook:

http://rcs.bu.edu/examples/python/data_analysis/dataScience.ipynb