Exploring Graphs and Social Network Analytics Using Python
*Tabular data with heterogeneously-typed columns.
*Ordered and unordered time series data.
*Arbitrary matrix data with row and column labels.
*Any other form of observational or statistical data sets.
*The data actually need not be labeled at all to be placed into a pandas data structure.
*Numpy is the core library for scientific computing in Python.
*It provides high-performance multidimensional array object and tools for working with these arrays.