Pandas is a must for data-science since it is one of the most used packages for analyzing data, data exploration, and manipulation. It provides fast, expressive, and flexible data structures to easily (and intuitively) work with structured (tabular, multidimensional, potentially heterogeneous) and time-series data. Pandas can do well:
     i) Easy handling of missing floating point and non-floating point data
     ii) Columns can be inserted and deleted from DataFrame and higher dimensional objects
     iii) Automatic and explicit data alignment: objects can be aligned or be ignored to a set of labels
     iv) Powerful, flexible grouping
     v) Convert ragged, differently-indexed data in other data structures into DataFrame objects
     vi) Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
     vii) Intuitive merging and joining data sets
     viii) Reshaping and pivoting of data sets
     ix) Hierarchical labeling of axes
     x) Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases
     xi) Compatible with ultrafast HDF5 format
     xii) Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging

=================================================

Change data format, print head, print statistic summary of the data, replace empty cells with anything, sort/group columns: code:
        
        
Input:         
        
Output: