Pandas 101 (Introduction)

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What is Pandas?

    Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals. Its name is a play on the phrase "Python data analysis" itself. Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.



Library Highlights

  • A fast and efficient DataFrame object for data manipulation with integrated indexing;

  • Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;

  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;

  • Flexible reshaping and pivoting of data sets;

  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;

  • Columns can be inserted and deleted from data structures for size mutability;

  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;

  • High performance merging and joining of data sets;

  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;

  • Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;

  • Highly optimized for performance, with critical code paths written in Cython or C.

  • Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Why use Pandas? 

1.      Convenient method of handling missed data.

2.       It uses Series for one-dimensional data structure and DataFrame for multi-dimensional data structure.

3.      Easy to slice data.

4.      Make merging, concatenation data like a piece of cake.

5.      Built-in strong time series.  


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