Book Image

Mastering pandas

By : Femi Anthony
Book Image

Mastering pandas

By: Femi Anthony

Overview of this book

<p>Python is a ground breaking language for its simplicity and succinctness, allowing the user to achieve a great deal with a few lines of code, especially compared to other programming languages. The pandas brings these features of Python into the data analysis realm, by providing expressiveness, simplicity, and powerful capabilities for the task of data analysis. By mastering pandas, users will be able to do complex data analysis in a short period of time, as well as illustrate their findings using the rich visualization capabilities of related tools such as IPython and matplotlib.</p> <p>This book is an in-depth guide to the use of pandas for data analysis, for either the seasoned data analysis practitioner or the novice user. It provides a basic introduction to the pandas framework, and takes users through the installation of the library and the IPython interactive environment. Thereafter, you will learn basic as well as advanced features, such as MultiIndexing, modifying data structures, and sampling data, which provide powerful capabilities for data analysis.</p>
Table of Contents (18 chapters)
Mastering pandas
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Description of pandas' modules and files


In this section, we provide brief descriptions of the various submodules and files that make up pandas' library.

pandas/core

This module contains the core submodules of pandas. They are discussed as follows:

  • api.py: This imports some key modules for later use.

  • array.py: This isolates pandas' exposure to numPy, that is, all direct numPy usage.

  • base.py: This defines fundamental classes, such as StringMixin, PandasObject which is the base class for various pandas objects such as Period, PandasSQLTable, sparse.array.SparseArray/SparseList, internals.Block, internals.BlockManager, generic.NDFrame, groupby.GroupBy, base.FrozenList, base.FrozenNDArray, io.sql.PandasSQL, io.sql.PandasSQLTable, tseries.period.Period, FrozenList, FrozenNDArray: IndexOpsMixin, and DatetimeIndexOpsMixin.

  • common.py: This defines common utility methods for handling data structures. For example isnull object detects missing values.

  • config.py: This is the module for handling package-wide...