- 364 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Mastering pandas
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Information
Mastering pandas
Table of Contents
Mastering pandas
Credits
About the Author
About the Reviewers
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Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Introduction to pandas and Data Analysis
Motivation for data analysis
We live in a big data world
4 V's of big data
Volume of big data
Velocity of big data
Variety of big data
Veracity of big data
So much data, so little time for analysis
The move towards real-time analytics
How Python and pandas fit into the data analytics mix
What is pandas?
Benefits of using pandas
Summary
2. Installation of pandas and the Supporting Software
Selecting a version of Python to use
Python installation
Linux
Installing Python from compressed tarball
Windows
Core Python installation
Third-party Python software installation
Mac OS X
Installation using a package manager
Installation of Python and pandas from a third-party vendor
Continuum Analytics Anaconda
Installing Anaconda
Linux
Mac OS X
Windows
Final step for all platforms
Other numeric or analytics-focused Python distributions
Downloading and installing pandas
Linux
Ubuntu/Debian
Red Hat
Ubuntu/Debian
Fedora
OpenSuse
Mac
Source installation
Binary installation
Windows
Binary Installation
Source installation
IPython
IPython Notebook
IPython installation
Linux
Windows
Mac OS X
Install via Anaconda (for Linux/Mac OS X)
Wakari by Continuum Analytics
Virtualenv
Virtualenv installation and usage
Summary
3. The pandas Data Structures
NumPy ndarrays
NumPy array creation
NumPy arrays via numpy.array
NumPy array via numpy.arange
NumPy array via numpy.linspace
NumPy array via various other functions
numpy.ones
numpy.zeros
numpy.eye
numpy.diag
numpy.random.rand
numpy.empty
numpy.tile
NumPy datatypes
NumPy indexing and slicing
Array slicing
Array masking
Complex indexing
Copies and views
Operations
Basic operations
Reduction operations
Statistical operators
Logical operators
Broadcasting
Array shape manipulation
Flattening a multi-dimensional array
Reshaping
Resizing
Adding a dimension
Array sorting
Data structures in pandas
Series
Series creation
Using numpy.ndarray
Using Python dictionary
Using scalar values
Operations on Series
Assignment
Slicing
Other operations
DataFrame
DataFrame Creation
Using dictionaries of Series
Using a dictionary of ndarrays/lists
Using a structured array
Using a Series structure
Operations
Selection
Assignment
Deletion
Alignment
Other mathematical operations
Panel
Using 3D NumPy array with axis labels
Using a Python dictionary of DataFrame objects
Using the DataFrame.to_panel method
Other operations
Summary
4. Operations in pandas, Part I – Indexing and Selecting
Basic indexing
Accessing attributes using dot operator
Range slicing
Label, integer, and mixed indexing
Label-oriented indexing
Selection using a Boolean array
Integer-oriented indexing
The .iat and .at operators
Mixed indexing with the .ix operator
MultiIndexing
Swapping and reordering levels
Cross sections
Boolean indexing
The is in and any all methods
Using the where() method
Operations on indexes
Summary
5. Operations in pandas, Part II – Grouping, Merging, and Reshaping of Data
Grouping of data
The groupby operation
Using groupby with a MultiIndex
Using the aggregate method
Applying multiple functions
The transform() method
Filtering
Merging and joining
The concat function
Using append
Appending a single row to a ...
Table of contents
- Mastering pandas
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