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Learning Pandas

By : Michael Heydt
Book Image

Learning Pandas

By: Michael Heydt

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Learning Pandas
Michael Heydt
Apr, 2015 | 504 pages
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Table of Contents (19 chapters)
Learning pandas
Learning pandas
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
A Tour of pandas
A Tour of pandas
pandas and why it is important
pandas and IPython Notebooks
Referencing pandas in the application
Primary pandas objects
Loading data from files and the Web
Simplicity of visualization of pandas data
Summary
2
Installing pandas
Installing pandas
Getting Anaconda
Installing Anaconda
Ensuring pandas is up to date
Running a small pandas sample in IPython
Starting the IPython Notebook server
Installing and running IPython Notebooks
Using Wakari for pandas
Summary
3
NumPy for pandas
NumPy for pandas
Installing and importing NumPy
Benefits and characteristics of NumPy arrays
Creating NumPy arrays and performing basic array operations
Selecting array elements
Logical operations on arrays
Slicing arrays
Reshaping arrays
Combining arrays
Splitting arrays
Useful numerical methods of NumPy arrays
Summary
4
The pandas Series Object
The pandas Series Object
The Series object
Importing pandas
Creating Series
Size, shape, uniqueness, and counts of values
Peeking at data with heads, tails, and take
Looking up values in Series
Arithmetic operations
The special case of Not-A-Number (NaN)
Boolean selection
Reindexing a Series
Slicing a Series
Summary
5
The pandas DataFrame Object
The pandas DataFrame Object
Creating DataFrame from scratch
Example data
Selecting columns of a DataFrame
Selecting rows and values of a DataFrame using the index
Selecting rows of a DataFrame by Boolean selection
Modifying the structure and content of DataFrame
Arithmetic on a DataFrame
Resetting and reindexing
Hierarchical indexing
Summarized data and descriptive statistics
Summary
6
Accessing Data
Accessing Data
Setting up the IPython notebook
Reading and writing JSON files
Accessing data on the web and in the cloud
Reading and writing from/to SQL databases
Reading data from remote data services
Summary
7
Tidying Up Your Data
Tidying Up Your Data
What is tidying your data?
Setting up the IPython notebook
Working with missing data
Handling duplicate data
Transforming Data
Summary
8
Combining and Reshaping Data
Combining and Reshaping Data
Setting up the IPython notebook
Concatenating data
Merging and joining data
Stacking and unstacking
Performance benefits of stacked data
Summary
9
Grouping and Aggregating Data
Grouping and Aggregating Data
Setting up the IPython notebook
The split, apply, and combine (SAC) pattern
Split
Apply
Discretization and Binning
Summary
10
Time-series Data
Time-series Data
Setting up the IPython notebook
Representation of dates, time, and intervals
Introducing time-series data
Calculating new dates using offsets
Handling holidays using calendars
Normalizing timestamps using time zones
Manipulating time-series data
Summary
11
Visualization
Visualization
Setting up the IPython notebook
Plotting basics with pandas
Common plots used in statistical analyses
Multiple plots in a single chart
Summary
12
Applications to Finance
Applications to Finance
Setting up the IPython notebook
Obtaining and organizing stock data from Yahoo!
Plotting time-series prices
Performing a moving-average calculation
Volatility calculation
Determining risk relative to expected returns
Summary
Index
Index

Running a small pandas sample in IPython

Now that Python and pandas is installed, let's write our first pandas application. We will write it in the IPython interpreter. IPython is an alternative shell for executing Python applications, and it conveniently provides numeric sequence numbers for thin input and output for example purposes. This is convenient for matching specific code examples in the book and will be used in all examples.

Note

IPython or IPython Notebooks will be the tools for all remaining examples in the book.

IPython is started using the ipython command from the shell or command line:

Michaels-MacBook-Pro:~ michaelheydt$ ipython
Python 2.7.8 |Anaconda 2.1.0 (x86_64)| (default, Aug 21 2014, 15:21:46) 
Type "copyright", "credits" or "license" for more information.

IPython 2.2.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
?         -> Introduction and overview of IPython...
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