Learning pandas

Get to grips with pandas - a versatile and high-performance Python library for data manipulation, analysis, and discovery
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Learning pandas

Michael Heydt

6 customer reviews
Get to grips with pandas - a versatile and high-performance Python library for data manipulation, analysis, and discovery
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Book Details

ISBN 139781783985128
Paperback504 pages

Book Description

This learner's guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.

This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas' Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.

With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: Tidying Up Your Data
What is tidying your data?
Setting up the IPython notebook
Working with missing data
Handling duplicate data
Transforming Data
Summary
Chapter 8: Combining and Reshaping Data
Setting up the IPython notebook
Concatenating data
Merging and joining data
Stacking and unstacking
Performance benefits of stacked data
Summary
Chapter 9: Grouping and Aggregating Data
Setting up the IPython notebook
The split, apply, and combine (SAC) pattern
Split
Apply
Discretization and Binning
Summary
Chapter 10: 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
Chapter 11: Visualization
Setting up the IPython notebook
Plotting basics with pandas
Common plots used in statistical analyses
Multiple plots in a single chart
Summary
Chapter 12: 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

What You Will Learn

  • Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution
  • Learn how pandas builds on NumPy to implement flexible indexed data
  • Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs
  • Index, slice, and transform data to derive meaning from information
  • Load data from files, databases, and web services
  • Manipulate dates, times, and time series data
  • Group, aggregate, and summarize data
  • Visualize techniques for pandas and statistical data

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: Tidying Up Your Data
What is tidying your data?
Setting up the IPython notebook
Working with missing data
Handling duplicate data
Transforming Data
Summary
Chapter 8: Combining and Reshaping Data
Setting up the IPython notebook
Concatenating data
Merging and joining data
Stacking and unstacking
Performance benefits of stacked data
Summary
Chapter 9: Grouping and Aggregating Data
Setting up the IPython notebook
The split, apply, and combine (SAC) pattern
Split
Apply
Discretization and Binning
Summary
Chapter 10: 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
Chapter 11: Visualization
Setting up the IPython notebook
Plotting basics with pandas
Common plots used in statistical analyses
Multiple plots in a single chart
Summary
Chapter 12: 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

Book Details

ISBN 139781783985128
Paperback504 pages
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From 6 reviews

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