Learning pandas

Michael Heydt

• Publisher: Packt Publishing
• Year: 2015
• ISBN:
  9781783985128
• Topic: Ciencia de la computación
• Subtopic: Programación en Python

Learning pandas

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Table of Contents

Learning pandas

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Why subscribe?

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

Downloading the color images of this book

Errata

Piracy

Questions

1. A Tour of pandas

pandas and why it is important

pandas and IPython Notebooks

Referencing pandas in the application

Primary pandas objects

The pandas Series object

The pandas DataFrame object

Loading data from files and the Web

Loading CSV data from files

Loading data from the Web

Simplicity of visualization of pandas data

Summary

2. Installing pandas

Getting Anaconda

Installing Anaconda

Installing Anaconda on Linux

Installing Anaconda on Mac OS X

Installing Anaconda on Windows

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

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 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

Alignment via index labels

Arithmetic operations

The special case of Not-A-Number (NaN)

Boolean selection

Reindexing a Series

Modifying a Series in-place

Slicing a Series

Summary

5. The pandas DataFrame Object

Creating DataFrame from scratch

Example data

S&P 500

Monthly stock historical prices

Selecting columns of a DataFrame

Selecting rows and values of a DataFrame using the index

Slicing using the [] operator

Selecting rows by index label and location: .loc[] and .iloc[]

Selecting rows by index label and/or location: .ix[]

Scalar lookup by label or location using .at[] and .iat[]

Selecting rows of a DataFrame by Boolean selection

Modifying the structure and content of DataFrame

Renaming columns

Adding and inserting columns

Replacing the contents of a column

Deleting columns in a DataFrame

Adding rows to a DataFrame

Appending rows with .append()

Concatenating DataFrame objects with pd.concat()

Adding rows (and columns) via setting with enlargement

Removing rows from a DataFrame

Removing rows using .drop()

Removing rows using Boolean selection

Removing rows using a slice

Changing scalar values in a DataFrame

Arithmetic on a DataFrame

Resetting and reindexing

Hierarchical indexing

Summarized data and descriptive statistics

Summary

6. Accessing Data

Setting up the IPython notebook

CSV and Text/Tabular format

The sample CSV data set

Reading a CSV file into a DataFrame

Specifying the index column when reading a CSV file

Data type inference and specification

Specifying column names

Specifying specific columns to load

Saving DataFrame to a CSV file

General field-delimited data

Handling noise rows in field-delimited data

Reading and writing data in an Excel format

Reading and writing JSON files

Reading HTML data from the Web

Reading and writing HDF5 format files

Accessing data on the web and in the cloud

Reading and writing from/to SQL databases

Reading data from remote data services

Reading stock data from Yahoo! and Google Finance

Retrieving data from Yahoo! Finance Options

Reading economic data from the Federal Reserve Bank of St. Louis

Accessing Kenneth French's data

Reading from the World Bank

Summary

7. Tidying Up ...