Packt+ | Advance your knowledge in tech

You're reading from Learning Pandas Get to grips with pandas - a versatile and high-performance Python library for data manipulation, analysis, and discovery

Product type Paperback

Published in Apr 2015

Publisher Packt

ISBN-13 9781783985128

Length 504 pages

Edition 1st Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Michael Heydt

View More author details

Table of Contents (14) Chapters

Preface

1. A Tour of pandas

2. Installing pandas FREE CHAPTER

3. NumPy for pandas

4. The pandas Series Object

5. The pandas DataFrame Object

6. Accessing Data

7. Tidying Up Your Data

8. Combining and Reshaping Data

9. Grouping and Aggregating Data

10. Time-series Data

11. Visualization

12. Applications to Finance

Index

Reading data from remote data services

pandas has direct support for various web-based data source classes in the pandas.io.data namespace. The primary class of interest is pandas.io.data.DataReader, which is implemented to read data from various supported sources and return it to the application directly as DataFrame.

Currently, support exists for the following sources via the DataReader class:

Daily historical prices' stock from either Yahoo! and Google Finance
Yahoo! Options
The Federal Reserve Economic Data library
Kenneth French's Data Library
The World Bank

The specific source of data is specified via the DataReader object's data_source parameter. The specific items to be retrieved are specified using the name parameter. If the data source supports selecting data between a range of dates, these dates can be specified with the start and end parameters. We will now take a look at reading data from each of these sources.