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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

pandas DataFrames

A pandas DataFrame is a data structure, which is a labeled two-dimensional object and is similar in spirit to an Excel worksheet or a relational database table. A similar concept, by the way, was invented originally in the R programming language. (For more information, refer to http://www.r-tutor.com/r-introduction/data-frame.) A DataFrame can be created in the following ways:

  • From another DataFrame.
  • From a NumPy array or a composite of arrays that has a two-dimensional shape.
  • Likewise, we can create a DataFrame out of another pandas data structure called Series. We will learn about Series in the following section.
  • A DataFrame can also be produced from a file, such as a CSV file.

As an example, we will use data that can be retrieved from http://www.exploredata.net/Downloads/WHO-Data-Set. The original datafile is quite big and has many columns, so we will use an edited file instead, which only contains the first nine columns and is called WHO_first9cols.csv; the file is in the...

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