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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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 (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 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

The Pandas DataFrames


A Pandas DataFrame is a labeled two-dimensional data structure and is similar in spirit to a worksheet in Google Sheets or Microsoft Excel, or a relational database table. The columns in Pandas DataFrame can be of different types. 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:

  • Using another DataFrame.

  • Using 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.

  • From a dictionary of one-dimensional structures, such as one-dimensional NumPy arrays, lists, dicts, or Pandas Series.

As an example, we will use data that can be retrieved from http://www.exploredata.net/Downloads/WHO-Data-Set...

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