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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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Toc

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 Series

The pandas Series data structure is a one-dimensional heterogeneous array with labels. We can create a pandas Series data structure as follows:

  • From a Python dict
  • From a NumPy array
  • From a single scalar value

When creating a Series, we can hand the constructor a list of axis labels, which is commonly referred to as the index. The index is an optional parameter. By default, if we use a NumPy array as the input data, pandas will index values by autoincrementing the index commencing from 0. If the data handed to the constructor is a Python dict, the sorted dict keys will become the index. In the case of a scalar value as the input data, we are required to supply the index. For each new value in the index, the scalar input value will be reiterated. The pandas Series and DataFrame interfaces have features and behaviors borrowed from NumPy arrays and Python dictionaries, such as slicing, lookup using a key, and vectorized operations. Performing a lookup on a DataFrame column returns...

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