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Learning Pandas

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

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781783985128
Length 504 pages
Edition 1st Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (14) Chapters Close

Preface 1. A Tour of pandas FREE CHAPTER 2. Installing pandas 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

Hierarchical indexing


Hierarchical indexing is a feature of pandas that allows specifying two or more index levels on an axis. The specification of multiple levels in an index allows for efficient selection of subsets of data. A pandas index that has multiple levels of hierarchy is referred to as a MultiIndex.

We can demonstrate creating a MultiIndex using the sp500 data. Suppose we want to organize this data by both the Sector and Symbol. We can accomplish this with the following code:

In [107]:
   # first, push symbol into a column
   reindexed = sp500.reset_index()
   # and now index sp500 by sector and symbol
   multi_fi = reindexed.set_index(['Sector', 'Symbol'])
   multi_fi

Out[107]:
                                   Price  BookValue
   Sector                 Symbol                   
   Industrials            MMM     141.14     26.668
   Health Care            ABT      39.60     15.573
                          ABBV     53.95      2.954
   Information Technology ACN      79.79  ...
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