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Getting Started with Python Data Analysis

You're reading from  Getting Started with Python Data Analysis

Product type Book
Published in Nov 2015
Publisher
ISBN-13 9781785285110
Pages 188 pages
Edition 1st Edition
Languages

Table of Contents (15) Chapters

Getting Started with Python Data Analysis
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Introducing Data Analysis and Libraries 2. NumPy Arrays and Vectorized Computation 3. Data Analysis with Pandas 4. Data Visualization 5. Time Series 6. Interacting with Databases 7. Data Analysis Application Examples 8. Machine Learning Models with scikit-learn Index

The essential basic functionality


Pandas supports many essential functionalities that are useful to manipulate Pandas data structures. In this book, we will focus on the most important features regarding exploration and analysis.

Reindexing and altering labels

Reindex is a critical method in the Pandas data structures. It confirms whether the new or modified data satisfies a given set of labels along a particular axis of Pandas object.

First, let's view a reindex example on a Series object:

>>> s2.reindex([0, 2, 'b', 3])
0    0.6913
2    0.8627
b    NaN
3    0.7286
dtype: float64

When reindexed labels do not exist in the data object, a default value of NaN will be automatically assigned to the position; this holds true for the DataFrame case as well:

>>> df1.reindex(index=[0, 2, 'b', 3],
        columns=['Density', 'Year', 'Median_Age','C'])
   Density  Year  Median_Age        C
0      244  2000        24.2      NaN
2      268  2010        28.5      NaN
b      NaN   NaN  ...
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