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Learning pandas

You're reading from   Learning pandas High performance data manipulation and analysis using Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd 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 (16) Chapters Close

Preface 1. pandas and Data Analysis FREE CHAPTER 2. Up and Running with pandas 3. Representing Univariate Data with the Series 4. Representing Tabular and Multivariate Data with the DataFrame 5. Manipulating DataFrame Structure 6. Indexing Data 7. Categorical Data 8. Numerical and Statistical Methods 9. Accessing Data 10. Tidying Up Your Data 11. Combining, Relating, and Reshaping Data 12. Data Aggregation 13. Time-Series Modelling 14. Visualization 15. Historical Stock Price Analysis

How to work with missing data

Data is missing in pandas when it has a value of NaN (also seen as np.nan - the form from NumPy). This NaN value means that there is no value specified for the particular index label in a particular Series.

How can data be missing? There are a number of reasons why a value can be NaN:

  • A join of two sets of data does not have matched values
  • Data that you retrieved from an external source is incomplete
  • The NaN value is not known at a given point in time and will be filled in later
  • There is a data collection error retrieving a value, but the event must still be recorded in the index
  • Reindexing of data has resulted in an index that does not have a value
  • The shape of data has changed and there are now additional rows or columns, which at the time of reshaping could not be determined
  • There are likely more reasons, but the general point is that these situations...
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