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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 2. Up and Running with pandas FREE CHAPTER 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

Manipulating DataFrame Structure

pandas provides a powerful manipulation engine for you to use to explore your data. This exploration often involves making modifications to the structure of DataFrame objects to remove unnecessary data, change the format of existing data, or to create derived data from data in other rows or columns. These chapter will demonstrate how to perform these powerful and important operations.

Specifically, in this chapter we will cover:

  • Renaming columns
  • Adding new columns with [] and .insert()
  • Adding columns through enlargement
  • Adding columns using concatenation
  • Reordering columns
  • Replacing the contents of a column
  • Deleting columns
  • Adding new rows
  • Concatenating rows
  • Adding and replacing rows via enlargement
  • Removing rows using .drop()
  • Removing rows using Boolean selection
  • Removing rows using a slice
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