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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

Adding columns using concatenation

Both the [] operator and .insert() method modify the target data frame in-place. If a new data frame with the additional columns is desired (leaving the original unchanged) then we can use the pd.concat() function. This function creates a new data frame with all of the specified DataFrame objects concatenated in the order of specification.

This following creates a new DataFrame with a single column containing the rounded price. It then uses pd.concat() with axis=1 to signify that the given DataFrame objects should be concatenated along the columns axis (as compared to rows which would use axis=0) .

Concatenation will be covered in more detail in Chapter 11, Combining, Relating, and Reshaping Data.

It is possible to have duplicate column names as a result of a concatenation. To demonstrate this happening, let's recreate rounded_price but...

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