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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Renaming column names

One of the most common operations on a DataFrame is to rename the column names. I like to rename my columns so that they are also valid Python attribute names. This means that they do not start with numbers and are lowercased alphanumerics with underscores. Good column names should also be descriptive, brief, and not clash with existing DataFrame or Series attributes.

In this recipe, the column names are renamed. The motivation for renaming is to make your code easier to understand, and also let your environment assist you. Recall that Jupyter will allow you to complete Series methods if you accessed the Series using dot notation (but will not allow method completion on index access).

How to do it…

  1. Read in the movie dataset, and make the index meaningful by setting it as the movie title:
    >>> movies = pd.read_csv("data/movie.csv")
    
  2. The renamed DataFrame method accepts dictionaries that map the old...
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