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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
Languages
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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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Toc

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

Tidying when multiple variables are stored as a single column

Tidy datasets must have a single column for each variable. Occasionally, multiple variable names are placed in a single column with their corresponding value placed in another.

In this recipe, we identify the column containing the improperly structured variables and pivot it to create tidy data.

How to do it…

  1. Read in the restaurant inspections dataset, and convert the Date column data type to datetime64:
    >>> inspections = pd.read_csv('data/restaurant_inspections.csv',
    ...     parse_dates=['Date'])
    >>> inspections
                                  Name  ...
    0                E & E Grill House  ...
    1                E & E Grill House  ...
    2                E & E Grill House  ...
    3                E & E Grill House  ...
    4                E & E Grill House  ...
    ..                             ...  ...
    495  PIER SIXTY ONE-THE LIGHTHOUSE  ...
    496...
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