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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
Tools
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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

Using Excel files

While CSV files are common, it seems that the world is ruled by Excel. I've been surprised in my consulting work to see how many companies are using Excel as a critical if not the critical tool for making decisions.

In this recipe, we will show how to create and read Excel files. You may need to install xlwt or openpyxl to write XLS or XLSX files, respectively.

How to do it...

  1. Create an Excel file using the .to_excel method. You can write either xls files or xlsx files:
    >>> beatles.to_excel("beat.xls")
    >>> beatles.to_excel("beat.xlsx")
    
    Excel file

    Excel file

  2. Read the Excel file with the read_excel function:
    >>> beat2 = pd.read_excel("/tmp/beat.xls")
    >>> beat2
       Unnamed: 0    first       last  birth
    0           0     Paul  McCartney   1942
    1           1     John     Lennon   1940
    2           2  Richard    Starkey   1940
    3           3 ...
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