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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
Author Profile Icon Matthew Harrison
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

Concatenating multiple DataFrames together

The concat function enables concatenating two or more DataFrames (or Series) together, both vertically and horizontally. As per usual, when dealing with multiple pandas objects simultaneously, concatenation doesn't happen haphazardly but aligns each object by their index.

In this recipe, we combine DataFrames both horizontally and vertically with the concat function and then change the parameter values to yield different results.

How to do it…

  1. Read in the 2016 and 2017 stock datasets, and make their ticker symbol the index:
    >>> stocks_2016 = pd.read_csv('data/stocks_2016.csv',
    ...     index_col='Symbol')
    >>> stocks_2017 = pd.read_csv('data/stocks_2017.csv',
    ...     index_col='Symbol')
    >>> stocks_2016
            Shares  Low  High
    Symbol                   
    AAPL        80   95   110
    TSLA        50   80   130
    WMT         40   55    70
    ...
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