October 2019
Beginner to intermediate
674 pages
16h 43m
English
Content preview from Mastering pandas - Second Edition
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The stack() function
When stacking, a set of column labels get converted to an index level. To explore stacking further, let's use a DataFrame with a MultiIndex along the row-index and column-index:
multi_df = sales_data[["Sales", "Quantity", "Category", "ShipMode"]].groupby(["Category", "ShipMode"]).agg([np.sum, np.mean])multi_df
The following will be the output:
Hierarchical data for stacking and unstacking
Applying stack() makes a wide DataFrame longer. Let's apply stack() on the preceding DataFrame. The column labels on the last level get added to the MultiIndex:
multi_df.stack()
The following will be the output:
Result of stacking
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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