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Effective Data Manipulation with pandas

Stacking and Flattening Data

Explore techniques to reshape data using pandas by mastering stack and unstack methods. Understand how to work with multi-indexes, convert hierarchical columns into flat structures, and improve data manipulation for easier analysis and visualization.

We'll cover the following...

Unstacking

We’ve used the unstack method previously but not discussed it. It’s a powerful method (along with its complement, stack) to reshape our data. At a high level, unstack moves an index level into the columns. Usually, we use this operation on multi-index data, moving one of the indices into the columns (creating hierarchical columns).

Jupyter showing a transposed view of DataFrame. Notice that we see ten complete samples of data showing on the screen without scrolling.
Jupyter showing a transposed view of DataFrame. Notice that we see ten complete samples of data showing on the screen without scrolling.

The stack method does the reverse, moving a multilevel column into the index.

Python 3.8
import pandas as pd
url = 'https://github.com/mattharrison/datasets/raw/master/data/'\
      '2020-jetbrains-python-survey.csv'
jb = pd.read_csv(url)
jb2 = tweak_jb(jb)
print(jb2)

We’ll create a hierarchical index or multi-index by grouping with multiple columns. Let’s take the size of responses to are_you_datascientist column by country:

Python 3.8
print(jb2
  .groupby(['country_live', 'are_you_datascientist'])
  .size()
)

Notice that the result is a series with a multi-index. This result is useful but a little hard to scan through. It would be easier if we had countries in the index and each of the responses to are_you_datascientist as their own ...