liveBook · Manning

Series

As explained in Fast Python: High performance techniques for large datasets

A Series is pandas’ one-dimensional labeled data structure that associates values with an index.

A Series is pandas’ one-dimensional labeled data structure, associating values with an index. In data-processing code, Series objects can participate directly in column-wise arithmetic, avoiding explicit row iteration. section 7.2.2

Computing a ratio from two Series

Select the tip_amount and total_amount Series, divide them element by element, and calculate the resulting Series’ mean. This vectorized approach replaces row-wise callbacks and reduced the measured operation from seconds to milliseconds in the example. section 7.2.2

def get_tip_mean_vector(df):
    frac_tip = df["tip_amount"] / df["total_amount"]
    return frac_tip.mean()

This listing shows Series-based arithmetic and aggregation for a concise pandas computation. section 7.2.2

How Fast Python: High performance techniques for large datasets covers it

How deep it goes
Demonstrates Series in a vectorized calculation but stops short of explaining indexing internals or implementation details.
Examples
Uses tip_amount and total_amount Series to compute a mean tip fraction.
What you can do after
Apply it via pandas to replace row-wise calculations with vectorized column operations.
How it connects
Connects Series with pandas, DataFrame columns, vectorization, aggregation, and row iteration.

Also explained in

Compare how 4 books explain Series →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Fast Python: High performance techniques for large datasets. The section links point to the parts of the book it draws on.