liveBook · Manning

Scatter plot

As explained in Starting Data Analytics with Generative AI and Python

A scatter plot is a chart that represents observations as points positioned according to two quantitative or categorical variables.

A scatter plot represents observations as points positioned by two variables. It helps reveal patterns, differences, and unusual values before drawing more specific conclusions. section 3.3.4 section 3.3

For delivery analysis, place customer states on one axis and seller states on the other. Color each point by delivery time so regional differences become visible. section 3.3.4

Make the resulting chart easier to inspect:

  • Use a logarithmic color scale when low values dominate and high-value outliers flatten the rest of the color range. section 3.3.4
  • Sort categorical axis values consistently so related state pairs are easier to find. section 3.3.4

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Demonstrates data preparation and visual refinement, but defers detailed interpretation and further analysis.
Examples
Demonstrates delivery time by customer state and seller state, including a log-scaled, consistently ordered scatter plot.
What it assumes
Assumes tabular data and basic DataFrame operations, while demonstrating timestamp calculations and table merging.
What you can do after
Apply it via Python, pandas, Matplotlib, and Seaborn to inspect patterns in prepared business data.
How it connects
Connects descriptive statistics, data visualization, feature engineering, categorical variables, and logarithmic scaling.

Also explained in

Compare how 8 books explain scatter plot →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.