Style pivot table to create heat matrix

Learn how to highlight the most valuable cells in a Pandas pivot table that summarizes information on billionaires by country and industry.

Even though Hong Kong is small compared to other countries, its billionaires in Real Estate have accumulated wealth amounting to 132 billion dollars.

They are only surpassed by the United States, which has 192 billion dollars in Real Estate.

Heat matrix displaying billionaire wealth across various countries and industries, with notable highlights on Hong Kong and the USA.
Heat matrix of global billionaire wealth distribution

How can we build such a heat matrix from a long-format dataset?


Each row represents a billionaire, and the columns represent their attributes.

The dataset is a subset of the original billionaires dataset from Kaggle.

df = pd.read_csv('data.csv')
Preview of the billionaire dataset showing names, countries, industries, and net worth.
Dataset showing billionaire attributes


  1. What is a Pandas pivot table, and how is it used in data analysis?
  2. How can you summarize the total worth by country and category?
  3. How to style a DataFrame to create a heat matrix?
  4. Why is formatting a heat matrix crucial for optimal readability?
  5. What insights can be derived from analyzing the heat matrix?

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