Data Transformation

  • Reshaping Data (Pivoting and Melting)
  • Applying Functions to Data
  • Creating New Columns
  • String Manipulation
  • Handling Categorical Data

Using pandas to_html to Convert DataFrames to HTML Tables

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Pandas apply: Transform Data with Functions Complete Guide

What is apply()?

The apply() method applies a function along an axis (rows or columns) of a DataFrame. It’s a powerful tool for data transformation when built-in methods aren’t sufficient.

When to use apply():

  • Transform data with custom logic that pandas doesn’t provide
  • Apply same operation to every row or column
  • Conditional transformations based on multiple columns
  • Convert data types or formats
  • Create new calculated columns

Key variants:

  • apply(): Apply function to rows or columns of DataFrame
  • applymap(): Apply function to each element (deprecated in pandas 2.1, use map())
  • map(): Apply function to Series elements
⚠️ Performance Warning: apply() can be slow on large datasets. Vectorization is almost always faster!

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Merge DataFrames on Multiple Columns in Pandas

Merging DataFrames on multiple columns is essential when working with real-world datasets. While merging on a single key is common, many scenarios require matching on multiple columns to ensure accurate combinations. This guide covers everything you need to know about merging on multiple columns in Pandas, from basic syntax to advanced techniques.

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Pandas groupby(): Complete Guide with Examples

The groupby() function is one of the most powerful and frequently used methods in Pandas. It allows you to split a DataFrame into groups based on one or more columns, apply operations to each group independently, and combine the results back together. This split-apply-combine workflow is essential for data analysis, aggregation, and summarization tasks.

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Custom Aggregations: Using apply and map for Complex Data Transformations

Custom aggregations in Pandas, involving apply and map functions, are powerful tools for performing complex data transformations. These functions allow for more nuanced and sophisticated data analysis than what is possible with standard aggregation methods like sum, mean, etc. Here’s how they work and how they can be used for complex data transformations: (more…)

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