An Introduction to DataFrames for pandas Users
summary
Shows how to reproduce pandas-style DataFrame workflows in Julia using DataFrames.jl, with line-by-line notebook conversions that cover reading CSV/Parquet files, inspecting shapes, and handling missing values. Demonstrates Julia package manager, Jupyter/Pluto notebooks, groupby/combine with piping for aggregation and new-column transforms, joins, frequency tables, and plotting hooks, with working code available in the repository.
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chapters (8)
- 0:00Welcome!
- 0:15Why use DataFrames.jl
- 4:19Hands-on working with DataFrames in Julia
- 8:05Reading a DataFrame
- 10:29Simple operations on a DataFrame (Julia vs Pandas)
- 21:37Aggregation functions
- 25:35Piping
- 27:17Noteworthy packages in the Julia Ecosystem
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