Working with DataFrames.jl | Workshop Part 1 | JuliaCon 2023

Data science pipelines created in Julia typically need to be integrated into larger workflows involving various tools and technologies. Therefore an important aspect is ensuring interoperability, especially for the case of large data that does not fit in RAM of a single machine. During the workshop we discuss working with the following data formats: Section 1: https://github.com/bkamins/JuliaCon20... statistical packages (Stata/SAS/SPSS and RData); databases (SQLite and DuckDB); Apache Parquet. Section 2: https://github.com/quinnj/JuliaCon202... CSV; Apache Arrow; JSON. The examples will DataFrames.jl that provides a representative implementation of Tables.jl table. Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/You... Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/You...