โ๏ธ ๐๐๐ ๐บ๐ฒ ๐ฎ ๐ฐ๐ผ๐ณ๐ณ๐ฒ๐ฒ:
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This video shows how to use SQL to query Polars DataFrames in Marimo Notebooks, using SQL Cells. We perform an analytical query using the LAG window function and a common-table expression to get insight into the data we read in. This video also dives into how to configure Marimo notebooks through the user interface, and from a .marimo.toml file.
๐ ๐๐ต๐ฎ๐ฝ๐๐ฒ๐ฟ๐:
00:00 Intro
00:43 Installing SQL dependencies - DuckDB and Pyarrow
02:21 Querying dataframes with SQL
05:32 Converting date column to Date data-type
07:30 Comparing values to previous row with SQL LAG window function
10:05 Getting months with largest increase in popularity using SQL Common Table Expression
12:50 Marimo SQL cells - under the hood
13:46 More SQL analytical queries
15:38 Plotting SQL query outputs
17:48 Marimo configuration
๐ฆ๐ผ๐ฐ๐ถ๐ฎ๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ:
๐ Blog: https://bugbytes.io/posts/
๐พ Github: https://github.com/bugbytes-io
๐ ๐๐๐ฟ๐๐ต๐ฒ๐ฟ ๐ฟ๐ฒ๐ฎ๐ฑ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ถ๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป:
Marimo: https://marimo.io/
Marimo configuration: https://docs.marimo.io/guides/configuration
Marimo SQL: https://docs.marimo.io/guides/working_with_data/sql
Polars: https://pola.rs/
DuckDB: https://www.youtube.com/watch?v=HJGiMTLcpDs
Kaggle data: https://www.kaggle.com/datasets/muhammadkhalid/most-popular-programming-languages-since-2004
#python #datascience #dataanalytics #dataengineering