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Polars-readme-2.py(file creato)
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1 | + | >>> df = pl.scan_csv("docs/assets/data/iris.csv") | |
2 | + | >>> ## OPTION 1 | |
3 | + | >>> # run SQL queries on frame-level | |
4 | + | >>> df.sql(""" | |
5 | + | ... SELECT species, | |
6 | + | ... AVG(sepal_length) AS avg_sepal_length | |
7 | + | ... FROM self | |
8 | + | ... GROUP BY species | |
9 | + | ... """).collect() | |
10 | + | shape: (3, 2) | |
11 | + | ┌────────────┬──────────────────┐ | |
12 | + | │ species ┆ avg_sepal_length │ | |
13 | + | │ --- ┆ --- │ | |
14 | + | │ str ┆ f64 │ | |
15 | + | ╞════════════╪══════════════════╡ | |
16 | + | │ Virginica ┆ 6.588 │ | |
17 | + | │ Versicolor ┆ 5.936 │ | |
18 | + | │ Setosa ┆ 5.006 │ | |
19 | + | └────────────┴──────────────────┘ | |
20 | + | >>> ## OPTION 2 | |
21 | + | >>> # use pl.sql() to operate on the global context | |
22 | + | >>> df2 = pl.LazyFrame({ | |
23 | + | ... "species": ["Setosa", "Versicolor", "Virginica"], | |
24 | + | ... "blooming_season": ["Spring", "Summer", "Fall"] | |
25 | + | ...}) | |
26 | + | >>> pl.sql(""" | |
27 | + | ... SELECT df.species, | |
28 | + | ... AVG(df.sepal_length) AS avg_sepal_length, | |
29 | + | ... df2.blooming_season | |
30 | + | ... FROM df | |
31 | + | ... LEFT JOIN df2 ON df.species = df2.species | |
32 | + | ... GROUP BY df.species, df2.blooming_season | |
33 | + | ... """).collect() |
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