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Multiple

Dealing with multiple files.

Polars can deal with multiple files differently depending on your needs and memory strain.

Let's create some files to give us some context:

write_csv 

import polars as pl

df = pl.DataFrame({"foo": [1, 2, 3], "bar": [None, "ham", "spam"]})

for i in range(5):
    df.write_csv(f"docs/assets/data/my_many_files_{i}.csv")

Reading into a single DataFrame

To read multiple files into a single DataFrame, we can use globbing patterns:

read_csv 

df = pl.read_csv("docs/assets/data/my_many_files_*.csv")
print(df)


shape: (15, 2)
┌─────┬──────┐
│ foo ┆ bar  │
│ --- ┆ ---  │
│ i64 ┆ str  │
╞═════╪══════╡
│ 1   ┆ null │
│ 2   ┆ ham  │
│ 3   ┆ spam │
│ 1   ┆ null │
│ 2   ┆ ham  │
│ …   ┆ …    │
│ 2   ┆ ham  │
│ 3   ┆ spam │
│ 1   ┆ null │
│ 2   ┆ ham  │
│ 3   ┆ spam │
└─────┴──────┘

To see how this works we can take a look at the query plan. Below we see that all files are read separately and concatenated into a single DataFrame. Polars will try to parallelize the reading.

show_graph 

pl.scan_csv("docs/assets/data/my_many_files_*.csv").show_graph()

Reading and processing in parallel

If your files don't have to be in a single table you can also build a query plan for each file and execute them in parallel on the Polars thread pool.

All query plan execution is embarrassingly parallel and doesn't require any communication.

scan_csv 

import glob

import polars as pl

queries = []
for file in glob.glob("docs/assets/data/my_many_files_*.csv"):
    q = pl.scan_csv(file).group_by("bar").agg(pl.len(), pl.sum("foo"))
    queries.append(q)

dataframes = pl.collect_all(queries)
print(dataframes)


[shape: (3, 3)
┌──────┬─────┬─────┐
│ bar  ┆ len ┆ foo │
│ ---  ┆ --- ┆ --- │
│ str  ┆ u32 ┆ i64 │
╞══════╪═════╪═════╡
│ spam ┆ 1   ┆ 3   │
│ null ┆ 1   ┆ 1   │
│ ham  ┆ 1   ┆ 2   │
└──────┴─────┴─────┘, shape: (3, 3)
┌──────┬─────┬─────┐
│ bar  ┆ len ┆ foo │
│ ---  ┆ --- ┆ --- │
│ str  ┆ u32 ┆ i64 │
╞══════╪═════╪═════╡
│ spam ┆ 1   ┆ 3   │
│ ham  ┆ 1   ┆ 2   │
│ null ┆ 1   ┆ 1   │
└──────┴─────┴─────┘, shape: (3, 3)
┌──────┬─────┬─────┐
│ bar  ┆ len ┆ foo │
│ ---  ┆ --- ┆ --- │
│ str  ┆ u32 ┆ i64 │
╞══════╪═════╪═════╡
│ spam ┆ 1   ┆ 3   │
│ null ┆ 1   ┆ 1   │
│ ham  ┆ 1   ┆ 2   │
└──────┴─────┴─────┘, shape: (3, 3)
┌──────┬─────┬─────┐
│ bar  ┆ len ┆ foo │
│ ---  ┆ --- ┆ --- │
│ str  ┆ u32 ┆ i64 │
╞══════╪═════╪═════╡
│ spam ┆ 1   ┆ 3   │
│ ham  ┆ 1   ┆ 2   │
│ null ┆ 1   ┆ 1   │
└──────┴─────┴─────┘, shape: (3, 3)
┌──────┬─────┬─────┐
│ bar  ┆ len ┆ foo │
│ ---  ┆ --- ┆ --- │
│ str  ┆ u32 ┆ i64 │
╞══════╪═════╪═════╡
│ null ┆ 1   ┆ 1   │
│ ham  ┆ 1   ┆ 2   │
│ spam ┆ 1   ┆ 3   │
└──────┴─────┴─────┘]