How can Polars be used to export API results to a file when the stream size exceeds memory?

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Polars can be used to export API results to a file when the stream size exceeds memory using the to_csv() method with the chunk_size parameter. This parameter specifies the number of rows to write to the file at once, allowing large data sets to be written to disk in smaller chunks that can fit into memory.

Here is an example code snippet demonstrating how to use the chunk_size parameter:

import pandas as pd
import polars as pl

# Assume we have an API endpoint that returns a large data set
api_results = my_api_call()

# Convert API response to a Polars DataFrame
df = pl.from_pandas(pd.DataFrame(api_results))

# Set chunk_size to limit memory usage when writing to disk
chunk_size = 100000 

# Write the data to file in chunks
with pl.PolarsCsv() as sink:
    for i, chunk in enumerate(df.chunked(chunk_size)):
        sink.write(chunk, f"output_chunk_{i}.csv")

In this example, the chunk_size parameter is set to 100000, which limits the number of rows written to each file chunk. The with statement creates a Polars CSV sink and loops through the data in chunk_size-sized chunks, writing each chunk to a separate file. This allows large data sets to be written to disk without running out of memory.