How do you read and write Parquet files in Python with PyArrow?

PyArrow provides a low-level Parquet library that is fast, pure Python-friendly, and interoperates with Pandas:

Write:

import pyarrow as pa
import pyarrow.parquet as pq

table = pa.Table.from_pandas(df)
pq.write_table(table, "output.parquet", compression="zstd")

Read:

table = pq.read_table("output.parquet")
df = table.to_pandas()

Read specific columns only (column pruning):

table = pq.read_table("output.parquet", columns=["user_id", "revenue"])

Read with filter pushdown:

import pyarrow.dataset as ds
dataset = ds.dataset("s3://bucket/data/", format="parquet")
table = dataset.to_table(
    columns=["user_id", "revenue"],
    filter=ds.field("date") == "2026-01-01"
)

PyArrow's dataset API supports partitioned directories and applies filter pushdown automatically using Parquet statistics.

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