PyArrow
Apache Arrow columnar data format Python library — high-performance in-memory columnar data processing and Parquet/Feather file I/O. PyArrow features: pa.Table for columnar in-memory data, pa.array()/pa.chunked_array() for typed arrays, parquet.read_table()/write_table() for Parquet I/O, feather.read_feather()/write_feather(), pyarrow.dataset for partitioned datasets, compute functions (pc.filter, pc.sort_indices, pc.cast), pa.Schema for explicit typing, zero-copy conversion with pandas, Arrow IPC format for inter-process data transfer, S3/GCS/HDFS filesystem integration, and memory-mapped files. Standard library for Parquet I/O, columnar data processing, and zero-copy data sharing between Python processes.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
S3/GCS credentials use standard cloud IAM — store in environment variables or instance roles, not code. Parquet files may contain sensitive data — apply appropriate filesystem permissions. Memory-mapped files expose data to same-user processes.
⚡ Reliability
Best When
Reading/writing Parquet files, zero-copy data exchange between libraries (pandas, polars, DuckDB), or scanning large partitioned datasets on S3/GCS — PyArrow is the foundational data interchange format that all modern Python data tools understand.
Avoid When
You need row-level iteration, in-place mutation, or complex SQL analytics — use pandas or DuckDB for those instead.
Use Cases
- • Agent Parquet I/O — import pyarrow.parquet as pq; table = pq.read_table('data.parquet', columns=['id', 'value']); df = table.to_pandas() — read Parquet with column projection (only read needed columns); agent data pipeline reads 10GB Parquet file in seconds by skipping unused columns; write with pq.write_table(table, 'output.parquet', compression='snappy')
- • Agent zero-copy pandas interop — table = pa.Table.from_pandas(df); df2 = table.to_pandas(zero_copy_only=True) — convert pandas DataFrame to Arrow Table without copying data; agent passes large datasets between components without memory duplication; Arrow is the common currency between pandas, polars, and DuckDB
- • Agent partitioned dataset scanning — import pyarrow.dataset as ds; dataset = ds.dataset('s3://bucket/data/', format='parquet', partitioning='hive'); table = dataset.to_table(filter=ds.field('date') > '2024-01-01') — scan partitioned S3 Parquet dataset with predicate pushdown; agent reads only matching partitions instead of all data
- • Agent columnar compute — import pyarrow.compute as pc; filtered = pc.filter(table, pc.greater(table['score'], 0.5)); sorted_idx = pc.sort_indices(table, sort_keys=[('score', 'descending')]); top_n = table.take(sorted_idx[:100]) — filter and sort Arrow tables without pandas; faster than pandas for columnar operations on large tables
- • Agent IPC data transfer — sink = pa.BufferOutputStream(); writer = pa.ipc.new_stream(sink, table.schema); writer.write_table(table); writer.close(); buf = sink.getvalue() — serialize Arrow table to bytes for inter-process transfer; agent shares large datasets between workers via shared memory or network without serialization overhead
Not For
- • Row-oriented data processing — Arrow is columnar; row-level operations (iterating rows) are slow; use pandas or Python dicts for row-oriented workflows
- • In-place mutation — Arrow arrays are immutable; for mutable data use pandas or numpy arrays with Arrow only for I/O
- • Complex analytics — Arrow compute is limited; for complex aggregations use DuckDB or pandas on Arrow data
Interface
Authentication
No auth for local files. S3/GCS access uses boto3/google-auth credentials. HDFS uses Kerberos.
Pricing
PyArrow/Apache Arrow is Apache 2.0 licensed. Free for all use.
Agent Metadata
Known Gotchas
- ⚠ Column projection dramatically reduces read time — pq.read_table('data.parquet') reads ALL columns; for 100-column file reading 3 columns, use pq.read_table('data.parquet', columns=['a', 'b', 'c']); agent data pipelines reading wide Parquet files MUST specify columns to avoid reading 10-100x more data than needed
- ⚠ Arrow arrays are immutable — pa.array([1, 2, 3]) cannot be modified in place; agent code doing arr[0] = 5 raises TypeError; convert to list, modify, and create new array; or use pandas for mutable intermediate computation and convert back to Arrow for I/O
- ⚠ Type inference may not match schema — pa.Table.from_pandas(df) infers types; integer columns with NaN become float64; agent code must specify schema explicitly: pa.Table.from_pandas(df, schema=my_schema) to enforce types; especially important for nullable integers (use pa.int64() not python int)
- ⚠ to_pandas() copies data by default — table.to_pandas() always copies even when zero-copy is possible; use table.to_pandas(zero_copy_only=True) for zero-copy when data is contiguous; raises ArrowNotImplementedError if zero-copy impossible; agent large dataset pipelines should try zero_copy_only first
- ⚠ Partitioned datasets require partitioning scheme — ds.dataset('path/', partitioning='hive') assumes hive-style (date=2024-01-01/); wrong partitioning scheme causes wrong filter pushdown or ValueError; agent must match partitioning to how data was written
- ⚠ Large Parquet writes need row group size tuning — pq.write_table(table, 'out.parquet') uses 64MB row groups by default; for filter pushdown effectiveness, smaller row groups (1-10MB) enable finer skipping; agent write pipeline should tune: pq.write_table(table, path, row_group_size=50000)
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Scores are editorial opinions as of 2026-03-06.