pyarrow.parquet.ParquetFile¶

class pyarrow.parquet.ParquetFile(source, metadata=None, common_metadata=None, read_dictionary=None, memory_map=False, buffer_size=0, pre_buffer=False, coerce_int96_timestamp_unit=None)[source]¶

Bases: object

Reader interface for a single Parquet file.

Parameters

source (str, pathlib.Path, pyarrow.NativeFile, or file-like object) – Readable source. For passing bytes or buffer-like file containing a Parquet file, use pyarrow.BufferReader.
metadata (FileMetaData, default None) – Use existing metadata object, rather than reading from file.
common_metadata (FileMetaData, default None) – Will be used in reads for pandas schema metadata if not found in the main file’s metadata, no other uses at the moment.
memory_map (bool, default False) – If the source is a file path, use a memory map to read file, which can improve performance in some environments.
buffer_size (int, default 0) – If positive, perform read buffering when deserializing individual column chunks. Otherwise IO calls are unbuffered.
pre_buffer (bool, default False) – Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3). If True, Arrow will use a background I/O thread pool.
coerce_int96_timestamp_unit (str, default None.) – Cast timestamps that are stored in INT96 format to a particular resolution (e.g. ‘ms’). Setting to None is equivalent to ‘ns’ and therefore INT96 timestamps will be infered as timestamps in nanoseconds.

__init__(source, metadata=None, common_metadata=None, read_dictionary=None, memory_map=False, buffer_size=0, pre_buffer=False, coerce_int96_timestamp_unit=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(source[, metadata, …])	Initialize self.
`iter_batches`([batch_size, row_groups, …])	Read streaming batches from a Parquet file
`read`([columns, use_threads, use_pandas_metadata])	Read a Table from Parquet format,
`read_row_group`(i[, columns, use_threads, …])	Read a single row group from a Parquet file.
`read_row_groups`(row_groups[, columns, …])	Read a multiple row groups from a Parquet file.
`scan_contents`([columns, batch_size])	Read contents of file for the given columns and batch size.

Attributes

`metadata`
`num_row_groups`
`schema`	Return the Parquet schema, unconverted to Arrow types
`schema_arrow`	Return the inferred Arrow schema, converted from the whole Parquet file’s schema

iter_batches(batch_size=65536, row_groups=None, columns=None, use_threads=True, use_pandas_metadata=False)[source]¶

Read streaming batches from a Parquet file

Parameters

batch_size (int, default 64K) – Maximum number of records to yield per batch. Batches may be smaller if there aren’t enough rows in the file.
row_groups (list) – Only these row groups will be read from the file.
columns (list) – If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. ‘a’ will select ‘a.b’, ‘a.c’, and ‘a.d.e’.
use_threads (boolean, default True) – Perform multi-threaded column reads.
use_pandas_metadata (boolean, default False) – If True and file has custom pandas schema metadata, ensure that index columns are also loaded.

Returns

iterator of pyarrow.RecordBatch – Contents of each batch as a record batch

property metadata¶

property num_row_groups¶

read(columns=None, use_threads=True, use_pandas_metadata=False)[source]¶

Read a Table from Parquet format,

Parameters

columns (list) – If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. ‘a’ will select ‘a.b’, ‘a.c’, and ‘a.d.e’.
use_threads (bool, default True) – Perform multi-threaded column reads.
use_pandas_metadata (bool, default False) – If True and file has custom pandas schema metadata, ensure that index columns are also loaded.

Returns

pyarrow.table.Table – Content of the file as a table (of columns).

read_row_group(i, columns=None, use_threads=True, use_pandas_metadata=False)[source]¶

Read a single row group from a Parquet file.

Parameters

columns (list) – If not None, only these columns will be read from the row group. A column name may be a prefix of a nested field, e.g. ‘a’ will select ‘a.b’, ‘a.c’, and ‘a.d.e’.
use_threads (bool, default True) – Perform multi-threaded column reads.
use_pandas_metadata (bool, default False) – If True and file has custom pandas schema metadata, ensure that index columns are also loaded.

Returns

pyarrow.table.Table – Content of the row group as a table (of columns)

read_row_groups(row_groups, columns=None, use_threads=True, use_pandas_metadata=False)[source]¶

Read a multiple row groups from a Parquet file.

Parameters

row_groups (list) – Only these row groups will be read from the file.
columns (list) – If not None, only these columns will be read from the row group. A column name may be a prefix of a nested field, e.g. ‘a’ will select ‘a.b’, ‘a.c’, and ‘a.d.e’.
use_threads (bool, default True) – Perform multi-threaded column reads.
use_pandas_metadata (bool, default False) – If True and file has custom pandas schema metadata, ensure that index columns are also loaded.

Returns

pyarrow.table.Table – Content of the row groups as a table (of columns).

scan_contents(columns=None, batch_size=65536)[source]¶

Read contents of file for the given columns and batch size.

Notes

This function’s primary purpose is benchmarking. The scan is executed on a single thread.

Parameters

columns (list of integers, default None) – Select columns to read, if None scan all columns.
batch_size (int, default 64K) – Number of rows to read at a time internally.

Returns

num_rows (number of rows in file)

property schema¶: Return the Parquet schema, unconverted to Arrow types

property schema_arrow¶: Return the inferred Arrow schema, converted from the whole Parquet file’s schema

pyarrow.parquet.ParquetDataset pyarrow.parquet.ParquetWriter