pyarrow.parquet.read_table#

pyarrow.parquet.read_table(source, columns=None, use_threads=True, metadata=None, schema=None, use_pandas_metadata=False, memory_map=False, read_dictionary=None, filesystem=None, filters=None, buffer_size=0, partitioning='hive', use_legacy_dataset=False, ignore_prefixes=None, pre_buffer=True, coerce_int96_timestamp_unit=None, decryption_properties=None)[source]#

Read a Table from Parquet format

Note: starting with pyarrow 1.0, the default for use_legacy_dataset is switched to False.

Parameters

sourcestr, pyarrow.NativeFile, or file-like object

If a string passed, can be a single file name or directory name. For file-like objects, only read a single file. Use pyarrow.BufferReader to read a file contained in a bytes or buffer-like object.

columnslist

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’. If empty, no columns will be read. Note that the table will still have the correct num_rows set despite having no columns.

use_threadsbool, default True

Perform multi-threaded column reads.

metadataFileMetaData

If separately computed

schemaSchema, optional

Optionally provide the Schema for the parquet dataset, in which case it will not be inferred from the source.

read_dictionarylist, default None

List of names or column paths (for nested types) to read directly as DictionaryArray. Only supported for BYTE_ARRAY storage. To read a flat column as dictionary-encoded pass the column name. For nested types, you must pass the full column “path”, which could be something like level1.level2.list.item. Refer to the Parquet file’s schema to obtain the paths.

memory_mapbool, default False

If the source is a file path, use a memory map to read file, which can improve performance in some environments.

buffer_sizeint, default 0

If positive, perform read buffering when deserializing individual column chunks. Otherwise IO calls are unbuffered.

partitioningpyarrow.dataset.Partitioning or str or list of str, default “hive”

The partitioning scheme for a partitioned dataset. The default of “hive” assumes directory names with key=value pairs like “/year=2009/month=11”. In addition, a scheme like “/2009/11” is also supported, in which case you need to specify the field names or a full schema. See the pyarrow.dataset.partitioning() function for more details.

use_pandas_metadatabool, default False

If True and file has custom pandas schema metadata, ensure that index columns are also loaded.

use_legacy_datasetbool, default False

By default, read_table uses the new Arrow Datasets API since pyarrow 1.0.0. Among other things, this allows to pass filters for all columns and not only the partition keys, enables different partitioning schemes, etc. Set to True to use the legacy behaviour (this option is deprecated, and the legacy implementation will be removed in a future version).

ignore_prefixeslist, optional

Files matching any of these prefixes will be ignored by the discovery process if use_legacy_dataset=False. This is matched to the basename of a path. By default this is [‘.’, ‘_’]. Note that discovery happens only if a directory is passed as source.

filesystemFileSystem, default None

If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem.

filtersList[Tuple] or List[List[Tuple]] or None (default)

Rows which do not match the filter predicate will be removed from scanned data. Partition keys embedded in a nested directory structure will be exploited to avoid loading files at all if they contain no matching rows. If use_legacy_dataset is True, filters can only reference partition keys and only a hive-style directory structure is supported. When setting use_legacy_dataset to False, also within-file level filtering and different partitioning schemes are supported.

Predicates are expressed in disjunctive normal form (DNF), like [[('x', '=', 0), ...], ...]. DNF allows arbitrary boolean logical combinations of single column predicates. The innermost tuples each describe a single column predicate. The list of inner predicates is interpreted as a conjunction (AND), forming a more selective and multiple column predicate. Finally, the most outer list combines these filters as a disjunction (OR).

Predicates may also be passed as List[Tuple]. This form is interpreted as a single conjunction. To express OR in predicates, one must use the (preferred) List[List[Tuple]] notation.

Each tuple has format: (key, op, value) and compares the key with the value. The supported op are: = or ==, !=, <, >, <=, >=, in and not in. If the op is in or not in, the value must be a collection such as a list, a set or a tuple.

Examples:

('x', '=', 0)
('y', 'in', ['a', 'b', 'c'])
('z', 'not in', {'a','b'})

pre_bufferbool, default True

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. This option is only supported for use_legacy_dataset=False. If using a filesystem layer that itself performs readahead (e.g. fsspec’s S3FS), disable readahead for best results.

coerce_int96_timestamp_unitstr, 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 inferred as timestamps in nanoseconds.

decryption_propertiesFileDecryptionProperties or None

File-level decryption properties. The decryption properties can be created using CryptoFactory.file_decryption_properties().

Returns

——-

pyarrow.Table

Content of the file as a table (of columns)

Examples

Generate an example PyArrow Table and write it to a partitioned dataset:

>>> import pyarrow as pa
>>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
...                   'n_legs': [2, 2, 4, 4, 5, 100],
...                   'animal': ["Flamingo", "Parrot", "Dog", "Horse",
...                              "Brittle stars", "Centipede"]})
>>> import pyarrow.parquet as pq
>>> pq.write_to_dataset(table, root_path='dataset_name_2',
...                     partition_cols=['year'])

Read the data:

>>> pq.read_table('dataset_name_2').to_pandas()
   n_legs         animal  year
     5  Brittle stars  2019
     2       Flamingo  2020
     4            Dog  2021
   100      Centipede  2021
     2         Parrot  2022
     4          Horse  2022

Read only a subset of columns:

>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"])
pyarrow.Table
n_legs: int64
animal: string
----
n_legs: [[5],[2],...,[2],[4]]
animal: [["Brittle stars"],["Flamingo"],...,["Parrot"],["Horse"]]

Read a subset of columns and read one column as DictionaryArray:

>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"],
...               read_dictionary=["animal"])
pyarrow.Table
n_legs: int64
animal: dictionary<values=string, indices=int32, ordered=0>
----
n_legs: [[5],[2],...,[2],[4]]
animal: [  -- dictionary:
["Brittle stars"]  -- indices:
[0],  -- dictionary:
["Flamingo"]  -- indices:
[0],...,  -- dictionary:
["Parrot"]  -- indices:
[0],  -- dictionary:
["Horse"]  -- indices:
[0]]

Read the table with filter:

>>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"],
...               filters=[('n_legs','<',4)]).to_pandas()
   n_legs    animal
0       2  Flamingo
1       2    Parrot

Read data from a single Parquet file:

>>> pq.write_table(table, 'example.parquet')
>>> pq.read_table('dataset_name_2').to_pandas()
   n_legs         animal  year
0       5  Brittle stars  2019
1       2       Flamingo  2020
2     100      Centipede  2021
3       4            Dog  2021
4       2         Parrot  2022
5       4          Horse  2022

pyarrow.parquet.ParquetWriter

pyarrow.parquet.read_metadata