/usr/local/lib64/python3.6/site-packages/pyarrow/__pycache__
Edit: /usr/local/lib64/python3.6/site-packages/pyarrow/__pycache__/dataset.cpython-36.pyc (27970B)
3
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|dk r t d|dkr4tj| dS t| |S |dk rnt|trZtj|S t djt|qt dnr|dkr|dk rt dq| dk rt| tjr|dkrt j| dS t | |S t d jt| qt j S nt d
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Specify a partitioning scheme.
The supported schemes include:
- "DirectoryPartitioning": this scheme expects one segment in the file path
for each field in the specified schema (all fields are required to be
present). For example given schema the path
"/2009/11" would be parsed to ("year"_ == 2009 and "month"_ == 11).
- "HivePartitioning": a scheme for "/$key=$value/" nested directories as
found in Apache Hive. This is a multi-level, directory based partitioning
scheme. Data is partitioned by static values of a particular column in
the schema. Partition keys are represented in the form $key=$value in
directory names. Field order is ignored, as are missing or unrecognized
field names.
For example, given schema, a possible
path would be "/year=2009/month=11/day=15" (but the field order does not
need to match).
Parameters
----------
schema : pyarrow.Schema, default None
The schema that describes the partitions present in the file path.
If not specified, and `field_names` and/or `flavor` are specified,
the schema will be inferred from the file path (and a
PartitioningFactory is returned).
field_names : list of str, default None
A list of strings (field names). If specified, the schema's types are
inferred from the file paths (only valid for DirectoryPartitioning).
flavor : str, default None
The default is DirectoryPartitioning. Specify ``flavor="hive"`` for
a HivePartitioning.
dictionaries : Dict[str, Array]
If the type of any field of `schema` is a dictionary type, the
corresponding entry of `dictionaries` must be an array containing
every value which may be taken by the corresponding column or an
error will be raised in parsing. Alternatively, pass `infer` to have
Arrow discover the dictionary values, in which case a
PartitioningFactory is returned.
Returns
-------
Partitioning or PartitioningFactory
Examples
--------
Specify the Schema for paths like "/2009/June":
>>> partitioning(pa.schema([("year", pa.int16()), ("month", pa.string())]))
or let the types be inferred by only specifying the field names:
>>> partitioning(field_names=["year", "month"])
For paths like "/2009/June", the year will be inferred as int32 while month
will be inferred as string.
Specify a Schema with dictionary encoding, providing dictionary values:
>>> partitioning(
... pa.schema([
... ("year", pa.int16()),
... ("month", pa.dictionary(pa.int8(), pa.string()))
... ]),
... dictionaries={
... "month": pa.array(["January", "February", "March"]),
... })
Alternatively, specify a Schema with dictionary encoding, but have Arrow
infer the dictionary values:
>>> partitioning(
... pa.schema([
... ("year", pa.int16()),
... ("month", pa.dictionary(pa.int8(), pa.string()))
... ]),
... dictionaries="infer")
Create a Hive scheme for a path like "/year=2009/month=11":
>>> partitioning(
... pa.schema([("year", pa.int16()), ("month", pa.int8())]),
... flavor="hive")
A Hive scheme can also be discovered from the directory structure (and
types will be inferred):
>>> partitioning(flavor="hive")
Nz.Cannot specify both 'schema' and 'field_names'Zinfer)schemaz$Expected list of field names, got {}zSFor the default directory flavor, need to specify a Schema or a list of field namesZhivez.Cannot specify 'field_names' for flavor 'hive'z$Expected Schema for 'schema', got {}zUnsupported flavor)
ValueErrorr
Zdiscover
isinstancelistr+ typepaZSchemar )r3 field_namesflavorZ dictionariesr- r- r. partitionings s: ]
r; c C s\ | dkr
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Validate input and return a Partitioning(Factory).
It passes None through if no partitioning scheme is defined.
N)r: )r9 z4Expected Partitioning or PartitioningFactory, got {}) r5 strr; r6 r r r4 r+ r7 )schemer- r- r. _ensure_partitioning s
r> c C sd t | tr| S | dkr t S | dkr*t S | dkr8t S | dkrRtsLttt S tdj | d S ) NparquetipcarrowfeathercsvZorcz format '{}' is not supported> rB rA r@ )
r5 r r r r r' r4 r) r& r+ )objr- r- r. _ensure_format
s
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Treat a list of paths as files belonging to a single file system
If the file system is local then also validates that all paths
are referencing existing *files* otherwise any non-file paths will be
silently skipped (for example on a remote filesystem).
Parameters
----------
paths : list of path-like
Note that URIs are not allowed.
filesystem : FileSystem or str, optional
If an URI is passed, then its path component will act as a prefix for
the file paths.
Returns
-------
(FileSystem, list of str)
File system object and a list of normalized paths.
Raises
------
TypeError
If the passed filesystem has wrong type.
IOError
If the file system is local and a referenced path is not available or
not a file.
r )LocalFileSystemSubTreeFileSystem_MockFileSystemFileType_ensure_filesystemNc s g | ]} j t|qS r- )normalize_pathr ).0p)
filesystemr- r.
K s z,_ensure_multiple_sources..zPath {} points to a directory, but only file paths are supported. To construct a nested or union dataset pass a list of dataset objects instead.zPath {} exists but its type is unknown (could be a special file such as a Unix socket or character device, or Windows NUL / CON / ...))
pyarrow.fsrF rG rH rI rJ r5 Zbase_fs
get_file_infor7 FileZNotFoundFileNotFoundErrorpath DirectoryIsADirectoryErrorr+ IOError)
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r\ c C st ddl m}m}m} || |\}} |j| } |j| }|j|jkrP|| dd}n |j|jkrd| g}nt | ||fS )a
Treat path as either a recursively traversable directory or a single file.
Parameters
----------
path : path-like
filesystem : FileSystem or str, optional
If an URI is passed, then its path component will act as a prefix for
the file paths.
Returns
-------
(FileSystem, list of str or fs.Selector)
File system object and either a single item list pointing to a file or
an fs.Selector object pointing to a directory.
Raises
------
TypeError
If the passed filesystem has wrong type.
FileNotFoundError
If the referenced file or directory doesn't exist.
r )rI FileSelector _resolve_filesystem_and_pathT) recursive)
rP rI r] r^ rK rQ r7 rU rR rS )rT rN rI r] r^ file_infopaths_or_selectorr- r- r. _ensure_single_sourceg s
rb c C sh t |pd}t|}t| ttfr2t| |\}} nt| |\}} t||||d}
t|| ||
} | j |S )z
Create a FileSystemDataset which can be used to build a Dataset.
Parameters are documented in the dataset function.
Returns
-------
FileSystemDataset
r? )r; partition_base_direxclude_invalid_filesselector_ignore_prefixes)
rE r> r5 r6 tupler\ rb r r finish) sourcer3 rN r; r+ rc rd re fsra optionsfactoryr- r- r. _filesystem_dataset s
rl c K s( t dd |j D r tdt| |S )Nc s s | ]}|d k V qd S )Nr- )rL vr- r- r. s z%_in_memory_dataset..z@For in-memory datasets, you cannot pass any additional arguments)anyvaluesr4 r )rh r3 kwargsr- r- r. _in_memory_dataset s rr c sV t dd |j D r td d kr:tjdd | D fdd| D } t | S )Nc s s | ]}|d k V qd S )Nr- )rL rm r- r- r. rn s z!_union_dataset..zIWhen passing a list of Datasets, you cannot pass any additional argumentsc S s g | ]
}|j qS r- )r3 )rL childr- r- r. rO s z"_union_dataset..c s g | ]}|j qS r- )Zreplace_schema)rL rs )r3 r- r. rO s )ro rp r4 r8 Z
unify_schemasr" )childrenr3 rq r- )r3 r. _union_dataset s ru c
C s ddl m}m} |dkr t }nt|ts2td|dkrB| }n||}|jt| } t|t |d}t
| |||d} | j |S )ax
Create a FileSystemDataset from a `_metadata` file created via
`pyarrrow.parquet.write_metadata`.
Parameters
----------
metadata_path : path,
Path pointing to a single file parquet metadata file
schema : Schema, optional
Optionally provide the Schema for the Dataset, in which case it will
not be inferred from the source.
filesystem : FileSystem or URI string, default None
If a single path is given as source and filesystem is None, then the
filesystem will be inferred from the path.
If an URI string is passed, then a filesystem object is constructed
using the URI's optional path component as a directory prefix. See the
examples below.
Note that the URIs on Windows must follow 'file:///C:...' or
'file:/C:...' patterns.
format : ParquetFileFormat
An instance of a ParquetFileFormat if special options needs to be
passed.
partitioning : Partitioning, PartitioningFactory, str, list of str
The partitioning scheme specified with the ``partitioning()``
function. A flavor string can be used as shortcut, and with a list of
field names a DirectionaryPartitioning will be inferred.
partition_base_dir : str, optional
For the purposes of applying the partitioning, paths will be
stripped of the partition_base_dir. Files not matching the
partition_base_dir prefix will be skipped for partitioning discovery.
The ignored files will still be part of the Dataset, but will not
have partition information.
Returns
-------
FileSystemDataset
r )rF rJ Nz+format argument must be a ParquetFileFormat)rc r; )rj ) rP rF rJ r r5 r4 rK r r r> r rg )
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rv c C s t |||||||d}t| r*t| f|S t| ttfrtdd | D rVt| f|S tdd | D rtt| f|S tdd | D rt| f|S t dd | D } dj
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Open a dataset.
Datasets provides functionality to efficiently work with tabular,
potentially larger than memory and multi-file dataset.
- A unified interface for different sources, like Parquet and Feather
- Discovery of sources (crawling directories, handle directory-based
partitioned datasets, basic schema normalization)
- Optimized reading with predicate pushdown (filtering rows), projection
(selecting columns), parallel reading or fine-grained managing of tasks.
Note that this is the high-level API, to have more control over the dataset
construction use the low-level API classes (FileSystemDataset,
FilesystemDatasetFactory, etc.)
Parameters
----------
source : path, list of paths, dataset, list of datasets, (list of) batchesor tables, iterable of batches, RecordBatchReader, or URI
Path pointing to a single file:
Open a FileSystemDataset from a single file.
Path pointing to a directory:
The directory gets discovered recursively according to a
partitioning scheme if given.
List of file paths:
Create a FileSystemDataset from explicitly given files. The files
must be located on the same filesystem given by the filesystem
parameter.
Note that in contrary of construction from a single file, passing
URIs as paths is not allowed.
List of datasets:
A nested UnionDataset gets constructed, it allows arbitrary
composition of other datasets.
Note that additional keyword arguments are not allowed.
(List of) batches or tables, iterable of batches, or RecordBatchReader:
Create an InMemoryDataset. If an iterable or empty list is given,
a schema must also be given. If an iterable or RecordBatchReader
is given, the resulting dataset can only be scanned once; further
attempts will raise an error.
schema : Schema, optional
Optionally provide the Schema for the Dataset, in which case it will
not be inferred from the source.
format : FileFormat or str
Currently "parquet" and "ipc"/"arrow"/"feather" are supported. For
Feather, only version 2 files are supported.
filesystem : FileSystem or URI string, default None
If a single path is given as source and filesystem is None, then the
filesystem will be inferred from the path.
If an URI string is passed, then a filesystem object is constructed
using the URI's optional path component as a directory prefix. See the
examples below.
Note that the URIs on Windows must follow 'file:///C:...' or
'file:/C:...' patterns.
partitioning : Partitioning, PartitioningFactory, str, list of str
The partitioning scheme specified with the ``partitioning()``
function. A flavor string can be used as shortcut, and with a list of
field names a DirectionaryPartitioning will be inferred.
partition_base_dir : str, optional
For the purposes of applying the partitioning, paths will be
stripped of the partition_base_dir. Files not matching the
partition_base_dir prefix will be skipped for partitioning discovery.
The ignored files will still be part of the Dataset, but will not
have partition information.
exclude_invalid_files : bool, optional (default True)
If True, invalid files will be excluded (file format specific check).
This will incur IO for each files in a serial and single threaded
fashion. Disabling this feature will skip the IO, but unsupported
files may be present in the Dataset (resulting in an error at scan
time).
ignore_prefixes : list, optional
Files matching any of these prefixes will be ignored by the
discovery process. 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.
Returns
-------
dataset : Dataset
Either a FileSystemDataset or a UnionDataset depending on the source
parameter.
Examples
--------
Opening a single file:
>>> dataset("path/to/file.parquet", format="parquet")
Opening a single file with an explicit schema:
>>> dataset("path/to/file.parquet", schema=myschema, format="parquet")
Opening a dataset for a single directory:
>>> dataset("path/to/nyc-taxi/", format="parquet")
>>> dataset("s3://mybucket/nyc-taxi/", format="parquet")
Opening a dataset from a list of relatives local paths:
>>> dataset([
... "part0/data.parquet",
... "part1/data.parquet",
... "part3/data.parquet",
... ], format='parquet')
With filesystem provided:
>>> paths = [
... 'part0/data.parquet',
... 'part1/data.parquet',
... 'part3/data.parquet',
... ]
>>> dataset(paths, filesystem='file:///directory/prefix, format='parquet')
Which is equivalent with:
>>> fs = SubTreeFileSystem("/directory/prefix", LocalFileSystem())
>>> dataset(paths, filesystem=fs, format='parquet')
With a remote filesystem URI:
>>> paths = [
... 'nested/directory/part0/data.parquet',
... 'nested/directory/part1/data.parquet',
... 'nested/directory/part3/data.parquet',
... ]
>>> dataset(paths, filesystem='s3://bucket/', format='parquet')
Similarly to the local example, the directory prefix may be included in the
filesystem URI:
>>> dataset(paths, filesystem='s3://bucket/nested/directory',
... format='parquet')
Construction of a nested dataset:
>>> dataset([
... dataset("s3://old-taxi-data", format="parquet"),
... dataset("local/path/to/data", format="ipc")
... ])
)r3 rN r; r+ rc rd re c s s | ]}t |V qd S )N)r )rL elemr- r- r. rn s zdataset..c s s | ]}t |tV qd S )N)r5 r )rL rw r- r- r. rn s c s s | ]}t |tjtjfV qd S )N)r5 r8 RecordBatchTable)rL rw r- r- r. rn s c s s | ]}t |jV qd S )N)r7 __name__)rL rw r- r- r. rn s z, c s s | ]}d j |V qdS )z{}N)r+ )rL tr- r- r. rn s zExpected a list of path-like or dataset objects, or a list of batches or tables. The given list contains the following types: {}z\Expected a path-like, list of path-likes or a list of Datasets instead of the given type: {}N)dictr rl r5 rf r6 allru rr setjoin TypeErrorr+ r8 rx ry r7 rz ) rh r3 r+ rN r; rc rd Zignore_prefixesrq Z unique_types
type_namesr- r- r. dataset s:
r c s t | trtdt | tr*|r*tdnJt | ttfrZttj fdd| D |d} n| d krtttjg |d} t | tstd| S )NzhA PartitioningFactory cannot be used. Did you call the partitioning function without supplying a schema?zKProviding a partitioning_flavor with a Partitioning object is not supportedc s g | ]} j |qS r- )r0 )rL f)r3 r- r. rO s z._ensure_write_partitioning..)r3 r: )r: zDpartitioning must be a Partitioning object or a list of column names) r5 r r4 r rf r6 r; r8 r3 )partr3 r: r- )r3 r. _ensure_write_partitioning s
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Write a dataset to a given format and partitioning.
Parameters
----------
data : Dataset, Table/RecordBatch, RecordBatchReader, list of
Table/RecordBatch, or iterable of RecordBatch
The data to write. This can be a Dataset instance or
in-memory Arrow data. If an iterable is given, the schema must
also be given.
base_dir : str
The root directory where to write the dataset.
basename_template : str, optional
A template string used to generate basenames of written data files.
The token '{i}' will be replaced with an automatically incremented
integer. If not specified, it defaults to
"part-{i}." + format.default_extname
format : FileFormat or str
The format in which to write the dataset. Currently supported:
"parquet", "ipc"/"feather". If a FileSystemDataset is being written
and `format` is not specified, it defaults to the same format as the
specified FileSystemDataset. When writing a Table or RecordBatch, this
keyword is required.
partitioning : Partitioning or list[str], optional
The partitioning scheme specified with the ``partitioning()``
function or a list of field names. When providing a list of
field names, you can use ``partitioning_flavor`` to drive which
partitioning type should be used.
partitioning_flavor : str, optional
One of the partitioning flavors supported by
``pyarrow.dataset.partitioning``. If omitted will use the
default of ``partitioning()`` which is directory partitioning.
schema : Schema, optional
filesystem : FileSystem, optional
file_options : FileWriteOptions, optional
FileFormat specific write options, created using the
``FileFormat.make_write_options()`` function.
use_threads : bool, default True
Write files in parallel. If enabled, then maximum parallelism will be
used determined by the number of available CPU cores.
max_partitions : int, default 1024
Maximum number of partitions any batch may be written into.
file_visitor : Function
If set, this function will be called with a WrittenFile instance
for each file created during the call. This object will have both
a path attribute and a metadata attribute.
The path attribute will be a string containing the path to
the created file.
The metadata attribute will be the parquet metadata of the file.
This metadata will have the file path attribute set and can be used
to build a _metadata file. The metadata attribute will be None if
the format is not parquet.
Example visitor which simple collects the filenames created::
visited_paths = []
def file_visitor(written_file):
visited_paths.append(written_file.path)
existing_data_behavior : 'error' | 'overwrite_or_ignore' | 'delete_matching'
Controls how the dataset will handle data that already exists in
the destination. The default behavior ('error') is to raise an error
if any data exists in the destination.
'overwrite_or_ignore' will ignore any existing data and will
overwrite files with the same name as an output file. Other
existing files will be ignored. This behavior, in combination
with a unique basename_template for each write, will allow for
an append workflow.
'delete_matching' is useful when you are writing a partitioned
dataset. The first time each partition directory is encountered
the entire directory will be deleted. This allows you to overwrite
old partitions completely.
r )r^ )r3 T)r3 use_asyncNzOnly Dataset, Scanner, Table/RecordBatch, RecordBatchReader, a list of Tables/RecordBatches, or iterable of batches are supported.zTSupplied FileWriteOptions have format {}, which doesn't match supplied FileFormat {}z part-{i}.i )r3 r: ) use_threadsr z.Cannot specify a schema when writing a Scanner)rP r^ r5 r6 rf r3 r r8 rx ry r@ ZRecordBatchReaderr r Z from_batchesr r4 r
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