apache_beam.io.textio module¶
A source and a sink for reading from and writing to text files.
-
class
apache_beam.io.textio.
ReadAllFromText
(min_bundle_size=0, desired_bundle_size=67108864, compression_type='auto', strip_trailing_newlines=True, validate=False, coder=StrUtf8Coder, skip_header_lines=0, with_filename=False, delimiter=None, escapechar=None, **kwargs)[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
A
PTransform
for reading aPCollection
of text files.Reads aPCollection
of text files or file patterns and produces aPCollection
of strings.Parses a text file as newline-delimited elements, by default assuming UTF-8 encoding. Supports newline delimiters ‘n’ and ‘rn’.
If with_filename is
True
the output will include the file name. This is similar toReadFromTextWithFilename
but thisPTransform
can be placed anywhere in the pipeline.If reading from a text file that that requires a different encoding, you may provide a custom
Coder
that encodes and decodes with the appropriate codec. For example, see the implementation ofStrUtf8Coder
.This does not support
UTF-16
orUTF-32
encodings.This implementation is only tested with batch pipeline. In streaming, reading may happen with delay due to the limitation in ReShuffle involved.
Initialize the
ReadAllFromText
transform.Parameters: - min_bundle_size – Minimum size of bundles that should be generated when
splitting this source into bundles. See
FileBasedSource
for more details. - desired_bundle_size – Desired size of bundles that should be generated when
splitting this source into bundles. See
FileBasedSource
for more details. - compression_type – Used to handle compressed input files. Typical value
is
CompressionTypes.AUTO
, in which case the underlying file_path’s extension will be used to detect the compression. - strip_trailing_newlines – Indicates whether this source should remove the newline char in each line it reads before decoding that line.
- validate – flag to verify that the files exist during the pipeline creation time.
- skip_header_lines – Number of header lines to skip. Same number is skipped from each source file. Must be 0 or higher. Large number of skipped lines might impact performance.
- coder – Coder used to decode each line.
- with_filename – If True, returns a Key Value with the key being the file name and the value being the actual data. If False, it only returns the data.
- delimiter (bytes) – delimiter to split records. Must not self-overlap, because self-overlapping delimiters cause ambiguous parsing.
- escapechar (bytes) – a single byte to escape the records delimiter, can also escape itself.
-
DEFAULT_DESIRED_BUNDLE_SIZE
= 67108864¶
- min_bundle_size – Minimum size of bundles that should be generated when
splitting this source into bundles. See
-
class
apache_beam.io.textio.
ReadAllFromTextContinuously
(file_pattern, **kwargs)[source]¶ Bases:
apache_beam.io.textio.ReadAllFromText
A
PTransform
for reading text files in given file patterns. This PTransform acts as a Source and produces continuously aPCollection
of strings.For more details, see
ReadAllFromText
for text parsing settings; seeapache_beam.io.fileio.MatchContinuously
for watching settings.ReadAllFromTextContinuously is experimental. No backwards-compatibility guarantees. Due to the limitation on Reshuffle, current implementation does not scale.
Initialize the
ReadAllFromTextContinuously
transform.Accepts args for constructor args of both
ReadAllFromText
andMatchContinuously
.
-
class
apache_beam.io.textio.
ReadFromText
(file_pattern=None, min_bundle_size=0, compression_type='auto', strip_trailing_newlines=True, coder=StrUtf8Coder, validate=True, skip_header_lines=0, delimiter=None, escapechar=None, **kwargs)[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
A
PTransform
for reading text files.Parses a text file as newline-delimited elements, by default assuming
UTF-8
encoding. Supports newline delimiters\n
and\r\n
or specified delimiter.If reading from a text file that that requires a different encoding, you may provide a custom
Coder
that encodes and decodes with the appropriate codec. For example, see the implementation ofStrUtf8Coder
.This does not support
UTF-16
orUTF-32
encodings.Initialize the
ReadFromText
transform.Parameters: - file_pattern (str) – The file path to read from as a local file path or a
GCS
gs://
path. The path can contain glob characters (*
,?
, and[...]
sets). - min_bundle_size (int) – Minimum size of bundles that should be generated
when splitting this source into bundles. See
FileBasedSource
for more details. - compression_type (str) – Used to handle compressed input files.
Typical value is
CompressionTypes.AUTO
, in which case the underlying file_path’s extension will be used to detect the compression. - strip_trailing_newlines (bool) – Indicates whether this source should remove the newline char in each line it reads before decoding that line.
- validate (bool) – flag to verify that the files exist during the pipeline creation time.
- skip_header_lines (int) – Number of header lines to skip. Same number is skipped from each source file. Must be 0 or higher. Large number of skipped lines might impact performance.
- coder (Coder) – Coder used to decode each line.
- delimiter (bytes) – delimiter to split records. Must not self-overlap, because self-overlapping delimiters cause ambiguous parsing.
- escapechar (bytes) – a single byte to escape the records delimiter, can also escape itself.
- file_pattern (str) – The file path to read from as a local file path or a
GCS
-
class
apache_beam.io.textio.
ReadFromTextWithFilename
(file_pattern=None, min_bundle_size=0, compression_type='auto', strip_trailing_newlines=True, coder=StrUtf8Coder, validate=True, skip_header_lines=0, delimiter=None, escapechar=None, **kwargs)[source]¶ Bases:
apache_beam.io.textio.ReadFromText
A
ReadFromText
for reading text files returning the name of the file and the content of the file.This class extend ReadFromText class just setting a different _source_class attribute.
Initialize the
ReadFromText
transform.Parameters: - file_pattern (str) – The file path to read from as a local file path or a
GCS
gs://
path. The path can contain glob characters (*
,?
, and[...]
sets). - min_bundle_size (int) – Minimum size of bundles that should be generated
when splitting this source into bundles. See
FileBasedSource
for more details. - compression_type (str) – Used to handle compressed input files.
Typical value is
CompressionTypes.AUTO
, in which case the underlying file_path’s extension will be used to detect the compression. - strip_trailing_newlines (bool) – Indicates whether this source should remove the newline char in each line it reads before decoding that line.
- validate (bool) – flag to verify that the files exist during the pipeline creation time.
- skip_header_lines (int) – Number of header lines to skip. Same number is skipped from each source file. Must be 0 or higher. Large number of skipped lines might impact performance.
- coder (Coder) – Coder used to decode each line.
- delimiter (bytes) – delimiter to split records. Must not self-overlap, because self-overlapping delimiters cause ambiguous parsing.
- escapechar (bytes) – a single byte to escape the records delimiter, can also escape itself.
- file_pattern (str) – The file path to read from as a local file path or a
GCS
-
class
apache_beam.io.textio.
WriteToText
(file_path_prefix, file_name_suffix='', append_trailing_newlines=True, num_shards=0, shard_name_template=None, coder=ToBytesCoder, compression_type='auto', header=None, footer=None, *, max_records_per_shard=None, max_bytes_per_shard=None, skip_if_empty=False)[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
A
PTransform
for writing to text files.Initialize a
WriteToText
transform.Parameters: - file_path_prefix (str) – The file path to write to. The files written will begin with this prefix, followed by a shard identifier (see num_shards), and end in a common extension, if given by file_name_suffix. In most cases, only this argument is specified and num_shards, shard_name_template, and file_name_suffix use default values.
- file_name_suffix (str) – Suffix for the files written.
- append_trailing_newlines (bool) – indicate whether this sink should write an additional newline char after writing each element.
- num_shards (int) – The number of files (shards) used for output. If not set, the service will decide on the optimal number of shards. Constraining the number of shards is likely to reduce the performance of a pipeline. Setting this value is not recommended unless you require a specific number of output files.
- shard_name_template (str) – A template string containing placeholders for
the shard number and shard count. Currently only
''
and'-SSSSS-of-NNNNN'
are patterns accepted by the service. When constructing a filename for a particular shard number, the upper-case lettersS
andN
are replaced with the0
-padded shard number and shard count respectively. This argument can be''
in which case it behaves as if num_shards was set to 1 and only one file will be generated. The default pattern used is'-SSSSS-of-NNNNN'
. - coder (Coder) – Coder used to encode each line.
- compression_type (str) – Used to handle compressed output files.
Typical value is
CompressionTypes.AUTO
, in which case the final file path’s extension (as determined by file_path_prefix, file_name_suffix, num_shards and shard_name_template) will be used to detect the compression. - header (str) – String to write at beginning of file as a header.
If not
None
and append_trailing_newlines is set,\n
will be added. - footer (str) – String to write at the end of file as a footer.
If not
None
and append_trailing_newlines is set,\n
will be added. - max_records_per_shard – Maximum number of records to write to any individual shard.
- max_bytes_per_shard – Target maximum number of bytes to write to any individual shard. This may be exceeded slightly, as a new shard is created once this limit is hit, but the remainder of a given record, a subsequent newline, and a footer may cause the actual shard size to exceed this value. This also tracks the uncompressed, not compressed, size of the shard.
- skip_if_empty – Don’t write any shards if the PCollection is empty. In case of an empty PCollection, this will still delete existing files having same file path and not create new ones.
-
apache_beam.io.textio.
ReadFromCsv
(path: str, *, splittable: bool = True, **kwargs)[source]¶ - A PTransform for reading comma-separated values (csv) files into a
PCollection.
- Args:
- path (str): The file path to read from. The path can contain glob
- characters such as
*
and?
. - splittable (bool): Whether the csv files are splittable at line
- boundaries, i.e. each line of this file represents a complete record. This should be set to False if single records span multiple lines (e.g. a quoted field has a newline inside of it). Setting this to false may disable liquid sharding.
**kwargs: Extra arguments passed to pandas.read_csv (see below).
- sep : str, default ‘,’
- Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python’s builtin sniffer
tool,
csv.Sniffer
. In addition, separators longer than 1 character and different from'\s+'
will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example:'\r\t'
. - delimiter : str, default
None
- Alias for sep.
- header : int, list of int, None, default ‘infer’
- Row number(s) to use as the column names, and the start of the
data. Default behavior is to infer the column names: if no names
are passed the behavior is identical to
header=0
and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical toheader=None
. Explicitly passheader=0
to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines ifskip_blank_lines=True
, soheader=0
denotes the first line of data rather than the first line of the file. - names : array-like, optional
- List of column names to use. If the file contains a header row,
then you should explicitly pass
header=0
to override the column names. Duplicates in this list are not allowed. - index_col : int, str, sequence of int / str, or False, optional, default
None
Column(s) to use as the row labels of the
DataFrame
, either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used.Note:
index_col=False
can be used to force pandas to not use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line.- usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in names or inferred from the document header row(s). If
names
are given, the document header row(s) are not taken into account. For example, a valid list-like usecols parameter would be[0, 1, 2]
or['foo', 'bar', 'baz']
. Element order is ignored, sousecols=[0, 1]
is the same as[1, 0]
. To instantiate a DataFrame fromdata
with element order preserved usepd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]
for columns in['foo', 'bar']
order orpd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]
for['bar', 'foo']
order.If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be
lambda x: x.upper() in ['AAA', 'BBB', 'DDD']
. Using this parameter results in much faster parsing time and lower memory usage.- dtype : Type name or dict of column -> type, optional
Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’} Use str or object together with suitable na_values settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.
New in version 1.5.0: Support for defaultdict was added. Specify a defaultdict as input where the default determines the dtype of the columns which are not explicitly listed.
- engine : {‘c’, ‘python’, ‘pyarrow’}, optional
Parser engine to use. The C and pyarrow engines are faster, while the python engine is currently more feature-complete. Multithreading is currently only supported by the pyarrow engine.
New in version 1.4.0: The “pyarrow” engine was added as an experimental engine, and some features are unsupported, or may not work correctly, with this engine.
- converters : dict, optional
- Dict of functions for converting values in certain columns. Keys can either be integers or column labels.
- true_values : list, optional
- Values to consider as True in addition to case-insensitive variants of “True”.
- false_values : list, optional
- Values to consider as False in addition to case-insensitive variants of “False”.
- skipinitialspace : bool, default False
- Skip spaces after delimiter.
- skiprows : list-like, int or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.
If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be
lambda x: x in [0, 2]
.- skipfooter : int, default 0
- Number of lines at bottom of file to skip (Unsupported with engine=’c’).
- nrows : int, optional
- Number of rows of file to read. Useful for reading pieces of large files.
- na_values : scalar, str, list-like, or dict, optional
- Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘None’, ‘n/a’, ‘nan’, ‘null’.
- keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:
- If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.
- If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.
- If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.
- If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.
Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored.
- na_filter : bool, default True
- Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file.
- verbose : bool, default False
- Indicate number of NA values placed in non-numeric columns.
- skip_blank_lines : bool, default True
- If True, skip over blank lines rather than interpreting as NaN values.
- parse_dates : bool or list of int or names or list of lists or dict, default False
The behavior is as follows:
- boolean. If True -> try parsing the index.
- list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
- list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
- dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’
If a column or index cannot be represented as an array of datetimes, say because of an unparsable value or a mixture of timezones, the column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use
pd.to_datetime
afterpd.read_csv
.Note: A fast-path exists for iso8601-formatted dates.
- infer_datetime_format : bool, default False
If True and parse_dates is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x.
Deprecated since version 2.0.0: A strict version of this argument is now the default, passing it has no effect.
- keep_date_col : bool, default False
- If True and parse_dates specifies combining multiple columns then keep the original columns.
- date_parser : function, optional
Function to use for converting a sequence of string columns to an array of datetime instances. The default uses
dateutil.parser.parser
to do the conversion. Pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and pass that; and 3) call date_parser once for each row using one or more strings (corresponding to the columns defined by parse_dates) as arguments.Deprecated since version 2.0.0: Use
date_format
instead, or read in asobject
and then applyto_datetime()
as-needed.- date_format : str or dict of column -> format, default
None
If used in conjunction with
parse_dates
, will parse dates according to this format. For anything more complex, please read in asobject
and then applyto_datetime()
as-needed.New in version 2.0.0.
- dayfirst : bool, default False
- DD/MM format dates, international and European format.
- cache_dates : bool, default True
- If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets.
- chunksize : int, optional
Return TextFileReader object for iteration. See the IO Tools docs for more information on
iterator
andchunksize
.Changed in version 1.2:
TextFileReader
is a context manager.- compression : str or dict, default ‘infer’
For on-the-fly decompression of on-disk data. If ‘infer’ and ‘filepath_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. Set to
None
for no decompression. Can also be a dict with key'method'
set to one of {'zip'
,'gzip'
,'bz2'
,'zstd'
,'tar'
} and other key-value pairs are forwarded tozipfile.ZipFile
,gzip.GzipFile
,bz2.BZ2File
,zstandard.ZstdDecompressor
ortarfile.TarFile
, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary:compression={'method': 'zstd', 'dict_data': my_compression_dict}
.New in version 1.5.0: Added support for .tar files.
Changed in version 1.4.0: Zstandard support.
- thousands : str, optional
- Thousands separator.
- decimal : str, default ‘.’
- Character to recognize as decimal point (e.g. use ‘,’ for European data).
- lineterminator : str (length 1), optional
- Character to break file into lines. Only valid with C parser.
- quotechar : str (length 1), optional
- The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.
- quoting : int or csv.QUOTE_* instance, default 0
- Control field quoting behavior per
csv.QUOTE_*
constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). - doublequote : bool, default
True
- When quotechar is specified and quoting is not
QUOTE_NONE
, indicate whether or not to interpret two consecutive quotechar elements INSIDE a field as a singlequotechar
element. - escapechar : str (length 1), optional
- One-character string used to escape other characters.
- comment : str, optional
- Indicates remainder of line should not be parsed. If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
single character. Like empty lines (as long as
skip_blank_lines=True
), fully commented lines are ignored by the parameter header but not by skiprows. For example, ifcomment='#'
, parsing#empty\na,b,c\n1,2,3
withheader=0
will result in ‘a,b,c’ being treated as the header. - encoding : str, optional, default “utf-8”
Encoding to use for UTF when reading/writing (ex. ‘utf-8’). List of Python standard encodings .
Changed in version 1.2: When
encoding
isNone
,errors="replace"
is passed toopen()
. Otherwise,errors="strict"
is passed toopen()
. This behavior was previously only the case forengine="python"
.Changed in version 1.3.0:
encoding_errors
is a new argument.encoding
has no longer an influence on how encoding errors are handled.- encoding_errors : str, optional, default “strict”
How encoding errors are treated. List of possible values .
New in version 1.3.0.
- dialect : str or csv.Dialect, optional
- If provided, this parameter will override values (default or not) for the following parameters: delimiter, doublequote, escapechar, skipinitialspace, quotechar, and quoting. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details.
- on_bad_lines : {‘error’, ‘warn’, ‘skip’} or callable, default ‘error’
Specifies what to do upon encountering a bad line (a line with too many fields). Allowed values are :
- ‘error’, raise an Exception when a bad line is encountered.
- ‘warn’, raise a warning when a bad line is encountered and skip that line.
- ‘skip’, skip bad lines without raising or warning when they are encountered.
New in version 1.3.0.
-
New in version 1.4.0:
callable, function with signature
(bad_line: list[str]) -> list[str] | None
that will process a single bad line.bad_line
is a list of strings split by thesep
. If the function returnsNone
, the bad line will be ignored. If the function returns a new list of strings with more elements than expected, aParserWarning
will be emitted while dropping extra elements. Only supported whenengine="python"
- delim_whitespace : bool, default False
- Specifies whether or not whitespace (e.g.
' '
or' '
) will be used as the sep. Equivalent to settingsep='\s+'
. If this option is set to True, nothing should be passed in for thedelimiter
parameter. - low_memory : bool, default True
- Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the dtype parameter. Note that the entire file is read into a single DataFrame regardless, use the chunksize or iterator parameter to return the data in chunks. (Only valid with C parser).
- memory_map : bool, default False
- If a filepath is provided for filepath_or_buffer, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead.
- float_precision : str, optional
Specifies which converter the C engine should use for floating-point values. The options are
None
or ‘high’ for the ordinary converter, ‘legacy’ for the original lower precision pandas converter, and ‘round_trip’ for the round-trip converter.Changed in version 1.2.
- storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllib.request.Request
as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.New in version 1.2.
- dtype_backend : {“numpy_nullable”, “pyarrow”}, defaults to NumPy backed DataFrames
Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when “numpy_nullable” is set, pyarrow is used for all dtypes if “pyarrow” is set.
The dtype_backends are still experimential.
New in version 2.0.
-
apache_beam.io.textio.
WriteToCsv
(path: str, num_shards: Optional[int] = None, file_naming: Optional[fileio.FileNaming] = None, **kwargs)[source]¶ - A PTransform for writing a schema’d PCollection as a (set of)
comma-separated values (csv) files.
- Args:
- path (str): The file path to write to. The files written will
- begin with this prefix, followed by a shard identifier (see num_shards) according to the file_naming parameter.
- num_shards (optional int): The number of shards to use in the distributed
- write. Defaults to None, letting the system choose an optimal value.
- file_naming (optional callable): A file-naming strategy, determining the
- actual shard names given their shard number, etc. See the section on file naming Defaults to fileio.default_file_naming, which names files as path-XXXXX-of-NNNNN.
**kwargs: Extra arguments passed to pandas.Dataframe.to_csv (see below).
- sep : str, default ‘,’
- String of length 1. Field delimiter for the output file.
- na_rep : str, default ‘’
- Missing data representation.
- float_format : str, Callable, default None
- Format string for floating point numbers. If a Callable is given, it takes precedence over other numeric formatting parameters, like decimal.
- columns : sequence, optional
- Columns to write.
- header : bool or list of str, default True
- Write out the column names. If a list of strings is given it is assumed to be aliases for the column names.
- mode : str, default ‘w’
- Python write mode. The available write modes are the same as
open()
. - encoding : str, optional
- A string representing the encoding to use in the output file, defaults to ‘utf-8’. encoding is not supported if path_or_buf is a non-binary file object.
- compression : str or dict, default ‘infer’
For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to
None
for no compression. Can also be a dict with key'method'
set to one of {'zip'
,'gzip'
,'bz2'
,'zstd'
,'tar'
} and other key-value pairs are forwarded tozipfile.ZipFile
,gzip.GzipFile
,bz2.BZ2File
,zstandard.ZstdCompressor
ortarfile.TarFile
, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}
.New in version 1.5.0: Added support for .tar files.
Changed in version 1.0.0: May now be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’.
Changed in version 1.1.0: Passing compression options as keys in dict is supported for compression modes ‘gzip’, ‘bz2’, ‘zstd’, and ‘zip’.
Changed in version 1.2.0: Compression is supported for binary file objects.
Changed in version 1.2.0: Previous versions forwarded dict entries for ‘gzip’ to gzip.open instead of gzip.GzipFile which prevented setting mtime.
- quoting : optional constant from csv module
- Defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.
- quotechar : str, default ‘”’
- String of length 1. Character used to quote fields.
- lineterminator : str, optional
The newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (‘\n’ for linux, ‘\r\n’ for Windows, i.e.).
Changed in version 1.5.0: Previously was line_terminator, changed for consistency with read_csv and the standard library ‘csv’ module.
- chunksize : int or None
- Rows to write at a time.
- date_format : str, default None
- Format string for datetime objects.
- doublequote : bool, default True
- Control quoting of quotechar inside a field.
- escapechar : str, default None
- String of length 1. Character used to escape sep and quotechar when appropriate.
- decimal : str, default ‘.’
- Character recognized as decimal separator. E.g. use ‘,’ for European data.
- errors : str, default ‘strict’
Specifies how encoding and decoding errors are to be handled. See the errors argument for
open()
for a full list of options.New in version 1.1.0.
- storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllib.request.Request
as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.New in version 1.2.0.
-
apache_beam.io.textio.
ReadFromJson
(path: str, *, orient: str = 'records', lines: bool = True, **kwargs)[source]¶ A PTransform for reading json values from files into a PCollection.
- Args:
- path (str): The file path to read from. The path can contain glob
- characters such as
*
and?
. - orient (str): Format of the json elements in the file.
- Default to ‘records’, meaning the file is expected to contain a list of json objects like {field1: value1, field2: value2, …}.
- lines (bool): Whether each line should be considered a separate record,
- as opposed to the entire file being a valid JSON object or list. Defaults to True (unlike Pandas).
**kwargs: Extra arguments passed to pandas.read_json (see below).
- orient : str, optional
Indication of expected JSON string format. Compatible JSON strings can be produced by
to_json()
with a corresponding orient value. The set of possible orients is:'split'
: dict like{index -> [index], columns -> [columns], data -> [values]}
'records'
: list like[{column -> value}, ... , {column -> value}]
'index'
: dict like{index -> {column -> value}}
'columns'
: dict like{column -> {index -> value}}
'values'
: just the values array
The allowed and default values depend on the value of the typ parameter.
- when
typ == 'series'
,- allowed orients are
{'split','records','index'}
- default is
'index'
- The Series index must be unique for orient
'index'
.
- allowed orients are
- when
typ == 'frame'
,- allowed orients are
{'split','records','index', 'columns','values', 'table'}
- default is
'columns'
- The DataFrame index must be unique for orients
'index'
and'columns'
. - The DataFrame columns must be unique for orients
'index'
,'columns'
, and'records'
.
- allowed orients are
- typ : {‘frame’, ‘series’}, default ‘frame’
- The type of object to recover.
- dtype : bool or dict, default None
If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don’t infer dtypes at all, applies only to the data.
For all
orient
values except'table'
, default is True.- convert_axes : bool, default None
Try to convert the axes to the proper dtypes.
For all
orient
values except'table'
, default is True.- convert_dates : bool or list of str, default True
- If True then default datelike columns may be converted (depending on keep_default_dates). If False, no dates will be converted. If a list of column names, then those columns will be converted and default datelike columns may also be converted (depending on keep_default_dates).
- keep_default_dates : bool, default True
If parsing dates (convert_dates is not False), then try to parse the default datelike columns. A column label is datelike if
- it ends with
'_at'
, - it ends with
'_time'
, - it begins with
'timestamp'
, - it is
'modified'
, or - it is
'date'
.
- it ends with
- precise_float : bool, default False
- Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality.
- date_unit : str, default None
- The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively.
- encoding : str, default is ‘utf-8’
- The encoding to use to decode py3 bytes.
- encoding_errors : str, optional, default “strict”
How encoding errors are treated. List of possible values .
New in version 1.3.0.
- lines : bool, default False
- Read the file as a json object per line.
- chunksize : int, optional
Return JsonReader object for iteration. See the line-delimited json docs for more information on
chunksize
. This can only be passed if lines=True. If this is None, the file will be read into memory all at once.Changed in version 1.2:
JsonReader
is a context manager.- compression : str or dict, default ‘infer’
For on-the-fly decompression of on-disk data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). If using ‘zip’ or ‘tar’, the ZIP file must contain only one data file to be read in. Set to
None
for no decompression. Can also be a dict with key'method'
set to one of {'zip'
,'gzip'
,'bz2'
,'zstd'
,'tar'
} and other key-value pairs are forwarded tozipfile.ZipFile
,gzip.GzipFile
,bz2.BZ2File
,zstandard.ZstdDecompressor
ortarfile.TarFile
, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary:compression={'method': 'zstd', 'dict_data': my_compression_dict}
.New in version 1.5.0: Added support for .tar files.
Changed in version 1.4.0: Zstandard support.
- nrows : int, optional
The number of lines from the line-delimited jsonfile that has to be read. This can only be passed if lines=True. If this is None, all the rows will be returned.
New in version 1.1.
- storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllib.request.Request
as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.New in version 1.2.0.
- dtype_backend : {“numpy_nullable”, “pyarrow”}, defaults to NumPy backed DataFrames
Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when “numpy_nullable” is set, pyarrow is used for all dtypes if “pyarrow” is set.
The dtype_backends are still experimential.
New in version 2.0.
- engine : {“ujson”, “pyarrow”}, default “ujson”
Parser engine to use. The
"pyarrow"
engine is only available whenlines=True
.New in version 2.0.
-
apache_beam.io.textio.
WriteToJson
(path: str, *, num_shards: Optional[int] = None, file_naming: Optional[fileio.FileNaming] = None, orient: str = 'records', lines: Optional[bool] = None, **kwargs)[source]¶ A PTransform for writing a PCollection as json values to files.
- Args:
- path (str): The file path to write to. The files written will
- begin with this prefix, followed by a shard identifier (see num_shards) according to the file_naming parameter.
- num_shards (optional int): The number of shards to use in the distributed
- write. Defaults to None, letting the system choose an optimal value.
- file_naming (optional callable): A file-naming strategy, determining the
- actual shard names given their shard number, etc. See the section on file naming Defaults to fileio.default_file_naming, which names files as path-XXXXX-of-NNNNN.
- orient (str): Format of the json elements in the file.
- Default to ‘records’, meaning the file will to contain a list of json objects like {field1: value1, field2: value2, …}.
- lines (bool): Whether each line should be considered a separate record,
- as opposed to the entire file being a valid JSON object or list. Defaults to True if orient is ‘records’ (unlike Pandas).
- **kwargs: Extra arguments passed to pandas.Dataframe.to_json
- (see below).
- orient : str
Indication of expected JSON string format.
Series:
- default is ‘index’
- allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}.
DataFrame:
- default is ‘columns’
- allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.
The format of the JSON string:
- ‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
- ‘records’ : list like [{column -> value}, … , {column -> value}]
- ‘index’ : dict like {index -> {column -> value}}
- ‘columns’ : dict like {column -> {index -> value}}
- ‘values’ : just the values array
- ‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}}
Describing the data, where data component is like
orient='records'
.
- date_format : {None, ‘epoch’, ‘iso’}
- Type of date conversion. ‘epoch’ = epoch milliseconds,
‘iso’ = ISO8601. The default depends on the orient. For
orient='table'
, the default is ‘iso’. For all other orients, the default is ‘epoch’. - double_precision : int, default 10
- The number of decimal places to use when encoding floating point values.
- force_ascii : bool, default True
- Force encoded string to be ASCII.
- date_unit : str, default ‘ms’ (milliseconds)
- The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.
- default_handler : callable, default None
- Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
- lines : bool, default False
- If ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like.
- compression : str or dict, default ‘infer’
For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to
None
for no compression. Can also be a dict with key'method'
set to one of {'zip'
,'gzip'
,'bz2'
,'zstd'
,'tar'
} and other key-value pairs are forwarded tozipfile.ZipFile
,gzip.GzipFile
,bz2.BZ2File
,zstandard.ZstdCompressor
ortarfile.TarFile
, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive:compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}
.New in version 1.5.0: Added support for .tar files.
Changed in version 1.4.0: Zstandard support.
- indent : int, optional
- Length of whitespace used to indent each record.
- storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to
urllib.request.Request
as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded tofsspec.open
. Please seefsspec
andurllib
for more details, and for more examples on storage options refer here.New in version 1.2.0.
- mode : str, default ‘w’ (writing)
- Specify the IO mode for output when supplying a path_or_buf. Accepted args are ‘w’ (writing) and ‘a’ (append) only. mode=’a’ is only supported when lines is True and orient is ‘records’.