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)

Bases: apache_beam.transforms.ptransform.PTransform

A PTransform for reading a PCollection of text files.

Reads a PCollection of text files or file patterns and produces a PCollection 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 to ReadFromTextWithFilename but this PTransform 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 of StrUtf8Coder.

This does not support UTF-16 or UTF-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

expand(pvalue)

class apache_beam.io.textio.ReadAllFromTextContinuously(file_pattern, **kwargs)

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 a PCollection of strings.

For more details, see ReadAllFromText for text parsing settings; see apache_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 and MatchContinuously.

expand(pbegin)

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)

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 of StrUtf8Coder.

This does not support UTF-16 or UTF-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.

expand(pvalue)

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)

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.

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)

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 letters S and N are replaced with the 0-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.

expand(pcoll)

apache_beam.io.textio.ReadFromCsv(path: str, *, splittable: bool = True, **kwargs)

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 to header=None. Explicitly pass header=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 if skip_blank_lines=True, so header=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, so usecols=[0, 1] is the same as [1, 0]. To instantiate a DataFrame from data with element order preserved use pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']] for columns in ['foo', 'bar'] order or pd.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 after pd.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 as object and then apply to_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 as object and then apply to_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 and chunksize.

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 to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor or tarfile.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 single quotechar 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, if comment='#', parsing #empty\na,b,c\n1,2,3 with header=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 is None, errors="replace" is passed to open(). Otherwise, errors="strict" is passed to open(). This behavior was previously only the case for engine="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 the sep. If the function returns None, the bad line will be ignored. If the function returns a new list of strings with more elements than expected, a ParserWarning will be emitted while dropping extra elements. Only supported when engine="python"

delim_whitespace : bool, default False

Specifies whether or not whitespace (e.g. ' ' or '    ') will be used as the sep. Equivalent to setting sep='\s+'. If this option is set to True, nothing should be passed in for the delimiter 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 to fsspec.open. Please see fsspec and urllib 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)

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 to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor or tarfile.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 to fsspec.open. Please see fsspec and urllib 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)

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'.
• 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'.

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'.

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 to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor or tarfile.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 to fsspec.open. Please see fsspec and urllib 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 when lines=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)

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 to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor or tarfile.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 to fsspec.open. Please see fsspec and urllib 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’.