class_docstr: A Dataset comprising lines from one or more CSV files.
class_docstr_tok: ["A","Dataset","comprising","lines","from","one","or","more","CSV","files","."]
docstr: Creates a `CsvDataset` by reading and decoding CSV files.
  
  The elements of this dataset correspond to records from the file(s).
  RFC 4180 format is expected for CSV files
  (https://tools.ietf.org/html/rfc4180)
  Note that we allow leading and trailing spaces with int or float field.
  
  
  For example, suppose we have a file 'my_file0.csv' with four CSV columns of
  different data types:
  ```
  abcdefg,4.28E10,5.55E6,12
  hijklmn,-5.3E14,,2
  ```
  
  We can construct a CsvDataset from it as follows:
  
  ```python
   dataset = tf.data.experimental.CsvDataset(
      "my_file*.csv",
      [tf.float32,  # Required field, use dtype or empty tensor
       tf.constant([0.0], dtype=tf.float32),  # Optional field, default to 0.0
       tf.int32,  # Required field, use dtype or empty tensor
       ],
      select_cols=[1,2,3]  # Only parse last three columns
  )
  ```
  
  The expected output of its iterations is:
  
  ```python
  for element in dataset:
    print(element)
  
  >> (4.28e10, 5.55e6, 12)
  >> (-5.3e14, 0.0, 2)
  ```
  
  Args:
    filenames: A `tf.string` tensor containing one or more filenames.
    record_defaults: A list of default values for the CSV fields. Each item in
      the list is either a valid CSV `DType` (float32, float64, int32, int64,
      string), or a `Tensor` object with one of the above types. One per
      column of CSV data, with either a scalar `Tensor` default value for the
      column if it is optional, or `DType` or empty `Tensor` if required. If
      both this and `select_columns` are specified, these must have the same
      lengths, and `column_defaults` is assumed to be sorted in order of
      increasing column index. If both this and 'exclude_cols' are specified,
      the sum of lengths of record_defaults and exclude_cols should equal the
      total number of columns in the CSV file.
    compression_type: (Optional.) A `tf.string` scalar evaluating to one of
      `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no
      compression.
    buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
      to buffer while reading files. Defaults to 4MB.
    header: (Optional.) A `tf.bool` scalar indicating whether the CSV file(s)
      have header line(s) that should be skipped when parsing. Defaults to
      `False`.
    field_delim: (Optional.) A `tf.string` scalar containing the delimiter
      character that separates fields in a record. Defaults to `","`.
    use_quote_delim: (Optional.) A `tf.bool` scalar. If `False`, treats double
      quotation marks as regular characters inside of string fields (ignoring
      RFC 4180, Section 2, Bullet 5). Defaults to `True`.
    na_value: (Optional.) A `tf.string` scalar indicating a value that will be
      treated as NA/NaN.
    select_cols: (Optional.) A sorted list of column indices to select from
      the input data. If specified, only this subset of columns will be
      parsed. Defaults to parsing all columns. At most one of `select_cols`
      and `exclude_cols` can be specified.
code: def __init__(
      self,
      filenames,
      record_defaults,
      compression_type=None,
      buffer_size=None,
      header=False,
      field_delim=",",
      use_quote_delim=True,
      na_value="",
      select_cols=None,
  ):
      """Creates a `CsvDataset` by reading and decoding CSV files.
  
      The elements of this dataset correspond to records from the file(s).
      RFC 4180 format is expected for CSV files
      (https://tools.ietf.org/html/rfc4180)
      Note that we allow leading and trailing spaces with int or float field.
  
  
      For example, suppose we have a file 'my_file0.csv' with four CSV columns of
      different data types:
      ```
      abcdefg,4.28E10,5.55E6,12
      hijklmn,-5.3E14,,2
      ```
  
      We can construct a CsvDataset from it as follows:
  
      ```python
       dataset = tf.data.experimental.CsvDataset(
          "my_file*.csv",
          [tf.float32,  # Required field, use dtype or empty tensor
           tf.constant([0.0], dtype=tf.float32),  # Optional field, default to 0.0
           tf.int32,  # Required field, use dtype or empty tensor
           ],
          select_cols=[1,2,3]  # Only parse last three columns
      )
      ```
  
      The expected output of its iterations is:
  
      ```python
      for element in dataset:
        print(element)
  
      >> (4.28e10, 5.55e6, 12)
      >> (-5.3e14, 0.0, 2)
      ```
  
      Args:
        filenames: A `tf.string` tensor containing one or more filenames.
        record_defaults: A list of default values for the CSV fields. Each item in
          the list is either a valid CSV `DType` (float32, float64, int32, int64,
          string), or a `Tensor` object with one of the above types. One per
          column of CSV data, with either a scalar `Tensor` default value for the
          column if it is optional, or `DType` or empty `Tensor` if required. If
          both this and `select_columns` are specified, these must have the same
          lengths, and `column_defaults` is assumed to be sorted in order of
          increasing column index. If both this and 'exclude_cols' are specified,
          the sum of lengths of record_defaults and exclude_cols should equal the
          total number of columns in the CSV file.
        compression_type: (Optional.) A `tf.string` scalar evaluating to one of
          `""` (no compression), `"ZLIB"`, or `"GZIP"`. Defaults to no
          compression.
        buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
          to buffer while reading files. Defaults to 4MB.
        header: (Optional.) A `tf.bool` scalar indicating whether the CSV file(s)
          have header line(s) that should be skipped when parsing. Defaults to
          `False`.
        field_delim: (Optional.) A `tf.string` scalar containing the delimiter
          character that separates fields in a record. Defaults to `","`.
        use_quote_delim: (Optional.) A `tf.bool` scalar. If `False`, treats double
          quotation marks as regular characters inside of string fields (ignoring
          RFC 4180, Section 2, Bullet 5). Defaults to `True`.
        na_value: (Optional.) A `tf.string` scalar indicating a value that will be
          treated as NA/NaN.
        select_cols: (Optional.) A sorted list of column indices to select from
          the input data. If specified, only this subset of columns will be
          parsed. Defaults to parsing all columns. At most one of `select_cols`
          and `exclude_cols` can be specified.
      """
      wrapped = CsvDatasetV2(
          filenames,
          record_defaults,
          compression_type,
          buffer_size,
          header,
          field_delim,
          use_quote_delim,
          na_value,
          select_cols,
      )
      super(CsvDatasetV1, self).__init__(wrapped)
  
imports: import collections
  import csv
  import functools
  import gzip
  import numpy
  from tensorflow.python import tf2
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.data.experimental.ops import error_ops
  from tensorflow.python.data.experimental.ops import parsing_ops
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import map_op
  from tensorflow.python.data.ops import options
  from tensorflow.python.data.ops import readers
  from tensorflow.python.data.util import convert
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new CsvDatasetV1 object from inherited base classes: dataset_ops with example: obj = CsvDatasetV1(filenames, record_defaults, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols)