Method zip

tensorflow/python/data/ops/dataset_ops.py:761–797

Creates a `Dataset` by zipping together the given datasets. This method has similar semantics to the built-in `zip()` function in Python, with the main difference being that the `datasets` argument can be an arbitrary nested structure of `Dataset` objects. For example: ```pytho

(datasets)

  @staticmethod
  def zip(datasets):
    """Creates a `Dataset` by zipping together the given datasets.

    This method has similar semantics to the built-in `zip()` function
    in Python, with the main difference being that the `datasets`
    argument can be an arbitrary nested structure of `Dataset` objects.
    For example:

    ```python
    a = Dataset.range(1, 4)  # ==> [ 1, 2, 3 ]
    b = Dataset.range(4, 7)  # ==> [ 4, 5, 6 ]
    c = Dataset.range(7, 13).batch(2)  # ==> [ [7, 8], [9, 10], [11, 12] ]
    d = Dataset.range(13, 15)  # ==> [ 13, 14 ]

    # The nested structure of the `datasets` argument determines the
    # structure of elements in the resulting dataset.
    Dataset.zip((a, b))  # ==> [ (1, 4), (2, 5), (3, 6) ]
    Dataset.zip((b, a))  # ==> [ (4, 1), (5, 2), (6, 3) ]

    # The `datasets` argument may contain an arbitrary number of
    # datasets.
    Dataset.zip((a, b, c))  # ==> [ (1, 4, [7, 8]),
                            #       (2, 5, [9, 10]),
                            #       (3, 6, [11, 12]) ]

    # The number of elements in the resulting dataset is the same as
    # the size of the smallest dataset in `datasets`.
    Dataset.zip((a, d))  # ==> [ (1, 13), (2, 14) ]
    ```

    Args:
      datasets: A nested structure of datasets.

    Returns:
      Dataset: A `Dataset`.
    """
    return ZipDataset(datasets)

  def concatenate(self, dataset):
    """Creates a `Dataset` by concatenating the given dataset with this dataset.