class torchdata.datapipes.iter.Rows2Columnar(source_datapipe: IterDataPipe[List[Union[Dict, List]]], column_names: Optional[List[str]] = None)

Accepts an input DataPipe with batches of data, and processes one batch at a time and yields a Dict for each batch, with column_names as keys and lists of corresponding values from each row as values (functional name: rows2columnar).

Within the input DataPipe, each row within a batch must either be a Dict or a List


If column_names are not given and each row is a Dict, the keys of that Dict will be used as column names.

  • source_datapipe – a DataPipe where each item is a batch. Within each batch, there are rows and each row is a List or Dict

  • column_names – if each element in a batch contains Dict, column_names act as a filter for matching keys; otherwise, these are used as keys to for the generated Dict of each batch


>>> # Each element in a batch is a `Dict`
>>> from torchdata.datapipes.iter import IterableWrapper
>>> dp = IterableWrapper([[{'a': 1}, {'b': 2, 'a': 1}], [{'a': 1, 'b': 200}, {'b': 2, 'c': 3, 'a': 100}]])
>>> row2col_dp = dp.rows2columnar()
>>> list(row2col_dp)
[defaultdict(<class 'list'>, {'a': [1, 1], 'b': [2]}),
 defaultdict(<class 'list'>, {'a': [1, 100], 'b': [200, 2], 'c': [3]})]
>>> row2col_dp = dp.rows2columnar(column_names=['a'])
>>> list(row2col_dp)
[defaultdict(<class 'list'>, {'a': [1, 1]}),
 defaultdict(<class 'list'>, {'a': [1, 100]})]
>>> # Each element in a batch is a `List`
>>> dp = IterableWrapper([[[0, 1, 2, 3], [4, 5, 6, 7]]])
>>> row2col_dp = dp.rows2columnar(column_names=["1st_in_batch", "2nd_in_batch", "3rd_in_batch", "4th_in_batch"])
>>> list(row2col_dp)
[defaultdict(<class 'list'>, {'1st_in_batch': [0, 4], '2nd_in_batch': [1, 5],
                              '3rd_in_batch': [2, 6], '4th_in_batch': [3, 7]})]


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