cudf.DataFrame.from_dict#
- classmethod DataFrame.from_dict(data: dict, orient: str = 'columns', dtype: Dtype | None = None, columns: list | None = None) DataFrame [source]#
Construct DataFrame from dict of array-like or dicts. Creates DataFrame object from dictionary by columns or by index allowing dtype specification.
- Parameters:
- datadict
Of the form {field : array-like} or {field : dict}.
- orient{‘columns’, ‘index’, ‘tight’}, default ‘columns’
The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’. If ‘tight’, assume a dict with keys [‘index’, ‘columns’, ‘data’, ‘index_names’, ‘column_names’].
- dtypedtype, default None
Data type to force, otherwise infer.
- columnslist, default None
Column labels to use when
orient='index'
. Raises aValueError
if used withorient='columns'
ororient='tight'
.
- Returns:
- DataFrame
See also
DataFrame.from_records
DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.
DataFrame
DataFrame object creation using constructor.
DataFrame.to_dict
Convert the DataFrame to a dictionary.
Examples
By default the keys of the dict become the DataFrame columns:
>>> import cudf >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']} >>> cudf.DataFrame.from_dict(data) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d
Specify
orient='index'
to create the DataFrame using dictionary keys as rows:>>> data = {'row_1': [3, 2, 1, 0], 'row_2': [10, 11, 12, 13]} >>> cudf.DataFrame.from_dict(data, orient='index') 0 1 2 3 row_1 3 2 1 0 row_2 10 11 12 13
When using the ‘index’ orientation, the column names can be specified manually:
>>> cudf.DataFrame.from_dict(data, orient='index', ... columns=['A', 'B', 'C', 'D']) A B C D row_1 3 2 1 0 row_2 10 11 12 13
Specify
orient='tight'
to create the DataFrame using a ‘tight’ format:>>> data = {'index': [('a', 'b'), ('a', 'c')], ... 'columns': [('x', 1), ('y', 2)], ... 'data': [[1, 3], [2, 4]], ... 'index_names': ['n1', 'n2'], ... 'column_names': ['z1', 'z2']} >>> cudf.DataFrame.from_dict(data, orient='tight') z1 x y z2 1 2 n1 n2 a b 1 3 c 2 4