pyspark.pandas.DataFrame.from_dict¶

static DataFrame.from_dict(data: Dict[Union[Any, Tuple[Any, …]], Sequence[Any]], orient: str = 'columns', dtype: Union[str, numpy.dtype, pandas.core.dtypes.base.ExtensionDtype] = None, columns: Optional[List[Union[Any, Tuple[Any, …]]]] = None) → pyspark.pandas.frame.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’}, 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’.
dtypedtype, default None: Data type to force, otherwise infer.
columnslist, default None: Column labels to use when orient='index'. Raises a ValueError if used with orient='columns'.

Returns

DataFrame

See also

DataFrame.from_records: DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.
DataFrame: DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': [10, 20, 30, 40]}
>>> ps.DataFrame.from_dict(data)
   col_1  col_2
0      3     10
1      2     20
2      1     30
3      0     40

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': [10, 20, 30, 40]}
>>> ps.DataFrame.from_dict(data, orient='index').sort_index()
        0   1   2   3
row_1   3   2   1   0
row_2  10  20  30  40

When using the ‘index’ orientation, the column names can be specified manually:

>>> ps.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D']).sort_index()
        A   B   C   D
row_1   3   2   1   0
row_2  10  20  30  40

pyspark.pandas.DataFrame.last_valid_index

pyspark.pandas.DataFrame.from_records