static DataFrame.from_records(data: Union[numpy.ndarray, List[tuple], dict, pandas.core.frame.DataFrame], index: Union[str, list, numpy.ndarray] = None, exclude: list = None, columns: list = None, coerce_float: bool = False, nrows: int = None) → pyspark.pandas.frame.DataFrame[source]

Convert structured or recorded ndarray to DataFrame.

datandarray (structured dtype), list of tuples, dict, or DataFrame
indexstring, list of fields, array-like

Field of array to use as the index, alternately a specific set of input labels to use

excludesequence, default None

Columns or fields to exclude

columnssequence, default None

Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns)

coerce_floatboolean, default False

Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets

nrowsint, default None

Number of rows to read if data is an iterator



Use dict as input

>>> ps.DataFrame.from_records({'A': [1, 2, 3]})
0  1
1  2
2  3

Use list of tuples as input

>>> ps.DataFrame.from_records([(1, 2), (3, 4)])
   0  1
0  1  2
1  3  4

Use NumPy array as input

>>> ps.DataFrame.from_records(np.eye(3))
     0    1    2
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0