pyspark.pandas.DataFrame.from_records¶
-
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.
- Parameters
- 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
- Returns
- dfDataFrame
Examples
Use dict as input
>>> ps.DataFrame.from_records({'A': [1, 2, 3]}) A 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