def __init__( # noqa: C901
self,
df: Any = None,
schema: Any = None,
metadata: Any = None,
pandas_df_wrapper: bool = False,
):
try:
apply_schema = True
if df is None:
schema = _input_schema(schema).assert_not_empty()
df = []
if isinstance(df, PandasDataFrame):
# TODO: This is useless if in this way and wrong
pdf = df.native
schema = None
elif isinstance(df, (pd.DataFrame, pd.Series)):
if isinstance(df, pd.Series):
df = df.to_frame()
pdf = df
schema = None if schema is None else _input_schema(schema)
if pandas_df_wrapper and schema is not None:
apply_schema = False
elif isinstance(df, Iterable):
schema = _input_schema(schema).assert_not_empty()
pdf = pd.DataFrame(df, columns=schema.names)
pdf = PD_UTILS.enforce_type(pdf,
schema.pa_schema,
null_safe=True)
if PD_UTILS.empty(pdf):
for k, v in schema.items():
pdf[k] = pdf[k].astype(v.type.to_pandas_dtype())
apply_schema = False
else:
raise ValueError(f"{df} is incompatible with PandasDataFrame")
if apply_schema:
pdf, schema = self._apply_schema(pdf, schema)
super().__init__(schema, metadata)
self._native = pdf
except Exception as e:
raise FugueDataFrameInitError from e
def to_df(self,
df: Any,
schema: Any = None,
metadata: Any = None) -> SparkDataFrame:
"""Convert a data structure to :class:`~fugue_spark.dataframe.SparkDataFrame`
:param data: :class:`~fugue.dataframe.dataframe.DataFrame`,
:class:`spark:pyspark.sql.DataFrame`, :class:`spark:pyspark.RDD`,
pandas DataFrame or list or iterable of arrays
:param schema: |SchemaLikeObject| or :class:`spark:pyspark.sql.types.StructType`
defaults to None.
:param metadata: |ParamsLikeObject|, defaults to None
:return: engine compatible dataframe
:Notice:
* if the input is already :class:`~fugue_spark.dataframe.SparkDataFrame`,
it should return itself
* For :class:`~spark:pyspark.RDD`, list or iterable of arrays,
``schema`` must be specified
* When ``schema`` is not None, a potential type cast may happen to ensure
the dataframe's schema.
* all other methods in the engine can take arbitrary dataframes and
call this method to convert before doing anything
"""
if isinstance(df, DataFrame):
assert_or_throw(
schema is None and metadata is None,
ValueError(
"schema and metadata must be None when df is a DataFrame"),
)
if isinstance(df, SparkDataFrame):
return df
if isinstance(df, ArrowDataFrame):
sdf = self.spark_session.createDataFrame(
df.as_array(), to_spark_schema(df.schema))
return SparkDataFrame(sdf, df.schema, df.metadata)
if isinstance(df, (ArrayDataFrame, IterableDataFrame)):
adf = ArrowDataFrame(df.as_array(type_safe=False), df.schema)
sdf = self.spark_session.createDataFrame(
adf.as_array(), to_spark_schema(df.schema))
return SparkDataFrame(sdf, df.schema, df.metadata)
if any(pa.types.is_struct(t) for t in df.schema.types):
sdf = self.spark_session.createDataFrame(
df.as_array(type_safe=True), to_spark_schema(df.schema))
else:
sdf = self.spark_session.createDataFrame(
df.as_pandas(), to_spark_schema(df.schema))
return SparkDataFrame(sdf, df.schema, df.metadata)
if isinstance(df, ps.DataFrame):
return SparkDataFrame(
df, None if schema is None else to_schema(schema), metadata)
if isinstance(df, RDD):
assert_arg_not_none(schema, "schema")
sdf = self.spark_session.createDataFrame(df,
to_spark_schema(schema))
return SparkDataFrame(sdf, to_schema(schema), metadata)
if isinstance(df, pd.DataFrame):
if PD_UTILS.empty(df):
temp_schema = to_spark_schema(PD_UTILS.to_schema(df))
sdf = self.spark_session.createDataFrame([], temp_schema)
else:
sdf = self.spark_session.createDataFrame(df)
return SparkDataFrame(sdf, schema, metadata)
# use arrow dataframe here to handle nulls in int cols
assert_or_throw(schema is not None,
FugueDataFrameInitError("schema can't be None"))
adf = ArrowDataFrame(df, to_schema(schema))
sdf = self.spark_session.createDataFrame(adf.as_array(),
to_spark_schema(adf.schema))
return SparkDataFrame(sdf, adf.schema, metadata)
