def test_namespace_access(self):
try:
self.assertEqual(ks.options.compute.max_rows,
ks.get_option("compute.max_rows"))
ks.options.compute.max_rows = 0
self.assertEqual(ks.options.compute.max_rows, 0)
self.assertTrue(isinstance(ks.options.compute, DictWrapper))
wrapper = ks.options.compute
self.assertEqual(wrapper.max_rows,
ks.get_option("compute.max_rows"))
wrapper.max_rows = 1000
self.assertEqual(ks.options.compute.max_rows, 1000)
self.assertRaisesRegex(config.OptionError, "No such option",
lambda: ks.options.compu)
self.assertRaisesRegex(config.OptionError, "No such option",
lambda: ks.options.compute.max)
self.assertRaisesRegex(config.OptionError, "No such option",
lambda: ks.options.max_rows1)
with self.assertRaisesRegex(config.OptionError, "No such option"):
ks.options.compute.max = 0
with self.assertRaisesRegex(config.OptionError, "No such option"):
ks.options.compute = 0
with self.assertRaisesRegex(config.OptionError, "No such option"):
ks.options.com = 0
finally:
ks.reset_option("compute.max_rows")
def test_get_set_reset_option(self):
self.assertEqual(ks.get_option('test.config'), 'default')
ks.set_option('test.config', 'value')
self.assertEqual(ks.get_option('test.config'), 'value')
ks.reset_option('test.config')
self.assertEqual(ks.get_option('test.config'), 'default')
def test_unknown_option(self):
with self.assertRaisesRegex(config.OptionError, 'No such option'):
ks.get_option('unknown')
with self.assertRaisesRegex(config.OptionError, "Available options"):
ks.set_option('unknown', 'value')
with self.assertRaisesRegex(config.OptionError, "test.config"):
ks.reset_option('unknown')
def test_unknown_option(self):
with self.assertRaisesRegex(config.OptionError, 'No such key'):
ks.get_option('unknown')
with self.assertRaisesRegex(config.OptionError, "No such key"):
ks.set_option('unknown', 'value')
with self.assertRaisesRegex(config.OptionError, "No such key"):
ks.reset_option('unknows')
def test_get_set_reset_option_different_types(self):
ks.set_option('test.config.list', [1, 2, 3, 4])
self.assertEqual(ks.get_option('test.config.list'), [1, 2, 3, 4])
ks.set_option('test.config.list', None)
self.assertEqual(ks.get_option('test.config.list'), None)
ks.set_option('test.config.float', None)
self.assertEqual(ks.get_option('test.config.float'), None)
ks.set_option('test.config.float', 5.0)
self.assertEqual(ks.get_option('test.config.float'), 5.0)
ks.set_option('test.config.int', 123)
self.assertEqual(ks.get_option('test.config.int'), 123)
def _transform_batch(self, func, return_schema):
from databricks.koalas.series import Series
from databricks import koalas as ks
if not isinstance(func, types.FunctionType):
f = func
func = lambda *args, **kwargs: f(*args, **kwargs)
if return_schema is None:
# TODO: In this case, it avoids the shortcut for now (but only infers schema)
# because it returns a series from a different DataFrame and it has a different
# anchor. We should fix this to allow the shortcut or only allow to infer
# schema.
limit = ks.get_option("compute.shortcut_limit")
pser = self._kser.head(limit)._to_internal_pandas()
transformed = pser.transform(func)
kser = Series(transformed)
spark_return_type = kser.spark.data_type
else:
spark_return_type = return_schema
pudf = pandas_udf(func,
returnType=spark_return_type,
functionType=PandasUDFType.SCALAR)
return self._kser._with_new_scol(scol=pudf(self._kser.spark.column))
def _transform_batch(self, func, return_type: Optional[Union[SeriesType, ScalarType]]):
from databricks.koalas.groupby import GroupBy
from databricks.koalas.series import Series, first_series
from databricks import koalas as ks
if not isinstance(func, types.FunctionType):
f = func
func = lambda *args, **kwargs: f(*args, **kwargs)
if return_type is None:
# TODO: In this case, it avoids the shortcut for now (but only infers schema)
# because it returns a series from a different DataFrame and it has a different
# anchor. We should fix this to allow the shortcut or only allow to infer
# schema.
limit = ks.get_option("compute.shortcut_limit")
pser = self._kser.head(limit + 1)._to_internal_pandas()
transformed = pser.transform(func)
kser = Series(transformed) # type: Series
spark_return_type = force_decimal_precision_scale(
as_nullable_spark_type(kser.spark.data_type)
)
dtype = kser.dtype
else:
spark_return_type = return_type.spark_type
dtype = return_type.dtype
kdf = self._kser.to_frame()
columns = kdf._internal.spark_column_names
def pandas_concat(series):
# The input can only be a DataFrame for struct from Spark 3.0.
# This works around to make the input as a frame. See SPARK-27240
pdf = pd.concat(series, axis=1)
pdf.columns = columns
return pdf
def apply_func(pdf):
return func(first_series(pdf)).to_frame()
return_schema = StructType([StructField(SPARK_DEFAULT_SERIES_NAME, spark_return_type)])
output_func = GroupBy._make_pandas_df_builder_func(
kdf, apply_func, return_schema, retain_index=False
)
pudf = pandas_udf(
lambda *series: first_series(output_func(pandas_concat(series))),
returnType=spark_return_type,
functionType=PandasUDFType.SCALAR,
)
return self._kser._with_new_scol(
scol=pudf(*kdf._internal.spark_columns).alias(
self._kser._internal.spark_column_names[0]
),
dtype=dtype,
)
def transform_batch(self, func, *args, **kwargs):
"""
Transform chunks with a function that takes pandas DataFrame and outputs pandas DataFrame.
The pandas DataFrame given to the function is of a batch used internally. The length of
each input and output should be the same.
See also `Transform and apply a function
<https://koalas.readthedocs.io/en/latest/user_guide/transform_apply.html>`_.
.. note:: the `func` is unable to access to the whole input frame. Koalas internally
splits the input series into multiple batches and calls `func` with each batch multiple
times. Therefore, operations such as global aggregations are impossible. See the example
below.
>>> # This case does not return the length of whole frame but of the batch internally
... # used.
... def length(pdf) -> ks.DataFrame[int]:
... return pd.DataFrame([len(pdf)] * len(pdf))
...
>>> df = ks.DataFrame({'A': range(1000)})
>>> df.koalas.transform_batch(length) # doctest: +SKIP
c0
0 83
1 83
2 83
...
.. note:: this API executes the function once to infer the type which is
potentially expensive, for instance, when the dataset is created after
aggregations or sorting.
To avoid this, specify return type in ``func``, for instance, as below:
>>> def plus_one(x) -> ks.DataFrame[float, float]:
... return x + 1
If the return type is specified, the output column names become
`c0, c1, c2 ... cn`. These names are positionally mapped to the returned
DataFrame in ``func``.
To specify the column names, you can assign them in a pandas friendly style as below:
>>> def plus_one(x) -> ks.DataFrame['a': float, 'b': float]:
... return x + 1
>>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]})
>>> def plus_one(x) -> ks.DataFrame[zip(pdf.dtypes, pdf.columns)]:
... return x + 1
Parameters
----------
func : function
Function to transform each pandas frame.
*args
Positional arguments to pass to func.
**kwargs
Keyword arguments to pass to func.
Returns
-------
DataFrame
See Also
--------
DataFrame.koalas.apply_batch: For row/columnwise operations.
Series.koalas.transform_batch: transform the search as each pandas chunks.
Examples
--------
>>> df = ks.DataFrame([(1, 2), (3, 4), (5, 6)], columns=['A', 'B'])
>>> df
A B
0 1 2
1 3 4
2 5 6
>>> def plus_one_func(pdf) -> ks.DataFrame[int, int]:
... return pdf + 1
>>> df.koalas.transform_batch(plus_one_func)
c0 c1
0 2 3
1 4 5
2 6 7
>>> def plus_one_func(pdf) -> ks.DataFrame['A': int, 'B': int]:
... return pdf + 1
>>> df.koalas.transform_batch(plus_one_func)
A B
0 2 3
1 4 5
2 6 7
>>> def plus_one_func(pdf) -> ks.Series[int]:
... return pdf.B + 1
>>> df.koalas.transform_batch(plus_one_func)
0 3
1 5
2 7
dtype: int32
You can also omit the type hints so Koalas infers the return schema as below:
>>> df.koalas.transform_batch(lambda pdf: pdf + 1)
A B
0 2 3
1 4 5
2 6 7
>>> (df * -1).koalas.transform_batch(abs)
A B
0 1 2
1 3 4
2 5 6
Note that you should not transform the index. The index information will not change.
>>> df.koalas.transform_batch(lambda pdf: pdf.B + 1)
0 3
1 5
2 7
Name: B, dtype: int64
You can also specify extra arguments as below.
>>> df.koalas.transform_batch(lambda pdf, a, b, c: pdf.B + a + b + c, 1, 2, c=3)
0 8
1 10
2 12
Name: B, dtype: int64
"""
from databricks.koalas.groupby import GroupBy
from databricks.koalas.frame import DataFrame
from databricks.koalas.series import first_series
from databricks import koalas as ks
assert callable(
func), "the first argument should be a callable function."
spec = inspect.getfullargspec(func)
return_sig = spec.annotations.get("return", None)
should_infer_schema = return_sig is None
original_func = func
func = lambda o: original_func(o, *args, **kwargs)
names = self._kdf._internal.to_internal_spark_frame.schema.names
should_by_pass = LooseVersion(pyspark.__version__) >= "3.0"
def pandas_concat(series):
# The input can only be a DataFrame for struct from Spark 3.0.
# This works around to make the input as a frame. See SPARK-27240
pdf = pd.concat(series, axis=1)
pdf = pdf.rename(columns=dict(zip(pdf.columns, names)))
return pdf
def pandas_extract(pdf, name):
# This is for output to work around a DataFrame for struct
# from Spark 3.0. See SPARK-23836
return pdf[name]
def pandas_series_func(f):
ff = f
return lambda *series: ff(pandas_concat(series))
def pandas_frame_func(f):
ff = f
return lambda *series: pandas_extract(ff(pandas_concat(series)),
field.name)
if should_infer_schema:
# Here we execute with the first 1000 to get the return type.
# If the records were less than 1000, it uses pandas API directly for a shortcut.
limit = ks.get_option("compute.shortcut_limit")
pdf = self._kdf.head(limit + 1)._to_internal_pandas()
transformed = func(pdf)
if not isinstance(transformed, (pd.DataFrame, pd.Series)):
raise ValueError(
"The given function should return a frame; however, "
"the return type was %s." % type(transformed))
if len(transformed) != len(pdf):
raise ValueError(
"transform_batch cannot produce aggregated results")
kdf_or_kser = ks.from_pandas(transformed)
if isinstance(kdf_or_kser, ks.Series):
kser = kdf_or_kser
pudf = pandas_udf(
func if should_by_pass else pandas_series_func(func),
returnType=kser.spark.data_type,
functionType=PandasUDFType.SCALAR,
)
columns = self._kdf._internal.spark_columns
# TODO: Index will be lost in this case.
internal = self._kdf._internal.copy(
column_labels=kser._internal.column_labels,
data_spark_columns=[
(pudf(F.struct(*columns)) if should_by_pass else pudf(
*columns)).alias(
kser._internal.data_spark_column_names[0])
],
column_label_names=kser._internal.column_label_names,
)
return first_series(DataFrame(internal))
else:
kdf = kdf_or_kser
if len(pdf) <= limit:
# only do the short cut when it returns a frame to avoid
# operations on different dataframes in case of series.
return kdf
return_schema = kdf._internal.to_internal_spark_frame.schema
# Force nullability.
return_schema = StructType([
StructField(field.name, field.dataType)
for field in return_schema.fields
])
self_applied = DataFrame(self._kdf._internal.resolved_copy)
output_func = GroupBy._make_pandas_df_builder_func(
self_applied, func, return_schema, retain_index=True)
columns = self_applied._internal.spark_columns
if should_by_pass:
pudf = pandas_udf(output_func,
returnType=return_schema,
functionType=PandasUDFType.SCALAR)
temp_struct_column = verify_temp_column_name(
self_applied._internal.spark_frame, "__temp_struct__")
applied = pudf(
F.struct(*columns)).alias(temp_struct_column)
sdf = self_applied._internal.spark_frame.select(applied)
sdf = sdf.selectExpr("%s.*" % temp_struct_column)
else:
applied = []
for field in return_schema.fields:
applied.append(
pandas_udf(
pandas_frame_func(output_func),
returnType=field.dataType,
functionType=PandasUDFType.SCALAR,
)(*columns).alias(field.name))
sdf = self_applied._internal.spark_frame.select(*applied)
return DataFrame(kdf._internal.with_new_sdf(sdf))
else:
return_type = infer_return_type(original_func)
return_schema = return_type.tpe
is_return_series = isinstance(return_type, SeriesType)
is_return_dataframe = isinstance(return_type, DataFrameType)
if not is_return_dataframe and not is_return_series:
raise TypeError(
"The given function should specify a frame or series as its type "
"hints; however, the return type was %s." % return_sig)
if is_return_series:
pudf = pandas_udf(
func if should_by_pass else pandas_series_func(func),
returnType=return_schema,
functionType=PandasUDFType.SCALAR,
)
columns = self._kdf._internal.spark_columns
internal = self._kdf._internal.copy(
column_labels=[None],
data_spark_columns=[
(pudf(F.struct(*columns)) if should_by_pass else pudf(
*columns)).alias(SPARK_DEFAULT_SERIES_NAME)
],
column_label_names=None,
)
return first_series(DataFrame(internal))
else:
self_applied = DataFrame(self._kdf._internal.resolved_copy)
output_func = GroupBy._make_pandas_df_builder_func(
self_applied, func, return_schema, retain_index=False)
columns = self_applied._internal.spark_columns
if should_by_pass:
pudf = pandas_udf(output_func,
returnType=return_schema,
functionType=PandasUDFType.SCALAR)
temp_struct_column = verify_temp_column_name(
self_applied._internal.spark_frame, "__temp_struct__")
applied = pudf(
F.struct(*columns)).alias(temp_struct_column)
sdf = self_applied._internal.spark_frame.select(applied)
sdf = sdf.selectExpr("%s.*" % temp_struct_column)
else:
applied = []
for field in return_schema.fields:
applied.append(
pandas_udf(
pandas_frame_func(output_func),
returnType=field.dataType,
functionType=PandasUDFType.SCALAR,
)(*columns).alias(field.name))
sdf = self_applied._internal.spark_frame.select(*applied)
return DataFrame(sdf)
def apply_batch(self, func, args=(), **kwds):
"""
Apply a function that takes pandas DataFrame and outputs pandas DataFrame. The pandas
DataFrame given to the function is of a batch used internally.
See also `Transform and apply a function
<https://koalas.readthedocs.io/en/latest/user_guide/transform_apply.html>`_.
.. note:: the `func` is unable to access to the whole input frame. Koalas internally
splits the input series into multiple batches and calls `func` with each batch multiple
times. Therefore, operations such as global aggregations are impossible. See the example
below.
>>> # This case does not return the length of whole frame but of the batch internally
... # used.
... def length(pdf) -> ks.DataFrame[int]:
... return pd.DataFrame([len(pdf)])
...
>>> df = ks.DataFrame({'A': range(1000)})
>>> df.koalas.apply_batch(length) # doctest: +SKIP
c0
0 83
1 83
2 83
...
10 83
11 83
.. note:: this API executes the function once to infer the type which is
potentially expensive, for instance, when the dataset is created after
aggregations or sorting.
To avoid this, specify return type in ``func``, for instance, as below:
>>> def plus_one(x) -> ks.DataFrame[float, float]:
... return x + 1
If the return type is specified, the output column names become
`c0, c1, c2 ... cn`. These names are positionally mapped to the returned
DataFrame in ``func``.
To specify the column names, you can assign them in a pandas friendly style as below:
>>> def plus_one(x) -> ks.DataFrame["a": float, "b": float]:
... return x + 1
>>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]})
>>> def plus_one(x) -> ks.DataFrame[zip(pdf.dtypes, pdf.columns)]:
... return x + 1
Parameters
----------
func : function
Function to apply to each pandas frame.
args : tuple
Positional arguments to pass to `func` in addition to the
array/series.
**kwds
Additional keyword arguments to pass as keywords arguments to
`func`.
Returns
-------
DataFrame
See Also
--------
DataFrame.apply: For row/columnwise operations.
DataFrame.applymap: For elementwise operations.
DataFrame.aggregate: Only perform aggregating type operations.
DataFrame.transform: Only perform transforming type operations.
Series.koalas.transform_batch: transform the search as each pandas chunks.
Examples
--------
>>> df = ks.DataFrame([(1, 2), (3, 4), (5, 6)], columns=['A', 'B'])
>>> df
A B
0 1 2
1 3 4
2 5 6
>>> def query_func(pdf) -> ks.DataFrame[int, int]:
... return pdf.query('A == 1')
>>> df.koalas.apply_batch(query_func)
c0 c1
0 1 2
>>> def query_func(pdf) -> ks.DataFrame["A": int, "B": int]:
... return pdf.query('A == 1')
>>> df.koalas.apply_batch(query_func)
A B
0 1 2
You can also omit the type hints so Koalas infers the return schema as below:
>>> df.koalas.apply_batch(lambda pdf: pdf.query('A == 1'))
A B
0 1 2
You can also specify extra arguments.
>>> def calculation(pdf, y, z) -> ks.DataFrame[int, int]:
... return pdf ** y + z
>>> df.koalas.apply_batch(calculation, args=(10,), z=20)
c0 c1
0 21 1044
1 59069 1048596
2 9765645 60466196
You can also use ``np.ufunc`` and built-in functions as input.
>>> df.koalas.apply_batch(np.add, args=(10,))
A B
0 11 12
1 13 14
2 15 16
>>> (df * -1).koalas.apply_batch(abs)
A B
0 1 2
1 3 4
2 5 6
"""
# TODO: codes here partially duplicate `DataFrame.apply`. Can we deduplicate?
from databricks.koalas.groupby import GroupBy
from databricks.koalas.frame import DataFrame
from databricks import koalas as ks
if not isinstance(func, types.FunctionType):
assert callable(
func), "the first argument should be a callable function."
f = func
func = lambda *args, **kwargs: f(*args, **kwargs)
spec = inspect.getfullargspec(func)
return_sig = spec.annotations.get("return", None)
should_infer_schema = return_sig is None
should_use_map_in_pandas = LooseVersion(pyspark.__version__) >= "3.0"
original_func = func
func = lambda o: original_func(o, *args, **kwds)
self_applied = DataFrame(self._kdf._internal.resolved_copy)
if should_infer_schema:
# Here we execute with the first 1000 to get the return type.
# If the records were less than 1000, it uses pandas API directly for a shortcut.
limit = ks.get_option("compute.shortcut_limit")
pdf = self_applied.head(limit + 1)._to_internal_pandas()
applied = func(pdf)
if not isinstance(applied, pd.DataFrame):
raise ValueError(
"The given function should return a frame; however, "
"the return type was %s." % type(applied))
kdf = ks.DataFrame(applied)
if len(pdf) <= limit:
return kdf
return_schema = kdf._internal.to_internal_spark_frame.schema
if should_use_map_in_pandas:
output_func = GroupBy._make_pandas_df_builder_func(
self_applied, func, return_schema, retain_index=True)
sdf = self_applied._internal.to_internal_spark_frame.mapInPandas(
lambda iterator: map(output_func, iterator),
schema=return_schema)
else:
sdf = GroupBy._spark_group_map_apply(
self_applied,
func, (F.spark_partition_id(), ),
return_schema,
retain_index=True)
# If schema is inferred, we can restore indexes too.
internal = kdf._internal.with_new_sdf(sdf)
else:
return_type = infer_return_type(original_func)
return_schema = return_type.tpe
is_return_dataframe = isinstance(return_type, DataFrameType)
if not is_return_dataframe:
raise TypeError(
"The given function should specify a frame as its type "
"hints; however, the return type was %s." % return_sig)
if should_use_map_in_pandas:
output_func = GroupBy._make_pandas_df_builder_func(
self_applied, func, return_schema, retain_index=False)
sdf = self_applied._internal.to_internal_spark_frame.mapInPandas(
lambda iterator: map(output_func, iterator),
schema=return_schema)
else:
sdf = GroupBy._spark_group_map_apply(
self_applied,
func, (F.spark_partition_id(), ),
return_schema,
retain_index=False)
# Otherwise, it loses index.
internal = InternalFrame(spark_frame=sdf, index_map=None)
return DataFrame(internal)
