body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1
value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
c4ea7ec89addebcd34c3a738b97307a0e2560f10bd37d525e0a72130f2c4efb7 | @property
def time_updated(self):
'\n **[Required]** Gets the time_updated of this CreateChannelResult.\n When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.\n\n __ https://tools.ietf.org/rfc/rfc3339\n\n\n :return: The time_updated of this ... | **[Required]** Gets the time_updated of this CreateChannelResult.
When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.
__ https://tools.ietf.org/rfc/rfc3339
:return: The time_updated of this CreateChannelResult.
:rtype: datetime | src/oci/oda/models/create_channel_result.py | time_updated | pabs3/oci-python-sdk | 0 | python | @property
def time_updated(self):
'\n **[Required]** Gets the time_updated of this CreateChannelResult.\n When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.\n\n __ https://tools.ietf.org/rfc/rfc3339\n\n\n :return: The time_updated of this ... | @property
def time_updated(self):
'\n **[Required]** Gets the time_updated of this CreateChannelResult.\n When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.\n\n __ https://tools.ietf.org/rfc/rfc3339\n\n\n :return: The time_updated of this ... |
9072b0605d4ef3c2e2cb13a1ce0b75540a061227e645e66d6c1a76fc2b34a1d0 | @time_updated.setter
def time_updated(self, time_updated):
'\n Sets the time_updated of this CreateChannelResult.\n When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.\n\n __ https://tools.ietf.org/rfc/rfc3339\n\n\n :param time_updated: The... | Sets the time_updated of this CreateChannelResult.
When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.
__ https://tools.ietf.org/rfc/rfc3339
:param time_updated: The time_updated of this CreateChannelResult.
:type: datetime | src/oci/oda/models/create_channel_result.py | time_updated | pabs3/oci-python-sdk | 0 | python | @time_updated.setter
def time_updated(self, time_updated):
'\n Sets the time_updated of this CreateChannelResult.\n When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.\n\n __ https://tools.ietf.org/rfc/rfc3339\n\n\n :param time_updated: The... | @time_updated.setter
def time_updated(self, time_updated):
'\n Sets the time_updated of this CreateChannelResult.\n When the resource was last updated. A date-time string as described in `RFC 3339`__, section 14.29.\n\n __ https://tools.ietf.org/rfc/rfc3339\n\n\n :param time_updated: The... |
8d3ea4b54fffcec14f59e6a8f852e84bc02d2ec922fcdf9d0460d72a35b2ffeb | @property
def freeform_tags(self):
'\n Gets the freeform_tags of this CreateChannelResult.\n Simple key-value pair that is applied without any predefined name, type, or scope.\n Example: `{"bar-key": "value"}`\n\n\n :return: The freeform_tags of this CreateChannelResult.\n :rtype:... | Gets the freeform_tags of this CreateChannelResult.
Simple key-value pair that is applied without any predefined name, type, or scope.
Example: `{"bar-key": "value"}`
:return: The freeform_tags of this CreateChannelResult.
:rtype: dict(str, str) | src/oci/oda/models/create_channel_result.py | freeform_tags | pabs3/oci-python-sdk | 0 | python | @property
def freeform_tags(self):
'\n Gets the freeform_tags of this CreateChannelResult.\n Simple key-value pair that is applied without any predefined name, type, or scope.\n Example: `{"bar-key": "value"}`\n\n\n :return: The freeform_tags of this CreateChannelResult.\n :rtype:... | @property
def freeform_tags(self):
'\n Gets the freeform_tags of this CreateChannelResult.\n Simple key-value pair that is applied without any predefined name, type, or scope.\n Example: `{"bar-key": "value"}`\n\n\n :return: The freeform_tags of this CreateChannelResult.\n :rtype:... |
f2b962bd297cd0c68127eb7bca50f72293d74e6f9459b797eaed92248f26de8f | @freeform_tags.setter
def freeform_tags(self, freeform_tags):
'\n Sets the freeform_tags of this CreateChannelResult.\n Simple key-value pair that is applied without any predefined name, type, or scope.\n Example: `{"bar-key": "value"}`\n\n\n :param freeform_tags: The freeform_tags of th... | Sets the freeform_tags of this CreateChannelResult.
Simple key-value pair that is applied without any predefined name, type, or scope.
Example: `{"bar-key": "value"}`
:param freeform_tags: The freeform_tags of this CreateChannelResult.
:type: dict(str, str) | src/oci/oda/models/create_channel_result.py | freeform_tags | pabs3/oci-python-sdk | 0 | python | @freeform_tags.setter
def freeform_tags(self, freeform_tags):
'\n Sets the freeform_tags of this CreateChannelResult.\n Simple key-value pair that is applied without any predefined name, type, or scope.\n Example: `{"bar-key": "value"}`\n\n\n :param freeform_tags: The freeform_tags of th... | @freeform_tags.setter
def freeform_tags(self, freeform_tags):
'\n Sets the freeform_tags of this CreateChannelResult.\n Simple key-value pair that is applied without any predefined name, type, or scope.\n Example: `{"bar-key": "value"}`\n\n\n :param freeform_tags: The freeform_tags of th... |
dc4260b72da1411079a72caca91a804278acda30b35e0284c3f2c6e7d4b63111 | @property
def defined_tags(self):
'\n Gets the defined_tags of this CreateChannelResult.\n Usage of predefined tag keys. These predefined keys are scoped to namespaces.\n Example: `{"foo-namespace": {"bar-key": "value"}}`\n\n\n :return: The defined_tags of this CreateChannelResult.\n ... | Gets the defined_tags of this CreateChannelResult.
Usage of predefined tag keys. These predefined keys are scoped to namespaces.
Example: `{"foo-namespace": {"bar-key": "value"}}`
:return: The defined_tags of this CreateChannelResult.
:rtype: dict(str, dict(str, object)) | src/oci/oda/models/create_channel_result.py | defined_tags | pabs3/oci-python-sdk | 0 | python | @property
def defined_tags(self):
'\n Gets the defined_tags of this CreateChannelResult.\n Usage of predefined tag keys. These predefined keys are scoped to namespaces.\n Example: `{"foo-namespace": {"bar-key": "value"}}`\n\n\n :return: The defined_tags of this CreateChannelResult.\n ... | @property
def defined_tags(self):
'\n Gets the defined_tags of this CreateChannelResult.\n Usage of predefined tag keys. These predefined keys are scoped to namespaces.\n Example: `{"foo-namespace": {"bar-key": "value"}}`\n\n\n :return: The defined_tags of this CreateChannelResult.\n ... |
b1b75bbdf8f0c9cdebe443526d43d7f5193d9c435ed40419277cac6390088b64 | @defined_tags.setter
def defined_tags(self, defined_tags):
'\n Sets the defined_tags of this CreateChannelResult.\n Usage of predefined tag keys. These predefined keys are scoped to namespaces.\n Example: `{"foo-namespace": {"bar-key": "value"}}`\n\n\n :param defined_tags: The defined_ta... | Sets the defined_tags of this CreateChannelResult.
Usage of predefined tag keys. These predefined keys are scoped to namespaces.
Example: `{"foo-namespace": {"bar-key": "value"}}`
:param defined_tags: The defined_tags of this CreateChannelResult.
:type: dict(str, dict(str, object)) | src/oci/oda/models/create_channel_result.py | defined_tags | pabs3/oci-python-sdk | 0 | python | @defined_tags.setter
def defined_tags(self, defined_tags):
'\n Sets the defined_tags of this CreateChannelResult.\n Usage of predefined tag keys. These predefined keys are scoped to namespaces.\n Example: `{"foo-namespace": {"bar-key": "value"}}`\n\n\n :param defined_tags: The defined_ta... | @defined_tags.setter
def defined_tags(self, defined_tags):
'\n Sets the defined_tags of this CreateChannelResult.\n Usage of predefined tag keys. These predefined keys are scoped to namespaces.\n Example: `{"foo-namespace": {"bar-key": "value"}}`\n\n\n :param defined_tags: The defined_ta... |
7db04180b61b40410820c09c2023d08f43899d816af22aff433bafc5a5058538 | async def async_run(sync_func, *args, with_context=False, **kwargs):
'\n\n Parameters\n ----------\n with_context:bool\n 是否要copy当前进程的context， 默认：False\n sync_func\n args\n kwargs:\n\n Returns\n -------\n\n '
loop = init_event_loop()
fn = partial(sync_func, *args, **kwar... | Parameters
----------
with_context:bool
是否要copy当前进程的context， 默认：False
sync_func
args
kwargs:
Returns
------- | jcutil/core/__init__.py | async_run | bspiritxp/jcutil | 0 | python | async def async_run(sync_func, *args, with_context=False, **kwargs):
'\n\n Parameters\n ----------\n with_context:bool\n 是否要copy当前进程的context， 默认：False\n sync_func\n args\n kwargs:\n\n Returns\n -------\n\n '
loop = init_event_loop()
fn = partial(sync_func, *args, **kwar... | async def async_run(sync_func, *args, with_context=False, **kwargs):
'\n\n Parameters\n ----------\n with_context:bool\n 是否要copy当前进程的context， 默认：False\n sync_func\n args\n kwargs:\n\n Returns\n -------\n\n '
loop = init_event_loop()
fn = partial(sync_func, *args, **kwar... |
ae151a44b3ada80d204fbb5327f78b7ea1939b0e9a07563a808e205037db61a5 | def _schedule_injective(_, outs, target):
'Generic schedule for binary bcast'
with tvm.target.create(target):
return topi.generic.schedule_injective(outs) | Generic schedule for binary bcast | Fujitsu/benchmarks/resnet/implementations/mxnet/3rdparty/tvm/nnvm/python/nnvm/top/tensor.py | _schedule_injective | mengkai94/training_results_v0.6 | 64 | python | def _schedule_injective(_, outs, target):
with tvm.target.create(target):
return topi.generic.schedule_injective(outs) | def _schedule_injective(_, outs, target):
with tvm.target.create(target):
return topi.generic.schedule_injective(outs)<|docstring|>Generic schedule for binary bcast<|endoftext|> |
67701b0f0f8a813db967d773e825ae27bca76461eebfdff73809586d398700b6 | def _compute_binary_scalar(f):
'auxiliary function'
@tvm.tag_scope(topi.tag.ELEMWISE)
def _compute(attrs, x, _):
x = x[0]
scalar = attrs.get_float('scalar')
scalar = tvm.const(scalar, x.dtype)
return tvm.compute(x.shape, (lambda *i: f(x(*i), scalar)))
return _compute | auxiliary function | Fujitsu/benchmarks/resnet/implementations/mxnet/3rdparty/tvm/nnvm/python/nnvm/top/tensor.py | _compute_binary_scalar | mengkai94/training_results_v0.6 | 64 | python | def _compute_binary_scalar(f):
@tvm.tag_scope(topi.tag.ELEMWISE)
def _compute(attrs, x, _):
x = x[0]
scalar = attrs.get_float('scalar')
scalar = tvm.const(scalar, x.dtype)
return tvm.compute(x.shape, (lambda *i: f(x(*i), scalar)))
return _compute | def _compute_binary_scalar(f):
@tvm.tag_scope(topi.tag.ELEMWISE)
def _compute(attrs, x, _):
x = x[0]
scalar = attrs.get_float('scalar')
scalar = tvm.const(scalar, x.dtype)
return tvm.compute(x.shape, (lambda *i: f(x(*i), scalar)))
return _compute<|docstring|>auxiliary f... |
5343c21da4fa6cc33133e9c63ef912345588df9d0899a42ed0f17b02ce25e83f | def _compute_unary(f):
'auxiliary function'
def _compute(attrs, x, _):
return f(x[0])
return _compute | auxiliary function | Fujitsu/benchmarks/resnet/implementations/mxnet/3rdparty/tvm/nnvm/python/nnvm/top/tensor.py | _compute_unary | mengkai94/training_results_v0.6 | 64 | python | def _compute_unary(f):
def _compute(attrs, x, _):
return f(x[0])
return _compute | def _compute_unary(f):
def _compute(attrs, x, _):
return f(x[0])
return _compute<|docstring|>auxiliary function<|endoftext|> |
03703d876c059048df3f4f6635644ba250c6491fdfa29095b76d51cff3bb6549 | def _compute_binary(f):
'auxiliary function'
def _compute(attrs, x, _):
return f(x[0], x[1])
return _compute | auxiliary function | Fujitsu/benchmarks/resnet/implementations/mxnet/3rdparty/tvm/nnvm/python/nnvm/top/tensor.py | _compute_binary | mengkai94/training_results_v0.6 | 64 | python | def _compute_binary(f):
def _compute(attrs, x, _):
return f(x[0], x[1])
return _compute | def _compute_binary(f):
def _compute(attrs, x, _):
return f(x[0], x[1])
return _compute<|docstring|>auxiliary function<|endoftext|> |
69b10294e638e2157d289de251dfd11c29d1c39fe71dfd8b3447e362df043e82 | def gather_images_in_dir(img_dir):
' Collect the paths of all the images ina given directory '
image_ids = os.listdir(img_dir)
image_paths = [os.path.join(img_dir, image_id) for image_id in image_ids]
return image_paths | Collect the paths of all the images ina given directory | projects/ImageNet/code/tools/data.py | gather_images_in_dir | hboekema/recolour | 0 | python | def gather_images_in_dir(img_dir):
' '
image_ids = os.listdir(img_dir)
image_paths = [os.path.join(img_dir, image_id) for image_id in image_ids]
return image_paths | def gather_images_in_dir(img_dir):
' '
image_ids = os.listdir(img_dir)
image_paths = [os.path.join(img_dir, image_id) for image_id in image_ids]
return image_paths<|docstring|>Collect the paths of all the images ina given directory<|endoftext|> |
a8f5c6153d919ca11617e07875856d2716fad77cdcf9a9f3b0f9dd41b7df038b | def load_images(img_paths, img_dim=(256, 256)):
'Load RGB images for which the paths are given. Convert to YUV form and preprocess them for model training/prediction.\n\n Parameters\n ----------\n img_paths : list of str\n List of valid paths to images to load\n img_dim : tuple of int\n Im... | Load RGB images for which the paths are given. Convert to YUV form and preprocess them for model training/prediction.
Parameters
----------
img_paths : list of str
List of valid paths to images to load
img_dim : tuple of int
Image dimensions to load images into
Returns
-------
list
Lists of x (input) and ... | projects/ImageNet/code/tools/data.py | load_images | hboekema/recolour | 0 | python | def load_images(img_paths, img_dim=(256, 256)):
'Load RGB images for which the paths are given. Convert to YUV form and preprocess them for model training/prediction.\n\n Parameters\n ----------\n img_paths : list of str\n List of valid paths to images to load\n img_dim : tuple of int\n Im... | def load_images(img_paths, img_dim=(256, 256)):
'Load RGB images for which the paths are given. Convert to YUV form and preprocess them for model training/prediction.\n\n Parameters\n ----------\n img_paths : list of str\n List of valid paths to images to load\n img_dim : tuple of int\n Im... |
d049fba09e30603626ffe443748452122ab453843a05f18597f23460a11ce6bc | def run(args):
'Handle keyring script.'
parser = argparse.ArgumentParser(description='Modify Home Assistant secrets in the default keyring. Use the secrets in configuration files with: !secret <name>')
parser.add_argument('--script', choices=['keyring'])
parser.add_argument('action', choices=['get', 'se... | Handle keyring script. | homeassistant/scripts/keyring.py | run | ellsclytn/home-assistant | 37 | python | def run(args):
parser = argparse.ArgumentParser(description='Modify Home Assistant secrets in the default keyring. Use the secrets in configuration files with: !secret <name>')
parser.add_argument('--script', choices=['keyring'])
parser.add_argument('action', choices=['get', 'set', 'del', 'info'], help... | def run(args):
parser = argparse.ArgumentParser(description='Modify Home Assistant secrets in the default keyring. Use the secrets in configuration files with: !secret <name>')
parser.add_argument('--script', choices=['keyring'])
parser.add_argument('action', choices=['get', 'set', 'del', 'info'], help... |
dd205cd45734fbd5a16975ad10c5b4b53954505be0dcf8087ae1d8a5f9e170d0 | def ThioalkalivibrioSpK90mix(directed: bool=False, verbose: int=2, cache_path: str='graphs/string', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the Thioalkalivibrio sp. K90mix graph.\n\n The graph is automatically retrieved from the STRING repository. \n\n\t\n\n Parameters\n ... | Return new instance of the Thioalkalivibrio sp. K90mix graph.
The graph is automatically retrieved from the STRING repository.
Parameters
-------------------
directed: bool = False,
Wether to load the graph as directed or undirected.
By default false.
verbose: int = 2,
Wether to show loading bars d... | bindings/python/ensmallen_graph/datasets/string/thioalkalivibriospk90mix.py | ThioalkalivibrioSpK90mix | caufieldjh/ensmallen_graph | 0 | python | def ThioalkalivibrioSpK90mix(directed: bool=False, verbose: int=2, cache_path: str='graphs/string', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the Thioalkalivibrio sp. K90mix graph.\n\n The graph is automatically retrieved from the STRING repository. \n\n\t\n\n Parameters\n ... | def ThioalkalivibrioSpK90mix(directed: bool=False, verbose: int=2, cache_path: str='graphs/string', **additional_graph_kwargs: Dict) -> EnsmallenGraph:
'Return new instance of the Thioalkalivibrio sp. K90mix graph.\n\n The graph is automatically retrieved from the STRING repository. \n\n\t\n\n Parameters\n ... |
06978c5b029252ebcb54006e3c5ac4b9011e36a3823202ac10ea3bdb484fb4ec | def _maybe_project_func(projection: Optional[List[str]]):
' Returns identity func if projection is empty or None, else returns\n a function that projects the specified columns. '
if projection:
return (lambda df: df[projection])
else:
return (lambda x: x) | Returns identity func if projection is empty or None, else returns
a function that projects the specified columns. | sdks/python/apache_beam/dataframe/frames.py | _maybe_project_func | labianchin/beam-1 | 5,279 | python | def _maybe_project_func(projection: Optional[List[str]]):
' Returns identity func if projection is empty or None, else returns\n a function that projects the specified columns. '
if projection:
return (lambda df: df[projection])
else:
return (lambda x: x) | def _maybe_project_func(projection: Optional[List[str]]):
' Returns identity func if projection is empty or None, else returns\n a function that projects the specified columns. '
if projection:
return (lambda df: df[projection])
else:
return (lambda x: x)<|docstring|>Returns identity func i... |
45b6b9fa895c19e0f5f8355c7b04c65ab5d81aed1016380a0facb9815c0ec227 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop(self, labels, axis, index, columns, errors, **kwargs):
'drop is not parallelizable when dropping from the index and\n ``errors="raise"`` is specified. I... | drop is not parallelizable when dropping from the index and
``errors="raise"`` is specified. It requires collecting all data on a single
node in order to detect if one of the index values is missing. | sdks/python/apache_beam/dataframe/frames.py | drop | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop(self, labels, axis, index, columns, errors, **kwargs):
'drop is not parallelizable when dropping from the index and\n ``errors="raise"`` is specified. I... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop(self, labels, axis, index, columns, errors, **kwargs):
'drop is not parallelizable when dropping from the index and\n ``errors="raise"`` is specified. I... |
6d0c439f21fb8da9baf2e18001c3a7bb245b6f6905a7c0f0ff9e2eba6639da48 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def fillna(self, value, method, axis, limit, **kwargs):
'When ``axis="index"``, both ``method`` and ``limit`` must be ``None``.\n otherwise this operation is ord... | When ``axis="index"``, both ``method`` and ``limit`` must be ``None``.
otherwise this operation is order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | fillna | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def fillna(self, value, method, axis, limit, **kwargs):
'When ``axis="index"``, both ``method`` and ``limit`` must be ``None``.\n otherwise this operation is ord... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def fillna(self, value, method, axis, limit, **kwargs):
'When ``axis="index"``, both ``method`` and ``limit`` must be ``None``.\n otherwise this operation is ord... |
061cafcbce09a596fae732c7a936cdac03dea5fcce4609a2e929a2f0cd63fae0 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def groupby(self, by, level, axis, as_index, group_keys, **kwargs):
'``as_index`` and ``group_keys`` must both be ``True``.\n\n Aggregations grouping by a categorical column with ``observ... | ``as_index`` and ``group_keys`` must both be ``True``.
Aggregations grouping by a categorical column with ``observed=False`` set
are not currently parallelizable
(`BEAM-11190 <https://issues.apache.org/jira/browse/BEAM-11190>`_). | sdks/python/apache_beam/dataframe/frames.py | groupby | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def groupby(self, by, level, axis, as_index, group_keys, **kwargs):
'``as_index`` and ``group_keys`` must both be ``True``.\n\n Aggregations grouping by a categorical column with ``observ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def groupby(self, by, level, axis, as_index, group_keys, **kwargs):
'``as_index`` and ``group_keys`` must both be ``True``.\n\n Aggregations grouping by a categorical column with ``observ... |
528355b449e68d220c713c316f28d58969a8928e090961627c301ab42b49c6cd | @property
@frame_base.with_docs_from(pd.DataFrame)
def iloc(self):
'Position-based indexing with `iloc` is order-sensitive in almost every\n case. Beam DataFrame users should prefer label-based indexing with `loc`.\n '
return _DeferredILoc(self) | Position-based indexing with `iloc` is order-sensitive in almost every
case. Beam DataFrame users should prefer label-based indexing with `loc`. | sdks/python/apache_beam/dataframe/frames.py | iloc | labianchin/beam-1 | 5,279 | python | @property
@frame_base.with_docs_from(pd.DataFrame)
def iloc(self):
'Position-based indexing with `iloc` is order-sensitive in almost every\n case. Beam DataFrame users should prefer label-based indexing with `loc`.\n '
return _DeferredILoc(self) | @property
@frame_base.with_docs_from(pd.DataFrame)
def iloc(self):
'Position-based indexing with `iloc` is order-sensitive in almost every\n case. Beam DataFrame users should prefer label-based indexing with `loc`.\n '
return _DeferredILoc(self)<|docstring|>Position-based indexing with `iloc` is order-sen... |
b460b62b2e0a4f6ced5267eaaf1bcb5361ea6aaa2b7e12342bc3b713ed42a476 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def reset_index(self, level=None, **kwargs):
'Dropping the entire index (e.g. with ``reset_index(level=None)``) is\n not parallelizable. It is also only guarante... | Dropping the entire index (e.g. with ``reset_index(level=None)``) is
not parallelizable. It is also only guaranteed that the newly generated
index values will be unique. The Beam DataFrame API makes no guarantee
that the same index values as the equivalent pandas operation will be
generated, because that implementation... | sdks/python/apache_beam/dataframe/frames.py | reset_index | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def reset_index(self, level=None, **kwargs):
'Dropping the entire index (e.g. with ``reset_index(level=None)``) is\n not parallelizable. It is also only guarante... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def reset_index(self, level=None, **kwargs):
'Dropping the entire index (e.g. with ``reset_index(level=None)``) is\n not parallelizable. It is also only guarante... |
612fd4f2716c4fe79d50688c431bfa31f0c0c5c4c02c15c30eabb4c57650fc75 | @frame_base.with_docs_from(pd.core.generic.NDFrame)
@frame_base.args_to_kwargs(pd.core.generic.NDFrame)
@frame_base.populate_defaults(pd.core.generic.NDFrame)
def astype(self, dtype, copy, errors):
'astype is not parallelizable when ``errors="ignore"`` is specified.\n\n ``copy=False`` is not supported because it... | astype is not parallelizable when ``errors="ignore"`` is specified.
``copy=False`` is not supported because it relies on memory-sharing
semantics.
``dtype="category`` is not supported because the type of the output column
depends on the data. Please use ``pd.CategoricalDtype`` with explicit
categories instead. | sdks/python/apache_beam/dataframe/frames.py | astype | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.core.generic.NDFrame)
@frame_base.args_to_kwargs(pd.core.generic.NDFrame)
@frame_base.populate_defaults(pd.core.generic.NDFrame)
def astype(self, dtype, copy, errors):
'astype is not parallelizable when ``errors="ignore"`` is specified.\n\n ``copy=False`` is not supported because it... | @frame_base.with_docs_from(pd.core.generic.NDFrame)
@frame_base.args_to_kwargs(pd.core.generic.NDFrame)
@frame_base.populate_defaults(pd.core.generic.NDFrame)
def astype(self, dtype, copy, errors):
'astype is not parallelizable when ``errors="ignore"`` is specified.\n\n ``copy=False`` is not supported because it... |
344382b8acdbb3d7e9a3a227eddeb96d271816f7a38b4118c4a3cfc8b7f7dbf9 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def replace(self, to_replace, value, limit, method, **kwargs):
'``method`` is not supported in the Beam DataFrame API because it is\n order-sensitive. It cannot ... | ``method`` is not supported in the Beam DataFrame API because it is
order-sensitive. It cannot be specified.
If ``limit`` is specified this operation is not parallelizable. | sdks/python/apache_beam/dataframe/frames.py | replace | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def replace(self, to_replace, value, limit, method, **kwargs):
'``method`` is not supported in the Beam DataFrame API because it is\n order-sensitive. It cannot ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def replace(self, to_replace, value, limit, method, **kwargs):
'``method`` is not supported in the Beam DataFrame API because it is\n order-sensitive. It cannot ... |
96698463978e1b7e7f984f76f94419b74edeb38d39d851df95dcc8b1d92b4547 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def tz_localize(self, ambiguous, **kwargs):
'``ambiguous`` cannot be set to ``"infer"`` as its semantics are\n order-sensitive. Similarly, specifying ``ambiguous`` as an\n :class:`~num... | ``ambiguous`` cannot be set to ``"infer"`` as its semantics are
order-sensitive. Similarly, specifying ``ambiguous`` as an
:class:`~numpy.ndarray` is order-sensitive, but you can achieve similar
functionality by specifying ``ambiguous`` as a Series. | sdks/python/apache_beam/dataframe/frames.py | tz_localize | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def tz_localize(self, ambiguous, **kwargs):
'``ambiguous`` cannot be set to ``"infer"`` as its semantics are\n order-sensitive. Similarly, specifying ``ambiguous`` as an\n :class:`~num... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def tz_localize(self, ambiguous, **kwargs):
'``ambiguous`` cannot be set to ``"infer"`` as its semantics are\n order-sensitive. Similarly, specifying ``ambiguous`` as an\n :class:`~num... |
fa48b2a75629dc43a73f2a16518088671f5119eb23672b2539c4f4198abc698c | def length(self):
'Alternative to ``len(df)`` which returns a deferred result that can be\n used in arithmetic with :class:`DeferredSeries` or\n :class:`DeferredDataFrame` instances.'
lengths = expressions.ComputedExpression('get_lengths', (lambda df: pd.Series(len(df))), [self._expr], requires_partition_... | Alternative to ``len(df)`` which returns a deferred result that can be
used in arithmetic with :class:`DeferredSeries` or
:class:`DeferredDataFrame` instances. | sdks/python/apache_beam/dataframe/frames.py | length | labianchin/beam-1 | 5,279 | python | def length(self):
'Alternative to ``len(df)`` which returns a deferred result that can be\n used in arithmetic with :class:`DeferredSeries` or\n :class:`DeferredDataFrame` instances.'
lengths = expressions.ComputedExpression('get_lengths', (lambda df: pd.Series(len(df))), [self._expr], requires_partition_... | def length(self):
'Alternative to ``len(df)`` which returns a deferred result that can be\n used in arithmetic with :class:`DeferredSeries` or\n :class:`DeferredDataFrame` instances.'
lengths = expressions.ComputedExpression('get_lengths', (lambda df: pd.Series(len(df))), [self._expr], requires_partition_... |
8737439ca9e477172e35c2de54d24d956d37a138b51fe29dbf232b750db72e1d | @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sort_values(self, axis, **kwargs):
'``sort_values`` is not implemented.\n\n It is not implemented for ``axis=index`` because it imposes an ordering on\n the dataset, and it likely will not be maintained (see\n https://... | ``sort_values`` is not implemented.
It is not implemented for ``axis=index`` because it imposes an ordering on
the dataset, and it likely will not be maintained (see
https://s.apache.org/dataframe-order-sensitive-operations).
It is not implemented for ``axis=columns`` because it makes the order of
the columns depend ... | sdks/python/apache_beam/dataframe/frames.py | sort_values | labianchin/beam-1 | 5,279 | python | @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sort_values(self, axis, **kwargs):
'``sort_values`` is not implemented.\n\n It is not implemented for ``axis=index`` because it imposes an ordering on\n the dataset, and it likely will not be maintained (see\n https://... | @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sort_values(self, axis, **kwargs):
'``sort_values`` is not implemented.\n\n It is not implemented for ``axis=index`` because it imposes an ordering on\n the dataset, and it likely will not be maintained (see\n https://... |
9c33e6c9b90322f855c1b4e20035ca05fde1b8091b33ffcc24b379185620e0d1 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def sort_index(self, axis, **kwargs):
'``axis=index`` is not allowed because it imposes an ordering on the\n dataset, and we cannot guarantee it will be maintain... | ``axis=index`` is not allowed because it imposes an ordering on the
dataset, and we cannot guarantee it will be maintained (see
https://s.apache.org/dataframe-order-sensitive-operations). Only
``axis=columns`` is allowed. | sdks/python/apache_beam/dataframe/frames.py | sort_index | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def sort_index(self, axis, **kwargs):
'``axis=index`` is not allowed because it imposes an ordering on the\n dataset, and we cannot guarantee it will be maintain... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def sort_index(self, axis, **kwargs):
'``axis=index`` is not allowed because it imposes an ordering on the\n dataset, and we cannot guarantee it will be maintain... |
fdd635b7178543ad2663be5d14ecdfd6914a273d9959230827c91aa7e2923070 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def where(self, cond, other, errors, **kwargs):
'where is not parallelizable when ``errors="ignore"`` is specified.'
requires = partitionings.Arbitrary()
de... | where is not parallelizable when ``errors="ignore"`` is specified. | sdks/python/apache_beam/dataframe/frames.py | where | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def where(self, cond, other, errors, **kwargs):
requires = partitionings.Arbitrary()
deferred_args = {}
actual_args = {}
if isinstance(cond, frame_... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def where(self, cond, other, errors, **kwargs):
requires = partitionings.Arbitrary()
deferred_args = {}
actual_args = {}
if isinstance(cond, frame_... |
a44bcafc6ec787b63a19cab3cc878a60e7fdb6969dbc232e107d79771d575515 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def mask(self, cond, **kwargs):
'mask is not parallelizable when ``errors="ignore"`` is specified.'
return self.where((~ cond), **kwargs) | mask is not parallelizable when ``errors="ignore"`` is specified. | sdks/python/apache_beam/dataframe/frames.py | mask | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def mask(self, cond, **kwargs):
return self.where((~ cond), **kwargs) | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def mask(self, cond, **kwargs):
return self.where((~ cond), **kwargs)<|docstring|>mask is not parallelizable when ``errors="ignore"`` is specified.<|endoftext|... |
838858f0429eedff59851fb97c86947e72679cb38ac102a392a6874284f80a36 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def xs(self, key, axis, level, **kwargs):
"Note that ``xs(axis='index')`` will raise a ``KeyError`` at execution\n time if the key does not exist in the index."
if (axis in ('columns'... | Note that ``xs(axis='index')`` will raise a ``KeyError`` at execution
time if the key does not exist in the index. | sdks/python/apache_beam/dataframe/frames.py | xs | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def xs(self, key, axis, level, **kwargs):
"Note that ``xs(axis='index')`` will raise a ``KeyError`` at execution\n time if the key does not exist in the index."
if (axis in ('columns'... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def xs(self, key, axis, level, **kwargs):
"Note that ``xs(axis='index')`` will raise a ``KeyError`` at execution\n time if the key does not exist in the index."
if (axis in ('columns'... |
d968b9e87a9d73c141ef8a3b6bfcc2936fac035f8bf036795ff4cd4a84e864d3 | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def append(self, to_append, ignore_index, verify_integrity, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if (not isinstance(t... | ``ignore_index=True`` is not supported, because it requires generating an
order-sensitive index. | sdks/python/apache_beam/dataframe/frames.py | append | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def append(self, to_append, ignore_index, verify_integrity, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if (not isinstance(t... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def append(self, to_append, ignore_index, verify_integrity, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if (not isinstance(t... |
37cfa1785f554570db9b2b5a0aa8052156ff6069f68921d3910a831a3006c83a | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def align(self, other, join, axis, level, method, **kwargs):
'Aligning per-level is not yet supported. Only the default,\n ``level=None``, is allowed.\n\n Filling NaN values via ``method`` is n... | Aligning per-level is not yet supported. Only the default,
``level=None``, is allowed.
Filling NaN values via ``method`` is not supported, because it is
`order-sensitive
<https://s.apache.org/dataframe-order-sensitive-operations>`_.
Only the default, ``method=None``, is allowed. | sdks/python/apache_beam/dataframe/frames.py | align | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def align(self, other, join, axis, level, method, **kwargs):
'Aligning per-level is not yet supported. Only the default,\n ``level=None``, is allowed.\n\n Filling NaN values via ``method`` is n... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def align(self, other, join, axis, level, method, **kwargs):
'Aligning per-level is not yet supported. Only the default,\n ``level=None``, is allowed.\n\n Filling NaN values via ``method`` is n... |
43c287c7684ff64c9a1d5bf2ddea9d32c1d2dc1e417d52a7e1cd044c1cee67ba | @frame_base.with_docs_from(pd.DataFrame)
def dot(self, other):
'``other`` must be a :class:`DeferredDataFrame` or :class:`DeferredSeries`\n instance. Computing the dot product with an array-like is not supported\n because it is order-sensitive.'
left = self._expr
if isinstance(other, DeferredSeries):
... | ``other`` must be a :class:`DeferredDataFrame` or :class:`DeferredSeries`
instance. Computing the dot product with an array-like is not supported
because it is order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | dot | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
def dot(self, other):
'``other`` must be a :class:`DeferredDataFrame` or :class:`DeferredSeries`\n instance. Computing the dot product with an array-like is not supported\n because it is order-sensitive.'
left = self._expr
if isinstance(other, DeferredSeries):
... | @frame_base.with_docs_from(pd.DataFrame)
def dot(self, other):
'``other`` must be a :class:`DeferredDataFrame` or :class:`DeferredSeries`\n instance. Computing the dot product with an array-like is not supported\n because it is order-sensitive.'
left = self._expr
if isinstance(other, DeferredSeries):
... |
e626057a403b5a945cd9d41b4b3ae8f51b94bce58085f818dd89759ad42da508 | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def quantile(self, q, **kwargs):
'quantile is not parallelizable. See\n `BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking\n the possible addition of an approximate, para... | quantile is not parallelizable. See
`BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking
the possible addition of an approximate, parallelizable implementation of
quantile. | sdks/python/apache_beam/dataframe/frames.py | quantile | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def quantile(self, q, **kwargs):
'quantile is not parallelizable. See\n `BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking\n the possible addition of an approximate, para... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def quantile(self, q, **kwargs):
'quantile is not parallelizable. See\n `BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking\n the possible addition of an approximate, para... |
5e51b0a688ba3457f46ce51ed212ba8b5e7c523ca3ade608602586a913dbe645 | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def var(self, axis, skipna, level, ddof, **kwargs):
'Per-level aggregation is not yet supported (BEAM-11777). Only the\n default, ``level=None``, is allowed.'
if (level is not None):
r... | Per-level aggregation is not yet supported (BEAM-11777). Only the
default, ``level=None``, is allowed. | sdks/python/apache_beam/dataframe/frames.py | var | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def var(self, axis, skipna, level, ddof, **kwargs):
'Per-level aggregation is not yet supported (BEAM-11777). Only the\n default, ``level=None``, is allowed.'
if (level is not None):
r... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def var(self, axis, skipna, level, ddof, **kwargs):
'Per-level aggregation is not yet supported (BEAM-11777). Only the\n default, ``level=None``, is allowed.'
if (level is not None):
r... |
5938017875b8d2f1431727e36fa7739244cd1e691c04c2ba11ad698e99a7a037 | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def corr(self, other, method, min_periods):
"Only ``method='pearson'`` is currently parallelizable."
if (method == 'pearson'):
(x, y) = self.dropna().align(other.dropna(), 'inner')
... | Only ``method='pearson'`` is currently parallelizable. | sdks/python/apache_beam/dataframe/frames.py | corr | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def corr(self, other, method, min_periods):
if (method == 'pearson'):
(x, y) = self.dropna().align(other.dropna(), 'inner')
return x._corr_aligned(y, min_periods)
else:
... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def corr(self, other, method, min_periods):
if (method == 'pearson'):
(x, y) = self.dropna().align(other.dropna(), 'inner')
return x._corr_aligned(y, min_periods)
else:
... |
967323f710d76b7c84a427bc4b3a13f0e77d1f0c8c7a7ecc0064e754b0cc22ad | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def duplicated(self, keep):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` ... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | duplicated | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def duplicated(self, keep):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` ... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def duplicated(self, keep):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` ... |
bdc6ba5009d9b3b416f50a1eea9611cd9d2c1b2597e53cb8998ce3a50bc0f23b | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def drop_duplicates(self, keep):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="an... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | drop_duplicates | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def drop_duplicates(self, keep):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="an... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def drop_duplicates(self, keep):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="an... |
d70a3ace0d1b0963f37180aeda176fb9079252d074303fca74fbbbd86236c6ad | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def sample(self, **kwargs):
'Only ``n`` and/or ``weights`` may be specified. ``frac``,\n ``random_state``, and ``replace=True`` are not yet supported.\n See `BEAM-124... | Only ``n`` and/or ``weights`` may be specified. ``frac``,
``random_state``, and ``replace=True`` are not yet supported.
See `BEAM-12476 <https://issues.apache.org/jira/BEAM-12476>`_.
Note that pandas will raise an error if ``n`` is larger than the length
of the dataset, while the Beam DataFrame API will simply return... | sdks/python/apache_beam/dataframe/frames.py | sample | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def sample(self, **kwargs):
'Only ``n`` and/or ``weights`` may be specified. ``frac``,\n ``random_state``, and ``replace=True`` are not yet supported.\n See `BEAM-124... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def sample(self, **kwargs):
'Only ``n`` and/or ``weights`` may be specified. ``frac``,\n ``random_state``, and ``replace=True`` are not yet supported.\n See `BEAM-124... |
357b720bc1cd4977b32146001bfc45bfc08d84cb2b12a994e38d5466507c7e13 | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def aggregate(self, func, axis, *args, **kwargs):
'Some aggregation methods cannot be parallelized, and computing\n them will require collecting all data on a single machine.'
if kwargs.get('s... | Some aggregation methods cannot be parallelized, and computing
them will require collecting all data on a single machine. | sdks/python/apache_beam/dataframe/frames.py | aggregate | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def aggregate(self, func, axis, *args, **kwargs):
'Some aggregation methods cannot be parallelized, and computing\n them will require collecting all data on a single machine.'
if kwargs.get('s... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def aggregate(self, func, axis, *args, **kwargs):
'Some aggregation methods cannot be parallelized, and computing\n them will require collecting all data on a single machine.'
if kwargs.get('s... |
c026275207c7e8d0c7dd0c3d1d939d90f7fb02edcdd349d6c9b28769b3c48654 | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nlargest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a Beam-spe... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | nlargest | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nlargest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a Beam-spe... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nlargest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a Beam-spe... |
9d18a85eda1f3952db43fbb6761bdcef4d880c6e7a67bb0f9fbba0b22cd21d7c | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nsmallest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a Beam-sp... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | nsmallest | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nsmallest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a Beam-sp... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nsmallest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a Beam-sp... |
b0cd4840a8d614fff4b00d79eaafd5ae859009bbd0abd58b6fefe8d727684183 | @frame_base.with_docs_from(pd.Series)
def unique(self, as_series=False):
'unique is not supported by default because it produces a\n non-deferred result: an :class:`~numpy.ndarray`. You can use the\n Beam-specific argument ``unique(as_series=True)`` to get the result as\n a :class:`DeferredSeries`'
if ... | unique is not supported by default because it produces a
non-deferred result: an :class:`~numpy.ndarray`. You can use the
Beam-specific argument ``unique(as_series=True)`` to get the result as
a :class:`DeferredSeries` | sdks/python/apache_beam/dataframe/frames.py | unique | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
def unique(self, as_series=False):
'unique is not supported by default because it produces a\n non-deferred result: an :class:`~numpy.ndarray`. You can use the\n Beam-specific argument ``unique(as_series=True)`` to get the result as\n a :class:`DeferredSeries`'
if ... | @frame_base.with_docs_from(pd.Series)
def unique(self, as_series=False):
'unique is not supported by default because it produces a\n non-deferred result: an :class:`~numpy.ndarray`. You can use the\n Beam-specific argument ``unique(as_series=True)`` to get the result as\n a :class:`DeferredSeries`'
if ... |
7f580846c15cb2153ac0d33da6ac5796941fc8ac9cb92f2c06b693376f4c3aea | @frame_base.with_docs_from(pd.Series)
def value_counts(self, sort=False, normalize=False, ascending=False, bins=None, dropna=True):
'``sort`` is ``False`` by default, and ``sort=True`` is not supported\n because it imposes an ordering on the dataset which likely will not be\n preserved.\n\n When ``bin`` is... | ``sort`` is ``False`` by default, and ``sort=True`` is not supported
because it imposes an ordering on the dataset which likely will not be
preserved.
When ``bin`` is specified this operation is not parallelizable. See
[BEAM-12441](https://issues.apache.org/jira/browse/BEAM-12441) tracking the
possible addition of a d... | sdks/python/apache_beam/dataframe/frames.py | value_counts | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
def value_counts(self, sort=False, normalize=False, ascending=False, bins=None, dropna=True):
'``sort`` is ``False`` by default, and ``sort=True`` is not supported\n because it imposes an ordering on the dataset which likely will not be\n preserved.\n\n When ``bin`` is... | @frame_base.with_docs_from(pd.Series)
def value_counts(self, sort=False, normalize=False, ascending=False, bins=None, dropna=True):
'``sort`` is ``False`` by default, and ``sort=True`` is not supported\n because it imposes an ordering on the dataset which likely will not be\n preserved.\n\n When ``bin`` is... |
b794b700e022813868fdc60163be36d32547ed6f6d0d0ef6af231d3dcb8121bd | @frame_base.with_docs_from(pd.Series)
def mode(self, *args, **kwargs):
'mode is not currently parallelizable. An approximate,\n parallelizable implementation of mode may be added in the future\n (`BEAM-12181 <https://issues.apache.org/jira/BEAM-12181>`_).'
return frame_base.DeferredFrame.wrap(expressions.... | mode is not currently parallelizable. An approximate,
parallelizable implementation of mode may be added in the future
(`BEAM-12181 <https://issues.apache.org/jira/BEAM-12181>`_). | sdks/python/apache_beam/dataframe/frames.py | mode | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
def mode(self, *args, **kwargs):
'mode is not currently parallelizable. An approximate,\n parallelizable implementation of mode may be added in the future\n (`BEAM-12181 <https://issues.apache.org/jira/BEAM-12181>`_).'
return frame_base.DeferredFrame.wrap(expressions.... | @frame_base.with_docs_from(pd.Series)
def mode(self, *args, **kwargs):
'mode is not currently parallelizable. An approximate,\n parallelizable implementation of mode may be added in the future\n (`BEAM-12181 <https://issues.apache.org/jira/BEAM-12181>`_).'
return frame_base.DeferredFrame.wrap(expressions.... |
c621b26855206f865472309a36e235ca1df8afeb00e02f2c06061f7f0d18eeaf | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def repeat(self, repeats, axis):
'``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are\n not supported because they make this operation order-sensitive.'
if isinstance(repeat... | ``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are
not supported because they make this operation order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | repeat | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def repeat(self, repeats, axis):
'``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are\n not supported because they make this operation order-sensitive.'
if isinstance(repeat... | @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def repeat(self, repeats, axis):
'``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are\n not supported because they make this operation order-sensitive.'
if isinstance(repeat... |
aa4ac4ffadf30c6942f4c7421da1d004bed49282bbabde50e63f3c6c5ce5835c | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def align(self, other, join, axis, copy, level, method, **kwargs):
'Aligning per level is not yet supported. Only the default,\n ``level=None``, is allowed.\n\n Filling NaN values via ... | Aligning per level is not yet supported. Only the default,
``level=None``, is allowed.
Filling NaN values via ``method`` is not supported, because it is
`order-sensitive
<https://s.apache.org/dataframe-order-sensitive-operations>`_. Only the
default, ``method=None``, is allowed.
``copy=False`` is not supported becaus... | sdks/python/apache_beam/dataframe/frames.py | align | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def align(self, other, join, axis, copy, level, method, **kwargs):
'Aligning per level is not yet supported. Only the default,\n ``level=None``, is allowed.\n\n Filling NaN values via ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def align(self, other, join, axis, copy, level, method, **kwargs):
'Aligning per level is not yet supported. Only the default,\n ``level=None``, is allowed.\n\n Filling NaN values via ... |
7b791c47a6410f65f88f25bc60b839e5ab4b45523ae0b2d2fb000669e8366515 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def append(self, other, ignore_index, verify_integrity, sort, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if (not i... | ``ignore_index=True`` is not supported, because it requires generating an
order-sensitive index. | sdks/python/apache_beam/dataframe/frames.py | append | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def append(self, other, ignore_index, verify_integrity, sort, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if (not i... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def append(self, other, ignore_index, verify_integrity, sort, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if (not i... |
63ca3bad3ebaffa02943b24bd338f83870a85f81775a73e6afce57513e84db66 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def set_index(self, keys, **kwargs):
'``keys`` must be a ``str`` or ``List[str]``. Passing an Index or Series\n is not yet supported (`BEAM-11711\n <https://i... | ``keys`` must be a ``str`` or ``List[str]``. Passing an Index or Series
is not yet supported (`BEAM-11711
<https://issues.apache.org/jira/browse/BEAM-11711>`_). | sdks/python/apache_beam/dataframe/frames.py | set_index | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def set_index(self, keys, **kwargs):
'``keys`` must be a ``str`` or ``List[str]``. Passing an Index or Series\n is not yet supported (`BEAM-11711\n <https://i... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def set_index(self, keys, **kwargs):
'``keys`` must be a ``str`` or ``List[str]``. Passing an Index or Series\n is not yet supported (`BEAM-11711\n <https://i... |
634ac6ed72fccf4fb0f11aadbc453cb3542b0d2f90b1b13098ae723e1bb26019 | @frame_base.with_docs_from(pd.DataFrame)
def assign(self, **kwargs):
'``value`` must be a ``callable`` or :class:`DeferredSeries`. Other types\n make this operation order-sensitive.'
for (name, value) in kwargs.items():
if ((not callable(value)) and (not isinstance(value, DeferredSeries))):
... | ``value`` must be a ``callable`` or :class:`DeferredSeries`. Other types
make this operation order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | assign | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
def assign(self, **kwargs):
'``value`` must be a ``callable`` or :class:`DeferredSeries`. Other types\n make this operation order-sensitive.'
for (name, value) in kwargs.items():
if ((not callable(value)) and (not isinstance(value, DeferredSeries))):
... | @frame_base.with_docs_from(pd.DataFrame)
def assign(self, **kwargs):
'``value`` must be a ``callable`` or :class:`DeferredSeries`. Other types\n make this operation order-sensitive.'
for (name, value) in kwargs.items():
if ((not callable(value)) and (not isinstance(value, DeferredSeries))):
... |
e31264ae5ae57b40d2d385a5ebb40044db5ca024e02d4af7b13af8578bc29232 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def insert(self, value, **kwargs):
'``value`` cannot be a ``List`` because aligning it with this\n DeferredDataFrame is order-sensitive.'
if isinstance(value, list):
raise fra... | ``value`` cannot be a ``List`` because aligning it with this
DeferredDataFrame is order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | insert | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def insert(self, value, **kwargs):
'``value`` cannot be a ``List`` because aligning it with this\n DeferredDataFrame is order-sensitive.'
if isinstance(value, list):
raise fra... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def insert(self, value, **kwargs):
'``value`` cannot be a ``List`` because aligning it with this\n DeferredDataFrame is order-sensitive.'
if isinstance(value, list):
raise fra... |
e32d9e4c5f4027fc80ede261cb3cfc0bb49fa808b73cb72b3828f7cd0d5ca648 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def duplicated(self, keep, subset):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Not... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | duplicated | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def duplicated(self, keep, subset):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Not... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def duplicated(self, keep, subset):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Not... |
56c2a40d76b0fd1e23367b52c493d31591c30d8de26b83d6aa88188a3619c3a3 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop_duplicates(self, keep, subset, ignore_index):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensi... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | drop_duplicates | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop_duplicates(self, keep, subset, ignore_index):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensi... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop_duplicates(self, keep, subset, ignore_index):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensi... |
f3bcc5412ab503af19653cd5d79eef4f23caed9cdb966c318488c0714da90ac2 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def clip(self, axis, **kwargs):
'``lower`` and ``upper`` must be :class:`DeferredSeries` instances, or\n constants. Array-like arguments are not supported becau... | ``lower`` and ``upper`` must be :class:`DeferredSeries` instances, or
constants. Array-like arguments are not supported because they are
order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | clip | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def clip(self, axis, **kwargs):
'``lower`` and ``upper`` must be :class:`DeferredSeries` instances, or\n constants. Array-like arguments are not supported becau... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def clip(self, axis, **kwargs):
'``lower`` and ``upper`` must be :class:`DeferredSeries` instances, or\n constants. Array-like arguments are not supported becau... |
bc5b635db962781a603c19fc744903fb6b6016d5917f17c2a194849ec4df0278 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def corr(self, method, min_periods):
'Only ``method="pearson"`` can be parallelized. Other methods require\n collecting all data on a single worker (see\n https://s.apache.org/datafram... | Only ``method="pearson"`` can be parallelized. Other methods require
collecting all data on a single worker (see
https://s.apache.org/dataframe-non-parallel-operations for details). | sdks/python/apache_beam/dataframe/frames.py | corr | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def corr(self, method, min_periods):
'Only ``method="pearson"`` can be parallelized. Other methods require\n collecting all data on a single worker (see\n https://s.apache.org/datafram... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def corr(self, method, min_periods):
'Only ``method="pearson"`` can be parallelized. Other methods require\n collecting all data on a single worker (see\n https://s.apache.org/datafram... |
1d543872f89d6f46d63cdc44a0eee8deab358dfb938aa9f432f4c0c6c5499c43 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sample(self, n, frac, replace, weights, random_state, axis):
"When ``axis='index'``, only ``n`` and/or ``weights`` may be specified.\n ``frac``, ``random_state``, and ``replace=True``... | When ``axis='index'``, only ``n`` and/or ``weights`` may be specified.
``frac``, ``random_state``, and ``replace=True`` are not yet supported.
See `BEAM-12476 <https://issues.apache.org/jira/BEAM-12476>`_.
Note that pandas will raise an error if ``n`` is larger than the length
of the dataset, while the Beam DataFrame ... | sdks/python/apache_beam/dataframe/frames.py | sample | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sample(self, n, frac, replace, weights, random_state, axis):
"When ``axis='index'``, only ``n`` and/or ``weights`` may be specified.\n ``frac``, ``random_state``, and ``replace=True``... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sample(self, n, frac, replace, weights, random_state, axis):
"When ``axis='index'``, only ``n`` and/or ``weights`` may be specified.\n ``frac``, ``random_state``, and ``replace=True``... |
6c9dff9fb674e2a516ee42b1fe33befb40735df425e4a4a6424c455663b1386e | @frame_base.with_docs_from(pd.DataFrame)
def mode(self, axis=0, *args, **kwargs):
'mode with axis="columns" is not implemented because it produces\n non-deferred columns.\n\n mode with axis="index" is not currently parallelizable. An approximate,\n parallelizable implementation of mode may be added in the ... | mode with axis="columns" is not implemented because it produces
non-deferred columns.
mode with axis="index" is not currently parallelizable. An approximate,
parallelizable implementation of mode may be added in the future
(`BEAM-12181 <https://issues.apache.org/jira/BEAM-12181>`_). | sdks/python/apache_beam/dataframe/frames.py | mode | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
def mode(self, axis=0, *args, **kwargs):
'mode with axis="columns" is not implemented because it produces\n non-deferred columns.\n\n mode with axis="index" is not currently parallelizable. An approximate,\n parallelizable implementation of mode may be added in the ... | @frame_base.with_docs_from(pd.DataFrame)
def mode(self, axis=0, *args, **kwargs):
'mode with axis="columns" is not implemented because it produces\n non-deferred columns.\n\n mode with axis="index" is not currently parallelizable. An approximate,\n parallelizable implementation of mode may be added in the ... |
4210fd6964f5f3aa79928329f749d057895a3bae82c5ec9a24bb5569016a9b8a | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def dropna(self, axis, **kwargs):
'dropna with axis="columns" specified cannot be parallelized.'
if (axis in (1, 'columns')):
requires_partition_by = pa... | dropna with axis="columns" specified cannot be parallelized. | sdks/python/apache_beam/dataframe/frames.py | dropna | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def dropna(self, axis, **kwargs):
if (axis in (1, 'columns')):
requires_partition_by = partitionings.Singleton(reason='dropna(axis=1) cannot currently ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def dropna(self, axis, **kwargs):
if (axis in (1, 'columns')):
requires_partition_by = partitionings.Singleton(reason='dropna(axis=1) cannot currently ... |
9ad43c4807b129c0ade04d88048f138b3a27df47804ec61da90a09104bedcf81 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def eval(self, expr, inplace, **kwargs):
'Accessing local variables with ``@<varname>`` is not yet supported\n (`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).\n\n A... | Accessing local variables with ``@<varname>`` is not yet supported
(`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).
Arguments ``local_dict``, ``global_dict``, ``level``, ``target``, and
``resolvers`` are not yet supported. | sdks/python/apache_beam/dataframe/frames.py | eval | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def eval(self, expr, inplace, **kwargs):
'Accessing local variables with ``@<varname>`` is not yet supported\n (`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).\n\n A... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def eval(self, expr, inplace, **kwargs):
'Accessing local variables with ``@<varname>`` is not yet supported\n (`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).\n\n A... |
f61b641d6fcd10ffe35f6f08e1a68ad6f4f585abdc8702069b6c30eee790f99f | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def query(self, expr, inplace, **kwargs):
'Accessing local variables with ``@<varname>`` is not yet supported\n (`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).\n\n ... | Accessing local variables with ``@<varname>`` is not yet supported
(`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).
Arguments ``local_dict``, ``global_dict``, ``level``, ``target``, and
``resolvers`` are not yet supported. | sdks/python/apache_beam/dataframe/frames.py | query | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def query(self, expr, inplace, **kwargs):
'Accessing local variables with ``@<varname>`` is not yet supported\n (`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).\n\n ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def query(self, expr, inplace, **kwargs):
'Accessing local variables with ``@<varname>`` is not yet supported\n (`BEAM-11202 <https://issues.apache.org/jira/browse/BEAM-11202>`_).\n\n ... |
1ff9d02d611c924b2a5129af17b9a9aacf991d73459bd66c19be82dea1f2c9c3 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def merge(self, right, on, left_on, right_on, left_index, right_index, suffixes, **kwargs):
'merge is not parallelizable unless ``left_index`` or ``right_index`` is\n ``True`, because it ... | merge is not parallelizable unless ``left_index`` or ``right_index`` is
``True`, because it requires generating an entirely new unique index.
See notes on :meth:`DeferredDataFrame.reset_index`. It is recommended to
move the join key for one of your columns to the index to avoid this issue.
For an example see the enrich... | sdks/python/apache_beam/dataframe/frames.py | merge | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def merge(self, right, on, left_on, right_on, left_index, right_index, suffixes, **kwargs):
'merge is not parallelizable unless ``left_index`` or ``right_index`` is\n ``True`, because it ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def merge(self, right, on, left_on, right_on, left_index, right_index, suffixes, **kwargs):
'merge is not parallelizable unless ``left_index`` or ``right_index`` is\n ``True`, because it ... |
9ecc2e90dbcae8d01fa4b3de7f29c1cd6e41ebdb4a3cdede74dc279ead9fca5f | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nlargest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | nlargest | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nlargest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nlargest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n a... |
dab3361e39159c668dfdf8d4ba812a89cc8650e9abcfeaeffcb5cb0ff08f80d0 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nsmallest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n ... | Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept. | sdks/python/apache_beam/dataframe/frames.py | nsmallest | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nsmallest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n ... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nsmallest(self, keep, **kwargs):
'Only ``keep=False`` and ``keep="any"`` are supported. Other values of\n ``keep`` make this an order-sensitive operation. Note ``keep="any"`` is\n ... |
8a84bb7e3bd6fffbe5ae440377ecc359939e160d6a3c5d0cce3099a1b61398cc | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def quantile(self, q, axis, **kwargs):
'``quantile(axis="index")`` is not parallelizable. See\n `BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking\n the possible... | ``quantile(axis="index")`` is not parallelizable. See
`BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking
the possible addition of an approximate, parallelizable implementation of
quantile.
When using quantile with ``axis="columns"`` only a single ``q`` value can be
specified. | sdks/python/apache_beam/dataframe/frames.py | quantile | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def quantile(self, q, axis, **kwargs):
'``quantile(axis="index")`` is not parallelizable. See\n `BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking\n the possible... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def quantile(self, q, axis, **kwargs):
'``quantile(axis="index")`` is not parallelizable. See\n `BEAM-12167 <https://issues.apache.org/jira/browse/BEAM-12167>`_ tracking\n the possible... |
5938ae63c0313c71545999335172482f7a46a15163b0f78002054baa89fccb3e | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.maybe_inplace
def rename(self, **kwargs):
'rename is not parallelizable when ``axis="index"`` and\n ``errors="raise"``. It requires collecting all data on a single\n node in order to detect if one of the index values... | rename is not parallelizable when ``axis="index"`` and
``errors="raise"``. It requires collecting all data on a single
node in order to detect if one of the index values is missing. | sdks/python/apache_beam/dataframe/frames.py | rename | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.maybe_inplace
def rename(self, **kwargs):
'rename is not parallelizable when ``axis="index"`` and\n ``errors="raise"``. It requires collecting all data on a single\n node in order to detect if one of the index values... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.maybe_inplace
def rename(self, **kwargs):
'rename is not parallelizable when ``axis="index"`` and\n ``errors="raise"``. It requires collecting all data on a single\n node in order to detect if one of the index values... |
f7efd424c976d99de7534cc6bfc4aa2a3bddd317685d16db097d38e684c31215 | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def shift(self, axis, freq, **kwargs):
'shift with ``axis="index" is only supported with ``freq`` specified and\n ``fill_value`` undefined. Other configurations make this operation\n o... | shift with ``axis="index" is only supported with ``freq`` specified and
``fill_value`` undefined. Other configurations make this operation
order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | shift | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def shift(self, axis, freq, **kwargs):
'shift with ``axis="index" is only supported with ``freq`` specified and\n ``fill_value`` undefined. Other configurations make this operation\n o... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def shift(self, axis, freq, **kwargs):
'shift with ``axis="index" is only supported with ``freq`` specified and\n ``fill_value`` undefined. Other configurations make this operation\n o... |
4f1b18caa17175328aa89a11d86d6f03617276609e9f126e691eb2500177551a | @frame_base.with_docs_from(pd.DataFrame)
def unstack(self, *args, **kwargs):
'unstack cannot be used on :class:`DeferredDataFrame` instances with\n multiple index levels, because the columns in the output depend on the\n data.'
if (self._expr.proxy().index.nlevels == 1):
return frame_base.Deferred... | unstack cannot be used on :class:`DeferredDataFrame` instances with
multiple index levels, because the columns in the output depend on the
data. | sdks/python/apache_beam/dataframe/frames.py | unstack | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
def unstack(self, *args, **kwargs):
'unstack cannot be used on :class:`DeferredDataFrame` instances with\n multiple index levels, because the columns in the output depend on the\n data.'
if (self._expr.proxy().index.nlevels == 1):
return frame_base.Deferred... | @frame_base.with_docs_from(pd.DataFrame)
def unstack(self, *args, **kwargs):
'unstack cannot be used on :class:`DeferredDataFrame` instances with\n multiple index levels, because the columns in the output depend on the\n data.'
if (self._expr.proxy().index.nlevels == 1):
return frame_base.Deferred... |
82bd018db39318ef2677c5c7476cc0a44e0d3a46717dbb2c882b9b1db76a319f | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def melt(self, ignore_index, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if ignore_index:
raise frame_base.... | ``ignore_index=True`` is not supported, because it requires generating an
order-sensitive index. | sdks/python/apache_beam/dataframe/frames.py | melt | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def melt(self, ignore_index, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if ignore_index:
raise frame_base.... | @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def melt(self, ignore_index, **kwargs):
'``ignore_index=True`` is not supported, because it requires generating an\n order-sensitive index.'
if ignore_index:
raise frame_base.... |
52656ffa8d48d5991484dd61e746be40c08c31a2560ddc0df43b357cb85bff8d | def __init__(self, expr, kwargs, ungrouped: expressions.Expression[pd.core.generic.NDFrame], ungrouped_with_index: expressions.Expression[pd.core.generic.NDFrame], grouping_columns, grouping_indexes, projection=None):
'This object represents the result of::\n\n ungrouped.groupby(level=[grouping_indexes + gro... | This object represents the result of::
ungrouped.groupby(level=[grouping_indexes + grouping_columns],
**kwargs)[projection]
:param expr: An expression to compute a pandas GroupBy object. Convenient
for unliftable aggregations.
:param ungrouped: An expression to compute the DataFrame pre-... | sdks/python/apache_beam/dataframe/frames.py | __init__ | labianchin/beam-1 | 5,279 | python | def __init__(self, expr, kwargs, ungrouped: expressions.Expression[pd.core.generic.NDFrame], ungrouped_with_index: expressions.Expression[pd.core.generic.NDFrame], grouping_columns, grouping_indexes, projection=None):
'This object represents the result of::\n\n ungrouped.groupby(level=[grouping_indexes + gro... | def __init__(self, expr, kwargs, ungrouped: expressions.Expression[pd.core.generic.NDFrame], ungrouped_with_index: expressions.Expression[pd.core.generic.NDFrame], grouping_columns, grouping_indexes, projection=None):
'This object represents the result of::\n\n ungrouped.groupby(level=[grouping_indexes + gro... |
484cde6139819711800e5c8e3d3f7ed7b0adfe8f7c6d35c0087477f9cd965568 | @frame_base.with_docs_from(DataFrameGroupBy)
def apply(self, func, *args, **kwargs):
'Note that ``func`` will be called once during pipeline construction time\n with an empty pandas object, so take care if ``func`` has a side effect.\n\n When called with an empty pandas object, ``func`` is expected to return ... | Note that ``func`` will be called once during pipeline construction time
with an empty pandas object, so take care if ``func`` has a side effect.
When called with an empty pandas object, ``func`` is expected to return an
object of the same type as what will be returned when the pipeline is
processing actual data. If t... | sdks/python/apache_beam/dataframe/frames.py | apply | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(DataFrameGroupBy)
def apply(self, func, *args, **kwargs):
'Note that ``func`` will be called once during pipeline construction time\n with an empty pandas object, so take care if ``func`` has a side effect.\n\n When called with an empty pandas object, ``func`` is expected to return ... | @frame_base.with_docs_from(DataFrameGroupBy)
def apply(self, func, *args, **kwargs):
'Note that ``func`` will be called once during pipeline construction time\n with an empty pandas object, so take care if ``func`` has a side effect.\n\n When called with an empty pandas object, ``func`` is expected to return ... |
e08a24fb3664f6960d1a569523bfae70bc362ca4b8f70dc7bc6ffcda0b77c46c | @frame_base.with_docs_from(DataFrameGroupBy)
def transform(self, fn, *args, **kwargs):
'Note that ``func`` will be called once during pipeline construction time\n with an empty pandas object, so take care if ``func`` has a side effect.\n\n When called with an empty pandas object, ``func`` is expected to retur... | Note that ``func`` will be called once during pipeline construction time
with an empty pandas object, so take care if ``func`` has a side effect.
When called with an empty pandas object, ``func`` is expected to return an
object of the same type as what will be returned when the pipeline is
processing actual data. The ... | sdks/python/apache_beam/dataframe/frames.py | transform | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(DataFrameGroupBy)
def transform(self, fn, *args, **kwargs):
'Note that ``func`` will be called once during pipeline construction time\n with an empty pandas object, so take care if ``func`` has a side effect.\n\n When called with an empty pandas object, ``func`` is expected to retur... | @frame_base.with_docs_from(DataFrameGroupBy)
def transform(self, fn, *args, **kwargs):
'Note that ``func`` will be called once during pipeline construction time\n with an empty pandas object, so take care if ``func`` has a side effect.\n\n When called with an empty pandas object, ``func`` is expected to retur... |
a1cd44ce0101d06696a4135d9d937dbc434f3a84f1e6b71f30f09aa79cd6c035 | @frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
@frame_base.populate_defaults(pd.core.strings.StringMethods)
def cat(self, others, join, **kwargs):
'If defined, ``others`` must be a :class:`DeferredSeries` or a ``list`` of\n ``DeferredSeries``.'... | If defined, ``others`` must be a :class:`DeferredSeries` or a ``list`` of
``DeferredSeries``. | sdks/python/apache_beam/dataframe/frames.py | cat | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
@frame_base.populate_defaults(pd.core.strings.StringMethods)
def cat(self, others, join, **kwargs):
'If defined, ``others`` must be a :class:`DeferredSeries` or a ``list`` of\n ``DeferredSeries``.'... | @frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
@frame_base.populate_defaults(pd.core.strings.StringMethods)
def cat(self, others, join, **kwargs):
'If defined, ``others`` must be a :class:`DeferredSeries` or a ``list`` of\n ``DeferredSeries``.'... |
e30c4b143a2358402da3b6687ad7a0b53f4e21c1925e1937d84742b659df3919 | @frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
def repeat(self, repeats):
'``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are\n not supported because they make this operation order-sensitive.'
if isinstance(repeats, int)... | ``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are
not supported because they make this operation order-sensitive. | sdks/python/apache_beam/dataframe/frames.py | repeat | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
def repeat(self, repeats):
'``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are\n not supported because they make this operation order-sensitive.'
if isinstance(repeats, int)... | @frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
def repeat(self, repeats):
'``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are\n not supported because they make this operation order-sensitive.'
if isinstance(repeats, int)... |
c1ec8f323a9d32fd9737c83f3741d1fac1d7210887b45e1ff8b1c8c8f323dcdf | @frame_base.with_docs_from(pd.core.indexes.accessors.DatetimeProperties)
def tz_localize(self, *args, ambiguous='infer', **kwargs):
'``ambiguous`` cannot be set to ``"infer"`` as its semantics are\n order-sensitive. Similarly, specifying ``ambiguous`` as an\n :class:`~numpy.ndarray` is order-sensitive, but yo... | ``ambiguous`` cannot be set to ``"infer"`` as its semantics are
order-sensitive. Similarly, specifying ``ambiguous`` as an
:class:`~numpy.ndarray` is order-sensitive, but you can achieve similar
functionality by specifying ``ambiguous`` as a Series. | sdks/python/apache_beam/dataframe/frames.py | tz_localize | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.core.indexes.accessors.DatetimeProperties)
def tz_localize(self, *args, ambiguous='infer', **kwargs):
'``ambiguous`` cannot be set to ``"infer"`` as its semantics are\n order-sensitive. Similarly, specifying ``ambiguous`` as an\n :class:`~numpy.ndarray` is order-sensitive, but yo... | @frame_base.with_docs_from(pd.core.indexes.accessors.DatetimeProperties)
def tz_localize(self, *args, ambiguous='infer', **kwargs):
'``ambiguous`` cannot be set to ``"infer"`` as its semantics are\n order-sensitive. Similarly, specifying ``ambiguous`` as an\n :class:`~numpy.ndarray` is order-sensitive, but yo... |
502d408987f400e04e5b722e1f25b1707122934deec75e7dba9d6a09039b8454 | @frame_base.with_docs_from(pd.DataFrame)
def value_counts(self, subset=None, sort=False, normalize=False, ascending=False, dropna=True):
'``sort`` is ``False`` by default, and ``sort=True`` is not supported\n because it imposes an ordering on the dataset which likely will not be\n preserved.'
if sort:... | ``sort`` is ``False`` by default, and ``sort=True`` is not supported
because it imposes an ordering on the dataset which likely will not be
preserved. | sdks/python/apache_beam/dataframe/frames.py | value_counts | labianchin/beam-1 | 5,279 | python | @frame_base.with_docs_from(pd.DataFrame)
def value_counts(self, subset=None, sort=False, normalize=False, ascending=False, dropna=True):
'``sort`` is ``False`` by default, and ``sort=True`` is not supported\n because it imposes an ordering on the dataset which likely will not be\n preserved.'
if sort:... | @frame_base.with_docs_from(pd.DataFrame)
def value_counts(self, subset=None, sort=False, normalize=False, ascending=False, dropna=True):
'``sort`` is ``False`` by default, and ``sort=True`` is not supported\n because it imposes an ordering on the dataset which likely will not be\n preserved.'
if sort:... |
bc842e1e9e62fcb007856b7bd05e6069128e385efd9d9df752dc854b8dc120d9 | def create_time_string(time_format='%Y%m%d%H%M%S'):
'Returns current time formatted as `time_format`\n\n Parameters\n ----------\n time_format : str\n Refer https://docs.python.org/3/library/time.html#time.strftime for options\n\n Returns\n -------\n str\n time as string in requested... | Returns current time formatted as `time_format`
Parameters
----------
time_format : str
Refer https://docs.python.org/3/library/time.html#time.strftime for options
Returns
-------
str
time as string in requested format | body/stretch_body/hello_utils.py | create_time_string | hello-robot/stretch_body | 19 | python | def create_time_string(time_format='%Y%m%d%H%M%S'):
'Returns current time formatted as `time_format`\n\n Parameters\n ----------\n time_format : str\n Refer https://docs.python.org/3/library/time.html#time.strftime for options\n\n Returns\n -------\n str\n time as string in requested... | def create_time_string(time_format='%Y%m%d%H%M%S'):
'Returns current time formatted as `time_format`\n\n Parameters\n ----------\n time_format : str\n Refer https://docs.python.org/3/library/time.html#time.strftime for options\n\n Returns\n -------\n str\n time as string in requested... |
85355e812e181b7d370aa59a6af6fd30d155b0182efeb8e2ffb06347e24875d1 | def get_stretch_directory(sub_directory=''):
'Returns path to stretch_user dir if HELLO_FLEET_PATH env var exists\n\n Parameters\n ----------\n sub_directory : str\n valid sub_directory within stretch_user/\n\n Returns\n -------\n str\n dirpath to stretch_user/ or dir within it if st... | Returns path to stretch_user dir if HELLO_FLEET_PATH env var exists
Parameters
----------
sub_directory : str
valid sub_directory within stretch_user/
Returns
-------
str
dirpath to stretch_user/ or dir within it if stretch_user/ exists, else /tmp | body/stretch_body/hello_utils.py | get_stretch_directory | hello-robot/stretch_body | 19 | python | def get_stretch_directory(sub_directory=):
'Returns path to stretch_user dir if HELLO_FLEET_PATH env var exists\n\n Parameters\n ----------\n sub_directory : str\n valid sub_directory within stretch_user/\n\n Returns\n -------\n str\n dirpath to stretch_user/ or dir within it if stre... | def get_stretch_directory(sub_directory=):
'Returns path to stretch_user dir if HELLO_FLEET_PATH env var exists\n\n Parameters\n ----------\n sub_directory : str\n valid sub_directory within stretch_user/\n\n Returns\n -------\n str\n dirpath to stretch_user/ or dir within it if stre... |
57609c5d74104cc6c580941e671cb0b1c5fd4d4fd7fdbb25fcc6e050ab0f8f38 | def read_fleet_yaml(f):
'Reads yaml by filename from fleet directory\n\n Parameters\n ----------\n f : str\n filename of the yaml\n\n Returns\n -------\n dict\n yaml as dictionary if valid file, else empty dict\n '
try:
with open((get_fleet_directory() + f), 'r') as s:... | Reads yaml by filename from fleet directory
Parameters
----------
f : str
filename of the yaml
Returns
-------
dict
yaml as dictionary if valid file, else empty dict | body/stretch_body/hello_utils.py | read_fleet_yaml | hello-robot/stretch_body | 19 | python | def read_fleet_yaml(f):
'Reads yaml by filename from fleet directory\n\n Parameters\n ----------\n f : str\n filename of the yaml\n\n Returns\n -------\n dict\n yaml as dictionary if valid file, else empty dict\n '
try:
with open((get_fleet_directory() + f), 'r') as s:... | def read_fleet_yaml(f):
'Reads yaml by filename from fleet directory\n\n Parameters\n ----------\n f : str\n filename of the yaml\n\n Returns\n -------\n dict\n yaml as dictionary if valid file, else empty dict\n '
try:
with open((get_fleet_directory() + f), 'r') as s:... |
6ff0a17ff60378ddefa2115c5de90788d5d3f18f75d19dacf29641d04e226cd8 | def pretty_print_dict(title, d):
'Print human readable representation of dictionary to terminal\n\n Parameters\n ----------\n title : str\n header title under which the dictionary is printed\n d : dict\n the dictionary to pretty print\n '
print('-------- {0} --------'.format(title))... | Print human readable representation of dictionary to terminal
Parameters
----------
title : str
header title under which the dictionary is printed
d : dict
the dictionary to pretty print | body/stretch_body/hello_utils.py | pretty_print_dict | hello-robot/stretch_body | 19 | python | def pretty_print_dict(title, d):
'Print human readable representation of dictionary to terminal\n\n Parameters\n ----------\n title : str\n header title under which the dictionary is printed\n d : dict\n the dictionary to pretty print\n '
print('-------- {0} --------'.format(title))... | def pretty_print_dict(title, d):
'Print human readable representation of dictionary to terminal\n\n Parameters\n ----------\n title : str\n header title under which the dictionary is printed\n d : dict\n the dictionary to pretty print\n '
print('-------- {0} --------'.format(title))... |
9b9b38ec9f2ad2ca20b8aea381b36742c6a9413d30eecff7a93338e5533cd04b | def get_loop_sleep_time(self):
'\n Returns\n -------\n float : Time to sleep for to hit target loop rate\n '
return max(0.0, self.sleep_time_s) | Returns
-------
float : Time to sleep for to hit target loop rate | body/stretch_body/hello_utils.py | get_loop_sleep_time | hello-robot/stretch_body | 19 | python | def get_loop_sleep_time(self):
'\n Returns\n -------\n float : Time to sleep for to hit target loop rate\n '
return max(0.0, self.sleep_time_s) | def get_loop_sleep_time(self):
'\n Returns\n -------\n float : Time to sleep for to hit target loop rate\n '
return max(0.0, self.sleep_time_s)<|docstring|>Returns
-------
float : Time to sleep for to hit target loop rate<|endoftext|> |
98dd5ffea7c024de3c78517d03c2354b8fa8673d38ca9afa14cb2d15201d177e | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, byte_match_tuples: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ByteMatchSetByteMatchTupleArgs']]]]]=None, name: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None):
'\n Provid... | Provides a WAF Byte Match Set Resource
## Example Usage
```python
import pulumi
import pulumi_aws as aws
byte_set = aws.waf.ByteMatchSet("byteSet", byte_match_tuples=[aws.waf.ByteMatchSetByteMatchTupleArgs(
field_to_match=aws.waf.ByteMatchSetByteMatchTupleFieldToMatchArgs(
data="referer",
type="H... | sdk/python/pulumi_aws/waf/byte_match_set.py | __init__ | elad-snyk/pulumi-aws | 0 | python | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, byte_match_tuples: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ByteMatchSetByteMatchTupleArgs']]]]]=None, name: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None):
'\n Provid... | def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, byte_match_tuples: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ByteMatchSetByteMatchTupleArgs']]]]]=None, name: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None):
'\n Provid... |
24f1a965f5c74e31faf57a3d2bffe10c1aa8ef17b3206227abde2d69d953b577 | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, byte_match_tuples: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ByteMatchSetByteMatchTupleArgs']]]]]=None, name: Optional[pulumi.Input[str]]=None) -> 'ByteMatchSet':
"\n Get an exist... | Get an existing ByteMatchSet resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Option... | sdk/python/pulumi_aws/waf/byte_match_set.py | get | elad-snyk/pulumi-aws | 0 | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, byte_match_tuples: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ByteMatchSetByteMatchTupleArgs']]]]]=None, name: Optional[pulumi.Input[str]]=None) -> 'ByteMatchSet':
"\n Get an exist... | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, byte_match_tuples: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ByteMatchSetByteMatchTupleArgs']]]]]=None, name: Optional[pulumi.Input[str]]=None) -> 'ByteMatchSet':
"\n Get an exist... |
ad0ec18cf11ce91ae4d880690a835f7749f780b1352a67ac0cad1f1f90720545 | @property
@pulumi.getter(name='byteMatchTuples')
def byte_match_tuples(self) -> pulumi.Output[Optional[Sequence['outputs.ByteMatchSetByteMatchTuple']]]:
'\n Specifies the bytes (typically a string that corresponds\n with ASCII characters) that you want to search for in web requests,\n the locat... | Specifies the bytes (typically a string that corresponds
with ASCII characters) that you want to search for in web requests,
the location in requests that you want to search, and other settings. | sdk/python/pulumi_aws/waf/byte_match_set.py | byte_match_tuples | elad-snyk/pulumi-aws | 0 | python | @property
@pulumi.getter(name='byteMatchTuples')
def byte_match_tuples(self) -> pulumi.Output[Optional[Sequence['outputs.ByteMatchSetByteMatchTuple']]]:
'\n Specifies the bytes (typically a string that corresponds\n with ASCII characters) that you want to search for in web requests,\n the locat... | @property
@pulumi.getter(name='byteMatchTuples')
def byte_match_tuples(self) -> pulumi.Output[Optional[Sequence['outputs.ByteMatchSetByteMatchTuple']]]:
'\n Specifies the bytes (typically a string that corresponds\n with ASCII characters) that you want to search for in web requests,\n the locat... |
a2882db495a353fe7d010d9976ec873274fc7998b82a75b52fef5e504ef1a354 | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n The name or description of the Byte Match Set.\n '
return pulumi.get(self, 'name') | The name or description of the Byte Match Set. | sdk/python/pulumi_aws/waf/byte_match_set.py | name | elad-snyk/pulumi-aws | 0 | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>The name or description of the Byte Match Set.<|endoftext|> |
5da650075565261241d60cfb2a1e4b7647607f475e3960e40f6c5fcf6da5f854 | def get_corner(self):
'Return left-bottom most point of geometric form'
raise NotImplementedError('Must implement this') | Return left-bottom most point of geometric form | 13_Object_Oriented_Programming/A_OOP_Part_1/A_GeometricForm.py | get_corner | Oscar-Oliveira/Python3 | 0 | python | def get_corner(self):
raise NotImplementedError('Must implement this') | def get_corner(self):
raise NotImplementedError('Must implement this')<|docstring|>Return left-bottom most point of geometric form<|endoftext|> |
78021a556dfa05a42ebae530edf735863bdd9f7a099e1565538e8dfb9de38d35 | @classmethod
def _get_data(cls, config):
'\n Iterable function that acquires data from a source iteratively based on constraints provided by config\n Passed into BaseDataHandler._publish_data and iterated to publish samples.\n @param config dict containing configuration parameters, may include ... | Iterable function that acquires data from a source iteratively based on constraints provided by config
Passed into BaseDataHandler._publish_data and iterated to publish samples.
@param config dict containing configuration parameters, may include constraints, formatters, etc
@retval an iterable that returns well-formed ... | ion/agents/data/handlers/slocum_data_handler.py | _get_data | ooici/coi-services | 3 | python | @classmethod
def _get_data(cls, config):
'\n Iterable function that acquires data from a source iteratively based on constraints provided by config\n Passed into BaseDataHandler._publish_data and iterated to publish samples.\n @param config dict containing configuration parameters, may include ... | @classmethod
def _get_data(cls, config):
'\n Iterable function that acquires data from a source iteratively based on constraints provided by config\n Passed into BaseDataHandler._publish_data and iterated to publish samples.\n @param config dict containing configuration parameters, may include ... |
4d5bd4b9c869017859f51d06e8d8026649f3930354f1acd999b4d94916b55eb7 | def __init__(self, url=None, header_size=17):
'\n Constructor for the parser. Initializes headers and data\n\n @param url the url/filepath of the file\n @param header_size number of header lines. This is information is in the header already, so it will be removed\n '
if (not url):
... | Constructor for the parser. Initializes headers and data
@param url the url/filepath of the file
@param header_size number of header lines. This is information is in the header already, so it will be removed | ion/agents/data/handlers/slocum_data_handler.py | __init__ | ooici/coi-services | 3 | python | def __init__(self, url=None, header_size=17):
'\n Constructor for the parser. Initializes headers and data\n\n @param url the url/filepath of the file\n @param header_size number of header lines. This is information is in the header already, so it will be removed\n '
if (not url):
... | def __init__(self, url=None, header_size=17):
'\n Constructor for the parser. Initializes headers and data\n\n @param url the url/filepath of the file\n @param header_size number of header lines. This is information is in the header already, so it will be removed\n '
if (not url):
... |
8258f145f74114f70ac68de3f52c4242dda312b06eef684ffd517561a1cc9319 | def findAnagrams(self, s, p):
'\n :type s: str\n :type p: str\n :rtype: List[int]\n '
res = []
cnt = collections.Counter(p)
cnt.subtract(s[:(len(p) - 1)])
for i in range((len(p) - 1), len(s)):
cnt.update({s[i]: (- 1)})
if (not any(cnt.values())):
... | :type s: str
:type p: str
:rtype: List[int] | problems/438.Find_All_Anagrams_in_a_String/solution-64412-29.py | findAnagrams | subramp-prep/leetcode | 0 | python | def findAnagrams(self, s, p):
'\n :type s: str\n :type p: str\n :rtype: List[int]\n '
res = []
cnt = collections.Counter(p)
cnt.subtract(s[:(len(p) - 1)])
for i in range((len(p) - 1), len(s)):
cnt.update({s[i]: (- 1)})
if (not any(cnt.values())):
... | def findAnagrams(self, s, p):
'\n :type s: str\n :type p: str\n :rtype: List[int]\n '
res = []
cnt = collections.Counter(p)
cnt.subtract(s[:(len(p) - 1)])
for i in range((len(p) - 1), len(s)):
cnt.update({s[i]: (- 1)})
if (not any(cnt.values())):
... |
e567b19cdc936aa4dfae5d71d6b02015da00385eb16d648241cd71d0c36d765a | def _Sleep(mins):
'Calls time.sleep(). Exists solely for better unit testing.\n\n Args:\n mins: The number of minutes to sleep().\n '
time.sleep((mins * 60)) | Calls time.sleep(). Exists solely for better unit testing.
Args:
mins: The number of minutes to sleep(). | upvote/gae/bigquery/tables.py | _Sleep | isabella232/upvote_py2 | 453 | python | def _Sleep(mins):
'Calls time.sleep(). Exists solely for better unit testing.\n\n Args:\n mins: The number of minutes to sleep().\n '
time.sleep((mins * 60)) | def _Sleep(mins):
'Calls time.sleep(). Exists solely for better unit testing.\n\n Args:\n mins: The number of minutes to sleep().\n '
time.sleep((mins * 60))<|docstring|>Calls time.sleep(). Exists solely for better unit testing.
Args:
mins: The number of minutes to sleep().<|endoftext|> |
5895bd608d064c2d13e8f68a6b3a445710ce7a6b810b5dccbf5527a00fe60c9b | def _RowValueToStr(v):
'Converts a row value to a string, primarily for safe row ID creation.\n\n Args:\n v: The row value to convert to a string.\n\n Returns:\n A string representation of the provided value.\n '
if isinstance(v, list):
return str([_RowValueToStr(i) for i in v])
elif isinst... | Converts a row value to a string, primarily for safe row ID creation.
Args:
v: The row value to convert to a string.
Returns:
A string representation of the provided value. | upvote/gae/bigquery/tables.py | _RowValueToStr | isabella232/upvote_py2 | 453 | python | def _RowValueToStr(v):
'Converts a row value to a string, primarily for safe row ID creation.\n\n Args:\n v: The row value to convert to a string.\n\n Returns:\n A string representation of the provided value.\n '
if isinstance(v, list):
return str([_RowValueToStr(i) for i in v])
elif isinst... | def _RowValueToStr(v):
'Converts a row value to a string, primarily for safe row ID creation.\n\n Args:\n v: The row value to convert to a string.\n\n Returns:\n A string representation of the provided value.\n '
if isinstance(v, list):
return str([_RowValueToStr(i) for i in v])
elif isinst... |
4710dedcfea4c2ba2bb83715fddd7d9c2b5df2257242129e7fdaf686160211d5 | def _SendToBigQuery(table, row_dict):
'Sends a row to BigQuery.\n\n For a reference of the possible errors that the BigQuery API can return, see:\n https://cloud.google.com/bigquery/troubleshooting-errors#errortable\n\n For more information about how BigQuery uses row IDs for deduplication, see:\n https://cloud... | Sends a row to BigQuery.
For a reference of the possible errors that the BigQuery API can return, see:
https://cloud.google.com/bigquery/troubleshooting-errors#errortable
For more information about how BigQuery uses row IDs for deduplication, see:
https://cloud.google.com/bigquery/streaming-data-into-bigquery#datacon... | upvote/gae/bigquery/tables.py | _SendToBigQuery | isabella232/upvote_py2 | 453 | python | def _SendToBigQuery(table, row_dict):
'Sends a row to BigQuery.\n\n For a reference of the possible errors that the BigQuery API can return, see:\n https://cloud.google.com/bigquery/troubleshooting-errors#errortable\n\n For more information about how BigQuery uses row IDs for deduplication, see:\n https://cloud... | def _SendToBigQuery(table, row_dict):
'Sends a row to BigQuery.\n\n For a reference of the possible errors that the BigQuery API can return, see:\n https://cloud.google.com/bigquery/troubleshooting-errors#errortable\n\n For more information about how BigQuery uses row IDs for deduplication, see:\n https://cloud... |
36268783c21d31cfe316d23bfab8b667544c9ff2cb7bb08770f0226d6f10978e | def _ValidateInsertion(self, **kwargs):
"Verifies that the row can be inserted into the target table.\n\n Verifies that the contents of kwargs matches up with the expectations of\n this particular table (e.g. names, types, columns). If something doesn't\n match, raise an exception.\n\n Args:\n **kw... | Verifies that the row can be inserted into the target table.
Verifies that the contents of kwargs matches up with the expectations of
this particular table (e.g. names, types, columns). If something doesn't
match, raise an exception.
Args:
**kwargs: Key/value pairs which correspond to the row being inserted.
Raise... | upvote/gae/bigquery/tables.py | _ValidateInsertion | isabella232/upvote_py2 | 453 | python | def _ValidateInsertion(self, **kwargs):
"Verifies that the row can be inserted into the target table.\n\n Verifies that the contents of kwargs matches up with the expectations of\n this particular table (e.g. names, types, columns). If something doesn't\n match, raise an exception.\n\n Args:\n **kw... | def _ValidateInsertion(self, **kwargs):
"Verifies that the row can be inserted into the target table.\n\n Verifies that the contents of kwargs matches up with the expectations of\n this particular table (e.g. names, types, columns). If something doesn't\n match, raise an exception.\n\n Args:\n **kw... |
858e149219542f30f8ccb177182df3d2fe045ad1b492139db69b9b4e008dd3b6 | def CreateUniqueId(self, **kwargs):
'Creates a unique identifier of the provided row (key, value) pairs.\n\n Args:\n **kwargs: The kwargs that InsertRow() is called with, representing the\n individual values of this particular row.\n\n Returns:\n A SHA256 hash of the provided row data.\n ... | Creates a unique identifier of the provided row (key, value) pairs.
Args:
**kwargs: The kwargs that InsertRow() is called with, representing the
individual values of this particular row.
Returns:
A SHA256 hash of the provided row data. | upvote/gae/bigquery/tables.py | CreateUniqueId | isabella232/upvote_py2 | 453 | python | def CreateUniqueId(self, **kwargs):
'Creates a unique identifier of the provided row (key, value) pairs.\n\n Args:\n **kwargs: The kwargs that InsertRow() is called with, representing the\n individual values of this particular row.\n\n Returns:\n A SHA256 hash of the provided row data.\n ... | def CreateUniqueId(self, **kwargs):
'Creates a unique identifier of the provided row (key, value) pairs.\n\n Args:\n **kwargs: The kwargs that InsertRow() is called with, representing the\n individual values of this particular row.\n\n Returns:\n A SHA256 hash of the provided row data.\n ... |
bc00906be13c32894436e19af7ff91f38353ee9035550ada3122cfa31d098f73 | def _DoInsertRow(self, **kwargs):
'Performs the actual BigQuery row insertion.\n\n Args:\n **kwargs: The kwargs that InsertRow() is called with, representing the\n individual values of this particular row.\n '
logging.info('Inserting row into the %s table: %s', self.name, kwargs)
try:
... | Performs the actual BigQuery row insertion.
Args:
**kwargs: The kwargs that InsertRow() is called with, representing the
individual values of this particular row. | upvote/gae/bigquery/tables.py | _DoInsertRow | isabella232/upvote_py2 | 453 | python | def _DoInsertRow(self, **kwargs):
'Performs the actual BigQuery row insertion.\n\n Args:\n **kwargs: The kwargs that InsertRow() is called with, representing the\n individual values of this particular row.\n '
logging.info('Inserting row into the %s table: %s', self.name, kwargs)
try:
... | def _DoInsertRow(self, **kwargs):
'Performs the actual BigQuery row insertion.\n\n Args:\n **kwargs: The kwargs that InsertRow() is called with, representing the\n individual values of this particular row.\n '
logging.info('Inserting row into the %s table: %s', self.name, kwargs)
try:
... |
08df1eafa74ddfb75fb3e3d08de07a414a87981f68c3c77589adb07df95840f8 | @classmethod
def memory_mode(cls):
'Which memory mode does this CPU require?'
return 'invalid' | Which memory mode does this CPU require? | src/cpu/BaseCPU.py | memory_mode | rafaelfmoura/gem5 | 30 | python | @classmethod
def memory_mode(cls):
return 'invalid' | @classmethod
def memory_mode(cls):
return 'invalid'<|docstring|>Which memory mode does this CPU require?<|endoftext|> |
7a1f70d667f2c58dae2c183141e31fcf448659ef16f1c50be3f2ab7d04ec789b | @classmethod
def require_caches(cls):
'Does the CPU model require caches?\n\n Some CPU models might make assumptions that require them to\n have caches.\n '
return False | Does the CPU model require caches?
Some CPU models might make assumptions that require them to
have caches. | src/cpu/BaseCPU.py | require_caches | rafaelfmoura/gem5 | 30 | python | @classmethod
def require_caches(cls):
'Does the CPU model require caches?\n\n Some CPU models might make assumptions that require them to\n have caches.\n '
return False | @classmethod
def require_caches(cls):
'Does the CPU model require caches?\n\n Some CPU models might make assumptions that require them to\n have caches.\n '
return False<|docstring|>Does the CPU model require caches?
Some CPU models might make assumptions that require them to
have caches.<... |
594ccb379639dbc1a3f0c6d5a8d71af6f313a5e91af0105080e33c9a77254f71 | @classmethod
def support_take_over(cls):
'Does the CPU model support CPU takeOverFrom?'
return False | Does the CPU model support CPU takeOverFrom? | src/cpu/BaseCPU.py | support_take_over | rafaelfmoura/gem5 | 30 | python | @classmethod
def support_take_over(cls):
return False | @classmethod
def support_take_over(cls):
return False<|docstring|>Does the CPU model support CPU takeOverFrom?<|endoftext|> |
a8c1700b79cd9e71209c6bfa9205a746e62c46bf87d1168ba7d009b68039015f | def generateDeviceTree(self, state):
'Generate cpu nodes for each thread and the corresponding part of the\n cpu-map node. Note that this implementation does not support clusters\n of clusters. Note that GEM5 is not compatible with the official way of\n numbering cores as defined in the Device ... | Generate cpu nodes for each thread and the corresponding part of the
cpu-map node. Note that this implementation does not support clusters
of clusters. Note that GEM5 is not compatible with the official way of
numbering cores as defined in the Device Tree documentation. Where the
cpu_id needs to reset to 0 for each clu... | src/cpu/BaseCPU.py | generateDeviceTree | rafaelfmoura/gem5 | 30 | python | def generateDeviceTree(self, state):
'Generate cpu nodes for each thread and the corresponding part of the\n cpu-map node. Note that this implementation does not support clusters\n of clusters. Note that GEM5 is not compatible with the official way of\n numbering cores as defined in the Device ... | def generateDeviceTree(self, state):
'Generate cpu nodes for each thread and the corresponding part of the\n cpu-map node. Note that this implementation does not support clusters\n of clusters. Note that GEM5 is not compatible with the official way of\n numbering cores as defined in the Device ... |
c191bb1b1cd727a30567c209cffed318e2a5e9285cfc8f24bff758d111f80266 | def up(self):
' Creates and starts the docker-compose environment.\n Also pulls new images for each service if necessary.\n '
try:
subprocess.run(['docker-compose', '-f', self.compose_file, 'pull'], check=True, capture_output=True)
subprocess.run(['docker-compose', '-f', self.c... | Creates and starts the docker-compose environment.
Also pulls new images for each service if necessary. | envy/lib/docker_manager/compose_manager.py | up | magmastonealex/fydp | 6 | python | def up(self):
' Creates and starts the docker-compose environment.\n Also pulls new images for each service if necessary.\n '
try:
subprocess.run(['docker-compose', '-f', self.compose_file, 'pull'], check=True, capture_output=True)
subprocess.run(['docker-compose', '-f', self.c... | def up(self):
' Creates and starts the docker-compose environment.\n Also pulls new images for each service if necessary.\n '
try:
subprocess.run(['docker-compose', '-f', self.compose_file, 'pull'], check=True, capture_output=True)
subprocess.run(['docker-compose', '-f', self.c... |
9be70f4254c2a0f4485b678c0f3e9dc5232b5764847a3ba94a2769543bb54cee | def down(self):
' Stops the docker-compose environment. Does not delete any containers or volumes.\n '
try:
subprocess.run(['docker-compose', '-f', self.compose_file, 'stop'], check=True, capture_output=True)
except subprocess.CalledProcessError as e:
print('Failed to stop sidecar ser... | Stops the docker-compose environment. Does not delete any containers or volumes. | envy/lib/docker_manager/compose_manager.py | down | magmastonealex/fydp | 6 | python | def down(self):
' \n '
try:
subprocess.run(['docker-compose', '-f', self.compose_file, 'stop'], check=True, capture_output=True)
except subprocess.CalledProcessError as e:
print('Failed to stop sidecar services. Command returned with error code {}'.format(e.returncode))
print(... | def down(self):
' \n '
try:
subprocess.run(['docker-compose', '-f', self.compose_file, 'stop'], check=True, capture_output=True)
except subprocess.CalledProcessError as e:
print('Failed to stop sidecar services. Command returned with error code {}'.format(e.returncode))
print(... |
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