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pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.head | def head(self, n=5):
"""
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
... | python | def head(self, n=5):
"""
Return first n rows of each group.
Essentially equivalent to ``.apply(lambda x: x.head(n))``,
except ignores as_index flag.
%(see_also)s
Examples
--------
>>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
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apache/spark | python/pyspark/mllib/util.py | MLUtils.appendBias | def appendBias(data):
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vec = _convert_to_vector(data)
if isinstance(vec, SparseVector):
newIndices = np.append(vec.indices, len(vec))
newValues = np.append(vec.v... | python | def appendBias(data):
"""
Returns a new vector with `1.0` (bias) appended to
the end of the input vector.
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apache/spark | python/pyspark/shuffle.py | ExternalGroupBy._spill | def _spill(self):
"""
dump already partitioned data into disks.
"""
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"""
dump already partitioned data into disks.
"""
global MemoryBytesSpilled, DiskBytesSpilled
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huggingface/pytorch-pretrained-BERT | pytorch_pretrained_bert/modeling_transfo_xl.py | TransfoXLPreTrainedModel.from_pretrained | def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
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"""
Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
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apache/spark | python/pyspark/rdd.py | RDD.aggregate | def aggregate(self, zeroValue, seqOp, combOp):
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Collection function: Locates the position of the first occurrence of the given value
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apache/spark | python/pyspark/sql/utils.py | require_minimum_pandas_version | def require_minimum_pandas_version():
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... | python | def require_minimum_pandas_version():
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apache/spark | python/pyspark/mllib/linalg/__init__.py | DenseMatrix.asML | def asML(self):
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Convert this matrix to the new mllib-local representation.
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:return: :py:class:`pyspark.ml.linalg.DenseMatrix`
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return newlinalg.DenseMatrix(self.numRows, self.numCo... | python | def asML(self):
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Convert this matrix to the new mllib-local representation.
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apache/spark | python/pyspark/heapq3.py | nsmallest | def nsmallest(n, iterable, key=None):
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Equivalent to: sorted(iterable, key=key)[:n]
"""
# Short-cut for n==1 is to use min()
if n == 1:
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"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
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apache/spark | python/pyspark/sql/context.py | SQLContext.getOrCreate | def getOrCreate(cls, sc):
"""
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext
"""
if cls._instantiatedContext is None:
jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
sparkSession = SparkSession(... | python | def getOrCreate(cls, sc):
"""
Get the existing SQLContext or create a new one with given SparkContext.
:param sc: SparkContext
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apache/spark | python/pyspark/serializers.py | ArrowStreamPandasSerializer.load_stream | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
import pyarrow as pa
for batch in batches:
yield [self.arrow_t... | python | def load_stream(self, stream):
"""
Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series.
"""
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apache/spark | python/pyspark/heapq3.py | heappush | def heappush(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
heap.append(item)
_siftdown(heap, 0, len(heap)-1) | python | def heappush(heap, item):
"""Push item onto heap, maintaining the heap invariant."""
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apache/spark | python/pyspark/streaming/context.py | StreamingContext.queueStream | def queueStream(self, rdds, oneAtATime=True, default=None):
"""
Create an input stream from a queue of RDDs or list. In each batch,
it will process either one or all of the RDDs returned by the queue.
.. note:: Changes to the queue after the stream is created will not be recognized.
... | python | def queueStream(self, rdds, oneAtATime=True, default=None):
"""
Create an input stream from a queue of RDDs or list. In each batch,
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apache/spark | python/pyspark/serializers.py | _hijack_namedtuple | def _hijack_namedtuple():
""" Hack namedtuple() to make it picklable """
# hijack only one time
if hasattr(collections.namedtuple, "__hijack"):
return
global _old_namedtuple # or it will put in closure
global _old_namedtuple_kwdefaults # or it will put in closure too
def _copy_func(f... | python | def _hijack_namedtuple():
""" Hack namedtuple() to make it picklable """
# hijack only one time
if hasattr(collections.namedtuple, "__hijack"):
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global _old_namedtuple # or it will put in closure
global _old_namedtuple_kwdefaults # or it will put in closure too
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