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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.
"""
vec = _convert_to_vector(data)
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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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path = self._get_spill_dir(self.spills)
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if not self.... | python | def _spill(self):
"""
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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Gets summary (e.g. residuals, deviance, pValues) of model on
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apache/spark | python/pyspark/sql/utils.py | require_minimum_pandas_version | def require_minimum_pandas_version():
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minimum_pandas_version = "0.19.2"
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... | python | def require_minimum_pandas_version():
""" Raise ImportError if minimum version of Pandas is not installed
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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)
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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):
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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
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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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pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.shift | def shift(self, periods=1, freq=None, axis=0, fill_value=None):
"""
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periods : integer, default 1
number of periods to shift
freq : frequency string
axis : axis to shift, default 0
... | python | def shift(self, periods=1, freq=None, axis=0, fill_value=None):
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Shift each group by periods observations.
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periods : integer, default 1
number of periods to shift
freq : frequency string
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.rdd | def rdd(self):
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if self._lazy_rdd is None:
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.hint | def hint(self, name, *parameters):
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huggingface/pytorch-pretrained-BERT | examples/lm_finetuning/pregenerate_training_data.py | create_masked_lm_predictions | def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
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apache/spark | python/pyspark/mllib/classification.py | LogisticRegressionWithLBFGS.train | def train(cls, data, iterations=100, initialWeights=None, regParam=0.0, regType="l2",
intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
"""
Train a logistic regression model on the given data.
:param data:
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intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2):
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Train a logistic regression model on the given data.
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apache/spark | python/pyspark/ml/fpm.py | PrefixSpan.setParams | def setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
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setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
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apache/spark | python/pyspark/mllib/fpm.py | FPGrowth.train | def train(cls, data, minSupport=0.3, numPartitions=-1):
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"""
Computes an FP-Growth model that contains frequent itemsets.
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ageitgey/face_recognition | examples/face_recognition_knn.py | show_prediction_labels_on_image | def show_prediction_labels_on_image(img_path, predictions):
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Shows the face recognition results visually.
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apache/spark | python/pyspark/rdd.py | RDD.sumApprox | def sumApprox(self, timeout, confidence=0.95):
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apache/spark | python/pyspark/streaming/dstream.py | DStream.groupByKeyAndWindow | def groupByKeyAndWindow(self, windowDuration, slideDuration, numPartitions=None):
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pandas-dev/pandas | pandas/core/sorting.py | decons_obs_group_ids | def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull):
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Parameters
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xnull: boolean,
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reconstruct labels from observed group ids
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xnull: boolean,
if nulls are excluded; i.e. -1 labels are passed through
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Returns a ChiSquared feature selector.
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pandas-dev/pandas | pandas/tseries/holiday.py | weekend_to_monday | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe
"""
if dt.weekday() == 6:
return dt + timedelta(1)
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return dt + timedelta(2)
retu... | python | def weekend_to_monday(dt):
"""
If holiday falls on Sunday or Saturday,
use day thereafter (Monday) instead.
Needed for holidays such as Christmas observation in Europe
"""
if dt.weekday() == 6:
return dt + timedelta(1)
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pandas-dev/pandas | pandas/core/panel.py | Panel.xs | def xs(self, key, axis=1):
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pandas-dev/pandas | pandas/core/groupby/groupby.py | GroupBy.nth | def nth(self, n, dropna=None):
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apache/spark | python/pyspark/sql/dataframe.py | _to_corrected_pandas_type | def _to_corrected_pandas_type(dt):
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return _ | python | def _create_window_function(name, doc=''):
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ndim = getattr(result, 'ndim', None)
# need to assume they are the same
if ndim is None:
if isinstance(result, dict):
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apache/spark | python/pyspark/cloudpickle.py | _fill_function | def _fill_function(*args):
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pandas-dev/pandas | pandas/core/indexes/base.py | Index._add_numeric_methods_binary | def _add_numeric_methods_binary(cls):
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Add in numeric methods.
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cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
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"""
Add in numeric methods.
"""
cls.__add__ = _make_arithmetic_op(operator.add, cls)
cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
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pandas-dev/pandas | pandas/core/sorting.py | nargsort | def nargsort(items, kind='quicksort', ascending=True, na_position='last'):
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handles NaNs. It adds ascending and na_position parameters.
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apache/spark | python/pyspark/rdd.py | _parse_memory | def _parse_memory(s):
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>>> _parse_memory("256m")
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>>> _parse_memory("2g")
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>>> _parse_memory("2g")
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apache/spark | python/pyspark/ml/tuning.py | TrainValidationSplit.setParams | def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
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apache/spark | python/pyspark/mllib/stat/KernelDensity.py | KernelDensity.setSample | def setSample(self, sample):
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raise TypeError("samples should be a RDD, received %s" % type(sample))
self._sample = sample | python | def setSample(self, sample):
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apache/spark | python/pyspark/sql/functions.py | map_concat | def map_concat(*cols):
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>>> from pyspark.sql.functions import map_concat
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>>> df.select(... | python | def map_concat(*cols):
"""Returns the union of all the given maps.
:param cols: list of column names (string) or list of :class:`Column` expressions
>>> from pyspark.sql.functions import map_concat
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c', 1, 'd') as map2")
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pandas-dev/pandas | pandas/_config/config.py | register_option | def register_option(key, defval, doc='', validator=None, cb=None):
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key - a fully-qualified key, e.g. "x.y.option - z".
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key - a fully-qualified key, e.g. "x.y.option - z".
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Return an RDD created by piping elements to a forked external process.
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pandas-dev/pandas | pandas/core/window.py | Window._apply_window | def _apply_window(self, mean=True, **kwargs):
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.randomSplit | def randomSplit(self, weights, seed=None):
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:param weights: list of doubles as weights with which to split the DataFrame. Weights will
be normalized if they don't sum up to 1.0.
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"""Randomly splits this :class:`DataFrame` with the provided weights.
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apache/spark | python/pyspark/rdd.py | RDD.meanApprox | def meanApprox(self, timeout, confidence=0.95):
"""
.. note:: Experimental
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>>> r = sum(range(1000)) / 1000.0
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"""
.. note:: Experimental
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apache/spark | python/pyspark/context.py | SparkContext.dump_profiles | def dump_profiles(self, path):
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"""
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pandas-dev/pandas | pandas/core/panel.py | Panel.round | def round(self, decimals=0, *args, **kwargs):
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Round each value in Panel to a specified number of decimal places.
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apache/spark | python/pyspark/sql/functions.py | lpad | def lpad(col, len, pad):
"""
Left-pad the string column to width `len` with `pad`.
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>>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
[Row(s=u'##abcd')]
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"""
Left-pad the string column to width `len` with `pad`.
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>>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
[Row(s=u'##abcd')]
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pandas-dev/pandas | pandas/core/computation/engines.py | _check_ne_builtin_clash | def _check_ne_builtin_clash(expr):
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apache/spark | python/pyspark/mllib/linalg/__init__.py | DenseVector.dot | def dot(self, other):
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"""
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.sample | def sample(self, withReplacement=None, fraction=None, seed=None):
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"""Returns a sampled subset of this :class:`DataFrame`.
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pandas-dev/pandas | pandas/core/indexes/base.py | Index._reindex_non_unique | def _reindex_non_unique(self, target):
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target : an iterable
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Create a new index with target's values (move/add/delete values as
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apache/spark | python/pyspark/rdd.py | RDD.treeReduce | def treeReduce(self, f, depth=2):
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>>> rdd.treeReduce(ad... | python | def treeReduce(self, f, depth=2):
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pandas-dev/pandas | pandas/tseries/holiday.py | AbstractHolidayCalendar.merge_class | def merge_class(base, other):
"""
Merge holiday calendars together. The base calendar
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"""
Merge holiday calendars together. The base calendar
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apache/spark | python/pyspark/rdd.py | RDD.partitionBy | def partitionBy(self, numPartitions, partitionFunc=portable_hash):
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apache/spark | python/pyspark/shuffle.py | ExternalList._spill | def _spill(self):
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apache/spark | python/pyspark/ml/tuning.py | CrossValidator._to_java | def _to_java(self):
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pandas-dev/pandas | pandas/core/window.py | _GroupByMixin._apply | def _apply(self, func, name, window=None, center=None,
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apache/spark | python/pyspark/mllib/util.py | MLUtils.saveAsLibSVMFile | def saveAsLibSVMFile(data, dir):
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.dropDuplicates | def dropDuplicates(self, subset=None):
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apache/spark | python/pyspark/sql/catalog.py | Catalog.listTables | def listTables(self, dbName=None):
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iter = self._j... | python | def listTables(self, dbName=None):
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apache/spark | python/pyspark/mllib/util.py | MLUtils._convert_labeled_point_to_libsvm | def _convert_labeled_point_to_libsvm(p):
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items = [str(p.label)]
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if isinstance(v, SparseVecto... | python | def _convert_labeled_point_to_libsvm(p):
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apache/spark | python/pyspark/heapq3.py | heappushpop | def heappushpop(heap, item):
"""Fast version of a heappush followed by a heappop."""
if heap and heap[0] < item:
item, heap[0] = heap[0], item
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pandas-dev/pandas | pandas/core/dtypes/inference.py | is_nested_list_like | def is_nested_list_like(obj):
"""
Check if the object is list-like, and that all of its elements
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.. versionadded:: 0.20.0
Parameters
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obj : The object to check
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"""
Check if the object is list-like, and that all of its elements
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.. versionadded:: 0.20.0
Parameters
----------
obj : The object to check
Returns
-------
is_list_like : bool
Whether `obj` has list-like properties.
... | [
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.. versionadded:: 0.20.0
Parameters
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Returns
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is_list_like : bool
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Examples
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>>> is_nested_... | [
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.summary | def summary(self, *statistics):
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apache/spark | python/pyspark/sql/dataframe.py | DataFrame.intersectAll | def intersectAll(self, other):
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""" Return a new :class:`DataFrame` containing rows in both this dataframe and other
dataframe while preserving duplicates.
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