BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def predict(self, x): """ Predict values for a single data point or an RDD of points using the model trained. """ if isinstance(x, RDD): return x.map(lambda v: self.predict(v)) x = _convert_to_vector(x) margin = self.weights.dot(x) + self.intercept ...
def save(self, sc, path): """ Save this model to the given path. """ java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel( _py2java(sc, self._coeff), self.intercept) java_model.save(sc._jsc.sc(), path)
def load(cls, sc, path): """ Load a model from the given path. """ java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load( sc._jsc.sc(), path) weights = _java2py(sc, java_model.weights()) intercept = java_model.intercept() threshold =...
def train(cls, data, lambda_=1.0): """ Train a Naive Bayes model given an RDD of (label, features) vectors. This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, i...
def heappush(heap, item): """Push item onto heap, maintaining the heap invariant.""" heap.append(item) _siftdown(heap, 0, len(heap)-1)
def heappop(heap): """Pop the smallest item off the heap, maintaining the heap invariant.""" lastelt = heap.pop() # raises appropriate IndexError if heap is empty if heap: returnitem = heap[0] heap[0] = lastelt _siftup(heap, 0) return returnitem return lastelt
def heapreplace(heap, item): """Pop and return the current smallest value, and add the new item. This is more efficient than heappop() followed by heappush(), and can be more appropriate when using a fixed-size heap. Note that the value returned may be larger than item! That constrains reasonable use...
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 _siftup(heap, 0) return item
def heapify(x): """Transform list into a heap, in-place, in O(len(x)) time.""" n = len(x) # Transform bottom-up. The largest index there's any point to looking at # is the largest with a child index in-range, so must have 2i + 1 < n, # or i < (n-1)/2. If n is even = 2j, this is (2*j-1)/2 = j-1/2...
def _heappop_max(heap): """Maxheap version of a heappop.""" lastelt = heap.pop() # raises appropriate IndexError if heap is empty if heap: returnitem = heap[0] heap[0] = lastelt _siftup_max(heap, 0) return returnitem return lastelt
def _heapreplace_max(heap, item): """Maxheap version of a heappop followed by a heappush.""" returnitem = heap[0] # raises appropriate IndexError if heap is empty heap[0] = item _siftup_max(heap, 0) return returnitem
def _heapify_max(x): """Transform list into a maxheap, in-place, in O(len(x)) time.""" n = len(x) for i in reversed(range(n//2)): _siftup_max(x, i)
def _siftdown_max(heap, startpos, pos): 'Maxheap variant of _siftdown' newitem = heap[pos] # Follow the path to the root, moving parents down until finding a place # newitem fits. while pos > startpos: parentpos = (pos - 1) >> 1 parent = heap[parentpos] if parent < newitem: ...
def _siftup_max(heap, pos): 'Maxheap variant of _siftup' endpos = len(heap) startpos = pos newitem = heap[pos] # Bubble up the larger child until hitting a leaf. childpos = 2*pos + 1 # leftmost child position while childpos < endpos: # Set childpos to index of larger child. ...
def merge(iterables, key=None, reverse=False): '''Merge multiple sorted inputs into a single sorted output. Similar to sorted(itertools.chain(*iterables)) but returns a generator, does not pull the data into memory all at once, and assumes that each of the input streams is already sorted (smallest to l...
def nsmallest(n, iterable, key=None): """Find the n smallest elements in a dataset. Equivalent to: sorted(iterable, key=key)[:n] """ # Short-cut for n==1 is to use min() if n == 1: it = iter(iterable) sentinel = object() if key is None: result = min(it, default...
def nlargest(n, iterable, key=None): """Find the n largest elements in a dataset. Equivalent to: sorted(iterable, key=key, reverse=True)[:n] """ # Short-cut for n==1 is to use max() if n == 1: it = iter(iterable) sentinel = object() if key is None: result = max...
def corr(dataset, column, method="pearson"): """ Compute the correlation matrix with specified method using dataset. :param dataset: A Dataset or a DataFrame. :param column: The name of the column of vectors for which the correlation coefficient needs to be...
def metrics(*metrics): """ Given a list of metrics, provides a builder that it turns computes metrics from a column. See the documentation of [[Summarizer]] for an example. The following metrics are accepted (case sensitive): - mean: a vector that contains the coefficient-wise...
def summary(self, featuresCol, weightCol=None): """ Returns an aggregate object that contains the summary of the column with the requested metrics. :param featuresCol: a column that contains features Vector object. :param weightCol: a column that contains weigh...
def corr(x, y=None, method=None): """ Compute the correlation (matrix) for the input RDD(s) using the specified method. Methods currently supported: I{pearson (default), spearman}. If a single RDD of Vectors is passed in, a correlation matrix comparing the columns in the...
def _parallelFitTasks(est, train, eva, validation, epm, collectSubModel): """ Creates a list of callables which can be called from different threads to fit and evaluate an estimator in parallel. Each callable returns an `(index, metric)` pair. :param est: Estimator, the estimator to be fit. :param ...
def baseOn(self, args): """ Sets the given parameters in this grid to fixed values. Accepts either a parameter dictionary or a list of (parameter, value) pairs. """ if isinstance(args[0], dict): self.baseOn(args[0].items()) else: for (param, valu...
def build(self): """ Builds and returns all combinations of parameters specified by the param grid. """ keys = self._param_grid.keys() grid_values = self._param_grid.values() def to_key_value_pairs(keys, values): return [(key, key.typeConverter(value)...
def _from_java_impl(cls, java_stage): """ Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams. """ # Load information from java_stage to the instance. estimator = JavaParams._from_java(java_stage.getEstimator()) evaluator = JavaParams._...
def _to_java_impl(self): """ Return Java estimator, estimatorParamMaps, and evaluator from this Python instance. """ gateway = SparkContext._gateway cls = SparkContext._jvm.org.apache.spark.ml.param.ParamMap java_epms = gateway.new_array(cls, len(self.getEstimatorParamM...
def _from_java(cls, java_stage): """ Given a Java CrossValidator, create and return a Python wrapper of it. Used for ML persistence. """ estimator, epms, evaluator = super(CrossValidator, cls)._from_java_impl(java_stage) numFolds = java_stage.getNumFolds() seed =...
def _to_java(self): """ Transfer this instance to a Java CrossValidator. Used for ML persistence. :return: Java object equivalent to this instance. """ estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl() _java_obj = JavaParams._new_java_obj("org.ap...
def copy(self, extra=None): """ Creates a copy of this instance with a randomly generated uid and some extra params. This copies the underlying bestModel, creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. It does not copy the...
def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75, parallelism=1, collectSubModels=False, seed=None): """ setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\ parallelism=1, collectSubMod...
def copy(self, extra=None): """ Creates a copy of this instance with a randomly generated uid and some extra params. This copies creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. :param extra: Extra parameters to copy to the new ins...
def _from_java(cls, java_stage): """ Given a Java TrainValidationSplit, create and return a Python wrapper of it. Used for ML persistence. """ estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage) trainRatio = java_stage.getTrainRatio(...
def _to_java(self): """ Transfer this instance to a Java TrainValidationSplit. Used for ML persistence. :return: Java object equivalent to this instance. """ estimator, epms, evaluator = super(TrainValidationSplit, self)._to_java_impl() _java_obj = JavaParams._new_java_...
def copy(self, extra=None): """ Creates a copy of this instance with a randomly generated uid and some extra params. This copies the underlying bestModel, creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. And, this creates a ...
def _from_java(cls, java_stage): """ Given a Java TrainValidationSplitModel, create and return a Python wrapper of it. Used for ML persistence. """ # Load information from java_stage to the instance. bestModel = JavaParams._from_java(java_stage.bestModel()) estim...
def _to_java(self): """ Transfer this instance to a Java TrainValidationSplitModel. Used for ML persistence. :return: Java object equivalent to this instance. """ sc = SparkContext._active_spark_context # TODO: persst validation metrics as well _java_obj = JavaPa...
def get(self, key, default=_NoValue): """Returns the value of Spark runtime configuration property for the given key, assuming it is set. """ self._checkType(key, "key") if default is _NoValue: return self._jconf.get(key) else: if default is not No...
def _checkType(self, obj, identifier): """Assert that an object is of type str.""" if not isinstance(obj, basestring): raise TypeError("expected %s '%s' to be a string (was '%s')" % (identifier, obj, type(obj).__name__))
def _create_function(name, doc=""): """Create a PySpark function by its name""" def _(col): sc = SparkContext._active_spark_context jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col) return Column(jc) _.__name__ = name _.__doc__ = doc return _
def _create_function_over_column(name, doc=""): """Similar with `_create_function` but creates a PySpark function that takes a column (as string as well). This is mainly for PySpark functions to take strings as column names. """ def _(col): sc = SparkContext._active_spark_context jc ...
def _wrap_deprecated_function(func, message): """ Wrap the deprecated function to print out deprecation warnings""" def _(col): warnings.warn(message, DeprecationWarning) return func(col) return functools.wraps(func)(_)
def _create_binary_mathfunction(name, doc=""): """ Create a binary mathfunction by name""" def _(col1, col2): sc = SparkContext._active_spark_context # For legacy reasons, the arguments here can be implicitly converted into floats, # if they are not columns or strings. if isinsta...
def _create_window_function(name, doc=''): """ Create a window function by name """ def _(): sc = SparkContext._active_spark_context jc = getattr(sc._jvm.functions, name)() return Column(jc) _.__name__ = name _.__doc__ = 'Window function: ' + doc return _
def approx_count_distinct(col, rsd=None): """Aggregate function: returns a new :class:`Column` for approximate distinct count of column `col`. :param rsd: maximum estimation error allowed (default = 0.05). For rsd < 0.01, it is more efficient to use :func:`countDistinct` >>> df.agg(approx_coun...
def broadcast(df): """Marks a DataFrame as small enough for use in broadcast joins.""" sc = SparkContext._active_spark_context return DataFrame(sc._jvm.functions.broadcast(df._jdf), df.sql_ctx)
def countDistinct(col, *cols): """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect() [Row(c=2)] >>> df.agg(countDistinct("age", "name").alias('c')).collect() [Row(c=2)] """ sc = SparkContext._active_spark...
def last(col, ignorenulls=False): """Aggregate function: returns the last value in a group. The function by default returns the last values it sees. It will return the last non-null value it sees when ignoreNulls is set to true. If all values are null, then null is returned. .. note:: The function is ...
def nanvl(col1, col2): """Returns col1 if it is not NaN, or col2 if col1 is NaN. Both inputs should be floating point columns (:class:`DoubleType` or :class:`FloatType`). >>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b")) >>> df.select(nanvl("a", "b").alias("r1"), n...
def rand(seed=None): """Generates a random column with independent and identically distributed (i.i.d.) samples from U[0.0, 1.0]. .. note:: The function is non-deterministic in general case. >>> df.withColumn('rand', rand(seed=42) * 3).collect() [Row(age=2, name=u'Alice', rand=2.4052597283576684),...
def round(col, scale=0): """ Round the given value to `scale` decimal places using HALF_UP rounding mode if `scale` >= 0 or at integral part when `scale` < 0. >>> spark.createDataFrame([(2.5,)], ['a']).select(round('a', 0).alias('r')).collect() [Row(r=3.0)] """ sc = SparkContext._active_spa...
def shiftLeft(col, numBits): """Shift the given value numBits left. >>> spark.createDataFrame([(21,)], ['a']).select(shiftLeft('a', 1).alias('r')).collect() [Row(r=42)] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.shiftLeft(_to_java_column(col), numBits))
def shiftRight(col, numBits): """(Signed) shift the given value numBits right. >>> spark.createDataFrame([(42,)], ['a']).select(shiftRight('a', 1).alias('r')).collect() [Row(r=21)] """ sc = SparkContext._active_spark_context jc = sc._jvm.functions.shiftRight(_to_java_column(col), numBits) r...
def expr(str): """Parses the expression string into the column that it represents >>> df.select(expr("length(name)")).collect() [Row(length(name)=5), Row(length(name)=3)] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.expr(str))
def when(condition, value): """Evaluates a list of conditions and returns one of multiple possible result expressions. If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions. :param condition: a boolean :class:`Column` expression. :param value: a literal value, or a :clas...
def log(arg1, arg2=None): """Returns the first argument-based logarithm of the second argument. If there is only one argument, then this takes the natural logarithm of the argument. >>> df.select(log(10.0, df.age).alias('ten')).rdd.map(lambda l: str(l.ten)[:7]).collect() ['0.30102', '0.69897'] >>...
def conv(col, fromBase, toBase): """ Convert a number in a string column from one base to another. >>> df = spark.createDataFrame([("010101",)], ['n']) >>> df.select(conv(df.n, 2, 16).alias('hex')).collect() [Row(hex=u'15')] """ sc = SparkContext._active_spark_context return Column(sc._...
def lag(col, offset=1, default=None): """ Window function: returns the value that is `offset` rows before the current row, and `defaultValue` if there is less than `offset` rows before the current row. For example, an `offset` of one will return the previous row at any given point in the window partitio...
def ntile(n): """ Window function: returns the ntile group id (from 1 to `n` inclusive) in an ordered window partition. For example, if `n` is 4, the first quarter of the rows will get value 1, the second quarter will get 2, the third quarter will get 3, and the last quarter will get 4. This is...
def date_format(date, format): """ Converts a date/timestamp/string to a value of string in the format specified by the date format given by the second argument. A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All pattern letters of the Java class `java.tim...
def date_add(start, days): """ Returns the date that is `days` days after `start` >>> df = spark.createDataFrame([('2015-04-08',)], ['dt']) >>> df.select(date_add(df.dt, 1).alias('next_date')).collect() [Row(next_date=datetime.date(2015, 4, 9))] """ sc = SparkContext._active_spark_context ...
def datediff(end, start): """ Returns the number of days from `start` to `end`. >>> df = spark.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2']) >>> df.select(datediff(df.d2, df.d1).alias('diff')).collect() [Row(diff=32)] """ sc = SparkContext._active_spark_context return Col...
def add_months(start, months): """ Returns the date that is `months` months after `start` >>> df = spark.createDataFrame([('2015-04-08',)], ['dt']) >>> df.select(add_months(df.dt, 1).alias('next_month')).collect() [Row(next_month=datetime.date(2015, 5, 8))] """ sc = SparkContext._active_spa...
def months_between(date1, date2, roundOff=True): """ Returns number of months between dates date1 and date2. If date1 is later than date2, then the result is positive. If date1 and date2 are on the same day of month, or both are the last day of month, returns an integer (time of day will be ignored)...
def to_date(col, format=None): """Converts a :class:`Column` of :class:`pyspark.sql.types.StringType` or :class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType` using the optionally specified format. Specify formats according to `DateTimeFormatter <https://docs.oracle.com/javas...
def date_trunc(format, timestamp): """ Returns timestamp truncated to the unit specified by the format. :param format: 'year', 'yyyy', 'yy', 'month', 'mon', 'mm', 'day', 'dd', 'hour', 'minute', 'second', 'week', 'quarter' >>> df = spark.createDataFrame([('1997-02-28 05:02:11',)], ['t']) >>...
def next_day(date, dayOfWeek): """ Returns the first date which is later than the value of the date column. Day of the week parameter is case insensitive, and accepts: "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun". >>> df = spark.createDataFrame([('2015-07-27',)], ['d']) >>> df.select(ne...
def last_day(date): """ Returns the last day of the month which the given date belongs to. >>> df = spark.createDataFrame([('1997-02-10',)], ['d']) >>> df.select(last_day(df.d).alias('date')).collect() [Row(date=datetime.date(1997, 2, 28))] """ sc = SparkContext._active_spark_context re...
def unix_timestamp(timestamp=None, format='yyyy-MM-dd HH:mm:ss'): """ Convert time string with given pattern ('yyyy-MM-dd HH:mm:ss', by default) to Unix time stamp (in seconds), using the default timezone and the default locale, return null if fail. if `timestamp` is None, then it returns current t...
def from_utc_timestamp(timestamp, tz): """ This is a common function for databases supporting TIMESTAMP WITHOUT TIMEZONE. This function takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in UTC, and renders that timestamp as a timestamp in the given time zone. However, t...
def window(timeColumn, windowDuration, slideDuration=None, startTime=None): """Bucketize rows into one or more time windows given a timestamp specifying column. Window starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window [12:05,12:10) but not in [12:00,12:05). Windows can...
def hash(*cols): """Calculates the hash code of given columns, and returns the result as an int column. >>> spark.createDataFrame([('ABC',)], ['a']).select(hash('a').alias('hash')).collect() [Row(hash=-757602832)] """ sc = SparkContext._active_spark_context jc = sc._jvm.functions.hash(_to_seq(s...
def concat_ws(sep, *cols): """ Concatenates multiple input string columns together into a single string column, using the given separator. >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd']) >>> df.select(concat_ws('-', df.s, df.d).alias('s')).collect() [Row(s=u'abcd-123')] """ ...
def decode(col, charset): """ Computes the first argument into a string from a binary using the provided character set (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16'). """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.decode(_to_java_column(...
def format_number(col, d): """ Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places with HALF_EVEN round mode, and returns the result as a string. :param col: the column name of the numeric value to be formatted :param d: the N decimal places >>> spark.createDataFr...
def format_string(format, *cols): """ Formats the arguments in printf-style and returns the result as a string column. :param col: the column name of the numeric value to be formatted :param d: the N decimal places >>> df = spark.createDataFrame([(5, "hello")], ['a', 'b']) >>> df.select(format...
def instr(str, substr): """ Locate the position of the first occurrence of substr column in the given string. Returns null if either of the arguments are null. .. note:: The position is not zero based, but 1 based index. Returns 0 if substr could not be found in str. >>> df = spark.createD...
def substring(str, pos, len): """ Substring starts at `pos` and is of length `len` when str is String type or returns the slice of byte array that starts at `pos` in byte and is of length `len` when str is Binary type. .. note:: The position is not zero based, but 1 based index. >>> df = spark...
def substring_index(str, delim, count): """ Returns the substring from string str before count occurrences of the delimiter delim. If count is positive, everything the left of the final delimiter (counting from left) is returned. If count is negative, every to the right of the final delimiter (counting ...
def levenshtein(left, right): """Computes the Levenshtein distance of the two given strings. >>> df0 = spark.createDataFrame([('kitten', 'sitting',)], ['l', 'r']) >>> df0.select(levenshtein('l', 'r').alias('d')).collect() [Row(d=3)] """ sc = SparkContext._active_spark_context jc = sc._jvm.f...
def locate(substr, str, pos=1): """ Locate the position of the first occurrence of substr in a string column, after position pos. .. note:: The position is not zero based, but 1 based index. Returns 0 if substr could not be found in str. :param substr: a string :param str: a Column of :cla...
def lpad(col, len, pad): """ Left-pad the string column to width `len` with `pad`. >>> df = spark.createDataFrame([('abcd',)], ['s',]) >>> df.select(lpad(df.s, 6, '#').alias('s')).collect() [Row(s=u'##abcd')] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.lp...
def repeat(col, n): """ Repeats a string column n times, and returns it as a new string column. >>> df = spark.createDataFrame([('ab',)], ['s',]) >>> df.select(repeat(df.s, 3).alias('s')).collect() [Row(s=u'ababab')] """ sc = SparkContext._active_spark_context return Column(sc._jvm.func...
def split(str, pattern, limit=-1): """ Splits str around matches of the given pattern. :param str: a string expression to split :param pattern: a string representing a regular expression. The regex string should be a Java regular expression. :param limit: an integer which controls the numbe...
def regexp_extract(str, pattern, idx): r"""Extract a specific group matched by a Java regex, from the specified string column. If the regex did not match, or the specified group did not match, an empty string is returned. >>> df = spark.createDataFrame([('100-200',)], ['str']) >>> df.select(regexp_extr...
def regexp_replace(str, pattern, replacement): r"""Replace all substrings of the specified string value that match regexp with rep. >>> df = spark.createDataFrame([('100-200',)], ['str']) >>> df.select(regexp_replace('str', r'(\d+)', '--').alias('d')).collect() [Row(d=u'-----')] """ sc = SparkC...
def translate(srcCol, matching, replace): """A function translate any character in the `srcCol` by a character in `matching`. The characters in `replace` is corresponding to the characters in `matching`. The translate will happen when any character in the string matching with the character in the `match...
def arrays_overlap(a1, a2): """ Collection function: returns true if the arrays contain any common non-null element; if not, returns null if both the arrays are non-empty and any of them contains a null element; returns false otherwise. >>> df = spark.createDataFrame([(["a", "b"], ["b", "c"]), (["a...
def slice(x, start, length): """ Collection function: returns an array containing all the elements in `x` from index `start` (or starting from the end if `start` is negative) with the specified `length`. >>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x']) >>> df.select(slice(df.x, 2, 2...
def array_join(col, delimiter, null_replacement=None): """ Concatenates the elements of `column` using the `delimiter`. Null values are replaced with `null_replacement` if set, otherwise they are ignored. >>> df = spark.createDataFrame([(["a", "b", "c"],), (["a", None],)], ['data']) >>> df.select(a...
def concat(*cols): """ Concatenates multiple input columns together into a single column. The function works with strings, binary and compatible array columns. >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd']) >>> df.select(concat(df.s, df.d).alias('s')).collect() [Row(s=u'abcd123')]...
def array_position(col, value): """ Collection function: Locates the position of the first occurrence of the given value in the given array. Returns null if either of the arguments are null. .. note:: The position is not zero based, but 1 based index. Returns 0 if the given value could not be f...
def element_at(col, extraction): """ Collection function: Returns element of array at given index in extraction if col is array. Returns value for the given key in extraction if col is map. :param col: name of column containing array or map :param extraction: index to check for in array or key to c...
def array_remove(col, element): """ Collection function: Remove all elements that equal to element from the given array. :param col: name of column containing array :param element: element to be removed from the array >>> df = spark.createDataFrame([([1, 2, 3, 1, 1],), ([],)], ['data']) >>> df...
def explode(col): """ Returns a new row for each element in the given array or map. Uses the default column name `col` for elements in the array and `key` and `value` for elements in the map unless specified otherwise. >>> from pyspark.sql import Row >>> eDF = spark.createDataFrame([Row(a=1, in...
def get_json_object(col, path): """ Extracts json object from a json string based on json path specified, and returns json string of the extracted json object. It will return null if the input json string is invalid. :param col: string column in json format :param path: path to the json object to e...
def json_tuple(col, *fields): """Creates a new row for a json column according to the given field names. :param col: string column in json format :param fields: list of fields to extract >>> data = [("1", '''{"f1": "value1", "f2": "value2"}'''), ("2", '''{"f1": "value12"}''')] >>> df = spark.creat...
def from_json(col, schema, options={}): """ Parses a column containing a JSON string into a :class:`MapType` with :class:`StringType` as keys type, :class:`StructType` or :class:`ArrayType` with the specified schema. Returns `null`, in the case of an unparseable string. :param col: string column in...
def schema_of_json(json, options={}): """ Parses a JSON string and infers its schema in DDL format. :param json: a JSON string or a string literal containing a JSON string. :param options: options to control parsing. accepts the same options as the JSON datasource .. versionchanged:: 3.0 It...
def schema_of_csv(csv, options={}): """ Parses a CSV string and infers its schema in DDL format. :param col: a CSV string or a string literal containing a CSV string. :param options: options to control parsing. accepts the same options as the CSV datasource >>> df = spark.range(1) >>> df.selec...
def to_csv(col, options={}): """ Converts a column containing a :class:`StructType` into a CSV string. Throws an exception, in the case of an unsupported type. :param col: name of column containing a struct. :param options: options to control converting. accepts the same options as the CSV datasour...

def predict(self, x): """ Predict values for a single data point or an RDD of points using the model trained. """ if isinstance(x, RDD): return x.map(lambda v: self.predict(v)) x = _convert_to_vector(x) margin = self.weights.dot(x) + self.intercept ...

def save(self, sc, path): """ Save this model to the given path. """ java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel( _py2java(sc, self._coeff), self.intercept) java_model.save(sc._jsc.sc(), path)

def load(cls, sc, path): """ Load a model from the given path. """ java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load( sc._jsc.sc(), path) weights = _java2py(sc, java_model.weights()) intercept = java_model.intercept() threshold =...

def train(cls, data, lambda_=1.0): """ Train a Naive Bayes model given an RDD of (label, features) vectors. This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can handle all kinds of discrete data. For example, by converting documents into TF-IDF vectors, i...

def heappush(heap, item): """Push item onto heap, maintaining the heap invariant.""" heap.append(item) _siftdown(heap, 0, len(heap)-1)

def heappop(heap): """Pop the smallest item off the heap, maintaining the heap invariant.""" lastelt = heap.pop() # raises appropriate IndexError if heap is empty if heap: returnitem = heap[0] heap[0] = lastelt _siftup(heap, 0) return returnitem return lastelt

def heapreplace(heap, item): """Pop and return the current smallest value, and add the new item. This is more efficient than heappop() followed by heappush(), and can be more appropriate when using a fixed-size heap. Note that the value returned may be larger than item! That constrains reasonable use...

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 _siftup(heap, 0) return item

def heapify(x): """Transform list into a heap, in-place, in O(len(x)) time.""" n = len(x) # Transform bottom-up. The largest index there's any point to looking at # is the largest with a child index in-range, so must have 2*i + 1 < n, # or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2...

def _heappop_max(heap): """Maxheap version of a heappop.""" lastelt = heap.pop() # raises appropriate IndexError if heap is empty if heap: returnitem = heap[0] heap[0] = lastelt _siftup_max(heap, 0) return returnitem return lastelt

def _heapreplace_max(heap, item): """Maxheap version of a heappop followed by a heappush.""" returnitem = heap[0] # raises appropriate IndexError if heap is empty heap[0] = item _siftup_max(heap, 0) return returnitem

def _heapify_max(x): """Transform list into a maxheap, in-place, in O(len(x)) time.""" n = len(x) for i in reversed(range(n//2)): _siftup_max(x, i)

def _siftdown_max(heap, startpos, pos): 'Maxheap variant of _siftdown' newitem = heap[pos] # Follow the path to the root, moving parents down until finding a place # newitem fits. while pos > startpos: parentpos = (pos - 1) >> 1 parent = heap[parentpos] if parent < newitem: ...

def _siftup_max(heap, pos): 'Maxheap variant of _siftup' endpos = len(heap) startpos = pos newitem = heap[pos] # Bubble up the larger child until hitting a leaf. childpos = 2*pos + 1 # leftmost child position while childpos < endpos: # Set childpos to index of larger child. ...

def merge(iterables, key=None, reverse=False): '''Merge multiple sorted inputs into a single sorted output. Similar to sorted(itertools.chain(*iterables)) but returns a generator, does not pull the data into memory all at once, and assumes that each of the input streams is already sorted (smallest to l...

def nsmallest(n, iterable, key=None): """Find the n smallest elements in a dataset. Equivalent to: sorted(iterable, key=key)[:n] """ # Short-cut for n==1 is to use min() if n == 1: it = iter(iterable) sentinel = object() if key is None: result = min(it, default...

def nlargest(n, iterable, key=None): """Find the n largest elements in a dataset. Equivalent to: sorted(iterable, key=key, reverse=True)[:n] """ # Short-cut for n==1 is to use max() if n == 1: it = iter(iterable) sentinel = object() if key is None: result = max...

def corr(dataset, column, method="pearson"): """ Compute the correlation matrix with specified method using dataset. :param dataset: A Dataset or a DataFrame. :param column: The name of the column of vectors for which the correlation coefficient needs to be...

def metrics(*metrics): """ Given a list of metrics, provides a builder that it turns computes metrics from a column. See the documentation of [[Summarizer]] for an example. The following metrics are accepted (case sensitive): - mean: a vector that contains the coefficient-wise...

def summary(self, featuresCol, weightCol=None): """ Returns an aggregate object that contains the summary of the column with the requested metrics. :param featuresCol: a column that contains features Vector object. :param weightCol: a column that contains weigh...

def corr(x, y=None, method=None): """ Compute the correlation (matrix) for the input RDD(s) using the specified method. Methods currently supported: I{pearson (default), spearman}. If a single RDD of Vectors is passed in, a correlation matrix comparing the columns in the...

def _parallelFitTasks(est, train, eva, validation, epm, collectSubModel): """ Creates a list of callables which can be called from different threads to fit and evaluate an estimator in parallel. Each callable returns an `(index, metric)` pair. :param est: Estimator, the estimator to be fit. :param ...

def baseOn(self, *args): """ Sets the given parameters in this grid to fixed values. Accepts either a parameter dictionary or a list of (parameter, value) pairs. """ if isinstance(args[0], dict): self.baseOn(*args[0].items()) else: for (param, valu...

def build(self): """ Builds and returns all combinations of parameters specified by the param grid. """ keys = self._param_grid.keys() grid_values = self._param_grid.values() def to_key_value_pairs(keys, values): return [(key, key.typeConverter(value)...

def _from_java_impl(cls, java_stage): """ Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams. """ # Load information from java_stage to the instance. estimator = JavaParams._from_java(java_stage.getEstimator()) evaluator = JavaParams._...

def _to_java_impl(self): """ Return Java estimator, estimatorParamMaps, and evaluator from this Python instance. """ gateway = SparkContext._gateway cls = SparkContext._jvm.org.apache.spark.ml.param.ParamMap java_epms = gateway.new_array(cls, len(self.getEstimatorParamM...

def _from_java(cls, java_stage): """ Given a Java CrossValidator, create and return a Python wrapper of it. Used for ML persistence. """ estimator, epms, evaluator = super(CrossValidator, cls)._from_java_impl(java_stage) numFolds = java_stage.getNumFolds() seed =...

def _to_java(self): """ Transfer this instance to a Java CrossValidator. Used for ML persistence. :return: Java object equivalent to this instance. """ estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl() _java_obj = JavaParams._new_java_obj("org.ap...

def copy(self, extra=None): """ Creates a copy of this instance with a randomly generated uid and some extra params. This copies the underlying bestModel, creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. It does not copy the...

def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75, parallelism=1, collectSubModels=False, seed=None): """ setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\ parallelism=1, collectSubMod...

def copy(self, extra=None): """ Creates a copy of this instance with a randomly generated uid and some extra params. This copies creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. :param extra: Extra parameters to copy to the new ins...

def _from_java(cls, java_stage): """ Given a Java TrainValidationSplit, create and return a Python wrapper of it. Used for ML persistence. """ estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage) trainRatio = java_stage.getTrainRatio(...

def _to_java(self): """ Transfer this instance to a Java TrainValidationSplit. Used for ML persistence. :return: Java object equivalent to this instance. """ estimator, epms, evaluator = super(TrainValidationSplit, self)._to_java_impl() _java_obj = JavaParams._new_java_...

def copy(self, extra=None): """ Creates a copy of this instance with a randomly generated uid and some extra params. This copies the underlying bestModel, creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over. And, this creates a ...

def _from_java(cls, java_stage): """ Given a Java TrainValidationSplitModel, create and return a Python wrapper of it. Used for ML persistence. """ # Load information from java_stage to the instance. bestModel = JavaParams._from_java(java_stage.bestModel()) estim...

def _to_java(self): """ Transfer this instance to a Java TrainValidationSplitModel. Used for ML persistence. :return: Java object equivalent to this instance. """ sc = SparkContext._active_spark_context # TODO: persst validation metrics as well _java_obj = JavaPa...

def get(self, key, default=_NoValue): """Returns the value of Spark runtime configuration property for the given key, assuming it is set. """ self._checkType(key, "key") if default is _NoValue: return self._jconf.get(key) else: if default is not No...

def _checkType(self, obj, identifier): """Assert that an object is of type str.""" if not isinstance(obj, basestring): raise TypeError("expected %s '%s' to be a string (was '%s')" % (identifier, obj, type(obj).__name__))

def _create_function(name, doc=""): """Create a PySpark function by its name""" def _(col): sc = SparkContext._active_spark_context jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col) return Column(jc) _.__name__ = name _.__doc__ = doc return _

def _create_function_over_column(name, doc=""): """Similar with `_create_function` but creates a PySpark function that takes a column (as string as well). This is mainly for PySpark functions to take strings as column names. """ def _(col): sc = SparkContext._active_spark_context jc ...

def _wrap_deprecated_function(func, message): """ Wrap the deprecated function to print out deprecation warnings""" def _(col): warnings.warn(message, DeprecationWarning) return func(col) return functools.wraps(func)(_)

def _create_binary_mathfunction(name, doc=""): """ Create a binary mathfunction by name""" def _(col1, col2): sc = SparkContext._active_spark_context # For legacy reasons, the arguments here can be implicitly converted into floats, # if they are not columns or strings. if isinsta...

def _create_window_function(name, doc=''): """ Create a window function by name """ def _(): sc = SparkContext._active_spark_context jc = getattr(sc._jvm.functions, name)() return Column(jc) _.__name__ = name _.__doc__ = 'Window function: ' + doc return _

def approx_count_distinct(col, rsd=None): """Aggregate function: returns a new :class:`Column` for approximate distinct count of column `col`. :param rsd: maximum estimation error allowed (default = 0.05). For rsd < 0.01, it is more efficient to use :func:`countDistinct` >>> df.agg(approx_coun...

def broadcast(df): """Marks a DataFrame as small enough for use in broadcast joins.""" sc = SparkContext._active_spark_context return DataFrame(sc._jvm.functions.broadcast(df._jdf), df.sql_ctx)

def countDistinct(col, *cols): """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect() [Row(c=2)] >>> df.agg(countDistinct("age", "name").alias('c')).collect() [Row(c=2)] """ sc = SparkContext._active_spark...

def last(col, ignorenulls=False): """Aggregate function: returns the last value in a group. The function by default returns the last values it sees. It will return the last non-null value it sees when ignoreNulls is set to true. If all values are null, then null is returned. .. note:: The function is ...

def nanvl(col1, col2): """Returns col1 if it is not NaN, or col2 if col1 is NaN. Both inputs should be floating point columns (:class:`DoubleType` or :class:`FloatType`). >>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b")) >>> df.select(nanvl("a", "b").alias("r1"), n...

def rand(seed=None): """Generates a random column with independent and identically distributed (i.i.d.) samples from U[0.0, 1.0]. .. note:: The function is non-deterministic in general case. >>> df.withColumn('rand', rand(seed=42) * 3).collect() [Row(age=2, name=u'Alice', rand=2.4052597283576684),...

def round(col, scale=0): """ Round the given value to `scale` decimal places using HALF_UP rounding mode if `scale` >= 0 or at integral part when `scale` < 0. >>> spark.createDataFrame([(2.5,)], ['a']).select(round('a', 0).alias('r')).collect() [Row(r=3.0)] """ sc = SparkContext._active_spa...

def shiftLeft(col, numBits): """Shift the given value numBits left. >>> spark.createDataFrame([(21,)], ['a']).select(shiftLeft('a', 1).alias('r')).collect() [Row(r=42)] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.shiftLeft(_to_java_column(col), numBits))

def shiftRight(col, numBits): """(Signed) shift the given value numBits right. >>> spark.createDataFrame([(42,)], ['a']).select(shiftRight('a', 1).alias('r')).collect() [Row(r=21)] """ sc = SparkContext._active_spark_context jc = sc._jvm.functions.shiftRight(_to_java_column(col), numBits) r...

def expr(str): """Parses the expression string into the column that it represents >>> df.select(expr("length(name)")).collect() [Row(length(name)=5), Row(length(name)=3)] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.expr(str))

def when(condition, value): """Evaluates a list of conditions and returns one of multiple possible result expressions. If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions. :param condition: a boolean :class:`Column` expression. :param value: a literal value, or a :clas...

def log(arg1, arg2=None): """Returns the first argument-based logarithm of the second argument. If there is only one argument, then this takes the natural logarithm of the argument. >>> df.select(log(10.0, df.age).alias('ten')).rdd.map(lambda l: str(l.ten)[:7]).collect() ['0.30102', '0.69897'] >>...

def conv(col, fromBase, toBase): """ Convert a number in a string column from one base to another. >>> df = spark.createDataFrame([("010101",)], ['n']) >>> df.select(conv(df.n, 2, 16).alias('hex')).collect() [Row(hex=u'15')] """ sc = SparkContext._active_spark_context return Column(sc._...

def lag(col, offset=1, default=None): """ Window function: returns the value that is `offset` rows before the current row, and `defaultValue` if there is less than `offset` rows before the current row. For example, an `offset` of one will return the previous row at any given point in the window partitio...

def ntile(n): """ Window function: returns the ntile group id (from 1 to `n` inclusive) in an ordered window partition. For example, if `n` is 4, the first quarter of the rows will get value 1, the second quarter will get 2, the third quarter will get 3, and the last quarter will get 4. This is...

def date_format(date, format): """ Converts a date/timestamp/string to a value of string in the format specified by the date format given by the second argument. A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All pattern letters of the Java class `java.tim...

def date_add(start, days): """ Returns the date that is `days` days after `start` >>> df = spark.createDataFrame([('2015-04-08',)], ['dt']) >>> df.select(date_add(df.dt, 1).alias('next_date')).collect() [Row(next_date=datetime.date(2015, 4, 9))] """ sc = SparkContext._active_spark_context ...

def datediff(end, start): """ Returns the number of days from `start` to `end`. >>> df = spark.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2']) >>> df.select(datediff(df.d2, df.d1).alias('diff')).collect() [Row(diff=32)] """ sc = SparkContext._active_spark_context return Col...

def add_months(start, months): """ Returns the date that is `months` months after `start` >>> df = spark.createDataFrame([('2015-04-08',)], ['dt']) >>> df.select(add_months(df.dt, 1).alias('next_month')).collect() [Row(next_month=datetime.date(2015, 5, 8))] """ sc = SparkContext._active_spa...

def months_between(date1, date2, roundOff=True): """ Returns number of months between dates date1 and date2. If date1 is later than date2, then the result is positive. If date1 and date2 are on the same day of month, or both are the last day of month, returns an integer (time of day will be ignored)...

def to_date(col, format=None): """Converts a :class:`Column` of :class:`pyspark.sql.types.StringType` or :class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType` using the optionally specified format. Specify formats according to `DateTimeFormatter <https://docs.oracle.com/javas...

def date_trunc(format, timestamp): """ Returns timestamp truncated to the unit specified by the format. :param format: 'year', 'yyyy', 'yy', 'month', 'mon', 'mm', 'day', 'dd', 'hour', 'minute', 'second', 'week', 'quarter' >>> df = spark.createDataFrame([('1997-02-28 05:02:11',)], ['t']) >>...

def next_day(date, dayOfWeek): """ Returns the first date which is later than the value of the date column. Day of the week parameter is case insensitive, and accepts: "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun". >>> df = spark.createDataFrame([('2015-07-27',)], ['d']) >>> df.select(ne...

def last_day(date): """ Returns the last day of the month which the given date belongs to. >>> df = spark.createDataFrame([('1997-02-10',)], ['d']) >>> df.select(last_day(df.d).alias('date')).collect() [Row(date=datetime.date(1997, 2, 28))] """ sc = SparkContext._active_spark_context re...

def unix_timestamp(timestamp=None, format='yyyy-MM-dd HH:mm:ss'): """ Convert time string with given pattern ('yyyy-MM-dd HH:mm:ss', by default) to Unix time stamp (in seconds), using the default timezone and the default locale, return null if fail. if `timestamp` is None, then it returns current t...

def from_utc_timestamp(timestamp, tz): """ This is a common function for databases supporting TIMESTAMP WITHOUT TIMEZONE. This function takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in UTC, and renders that timestamp as a timestamp in the given time zone. However, t...

def window(timeColumn, windowDuration, slideDuration=None, startTime=None): """Bucketize rows into one or more time windows given a timestamp specifying column. Window starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window [12:05,12:10) but not in [12:00,12:05). Windows can...

def hash(*cols): """Calculates the hash code of given columns, and returns the result as an int column. >>> spark.createDataFrame([('ABC',)], ['a']).select(hash('a').alias('hash')).collect() [Row(hash=-757602832)] """ sc = SparkContext._active_spark_context jc = sc._jvm.functions.hash(_to_seq(s...

def concat_ws(sep, *cols): """ Concatenates multiple input string columns together into a single string column, using the given separator. >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd']) >>> df.select(concat_ws('-', df.s, df.d).alias('s')).collect() [Row(s=u'abcd-123')] """ ...

def decode(col, charset): """ Computes the first argument into a string from a binary using the provided character set (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16'). """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.decode(_to_java_column(...

def format_number(col, d): """ Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places with HALF_EVEN round mode, and returns the result as a string. :param col: the column name of the numeric value to be formatted :param d: the N decimal places >>> spark.createDataFr...

def format_string(format, *cols): """ Formats the arguments in printf-style and returns the result as a string column. :param col: the column name of the numeric value to be formatted :param d: the N decimal places >>> df = spark.createDataFrame([(5, "hello")], ['a', 'b']) >>> df.select(format...

def instr(str, substr): """ Locate the position of the first occurrence of substr column in the given string. Returns null if either of the arguments are null. .. note:: The position is not zero based, but 1 based index. Returns 0 if substr could not be found in str. >>> df = spark.createD...

def substring(str, pos, len): """ Substring starts at `pos` and is of length `len` when str is String type or returns the slice of byte array that starts at `pos` in byte and is of length `len` when str is Binary type. .. note:: The position is not zero based, but 1 based index. >>> df = spark...

def substring_index(str, delim, count): """ Returns the substring from string str before count occurrences of the delimiter delim. If count is positive, everything the left of the final delimiter (counting from left) is returned. If count is negative, every to the right of the final delimiter (counting ...

def levenshtein(left, right): """Computes the Levenshtein distance of the two given strings. >>> df0 = spark.createDataFrame([('kitten', 'sitting',)], ['l', 'r']) >>> df0.select(levenshtein('l', 'r').alias('d')).collect() [Row(d=3)] """ sc = SparkContext._active_spark_context jc = sc._jvm.f...

def locate(substr, str, pos=1): """ Locate the position of the first occurrence of substr in a string column, after position pos. .. note:: The position is not zero based, but 1 based index. Returns 0 if substr could not be found in str. :param substr: a string :param str: a Column of :cla...

def lpad(col, len, pad): """ Left-pad the string column to width `len` with `pad`. >>> df = spark.createDataFrame([('abcd',)], ['s',]) >>> df.select(lpad(df.s, 6, '#').alias('s')).collect() [Row(s=u'##abcd')] """ sc = SparkContext._active_spark_context return Column(sc._jvm.functions.lp...

def repeat(col, n): """ Repeats a string column n times, and returns it as a new string column. >>> df = spark.createDataFrame([('ab',)], ['s',]) >>> df.select(repeat(df.s, 3).alias('s')).collect() [Row(s=u'ababab')] """ sc = SparkContext._active_spark_context return Column(sc._jvm.func...

def split(str, pattern, limit=-1): """ Splits str around matches of the given pattern. :param str: a string expression to split :param pattern: a string representing a regular expression. The regex string should be a Java regular expression. :param limit: an integer which controls the numbe...

def regexp_extract(str, pattern, idx): r"""Extract a specific group matched by a Java regex, from the specified string column. If the regex did not match, or the specified group did not match, an empty string is returned. >>> df = spark.createDataFrame([('100-200',)], ['str']) >>> df.select(regexp_extr...

def regexp_replace(str, pattern, replacement): r"""Replace all substrings of the specified string value that match regexp with rep. >>> df = spark.createDataFrame([('100-200',)], ['str']) >>> df.select(regexp_replace('str', r'(\d+)', '--').alias('d')).collect() [Row(d=u'-----')] """ sc = SparkC...

def translate(srcCol, matching, replace): """A function translate any character in the `srcCol` by a character in `matching`. The characters in `replace` is corresponding to the characters in `matching`. The translate will happen when any character in the string matching with the character in the `match...

def arrays_overlap(a1, a2): """ Collection function: returns true if the arrays contain any common non-null element; if not, returns null if both the arrays are non-empty and any of them contains a null element; returns false otherwise. >>> df = spark.createDataFrame([(["a", "b"], ["b", "c"]), (["a...

def slice(x, start, length): """ Collection function: returns an array containing all the elements in `x` from index `start` (or starting from the end if `start` is negative) with the specified `length`. >>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x']) >>> df.select(slice(df.x, 2, 2...

def array_join(col, delimiter, null_replacement=None): """ Concatenates the elements of `column` using the `delimiter`. Null values are replaced with `null_replacement` if set, otherwise they are ignored. >>> df = spark.createDataFrame([(["a", "b", "c"],), (["a", None],)], ['data']) >>> df.select(a...

def concat(*cols): """ Concatenates multiple input columns together into a single column. The function works with strings, binary and compatible array columns. >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd']) >>> df.select(concat(df.s, df.d).alias('s')).collect() [Row(s=u'abcd123')]...

def array_position(col, value): """ Collection function: Locates the position of the first occurrence of the given value in the given array. Returns null if either of the arguments are null. .. note:: The position is not zero based, but 1 based index. Returns 0 if the given value could not be f...

def element_at(col, extraction): """ Collection function: Returns element of array at given index in extraction if col is array. Returns value for the given key in extraction if col is map. :param col: name of column containing array or map :param extraction: index to check for in array or key to c...

def array_remove(col, element): """ Collection function: Remove all elements that equal to element from the given array. :param col: name of column containing array :param element: element to be removed from the array >>> df = spark.createDataFrame([([1, 2, 3, 1, 1],), ([],)], ['data']) >>> df...

def explode(col): """ Returns a new row for each element in the given array or map. Uses the default column name `col` for elements in the array and `key` and `value` for elements in the map unless specified otherwise. >>> from pyspark.sql import Row >>> eDF = spark.createDataFrame([Row(a=1, in...

def get_json_object(col, path): """ Extracts json object from a json string based on json path specified, and returns json string of the extracted json object. It will return null if the input json string is invalid. :param col: string column in json format :param path: path to the json object to e...

def json_tuple(col, *fields): """Creates a new row for a json column according to the given field names. :param col: string column in json format :param fields: list of fields to extract >>> data = [("1", '''{"f1": "value1", "f2": "value2"}'''), ("2", '''{"f1": "value12"}''')] >>> df = spark.creat...

def from_json(col, schema, options={}): """ Parses a column containing a JSON string into a :class:`MapType` with :class:`StringType` as keys type, :class:`StructType` or :class:`ArrayType` with the specified schema. Returns `null`, in the case of an unparseable string. :param col: string column in...

def schema_of_json(json, options={}): """ Parses a JSON string and infers its schema in DDL format. :param json: a JSON string or a string literal containing a JSON string. :param options: options to control parsing. accepts the same options as the JSON datasource .. versionchanged:: 3.0 It...

def schema_of_csv(csv, options={}): """ Parses a CSV string and infers its schema in DDL format. :param col: a CSV string or a string literal containing a CSV string. :param options: options to control parsing. accepts the same options as the CSV datasource >>> df = spark.range(1) >>> df.selec...

def to_csv(col, options={}): """ Converts a column containing a :class:`StructType` into a CSV string. Throws an exception, in the case of an unsupported type. :param col: name of column containing a struct. :param options: options to control converting. accepts the same options as the CSV datasour...