In case of conflicts (for example with {42: -1, 42.0: 1}) The position is not zero based, but 1 based index. Tests whether this instance contains a param with a given (string) name. efficient, because Spark needs to first compute the list of distinct values internally. This is equivalent to INTERSECT ALL in SQL. If the given schema is not Who counts as pupils or as a student in Germany? If stages is an empty list, the pipeline acts as an Returns a DataStreamReader that can be used to read data streams without duplicates. Changed in version 2.2: Added optional metadata argument. In this case, the only solution I found is to update spark to V 3.0.0. Sets a parameter in the embedded param map. The definition of the Timestamp type and how it relates to time zones. of the returned array in ascending order or at the end of the returned array in descending If the view has been cached before, then it will also be uncached. Formats the arguments in printf-style and returns the result as a string column. Spark Create DataFrame with Examples - Spark By {Examples} Functions exported from pyspark.sql.functions are thin wrappers around JVM code and, with a few exceptions which require special treatment, are generated automatically using helper methods. Estimator.fit() method will be called on the input method has been called, which signifies that the task is ready to generate data. Returns all params ordered by name. Window function: returns a sequential number starting at 1 within a window partition. Optionally, a schema can be provided as the schema of the returned DataFrame and values, and then merges them with extra values from input into Hence, it is strongly Introduction to Databricks and PySpark for SAS Developers Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The future of collective knowledge sharing. Loads a JSON file stream and returns the results as a DataFrame. In this article, I will explain how to use these two functions and learn the differences with examples. Durations are provided as strings, e.g. Currently only supports the Pearson Correlation Coefficient. (or starting from the end if start is negative) with the specified length. Returns a sort expression based on the descending order of the column, and null values to Hives partitioning scheme. Explains a single param and returns its name, doc, and optional versionchanged:: 3.4.0Since 3.4.0, it supports the list type. terminated with an exception, then the exception will be thrown. and converts to the byte representation of number. This is a shorthand for df.rdd.foreach(). # Compute the sum of earnings for each year by course with each course as a separate column, # Or without specifying column values (less efficient). The lifecycle of the methods are as follows. Generates a random column with independent and identically distributed (i.i.d.) a signed 32-bit integer. place and that the next person came in third. Making statements based on opinion; back them up with references or personal experience. timeout seconds. Joins with another DataFrame, using the given join expression. I solved this by using Python 2.7 and setting the path accordingly in .bashrc. Computes the character length of string data or number of bytes of binary data. getOffset must immediately reflect the addition). The DecimalType must have fixed precision (the maximum total number of digits) Returns an array of the most recent [[StreamingQueryProgress]] updates for this query. Applies the f function to all Row of this DataFrame. Checks whether a param is explicitly set by user or has : The user-defined functions do not support conditional expressions or short circuiting Returns a sort expression based on the descending order of the column, and null values A grouped map UDF defines transformation: A pandas.DataFrame -> A pandas.DataFrame Calculate the sample covariance for the given columns, specified by their names, as a Loads a CSV file stream and returns the result as a DataFrame. call to next(modelIterator) will return (index, model) where model was fit if timestamp is None, then it returns current timestamp. However, if youre doing a drastic coalesce, e.g. Window function: returns the relative rank (i.e. Aggregate function: returns the sum of all values in the expression. will be the distinct values of col2. the real data, or an exception will be thrown at runtime. pyspark - Spark context 'sc' not defined - Stack Overflow Computes the first argument into a string from a binary using the provided character set false otherwise. Calculates the MD5 digest and returns the value as a 32 character hex string. This method should only be used if the resulting array is expected If step is not set, incrementing by 1 if start is less than or equal to stop , otherwise -1. Loads a text file stream and returns a DataFrame whose schema starts with a Iterating a StructType will iterate its StructFields. databases, tables, functions etc. NameError: Name 'Spark' is not Defined - Spark By Examples queries, users need to stop all of them after any of them terminates with exception, and (From PyCharm). The object can have the following methods. For the DATE or TIMESTAMP sequences default step is INTERVAL '1' DAY and INTERVAL '-1' DAY respectively. Defines the ordering columns in a WindowSpec. object must match the specified type. This method is intended for testing. Returns a new DataFrame by adding a column or replacing the tables, execute SQL over tables, cache tables, and read parquet files. default values and user-supplied values. A SQLContext can be used create DataFrame, register DataFrame as The length of the returned pandas.DataFrame can be arbitrary. fraction is required and, withReplacement and seed are optional. Also made numPartitions Returns timestamp truncated to the unit specified by the format. Returns a sort expression based on the descending order of the given column name, and null values appear before non-null values. Aggregate function: returns the first value in a group. as a pandas.DataFrame containing all columns from the original Spark DataFrame. Collection function: creates a single array from an array of arrays. source present. id, containing elements in a range from start to end (exclusive) with in polar coordinates that corresponds to the point New in version 1.4.0. approximate quartiles (percentiles at 25%, 50%, and 75%), and max. to access this. In addition to a name and the function itself, the return type can be optionally specified. operations after the first time it is computed. E.g. Returns the content as an pyspark.RDD of Row. Defines the frame boundaries, from start (inclusive) to end (inclusive). The function by default returns the last values it sees. column col. Collection function: returns null if the array is null, true if the array contains the a new DataFrame that represents the stratified sample. to the natural ordering of the array elements. Throws an exception, in the case of an unsupported type. It will return null iff all parameters are null. and arbitrary replacement will be used. Loads a data stream from a data source and returns it as a :class`DataFrame`. Specify formats according to If no valid global default SparkSession exists, the method samples from Set the trigger for the stream query. Extract the year of a given date as integer. PySpark Pyspark StructType PySparkPyspark StructType PySpark PySparkCSVJSON . Randomly splits this DataFrame with the provided weights. Register a Java user-defined aggregate function as a SQL function. into memory, so the user should be aware of the potential OOM risk if data is skewed There are two versions of pivot function: one that requires the caller to specify the list Long data type, i.e. Saves the contents of the DataFrame to a data source. Computes the BASE64 encoding of a binary column and returns it as a string column. This syntax error is telling us that the name count is not defined. - min Invalidates and refreshes all the cached data (and the associated metadata) for any However, timestamp in Spark represents number of microseconds from the Unix epoch, which is not Extract the seconds of a given date as integer. Returns a StreamingQueryManager that allows managing all the The method accepts Returns a new DataFrame replacing a value with another value. a column from some other dataframe will raise an error. In Biopython, sequences are usually held as ` Seq` objects, which add various biological methods on top of string like behaviour. 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594. The data will still be passed in resetTerminated() to clear past terminations and wait for new terminations. be passed as the second argument. For those who may come across this, I figured it out! Parameters sepstr words separator. location of blocks. Returns a list of functions registered in the specified database. PySpark SQL expr() (Expression) Function - Spark By Examples If its not a pyspark.sql.types.StructType, it will be wrapped into a Note that null values will be ignored in numerical columns before calculation. This is a no-op if schema doesnt contain the given column name. [Row(age=2, name='Alice', randn=-0.7556247885860078), Row(age=5, name='Bob', randn=-0.0861619008451133)], [Row(r=[3, 1, 2]), Row(r=[1]), Row(r=[])], [Row(hash='3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')], Row(s='3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043'), Row(s='cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961'), [Row(s=[3, 1, 5, 20]), Row(s=[20, None, 3, 1])], [Row(size(data)=3), Row(size(data)=1), Row(size(data)=0)], [Row(r=[None, 1, 2, 3]), Row(r=[1]), Row(r=[])], [Row(r=[3, 2, 1, None]), Row(r=[1]), Row(r=[])], [Row(soundex='P362'), Row(soundex='U612')], [Row(struct=Row(age=2, name='Alice')), Row(struct=Row(age=5, name='Bob'))], [Row(json='[{"age":2,"name":"Alice"},{"age":3,"name":"Bob"}]')], [Row(json='[{"name":"Alice"},{"name":"Bob"}]')], [Row(dt=datetime.datetime(1997, 2, 28, 10, 30))], [Row(utc_time=datetime.datetime(1997, 2, 28, 18, 30))], [Row(utc_time=datetime.datetime(1997, 2, 28, 1, 30))], [Row(start='2016-03-11 09:00:05', end='2016-03-11 09:00:10', sum=1)]. table cache. The version of Spark on which this application is running. some input data. spark.sql.sources.default will be used. Create a multi-dimensional rollup for the current DataFrame using 0 means current row, while -1 means one off before the current row, Aggregate function: returns the last value in a group. The grouping key(s) will be passed as a tuple of numpy The fitted model from a NameError: name 'spark' is not defined, how to solve? Returns a DataFrameNaFunctions for handling missing values. The difference between this function and union() is that this function Return a new DataFrame with duplicate rows removed, An ARRAY of least common type of start and stop. The function is non-deterministic because its result depends on partition IDs. SimpleDateFormats. this may result in your computation taking place on fewer nodes than in the given array. Represents values comprising values of fields year, month and day, without a time-zone. Use the static methods in Window to create a WindowSpec. it will stay at the current number of partitions. Compute bitwise XOR of this expression with another expression. Estimator or a Transformer. If no application name is set, a randomly generated name will be used. Returns a new Column for the Pearson Correlation Coefficient for col1 Returns the first column that is not null. The timezone-agnostic. For correctly documenting exceptions across multiple algorithm (with some speed optimizations). Concatenates multiple input columns together into a single column. A window specification that defines the partitioning, ordering, Enables Hive support, including connectivity to a persistent Hive metastore, support takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in the given other options, installing sudo apt python (which is for 2.x ) is not appropriate. The batchId can be used deduplicate and transactionally write the output immediately (if the query has terminated with exception). Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. pyspark.sql.functions.regexp_replace(string: ColumnOrName, pattern: Union[str, pyspark.sql.column.Column], replacement: Union[str, pyspark.sql.column.Column]) pyspark.sql.column.Column [source] Replace all substrings of the specified string value that match regexp with replacement. could not be found in str. When I run 'pyspark' in the terminal is says. None if there were no progress updates as possible, which is equivalent to setting the trigger to processingTime='0 seconds'. If the schema parameter is not specified, this function goes Returns a sampled subset of this DataFrame. Buckets the output by the given columns.If specified, Collection function: sorts the input array in ascending order. Unlike posexplode, if the array/map is null or empty then the row (null, null) is produced. Group aggregate UDFs are used with pyspark.sql.GroupedData.agg() and throws TempTableAlreadyExistsException, if the view name already exists in the Returns a new DataFrame partitioned by the given partitioning expressions. supported for schema. Which denominations dislike pictures of people? Splits str around pattern (pattern is a regular expression). Aggregate function: returns population standard deviation of the expression in a group. a full shuffle is required. Creates a string column for the file name of the current Spark task. The result is rounded off to 8 digits unless roundOff is set to False. US Treasuries, explanation of numbers listed in IBKR. Returns a new DataFrame sorted by the specified column(s). sink. Parses the expression string into the column that it represents. Extract the quarter of a given date as integer. Compute aggregates and returns the result as a DataFrame. will be used to transform the dataset as the input to the next Aggregate function: returns the minimum value of the expression in a group. only one level of nesting is removed. when f is a user-defined function. See pyspark.sql.UDFRegistration.register(). If a query has terminated, then subsequent calls to awaitAnyTermination() will resolves columns by name (not by position): Marks the DataFrame as non-persistent, and remove all blocks for it from If source is not specified, the default data source configured by Collection function: returns an array containing all the elements in x from index start :return: a map. sep: An STRING expression. the same as that of the existing table. The returnType should be a StructType describing the schema of the returned When no explicit sort order is specified, ascending nulls first is assumed. DataFrame. >>> df.select(slice(df.x, 2, 2).alias(sliced)).collect() recommended that any initialization for writing data (e.g. Returns a sort expression based on the ascending order of the given column name. Aggregate function: returns a new Column for approximate distinct count of If step is not set, incrementing by 1 if start is less than or equal to stop, Only works with a partitioned table, and not a view. and certain groups are too large to fit in memory. pandas.Series, and can not be used as the column length. This is equivalent to UNION ALL in SQL. Can someone help me understand the intuition behind the query, key and value matrices in the transformer architecture? The current implementation puts the partition ID in the upper 31 bits, and the record number Aggregate function: indicates whether a specified column in a GROUP BY list is aggregated register(name, f, returnType=StringType()). Returns a checkpointed version of this Dataset. Important classes of Spark SQL and DataFrames: The entry point to programming Spark with the Dataset and DataFrame API. When ordering is not defined, an unbounded window frame (rowFrame, Spark 2.3.0. If exprs is a single dict mapping from string to string, then the key The data source is specified by the format and a set of options. Drops the global temporary view with the given view name in the catalog. Returns a sort expression based on the ascending order of the given column name, and null values appear after non-null values. As the Seq class Scaladoc states: " Seq has two principal subtraits, IndexedSeq and LinearSeq, which give different guarantees for performance. Unlike explode, if the array/map is null or empty then null is produced. Note that only Returns col1 if it is not NaN, or col2 if col1 is NaN. predicates is specified. The function is non-deterministic because its results depends on order of rows Available statistics are: That is, this id is generated when a query is started for the first time, and Converts a Column of pyspark.sql.types.StringType or The length of pandas.Series within a scalar UDF is not that of the whole input pyspark : NameError: name 'spark' is not defined locale, return null if fail. Use DataFrame.writeStream() using the given separator. Deprecated in 2.3.0. appear after non-null values. This function takes at least 2 parameters. Whether this streaming query is currently active or not. Does this type need to conversion between Python object and internal SQL object. Returns a new row for each element with position in the given array or map. from U[0.0, 1.0]. Calculates the cyclic redundancy check value (CRC32) of a binary column and starts are inclusive but the window ends are exclusive, e.g. Dont create too many partitions in parallel on a large cluster; Convert a number in a string column from one base to another. In this case, this API works as if otherwise Spark might crash your external database systems. Pipeline PySpark 3.4.1 documentation - Apache Spark For example, if n is 4, the first When ordering is defined, a growing window frame (rangeFrame, unboundedPreceding, currentRow) is used by default. returnType can be optionally specified when f is a Python function but not and 5 means the five off after the current row. Create a multi-dimensional cube for the current DataFrame using When replacing, the new value will be cast This includes all temporary views. Returns a new row for each element with position in the given array or map. Returns-------:class:`~pyspark.sql.Column`the literal instance. How did this hand from the 2008 WSOP eliminate Scott Montgomery? 592), Stack Overflow at WeAreDevelopers World Congress in Berlin, Temporary policy: Generative AI (e.g., ChatGPT) is banned. returnType of the pandas udf. Applies to: Databricks SQL Databricks Runtime. lowerBound`, ``upperBound and numPartitions library it uses might cache certain metadata about a table, such as the models. Scala Seq class: Method examples (map, filter, fold, reduce) Additionally, this method is only guaranteed to block until data that has been Window function: returns the value that is offset rows before the current row, and timezone, and renders that timestamp as a timestamp in UTC. As of Spark 2.0, this is replaced by SparkSession. interval strings are week, day, hour, minute, second, millisecond, microsecond. Saves the content of the DataFrame to an external database table via JDBC. A variant of Spark SQL that integrates with data stored in Hive. guarantee about the backward compatibility of the schema of the resulting DataFrame. throws StreamingQueryException, if this query has terminated with an exception. Returns the specified table as a DataFrame. It also explains the detail of time zone offset resolution, and the subtle behavior changes in the new time API in Java 8, which is used by Spark 3.0. the current row, and 5 means the fifth row after the current row. connection or starting a transaction) is done after the open() The lifetime of this temporary view is tied to this Spark application. Gets an existing SparkSession or, if there is no existing one, creates a Temporary tables exist only during the lifetime of this instance of SQLContext. as if computed by java.lang.Math.atan2(). measured in radians. In this case, this API works as if register(name, f). 2. Optionally overwriting any existing data. to Unix time stamp (in seconds), using the default timezone and the default processing one partition of the data generated in a distributed manner. Applicable for file-based data sources in combination with A Dataset that reads data from a streaming source memory, so the user should be aware of the potential OOM risk if data is skewed Collection function: Returns a map created from the given array of entries. catalog. is a list of list of floats. Persists the DataFrame with the default storage level (MEMORY_AND_DISK). If no database is specified, the current database is used. as Column. Computes the Levenshtein distance of the two given strings. Inserts the content of the DataFrame to the specified table. Returns a new Column for distinct count of col or cols. It also covers the calendar switch in Spark 3.0. It will return null iff all parameters are null. throws TempTableAlreadyExistsException, if the view name already exists in the concat_ws function - Azure Databricks - Databricks SQL Generate a sequence of integers from start to stop, incrementing by step. Changed in version 2.1: Added verifySchema. present in [[http://dx.doi.org/10.1145/375663.375670 DataFrameWriter.saveAsTable(). Trim the spaces from right end for the specified string value. Grouped map UDFs are used with pyspark.sql.GroupedData.apply(). A handle to a query that is executing continuously in the background as new data arrives. Returns the user-specified name of the query, or null if not specified. Is there a word in English to describe instances where a melody is sung by multiple singers/voices? This is equivalent to the RANK function in SQL. Extract the day of the year of a given date as integer. For example, Example: value = ['Mango', 'Apple', 'Orange'] print (values) After writing the above code, Ones you will print " values " then the error will appear as a " NameError: name 'values' is not defined ". Does this definition of an epimorphism work? datatype string after 2.0. Returns the substring from string str before count occurrences of the delimiter delim. Sets the Spark master URL to connect to, such as local to run locally, local[4] Changed in version 3.4.0: Supports Spark Connect. To do a SQL-style set The length of the returned pandas.Series must be of the same as the input pandas.Series. so we can run aggregation on them. Returns a new DataFrame that drops the specified column. specifies the behavior of the save operation when data already exists. using the optionally specified format. or strings. The elements of the input array (JSON Lines text format or newline-delimited JSON) at the Returns true if this Dataset contains one or more sources that continuously Defines an event time watermark for this DataFrame. This expression would return the following IDs: Utility functions for defining window in DataFrames. either return immediately (if the query was terminated by query.stop()), an offset of one will return the next row at any given point in the window partition. If the DataFrame has N elements and if we request the quantile at :param col: name of column or expression. By specifying the schema here, the underlying data source can skip the schema The characters in replace is corresponding to the characters in matching. Returns an MLReader instance for this class. I just had the same issue and found solution. the person that came in third place (after the ties) would register as coming in fifth. Left-pad the string column to width len with pad. (shorthand for df.groupBy.agg()). Creates a new row for a json column according to the given field names. Collection function: Returns element of array at given index in extraction if col is array. pyspark.sql.DataFrame.select(). Deprecated in 2.1, use degrees() instead. Window function: returns the value that is offset rows after the current row, and Loads a Parquet file stream, returning the result as a DataFrame. Int data type, i.e. Examples >>> Configuration for Hive is read from hive-site.xml on the classpath. MapType, StructType are currently not supported as output types. the dataset for the next stage. Note that, the return type of this method was None in Spark 2.0, but changed to Boolean To register a nondeterministic Python function, users need to first build Computes specified statistics for numeric and string columns. Returns a Column based on the given column name. [Row(age=2, name='Alice', height=80), Row(age=2, name='Alice', height=85), Row(age=5, name='Bob', height=80), Row(age=5, name='Bob', height=85)], [Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)], [Row(name='Alice', age=2, count=1), Row(name='Bob', age=5, count=1)], [Row(name=None, height=80), Row(name='Bob', height=85), Row(name='Alice', height=None)], [Row(name='Tom', height=80), Row(name='Bob', height=85), Row(name='Alice', height=None)], [Row(name='Alice', age=2), Row(name='Bob', age=5)], [Row(age=5, name='Bob'), Row(age=2, name='Alice')], StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true))), [Row(name='Alice', age=12), Row(name='Bob', age=15)], [Row((age * 2)=4, abs(age)=2), Row((age * 2)=10, abs(age)=5)], StorageLevel(False, False, False, False, 1), StorageLevel(True, False, False, False, 2), [Row(f1=2, f2='Alice'), Row(f1=5, f2='Bob')], [Row(age=2, name='Alice', age2=4), Row(age=5, name='Bob', age2=7)], [Row(age2=2, name='Alice'), Row(age2=5, name='Bob')], [Row(name='Alice', count(1)=1), Row(name='Bob', count(1)=1)], [Row(name='Alice', min(age)=2), Row(name='Bob', min(age)=5)], [Row(name='Alice', min_udf(age)=2), Row(name='Bob', min_udf(age)=5)], [Row(age=2, count=1), Row(age=5, count=1)], [Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)], [Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)], [Row(name=None), Row(name='Alice'), Row(name='Tom')], [Row(name='Alice'), Row(name='Tom'), Row(name=None)], [Row(name=None), Row(name='Tom'), Row(name='Alice')], [Row(name='Tom'), Row(name='Alice'), Row(name=None)], +-------------+---------------+----------------+, |(value = foo)|(value <=> foo)|(value <=> NULL)|, | true| true| false|, | null| false| true|, +----------------+---------------+----------------+, |(value <=> NULL)|(value <=> NaN)|(value <=> 42.0)|, | false| true| false|, | false| false| true|, | true| false| false|, +-----+-------------------------------------+, | name|CASE WHEN (age > 3) THEN 1 ELSE 0 END|, |Alice| 0|, | Bob| 1|, # df.select(rank().over(window), min('age').over(window)), +-----+------------------------------------------------------------+, | name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|, |Alice| -1|, | Bob| 1|, # ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, # PARTITION BY country ORDER BY date RANGE BETWEEN 3 PRECEDING AND 3 FOLLOWING, 'python/test_support/sql/parquet_partitioned', [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')], [('age', 'bigint'), ('aka', 'string'), ('name', 'string')], 'python/test_support/sql/orc_partitioned', [('a', 'bigint'), ('b', 'int'), ('c', 'int')], [Row(next_month=datetime.date(2015, 5, 8))], [Row(array_contains(data, a)=True), Row(array_contains(data, a)=False)], [Row(array_distinct(data)=[1, 2, 3]), Row(array_distinct(data)=[4, 5])], [Row(array_intersect(c1, c2)=['a', 'c'])], [Row(joined='a,b,c'), Row(joined='a,NULL')], [Row(array_position(data, a)=3), Row(array_position(data, a)=0)], [Row(array_remove(data, 1)=[2, 3]), Row(array_remove(data, 1)=[])], [Row(r=[1, 2, 3, None]), Row(r=[1]), Row(r=[])], [Row(array_union(c1, c2)=['b', 'a', 'c', 'd', 'f'])], [Row(zipped=[Row(vals1=1, vals2=2), Row(vals1=2, vals2=3), Row(vals1=3, vals2=4)])], [Row(arr=[1, 2, 3, 4, 5]), Row(arr=None)], [Row(map={'Alice': 2}), Row(map={'Bob': 5})], [Row(next_date=datetime.date(2015, 4, 9))], [Row(prev_date=datetime.date(2015, 4, 7))], [Row(year=datetime.datetime(1997, 1, 1, 0, 0))], [Row(month=datetime.datetime(1997, 2, 1, 0, 0))], [Row(element_at(data, 1)='a'), Row(element_at(data, 1)=None)], [Row(element_at(data, a)=1.0), Row(element_at(data, a)=None)], [Row(anInt=1), Row(anInt=2), Row(anInt=3)], [Row(length(name)=5), Row(length(name)=3)], [Row(local_time=datetime.datetime(1997, 2, 28, 2, 30))], [Row(local_time=datetime.datetime(1997, 2, 28, 19, 30))], [Row(key='1', c0='value1', c1='value2'), Row(key='2', c0='value12', c1=None)], [Row(r1=False, r2=False), Row(r1=True, r2=True)], "SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c', 1, 'd') as map2", "SELECT array(struct(1, 'a'), struct(2, 'b')) as data", [Row(hash='902fbdd2b1df0c4f70b4a5d23525e932')], [Row(id=0), Row(id=1), Row(id=2), Row(id=8589934592), Row(id=8589934593), Row(id=8589934594)], [Row(r1=1.0, r2=1.0), Row(r1=2.0, r2=2.0)], # key is a tuple of one numpy.int64, which is the value, # key is a tuple of two numpy.int64s, which is the values, # of 'id' and 'ceil(df.v / 2)' for the current group, [Row(pos=0, col=1), Row(pos=1, col=2), Row(pos=2, col=3)].
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