XIg3ddlmZddlmZmZmZmZmZmZddl m Z ddl m Z ddl mZddlmZdd lmZdd lmZerdd lmZd d gZdd dededefdZdedefdedeffdZGdd ZGdd Zy))ContributionsAcceptedError)Callable TYPE_CHECKINGoverloadDictUnionList)Column) SparkSession) DataFrame)_to_column_expr) ColumnOrName) NumericType) LiteralType GroupedDataGroupingselfnamecolsreturnc0djt|}|jrt|jnd}|jj }t |j jd||||}t||jS)N,apply) function_name function_aggr group_exprprojected_columns) joinlist _groupingstr get_columnsgetattr_dfrelationrsession)rrr expressionsgroup_by projectionsjdfs `/var/www/html/answerous/venv/lib/python3.12/site-packages/duckdb/experimental/spark/sql/group.py _api_internalr/!sx((4:&K&*nns4>>""H..,,.K -'$((##W -!%  C S$,, ''f.ctdddtdtffd }j|_j|_|S)Nrrrrc6j}t||g|SN)__name__r/)rrrr1s r._apizdf_varargs_api.._api.szzT4/$//r0)r$rr5__doc__)r1r6s` r.df_varargs_apir8-s:0=000JJDM99DL Kr0c&eZdZddZdefdZdZy)rcd|_|Dcgc] }t|c}|_d|vr|d}ddg}||vsJ||_yycc}w)Nrspecialcuberollup)_typer_cols)rrkwargsxr;accepted_specials r.__init__zGrouping.__init__8s] 267Qoa(7  Y'G &1 .. .. DJ 8sArctdj|jDcgc] }t|c}}|Scc}w)Nr)r!r?r$)rrAcolumnss r.r%zGrouping.get_columnsAs-((DJJ7qCF788s5ch|j}|jr|jdz|zdzS|S)N())r%r>)rrEs r.__str__zGrouping.__str__Es5""$ ::::#g-3 3r0N)rr)r5 __module__ __qualname__rCr$r%rIr0r.rr7s!Sr0ceZdZdZdedefdZdefdZdefdZ e dedefd Z dedefd Z e dedefd Z e dedefd Ze dedefd ZededefdZedeeefdefdZdeeeeeffdefdZy)rzo A set of methods for aggregations on a :class:`DataFrame`, created by :func:`DataFrame.groupBy`. groupingdfcB||_||_|j|_yr4)r#r'r))rrNrOs r.rCzGroupedData.__init__Ss!%'ZZ r0rc,t|jSr4)r$r'rs r.__repr__zGroupedData.__repr__Xs488}r0c:t|djddS)a~Counts the number of records for each group. Examples -------- >>> df = spark.createDataFrame( ... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"]) >>> df.show() +---+-----+ |age| name| +---+-----+ | 2|Alice| | 3|Alice| | 5| Bob| | 10| Bob| +---+-----+ Group-by name, and count each group. >>> df.groupBy(df.name).count().sort("name").show() +-----+-----+ | name|count| +-----+-----+ |Alice| 2| | Bob| 2| +-----+-----+ countz count_star())r/withColumnRenamedrRs r.rUzGroupedData.count[s6T7+==ngVVr0rcy)zComputes average values for each numeric columns for each group. :func:`mean` is an alias for :func:`avg`. Parameters ---------- cols : str column names. Non-numeric columns are ignored. NrLrrs r.meanzGroupedData.meanxr0ct|}t|dk(rT|jj}|jDcgc])}t |j ts|j+}}t|dg|Scc}w)aComputes average values for each numeric columns for each group. :func:`mean` is an alias for :func:`avg`. Parameters ---------- cols : str column names. Non-numeric columns are ignored. Examples -------- >>> df = spark.createDataFrame([ ... (2, "Alice", 80), (3, "Alice", 100), ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"]) >>> df.show() +---+-----+------+ |age| name|height| +---+-----+------+ | 2|Alice| 80| | 3|Alice| 100| | 5| Bob| 120| | 10| Bob| 140| +---+-----+------+ Group-by name, and calculate the mean of the age in each group. >>> df.groupBy("name").avg('age').sort("name").show() +-----+--------+ | name|avg(age)| +-----+--------+ |Alice| 2.5| | Bob| 7.5| +-----+--------+ Calculate the mean of the age and height in all data. >>> df.groupBy().avg('age', 'height').show() +--------+-----------+ |avg(age)|avg(height)| +--------+-----------+ | 5.0| 110.0| +--------+-----------+ ravg) r"lenr'schemafields isinstancedataTyperrr/)rrrEr^rAs r.r\zGroupedData.avgshXt* w<1 XX__F28--!gQ:ajjZeCf!&&!gG!gT53733"hs A;A;cy)a2Computes the max value for each numeric columns for each group. Examples -------- >>> df = spark.createDataFrame([ ... (2, "Alice", 80), (3, "Alice", 100), ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"]) >>> df.show() +---+-----+------+ |age| name|height| +---+-----+------+ | 2|Alice| 80| | 3|Alice| 100| | 5| Bob| 120| | 10| Bob| 140| +---+-----+------+ Group-by name, and calculate the max of the age in each group. >>> df.groupBy("name").max("age").sort("name").show() +-----+--------+ | name|max(age)| +-----+--------+ |Alice| 3| | Bob| 10| +-----+--------+ Calculate the max of the age and height in all data. >>> df.groupBy().max("age", "height").show() +--------+-----------+ |max(age)|max(height)| +--------+-----------+ | 10| 140| +--------+-----------+ NrLrXs r.maxzGroupedData.maxrZr0cy)aComputes the min value for each numeric column for each group. Parameters ---------- cols : str column names. Non-numeric columns are ignored. Examples -------- >>> df = spark.createDataFrame([ ... (2, "Alice", 80), (3, "Alice", 100), ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"]) >>> df.show() +---+-----+------+ |age| name|height| +---+-----+------+ | 2|Alice| 80| | 3|Alice| 100| | 5| Bob| 120| | 10| Bob| 140| +---+-----+------+ Group-by name, and calculate the min of the age in each group. >>> df.groupBy("name").min("age").sort("name").show() +-----+--------+ | name|min(age)| +-----+--------+ |Alice| 2| | Bob| 5| +-----+--------+ Calculate the min of the age and height in all data. >>> df.groupBy().min("age", "height").show() +--------+-----------+ |min(age)|min(height)| +--------+-----------+ | 2| 80| +--------+-----------+ NrLrXs r.minzGroupedData.minrZr0cy)aComputes the sum for each numeric columns for each group. Parameters ---------- cols : str column names. Non-numeric columns are ignored. Examples -------- >>> df = spark.createDataFrame([ ... (2, "Alice", 80), (3, "Alice", 100), ... (5, "Bob", 120), (10, "Bob", 140)], ["age", "name", "height"]) >>> df.show() +---+-----+------+ |age| name|height| +---+-----+------+ | 2|Alice| 80| | 3|Alice| 100| | 5| Bob| 120| | 10| Bob| 140| +---+-----+------+ Group-by name, and calculate the sum of the age in each group. >>> df.groupBy("name").sum("age").sort("name").show() +-----+--------+ | name|sum(age)| +-----+--------+ |Alice| 5| | Bob| 15| +-----+--------+ Calculate the sum of the age and height in all data. >>> df.groupBy().sum("age", "height").show() +--------+-----------+ |sum(age)|sum(height)| +--------+-----------+ | 20| 440| +--------+-----------+ NrLrXs r.sumzGroupedData.sum rZr0exprscyr4rL)rrhs r.aggzGroupedData.agg6 r0_GroupedData__exprscyr4rL)rrls r.rjzGroupedData.agg:rkr0c|sJdt|dk(rt|dtrtt d|DsJdt |j j}|j|Dcgc]}|jc}t|j }|jjj|d|i}t||jScc}w)a Compute aggregates and returns the result as a :class:`DataFrame`. The available aggregate functions can be: 1. built-in aggregation functions, such as `avg`, `max`, `min`, `sum`, `count` 2. group aggregate pandas UDFs, created with :func:`pyspark.sql.functions.pandas_udf` .. note:: There is no partial aggregation with group aggregate UDFs, i.e., a full shuffle is required. Also, all the data of a group will be loaded into memory, so the user should be aware of the potential OOM risk if data is skewed and certain groups are too large to fit in memory. .. seealso:: :func:`pyspark.sql.functions.pandas_udf` If ``exprs`` is a single :class:`dict` mapping from string to string, then the key is the column to perform aggregation on, and the value is the aggregate function. Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions. .. versionadded:: 1.3.0 .. versionchanged:: 3.4.0 Supports Spark Connect. Parameters ---------- exprs : dict a dict mapping from column name (string) to aggregate functions (string), or a list of :class:`Column`. Notes ----- Built-in aggregation functions and group aggregate pandas UDFs cannot be mixed in a single call to this function. Examples -------- >>> from pyspark.sql import functions as F >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> df = spark.createDataFrame( ... [(2, "Alice"), (3, "Alice"), (5, "Bob"), (10, "Bob")], ["age", "name"]) >>> df.show() +---+-----+ |age| name| +---+-----+ | 2|Alice| | 3|Alice| | 5| Bob| | 10| Bob| +---+-----+ Group-by name, and count each group. >>> df.groupBy(df.name) GroupedData[grouping...: [name...], value: [age: bigint, name: string], type: GroupBy] >>> df.groupBy(df.name).agg({"*": "count"}).sort("name").show() +-----+--------+ | name|count(1)| +-----+--------+ |Alice| 2| | Bob| 2| +-----+--------+ Group-by name, and calculate the minimum age. >>> df.groupBy(df.name).agg(F.min(df.age)).sort("name").show() +-----+--------+ | name|min(age)| +-----+--------+ |Alice| 2| | Bob| 5| +-----+--------+ Same as above but uses pandas UDF. >>> @pandas_udf('int', PandasUDFType.GROUPED_AGG) # doctest: +SKIP ... def min_udf(v): ... return v.min() ... >>> df.groupBy(df.name).agg(min_udf(df.age)).sort("name").show() # doctest: +SKIP +-----+------------+ | name|min_udf(age)| +-----+------------+ |Alice| 2| | Bob| 5| +-----+------------+ zexprs should not be emptyr rc3<K|]}t|tywr4)r`r ).0cs r. z"GroupedData.agg..s<z!V,st111u u:?z%(D9, ,Z Z<t~~334K   616 74>>*H*$((##**KIICdll++ 7s8C#N)r5rJrKr7rrrCr$rSrUr8rYr\rcrergrr rjrr rLr0r.rrLsG 00y0 #WyW:  #  )    141414f$ $ $ $ L) ) ) ) V) ) ) ) V & Y   4S> i  d,%S#X 67d,Id,r0N) exceptionrtypingrrrrr r columnr r)r dataframer functionsr_typingrtypesrr__all__r$r/r8rrrLr0r.rs$3GG! &!$ * % ( (S ( ( (hsI~.8CN3K*V,V,r0