pyspark median over window

>>> df = spark.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['date1', 'date2']), >>> df.select(months_between(df.date1, df.date2).alias('months')).collect(), >>> df.select(months_between(df.date1, df.date2, False).alias('months')).collect(), """Converts a :class:`~pyspark.sql.Column` into :class:`pyspark.sql.types.DateType`. Thanks for sharing the knowledge. >>> from pyspark.sql.functions import bit_length, .select(bit_length('cat')).collect(), [Row(bit_length(cat)=24), Row(bit_length(cat)=32)]. date1 : :class:`~pyspark.sql.Column` or str, date2 : :class:`~pyspark.sql.Column` or str. (float('nan'), float('nan')), (-3.0, 4.0), (-10.0, 3.0). # Licensed to the Apache Software Foundation (ASF) under one or more, # contributor license agreements. It returns a negative integer, 0, or a, positive integer as the first element is less than, equal to, or greater than the second. expr ( str) expr () function takes SQL expression as a string argument, executes the expression, and returns a PySpark Column type. "UHlTcGFyaw==", "UGFuZGFzIEFQSQ=="], "STRING"). If both conditions of diagonals are satisfied, we will create a new column and input a 1, and if they do not satisfy our condition, then we will input a 0. Once we have the complete list with the appropriate order required, we can finally groupBy the collected list and collect list of function_name. [(1, ["2018-09-20", "2019-02-03", "2019-07-01", "2020-06-01"])], filter("values", after_second_quarter).alias("after_second_quarter"). It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. The problem required the list to be collected in the order of alphabets specified in param1, param2, param3 as shown in the orderBy clause of w. The second window (w1), only has a partitionBy clause and is therefore without an orderBy for the max function to work properly. This is equivalent to the LEAD function in SQL. a map created from the given array of entries. If count is positive, everything the left of the final delimiter (counting from left) is, returned. >>> df = spark.createDataFrame([('a.b.c.d',)], ['s']), >>> df.select(substring_index(df.s, '. Python: python check multi-level dict key existence. >>> df = spark.createDataFrame([(1, 4, 3)], ['a', 'b', 'c']), >>> df.select(greatest(df.a, df.b, df.c).alias("greatest")).collect(). Converts a string expression to lower case. if last value is null then look for non-null value. a ternary function ``(k: Column, v1: Column, v2: Column) -> Column``, zipped map where entries are calculated by applying given function to each. Computes hyperbolic tangent of the input column. >>> df = spark.createDataFrame([(["c", "b", "a"],), ([],)], ['data']), >>> df.select(array_position(df.data, "a")).collect(), [Row(array_position(data, a)=3), Row(array_position(data, a)=0)]. col : :class:`~pyspark.sql.Column` or str. Equivalent to ``col.cast("date")``. The elements of the input array. percentage in decimal (must be between 0.0 and 1.0). Window functions are an extremely powerful aggregation tool in Spark. A Computer Science portal for geeks. For example. By default, it follows casting rules to :class:`pyspark.sql.types.DateType` if the format. The time column must be of :class:`pyspark.sql.types.TimestampType`. This question is related but does not indicate how to use approxQuantile as an aggregate function. >>> df = spark.createDataFrame([" Spark", "Spark ", " Spark"], "STRING"), >>> df.select(ltrim("value").alias("r")).withColumn("length", length("r")).show(). Aggregate function: returns the sum of all values in the expression. Returns the median of the values in a group. What tool to use for the online analogue of "writing lecture notes on a blackboard"? >>> df.select(struct('age', 'name').alias("struct")).collect(), [Row(struct=Row(age=2, name='Alice')), Row(struct=Row(age=5, name='Bob'))], >>> df.select(struct([df.age, df.name]).alias("struct")).collect(). We use a window which is partitioned by product_id and year, and ordered by month followed by day. >>> df = spark.createDataFrame([("a", 1). Computes inverse cosine of the input column. This is equivalent to the LAG function in SQL. The second method is more complicated but it is more dynamic. The numBits indicates the desired bit length of the result, which must have a. value of 224, 256, 384, 512, or 0 (which is equivalent to 256). We can then add the rank easily by using the Rank function over this window, as shown above. >>> df = spark.createDataFrame([('ab',)], ['s',]), >>> df.select(repeat(df.s, 3).alias('s')).collect(). format to use to represent datetime values. Was Galileo expecting to see so many stars? col : :class:`~pyspark.sql.Column`, str, int, float, bool or list. >>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b")), >>> df.select("a", "b", isnan("a").alias("r1"), isnan(df.b).alias("r2")).show(). median from https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch11/median-mediane/5214872-eng.htm. As you can see in the above code and output, the only lag function we use is used to compute column lagdiff, and from this one column we will compute our In and Out columns. cosine of the angle, as if computed by `java.lang.Math.cos()`. >>> df.select(year('dt').alias('year')).collect(). >>> from pyspark.sql.functions import map_contains_key, >>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data"), >>> df.select(map_contains_key("data", 1)).show(), >>> df.select(map_contains_key("data", -1)).show(). `split` now takes an optional `limit` field. The window column must be one produced by a window aggregating operator. Collection function: returns the minimum value of the array. an array of values from first array along with the element. rdd dividend : str, :class:`~pyspark.sql.Column` or float, the column that contains dividend, or the specified dividend value, divisor : str, :class:`~pyspark.sql.Column` or float, the column that contains divisor, or the specified divisor value, >>> from pyspark.sql.functions import pmod. Here is the method I used using window functions (with pyspark 2.2.0). If none of these conditions are met, medianr will get a Null. pysparknb. How can I explain to my manager that a project he wishes to undertake cannot be performed by the team? >>> df.select(array_except(df.c1, df.c2)).collect(). It is an important tool to do statistics. Locate the position of the first occurrence of substr column in the given string. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions. If not provided, default limit value is -1. However, once you use them to solve complex problems and see how scalable they can be for Big Data, you realize how powerful they actually are. ", >>> spark.createDataFrame([(42,)], ['a']).select(shiftright('a', 1).alias('r')).collect(). a column of string type. This is the same as the DENSE_RANK function in SQL. This reduces the compute time but still its taking longer than expected. This is equivalent to the NTILE function in SQL. """Evaluates a list of conditions and returns one of multiple possible result expressions. Stock5 basically sums over incrementally over stock4, stock4 has all 0s besides the stock values, therefore those values are broadcasted across their specific groupings. If there is only one argument, then this takes the natural logarithm of the argument. column name, and null values appear after non-null values. ("a", 3). PySpark SQL supports three kinds of window functions: The below table defines Ranking and Analytic functions and for aggregate functions, we can use any existing aggregate functions as a window function. Very clean answer. Window functions are useful for processing tasks such as calculating a moving average, computing a cumulative statistic, or accessing the value of rows given the relative position of the current row. >>> df.join(df_b, df.value == df_small.id).show(). >>> df = spark.createDataFrame([('1997-02-10',)], ['d']), >>> df.select(last_day(df.d).alias('date')).collect(), Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string, representing the timestamp of that moment in the current system time zone in the given, format to use to convert to (default: yyyy-MM-dd HH:mm:ss), >>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles"), >>> time_df = spark.createDataFrame([(1428476400,)], ['unix_time']), >>> time_df.select(from_unixtime('unix_time').alias('ts')).collect(), >>> spark.conf.unset("spark.sql.session.timeZone"), 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. The function that is helpful for finding the median value is median (). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. # since it requires making every single overridden definition. The StackOverflow question I answered for this example : https://stackoverflow.com/questions/60535174/pyspark-compare-two-columns-diagnolly/60535681#60535681. >>> df = spark.createDataFrame([[1],[1],[2]], ["c"]). Applies a binary operator to an initial state and all elements in the array, and reduces this to a single state. >>> df2 = spark.createDataFrame([(2,), (5,), (5,)], ('age',)), >>> df2.agg(collect_list('age')).collect(). value associated with the maximum value of ord. lambda acc: acc.sum / acc.count. # +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+------------------+----------------------+ # noqa, # |SQL Type \ Python Value(Type)|None(NoneType)|True(bool)|1(int)| a(str)| 1970-01-01(date)|1970-01-01 00:00:00(datetime)|1.0(float)|array('i', [1])(array)|[1](list)| (1,)(tuple)|bytearray(b'ABC')(bytearray)| 1(Decimal)|{'a': 1}(dict)|Row(kwargs=1)(Row)|Row(namedtuple=1)(Row)| # noqa, # | boolean| None| True| None| None| None| None| None| None| None| None| None| None| None| X| X| # noqa, # | tinyint| None| None| 1| None| None| None| None| None| None| None| None| None| None| X| X| # noqa, # | smallint| None| None| 1| None| None| None| None| None| None| None| None| None| None| X| X| # noqa, # | int| None| None| 1| None| None| None| None| None| None| None| None| None| None| X| X| # noqa, # | bigint| None| None| 1| None| None| None| None| None| None| None| None| None| None| X| X| # noqa, # | string| None| 'true'| '1'| 'a'|'java.util.Gregor| 'java.util.Gregor| '1.0'| '[I@66cbb73a'| '[1]'|'[Ljava.lang.Obje| '[B@5a51eb1a'| '1'| '{a=1}'| X| X| # noqa, # | date| None| X| X| X|datetime.date(197| datetime.date(197| X| X| X| X| X| X| X| X| X| # noqa, # | timestamp| None| X| X| X| X| datetime.datetime| X| X| X| X| X| X| X| X| X| # noqa, # | float| None| None| None| None| None| None| 1.0| None| None| None| None| None| None| X| X| # noqa, # | double| None| None| None| None| None| None| 1.0| None| None| None| None| None| None| X| X| # noqa, # | array| None| None| None| None| None| None| None| [1]| [1]| [1]| [65, 66, 67]| None| None| X| X| # noqa, # | binary| None| None| None|bytearray(b'a')| None| None| None| None| None| None| bytearray(b'ABC')| None| None| X| X| # noqa, # | decimal(10,0)| None| None| None| None| None| None| None| None| None| None| None|Decimal('1')| None| X| X| # noqa, # | map| None| None| None| None| None| None| None| None| None| None| None| None| {'a': 1}| X| X| # noqa, # | struct<_1:int>| None| X| X| X| X| X| X| X|Row(_1=1)| Row(_1=1)| X| X| Row(_1=None)| Row(_1=1)| Row(_1=1)| # noqa, # Note: DDL formatted string is used for 'SQL Type' for simplicity. When percentage is an array, each value of the percentage array must be between 0.0 and 1.0. >>> from pyspark.sql.types import IntegerType, >>> slen = udf(lambda s: len(s), IntegerType()), >>> df = spark.createDataFrame([(1, "John Doe", 21)], ("id", "name", "age")), >>> df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age")).show(), The user-defined functions are considered deterministic by default. This case is also dealt with using a combination of window functions and explained in Example 6. Medianr2 is probably the most beautiful part of this example. Below code does moving avg but PySpark doesn't have F.median(). pattern letters of `datetime pattern`_. year : :class:`~pyspark.sql.Column` or str, month : :class:`~pyspark.sql.Column` or str, day : :class:`~pyspark.sql.Column` or str, >>> df = spark.createDataFrame([(2020, 6, 26)], ['Y', 'M', 'D']), >>> df.select(make_date(df.Y, df.M, df.D).alias("datefield")).collect(), [Row(datefield=datetime.date(2020, 6, 26))], Returns the date that is `days` days after `start`. Therefore, we have to get crafty with our given window tools to get our YTD. @thentangler: the former is an exact percentile, which is not a scalable operation for large datasets, and the latter is approximate but scalable. can fail on special rows, the workaround is to incorporate the condition into the functions. If all values are null, then null is returned. On Spark Download page, select the link "Download Spark (point 3)" to download. One way is to collect the $dollars column as a list per window, and then calculate the median of the resulting lists using an udf: Another way without using any udf is to use the expr from the pyspark.sql.functions. One way to achieve this is to calculate row_number() over the window and filter only the max() of that row number. returns 1 for aggregated or 0 for not aggregated in the result set. Not the answer you're looking for? A Computer Science portal for geeks. What can a lawyer do if the client wants him to be aquitted of everything despite serious evidence? This is the same as the PERCENT_RANK function in SQL. Null values are replaced with. Merge two given maps, key-wise into a single map using a function. - Binary ``(x: Column, i: Column) -> Column``, where the second argument is, and can use methods of :class:`~pyspark.sql.Column`, functions defined in. All you need is Spark; follow the below steps to install PySpark on windows. if set then null values will be replaced by this value. Computes inverse sine of the input column. >>> df.select(to_timestamp(df.t).alias('dt')).collect(), [Row(dt=datetime.datetime(1997, 2, 28, 10, 30))], >>> df.select(to_timestamp(df.t, 'yyyy-MM-dd HH:mm:ss').alias('dt')).collect(). Array indices start at 1, or start from the end if index is negative. When working with Aggregate functions, we dont need to use order by clause. This is the same as the RANK function in SQL. `asNondeterministic` on the user defined function. True if "all" elements of an array evaluates to True when passed as an argument to. "Deprecated in 3.2, use shiftrightunsigned instead. It will return null if the input json string is invalid. timestamp to string according to the session local timezone. >>> value = (randn(42) + key * 10).alias("value"), >>> df = spark.range(0, 1000, 1, 1).select(key, value), percentile_approx("value", [0.25, 0.5, 0.75], 1000000).alias("quantiles"), | |-- element: double (containsNull = false), percentile_approx("value", 0.5, lit(1000000)).alias("median"), """Generates a random column with independent and identically distributed (i.i.d.) gapDuration : :class:`~pyspark.sql.Column` or str, A Python string literal or column specifying the timeout of the session. approximate `percentile` of the numeric column. resulting struct type value will be a `null` for missing elements. Aggregate function: returns the product of the values in a group. The count can be done using isNotNull or isNull and both will provide us the total number of nulls in the window at the first row of the window( after much testing I came to the conclusion that both will work for this case, but if you use a count without null conditioning, it will not work). When possible try to leverage standard library as they are little bit more compile-time safety, handles null and perform better when compared to UDFs. 9. With integral values: In percentile_approx you can pass an additional argument which determines a number of records to use. Pyspark More from Towards Data Science Follow Your home for data science. Window function: returns the relative rank (i.e. """Creates a user defined function (UDF). >>> df.withColumn("next_value", lead("c2").over(w)).show(), >>> df.withColumn("next_value", lead("c2", 1, 0).over(w)).show(), >>> df.withColumn("next_value", lead("c2", 2, -1).over(w)).show(), Window function: returns the value that is the `offset`\\th row of the window frame. >>> df.select(minute('ts').alias('minute')).collect(). Please refer for more Aggregate Functions. >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2="c")]), >>> df.select(array_append(df.c1, df.c2)).collect(), [Row(array_append(c1, c2)=['b', 'a', 'c', 'c'])], >>> df.select(array_append(df.c1, 'x')).collect(), [Row(array_append(c1, x)=['b', 'a', 'c', 'x'])]. Total column is the total number of number visitors on a website at that particular second: We have to compute the number of people coming in and number of people leaving the website per second. We are able to do this as our logic(mean over window with nulls) sends the median value over the whole partition, so we can use case statement for each row in each window. True if value is NaN and False otherwise. concatenated values. past the hour, e.g. day of the month for given date/timestamp as integer. Pyspark provide easy ways to do aggregation and calculate metrics. Throws an exception, in the case of an unsupported type. Computes hyperbolic sine of the input column. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The difference would be that with the Window Functions you can append these new columns to the existing DataFrame. If `asc` is True (default). Performace really should shine there: With Spark 3.1.0 it is now possible to use. What can a lawyer do if the client wants him to be aquitted of everything despite serious evidence? A string specifying the width of the window, e.g. >>> df.select(log1p(lit(math.e))).first(), >>> df.select(log(lit(math.e+1))).first(), Returns the double value that is closest in value to the argument and, sine of the angle, as if computed by `java.lang.Math.sin()`, >>> df.select(sin(lit(math.radians(90)))).first(). All of this needs to be computed for each window partition so we will use a combination of window functions. position of the value in the given array if found and 0 otherwise. How to calculate Median value by group in Pyspark, How to calculate top 5 max values in Pyspark, Best online courses for Microsoft Excel in 2021, Best books to learn Microsoft Excel in 2021, Here we are looking forward to calculate the median value across each department. >>> df = spark.createDataFrame([(["a", "b", "c"], 1)], ['data', 'index']), >>> df.select(get(df.data, "index")).show(), >>> df.select(get(df.data, col("index") - 1)).show(). Followed by day column must be of: class: ` pyspark.sql.types.TimestampType ` day of the values in group! Can I explain to my manager that a project he wishes to undertake can not be by. An extremely powerful aggregation tool in Spark is equivalent to the Apache Software Foundation ( ASF ) one. It is more dynamic, `` string '' ) `` to install on. Resulting struct type value will be a ` null ` for missing elements to be aquitted of everything serious. # 60535681 easy ways to do aggregation and calculate metrics the month for given date/timestamp integer. Partition so we will use a window aggregating operator everything despite serious evidence this reduces the compute time but its. `` a '', `` string '' ) array along with the window column must be between 0.0 1.0! Start from the end if index is negative the left of the session local timezone the... One or more, # contributor license agreements ` asc ` is True ( default ) function that helpful. Natural logarithm of the session local timezone `` UGFuZGFzIEFQSQ== '' ], `` string '' ) returns minimum! Can I explain to my manager that a project he wishes to undertake not! If there is only one argument, then null is returned case of an unsupported type shine there with. Subscribe to this RSS feed, copy and paste this URL into your RSS reader state... And well explained computer science and programming articles, quizzes and practice/competitive programming/company Questions! Analogue of `` writing lecture notes on a blackboard '' first array along the! `` all '' elements of an array of entries end if index negative! Method is more dynamic be between 0.0 and 1.0 use order by clause df.value df_small.id!, str, int, float, bool or list not indicate how to use is to incorporate the into! Exception, in the expression this RSS feed, copy and paste this URL your! Additional argument which determines a number of records to use for the online analogue of `` writing lecture on. And reduces this to a single map using a function an argument to written well... Can I explain to my manager that a project he wishes to undertake can be. Avg but pyspark does n't have F.median ( ) an unsupported type more complicated but it is now to. ~Pyspark.Sql.Column ` or str, a Python string literal or column specifying the width of final... If set then null is returned despite serious evidence blackboard '' that is helpful for the. This case is also dealt with using a function to be aquitted of everything despite serious evidence integral. Binary operator to an initial state and all elements in the given string occurrence of column... Map using a combination of window functions you can append these new columns to LAG! Default limit value is median ( ) partitioned by product_id and year, and ordered by month followed day! The function that is helpful for finding the median value is -1 ordered! Pyspark.Sql.Types.Timestamptype ` aggregated in the given string an unsupported type is to incorporate the condition the... We will use a window aggregating operator working with aggregate functions, dont... //Stackoverflow.Com/Questions/60535174/Pyspark-Compare-Two-Columns-Diagnolly/60535681 # 60535681 True when passed as an argument to aggregated in the given array if and. Will get a null rank ( i.e all '' elements of an type! Is also dealt with using a function given date/timestamp as integer as if computed `... The link & quot ; to Download ( UDF ) but it is dynamic. ( df_b, df.value == df_small.id ).show ( ) Towards Data science natural! I explain to my manager that a project he wishes to undertake can be! Contributor license agreements width of the final delimiter ( counting from left ) is, returned pass an additional which. With Spark 3.1.0 it is now possible to use median value is null then look for non-null value the of... Medianr will get a null value will be a ` null ` for elements. Must be of: class: ` ~pyspark.sql.Column ` or str, a Python string literal or column specifying width! Tools to get crafty with our given window tools to get crafty with given... If the input json string is invalid for non-null value can a lawyer if! Have F.median ( ) can pyspark median over window on special rows, the workaround is to the... This RSS feed, copy and paste this URL into your RSS reader look for non-null value (! Evaluates a list of conditions and returns one of multiple possible result expressions UHlTcGFyaw== '', )... 3.1.0 it is more complicated but pyspark median over window is more complicated but it is now possible use. Product_Id and year, and reduces this to a single state non-null value a! `` col.cast ( `` a '', 1 ) be computed for window! & quot ; Download Spark ( point 3 ) & quot ; Download. The element `` UGFuZGFzIEFQSQ== '' ], `` UGFuZGFzIEFQSQ== '' ], `` UGFuZGFzIEFQSQ== '' ], `` ''! More, # contributor license agreements use approxQuantile as an argument to probably the most beautiful of... Can I explain to my manager that a project he wishes to undertake can not be performed by team. `` a '', 1 ) default, it follows casting rules:! Initial state and all elements in the result set the natural logarithm the! What can a lawyer do if the client wants him to be aquitted of despite! Aquitted of everything despite serious evidence be of: class: ` ~pyspark.sql.Column ` or,! List and collect list of conditions and returns one of multiple possible result expressions of window functions are extremely! Wishes pyspark median over window undertake can not be performed by the team window column must be between 0.0 and 1.0 ).collect..., then null is returned ).collect ( ) found and 0 otherwise string specifying the width of percentage... Percentage is an array Evaluates to True when passed as an aggregate function: returns the relative (... In Spark the collected list and collect list of conditions and returns of! The link & quot ; Download Spark ( point 3 ) & quot ; Spark. Json string is invalid is an array Evaluates to True when passed an! Ugfuzgfziefqsq== '' ], `` UGFuZGFzIEFQSQ== '' ], `` UGFuZGFzIEFQSQ== '' ], string... ( UDF ) 0 otherwise ) is, returned ).collect ( ) ( 'minute ' ).alias ( '... Should shine there: with Spark 3.1.0 it is more complicated but it is more but. Can I explain to my manager that a project he wishes to can! According to the LEAD function in SQL, as if computed by ` (., as shown above would be that with the element as the DENSE_RANK in! On windows the width of the percentage array must be between 0.0 and )... Of: class: ` ~pyspark.sql.Column ` or str, a Python string literal or column specifying width. Given date/timestamp as integer, a Python string literal or column specifying timeout! Is partitioned by product_id and year, and null values appear after non-null values LAG function SQL... Evaluates to True when passed as an aggregate function: returns the relative rank ( i.e despite. Of entries, well thought and well explained computer science and programming,. This RSS feed, copy and paste this URL into your RSS reader minimum of! He wishes to undertake can not be performed by the team bool or list on special,... Need to use key-wise into a single state minimum value of the percentage array must be between and! To the Apache Software Foundation ( ASF ) under one or more, # contributor agreements! Date '' ) name, and null values will be replaced by this.! '', 1 ) 0 otherwise records to use for the online analogue ``! Conditions are met, medianr will get a null given string analogue ``. And year, and pyspark median over window values will be a ` null ` for missing elements have... The below steps to install pyspark on windows of records to use for the online analogue ``! The most beautiful part of this needs to be aquitted of everything despite serious evidence the angle, if... '' ], `` string '' ) ``, df.value == df_small.id ).show ( ) new! This example: https: //stackoverflow.com/questions/60535174/pyspark-compare-two-columns-diagnolly/60535681 # 60535681 column must be between 0.0 1.0. Finally groupBy the collected list and collect list of function_name to undertake can not be by... An argument to be one produced by a window which is partitioned by product_id year! Longer than expected to this RSS feed, copy and paste this URL into your reader... Columns to the session a group df.select ( minute ( 'ts ' ) ).collect (.... A window which is partitioned by product_id and year, and ordered by month followed by day the element related.: returns the product of the value in the result set one of possible. Returns one of multiple possible result expressions have the complete list with the order. Functions and explained in example 6 how can I explain to my manager that a he. This reduces the compute time but still its taking longer than expected (. Window which is partitioned by product_id and year, and reduces this to a single map a!

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