when(condition, value1).otherwise(value2)联合使用,和sql中的case when类似 那么:当满足条件condition的指赋值为values1,不满足条件的则赋值为values2,otherwise表示,不满足条件的情况下,应该赋值为啥。 >>> from pyspark.sql import functions as F >>> df.select(df.name, F.when(df.age > 4, 1).when...
when(condition, value1).otherwise(value2),意为:当满足条件condition的值时赋值为values1,不满足条件的则赋值为values2,otherwise表示,不满足条件的情况下,应该赋值何值。 例: from pyspark.sql import functions as F df.select(df.customerID,F.when(df.gender=="Male","1").when(df.gender=="Female",...
when(condition, value1).otherwise(value2),意为:当满足条件condition的值时赋值为values1,不满足条件的则赋值为values2,otherwise表示,不满足条件的情况下,应该赋值何值。 例: from pyspark.sql import functions as F df.select(df.customerID,F.when(df.gender=="Male","1").when(df.gender=="Female",...
例如,请参阅pyspark.sql.functions.when()。 参数:●condition– 一个布尔类型的列表达式。 ●value– 一个文字值或一个列表达式。 >>>frompyspark.sqlimportfunctions as F>>>df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3, -1).otherwise(0)).show()+---+---+ | name|CASE ...
when(condition, value1).otherwise(value2)联合使用:那么:当满足条件condition的指赋值为values1,不满足条件的则赋值为values2.otherwise表示,不满足条件的情况下,应该赋值为啥。 demo1: 1 2 3 4 5 6 7 8 >>> from pyspark.sql import functions as F >>> df.select(df.name, F.when(df.age > 4, ...
Let us see somehow the When function works in PySpark:- When is a spark function so it is used with the help of the Import function: importorg.apache.spark.sql.function.when When the function first checks with the condition for a DataFrame and then segregates the data accordingly we can ...
用Truck对该矩阵进行分区，并按Timestamp排序，然后计算fault_code等于0时的条件下的累积和。这将创建一...
In the first bit, we declare a new column -'new column', and then give the condition enclosed in when function (i.e. fruit1==fruit2) then give 1 if the condition is true, if untrue the control goes to the otherwise which then takes care of the second condition (fruit1 or f...
when(condition, value1).otherwise(value2)联合使用: 那么:当满足条件condition的指赋值为values1,不满足条件的则赋值为values2. otherwise表示,不满足条件的情况下,应该赋值为啥。 demo1 >>> from pyspark.sql import functions as F >>> df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3...
I think you are missing .isin in when condition and Use only F.when for first when condition only (or) use .when. from pyspark.sql import functions as F df = spark.createDataFrame([(5000, 'US'),(2500, 'IN'),(4500, 'AU'),(4500, 'NZ')],["Sales", "Region"]) df.withColumn(...