from pyspark.sql import SparkSession from pyspark.sql.functions import when # 创建SparkSession spark = SparkSession.builder.appName("Multiple WHEN Conditions").getOrCreate() # 创建示例数据 data = [("John", 25), ("Alice", 30), ("Mike", 35)] df = spark.createDataFrame(data, ["Name",...
PySpark provides us with the .withColumnRenamed() method that helps us rename columns. Conclusion In this tutorial, we’ve learned how to drop single and multiple columns using the .drop() and .select() methods. We also described alternative methods to leverage SQL expressions if we require ...
(2)使用条件语句when & otherwise # create a new col based on another col's value data = data.withColumn('newCol', F.when(condition, value)) # multiple conditions data = data.withColumn("newCol", F.when(condition1, value1) .when(condition2, value2) .otherwise(value3)) 自定义函数(UDF...
df.withColumn("row",row_number().over(windowSpec)) \ .withColumn("avg", avg(col("salary")).over(windowSpecAgg)) \ .withColumn("sum", sum(col("salary")).over(windowSpecAgg)) \ .withColumn("min", min(col("salary")).over(windowSpecAgg)) \ .withColumn("max", max(col("salary")...
The following example shows how to use pysparklit()function using withColumn to derive a new column based on some conditions. # Usage of lit() with withColumn()frompyspark.sql.functionsimportwhen,lit,col df3=df2.withColumn("lit_value2",when((col("Salary")>=40000)&(col("Salary")<=50000...
df_customer_flag_renamed = df_customer_flag.withColumnRenamed("balance_flag", "balance_flag_renamed") The alias method is especially helpful when you want to rename your columns as part of aggregations:Python Копирај from pyspark.sql.functions import avg df_segment_balance = df_cust...
pyspark 如何在Spark中使用when().otherwise函数来满足多个条件你可以使用一个技巧，将column.isNull（）...
I can create new columns in Spark using .withColumn(). I have yet found a convenient way to create multiple columns at once without chaining multiple .withColumn() methods. df2.withColumn('AgeTimesFare', df2.Age*df2.Fare).show() +---+---+---+---+---+ |PassengerId|Age|Fare|...
Multiple when conditions can be chained together. from pyspark.sql.functions import col, when df = auto_df.withColumn( "mpg_class", when(col("mpg") <= 20, "low") .when(col("mpg") <= 30, "mid") .when(col("mpg") <= 40, "high") .otherwise("very high"), ) # Code snippet...
Case when for语句具有从Pyspark转换的多个分组条件在SQL中，IN需要一个元素列表，因此需要在元素两边加上...