when 与 otherwise 配合使用 如果未调用Column.otherwise(),则对于不匹配的条件将返回None df = spark.createDataFrame( [(2, "Alice"), (5, "Bob")], ["age", "name"])df.show()+---+---+|age| name|+---+---+| 2|Alice|| 5| Bob|+---+---+# 查询条件进行筛选,当when不配合otherwis...
df.withColumn("new_column", concat(df["first_name"], lit(" "), df["last_name"])) 通过使用 withColumn() 方法,你可以按照需要对 DataFrame 进行列级别的变换和操作。它提供了一种灵活的方式来构建和转换 DataFrame,以适应特定的数据处理需求。when() otherwise()在PySpark 中,when() 函数用于执行条件...
when/otherwise:条件表达式。 coalesce:返回第一个非空的值。 isnull/isnotnull:检查是否为空/不为空。 from pyspark.sql.functions import when, coalesce, isnull, isnotnull # 条件表达式 df.withColumn("category", when(col("value") > 100, "high").when(col("value") < 50, "low").otherwise("...
lit, when # 创建SparkSession spark = SparkSession.builder.appName("withColumnExample").getOrCreate() # 创建初始DataFrame data = [ ("Alice", 25), ("Bob", 30), ("Charlie", 35) ] columns = ["Name", "Age"] df = spark.createDataFrame(data, columns) # 使用withColumn添加新列 df_with...
In this example, we used the “when” and “otherwise” functions to create a new “tax” column based on the “salary” column’s values. 5. Using a User-Defined Function (UDF) with “withColumn” we will create a User-Defined Function (UDF) to categorize employees into different groups...
Columns in PySpark can be transformed using various functions such aswithColumn,when, andotherwise. These functions allow you to apply conditional logic and transformations to columns. Here is an example of how to add a new column “is_old” based on the age column: ...
( "highter_than_next",when(col("lead").isNull(),0).otherwise(col("lead"))).withColumn( "lower_than_previous",when(col("lag").isNull(),0).otherwise(col("lag"))) diff.show() +---+---+---+---+---+---+---+---+ | depName|empNo| name|salary|lead| lag|highter_than...
col_with_mean = mean_of_pyspark_columns(df, numeric_cols)forcol, meanincol_with_mean: df = df.withColumn(col, when(df[col].isNull() ==True, F.lit(mean)).otherwise(df[col]))returndfif__name__ =='__main__':# df需要自行创建numeric_cols = ['age2','height2']# 需要填充空值...
withColumn('ratings', when(df['popularity']<3,'Low').when(df['popularity']<5,'Mid').otherwise('High')) df_with_newcols.show(15,False) 删除和重命名列 删除列 columns_to_drop=['budget_cat'] df_with_newcols=df_with_newcols.drop(*columns_to_drop) ...
--- 1.5 按条件筛选when / between --- 2、--- 增、改 --- --- 2.1 新建数据 --- --- 2.2 新增数据列 withColumn--- 一种方式通过functions **另一种方式通过另一个已有变量:** **修改原有df[“xx”]列的所有值:** **修改列的类型...