重命名 PySpark DataFrames 聚合的列

在本文中，我们将讨论如何使用 Pyspark 重命名 PySpark 数据框聚合的列。

正在使用的数据框：

在 PySpark 中，groupBy() 用于将相同的数据收集到 PySpark DataFrame 上的组中，并对分组数据执行聚合函数。这些在功能模块中可用：

方法一：使用别名（）

我们可以使用此方法更改聚合的列名。

语法：

dataframe.groupBy(‘column_name_group’).agg(aggregate_function(‘column_name’).alias(“new_column_name”))

在哪里，

• 数据框是输入数据框
• column_name_group 是分组列
• aggregate_function 是上述函数中的函数
• column_name 是执行聚合的列
• new_column_name 是 column_name 的新名称

示例 1：通过将 FEE 列名称更改为 Total Fee 来使用 sum() 和 avg() 聚合 DEPT 列

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
#import functions
from pyspark.sql import functions
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
  
# aggregating DEPT column with sum() and avg() 
# by changing FEE column name to Total Fee
dataframe.groupBy('DEPT').agg(functions.sum('FEE').alias(
    "Total Fee"), functions.avg('FEE').alias("Average Fee")).show()

输出：

示例 2：通过将 FEE 列名称更改为 Total Fee 来聚合 DEPT 列与 min()、count()、mean() 和 max()

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
#import functions
from pyspark.sql import functions
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
  
# aggregating DEPT column with min(),count(),mean() 
# and max() by changing FEE column name to Total Fee
dataframe.groupBy('DEPT').agg(functions.min('FEE').alias("Minimum Fee"),
                              functions.max('FEE').alias("Maximum Fee"), 
                              functions.count('FEE').alias("No of Fee"),
                              functions.mean('FEE').alias("Average Fee")).show()

输出：

方法 2：使用 withColumnRenamed()

这需要一个结果聚合列名并重命名此列。聚合后，它将返回列名称为 aggregate_operation(old_column)

所以使用它我们可以用我们的新列替换它

语法：

dataframe.groupBy(“column_name_group”).agg({“column_name”:”aggregate_operation”}).withColumnRenamed(“aggregate_operation(column_name)”, “new_column_name”)

示例：使用 sum() FEE 聚合 DEPT 列并重命名为 Total Fee

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
#import functions
from pyspark.sql import functions
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
  
# aggregating DEPT column with sum() FEE and rename to Total Fee
dataframe.groupBy("DEPT").agg({"FEE": "sum"}).withColumnRenamed(
    "sum(FEE)", "Total Fee").show()

输出：