如何按索引重命名 PySpark 数据框列？

在本文中，我们将了解如何使用Python按索引重命名 PySpark Dataframe 列。我们可以使用 Dataframe.withColumnRenamed() 和 Dataframe.columns[] 方法按索引重命名列。在 Dataframe.columns[] 的帮助下，我们获得了特定索引上列的名称，然后我们使用 withColumnRenamed() 方法将该名称替换为另一个名称。

示例 1：以下程序是通过索引重命名列。

Python3

# importing required module
import pyspark
from pyspark.sql import SparkSession
  
# creating sparksession and giving
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# demo data of college students
data = [["Mukul", 23, "BBA"],
        ["Robin", 21, "BCA"],
        ["Rohit", 24, "MBA"],
        ["Suraj", 25, "MBA"],
        ["Krish", 22, "BCA"]]
  
# giving column names of dataframe
columns = ["Name", "Age", "Course"]
  
# creating a dataframe
dataframe = spark.createDataFrame(data, columns)
  
# Rename dataframe
df = dataframe.withColumnRenamed(dataframe.columns[0],
                                 "Student Name")
  
# Original dataframe
print("Original Dataframe")
dataframe.show()
  
# Dataframe after rename column
print("Dataframe after rename 0 index column")
df.show()

输出：

示例2：下面的程序是通过这些索引重命名多个列。

Python3

# importing module
import pyspark
  
# importing sparksession from
# pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving
# an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of students  data
data = [[123, "Sagar",   "Rajveer", 22, "BBA"],
        [124, "Rajeev",  "Mukesh",  23, "BBA"],
        [125, "Harish",  "Parveen", 25, "BBA"],
        [126, "Gagan",   "Rohit",   24, "BBA"],
        [127, "Rakesh",  "Mayank",  25, "BBA"],
        [128, "Gnanesh", "Dleep",   26, "BBA"]]
  
# specify column names
columns = ['ID', 'Name', 'Father Name',
           'Age', "Course", ]
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# display original dataframe
print('Actual data in dataframe')
dataframe.show()
  
# Rename column
df = dataframe.withColumnRenamed(dataframe.columns[1], 
                                 "Student Name").withColumnRenamed(
    dataframe.columns[3], "Student Age")
  
# display dataframe after rename column
print('After rename 1 and 3 index column')
df.show()

输出：