如何将字典列表转换为 Pyspark DataFrame ？

在本文中，我们将讨论从字典列表中创建 Pyspark 数据框。

我们将使用带有帮助 createDataFrame() 方法的字典列表在 PySpark 中创建一个数据框。 data 属性采用字典列表，columns 属性采用名称列表。

dataframe = spark.createDataFrame(data, columns)

示例 1：

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 dictionaries of students  data
data = [{"Student ID": 1, "Student name": "sravan"},
        {"Student ID": 2, "Student name": "Jyothika"},
        {"Student ID": 3, "Student name": "deepika"},
        {"Student ID": 4, "Student name": "harsha"}]
  
# creating a dataframe
dataframe = spark.createDataFrame(data)
  
# display dataframe
dataframe.show()

输出：

示例 2：

蟒蛇3

# 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 dictionaries of crop  data
data = [{"Crop ID": 1, "name": "rose", "State": "AP"},
        {"Crop ID": 2, "name": "lilly", "State": "TS"},
        {"Crop ID": 3, "name": "lotus", "State": "Maharashtra"},
        {"Crop ID": 4, "name": "jasmine", "State": "AP"}]
  
# creating a dataframe
dataframe = spark.createDataFrame(data)
  
# display dataframe
dataframe.show()

输出：

示例 3：

蟒蛇3

# 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 dictionaries of crop  data
data = [{"Crop ID": 1, "name": "rose", "State": "AP"},
        {"Crop ID": 2, "name": "lilly", "State": "TS"},
        {"Crop ID": 3, "name": "lotus", "State": "Maharashtra"},
        {"Crop ID": 4, "name": "jasmine", "State": "AP"}]
  
# creating a dataframe
dataframe = spark.createDataFrame(data)
  
# display dataframe count
dataframe.count()

输出：

4