PySpark – 从两列数据创建字典

在本文中，我们将了解如何使用Python从 PySpark 中两列中的数据创建字典。

方法 1：使用字典理解

在这里，我们将创建具有两列的数据框，然后使用字典推导将其转换为字典。

Python

# importing pyspark
# make sure you have installed the pyspark library
import pyspark
  
# Importing and creating a SparkSession
# to work on DataFrames
# The session name is 'Practice_Session'
from pyspark.sql import SparkSession
spark_session = SparkSession.builder.appName(
  'Practice_Session').getOrCreate()
  
# Creating a DataFrame using createDataFrame()
# method, with hard coded data.
rows = [['John', 54],
        ['Adam', 65],
        ['Michael', 56],
        ['Kelsey', 37],
        ['Chris', 49],
        ['Jonathan', 28],
        ['Anthony', 26],
        ['Esther', 48],
        ['Rachel', 52],
        ['Joseph', 56],
        ['Richard', 49],
        ]
  
columns = ['Name', 'Age']
df_pyspark = spark_session.createDataFrame(rows, columns)
  
# printing the DataFrame
df_pyspark.show()
  
# dictionary comprehension is used here
# Name column here is the key while Age
# columns is the value
# You can also use {row['Age']:row['Name']
# for row in df_pyspark.collect()},
# to reverse the key,value pairs
  
  
# collect() gives a list of
# rows in the DataFrame
result_dict = {row['Name']: row['Age'] 
               for row in df_pyspark.collect()}
  
# Printing a few key:value pairs of
# our final resultant dictionary
print(result_dict['John'])
print(result_dict['Michael'])
print(result_dict['Adam'])

输出 ：

方法二：转换 PySpark DataFrame 并使用 to_dict() 方法

以下是 to_dict() 方法的详细信息：

to_dict() : PandasDataFrame.to_dict(orient=’dict’)

Parameters: 

• orient : str {‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’}
• Determines the type of the values of the dictionary.

Return: It returns a Python dictionary corresponding to the DataFrame

Python

# importing pyspark
# make sure you have installed
# the pyspark library
import pyspark
  
# Importing and creating a SparkSession
# to work on DataFrames
# The session name is 'Practice_Session'
from pyspark.sql import SparkSession
spark_session = SparkSession.builder.appName(
  'Practice_Session').getOrCreate()
  
# Creating a DataFrame using createDataFrame()
# method, with hard coded data.
rows = [['John', 54],
        ['Adam', 65],
        ['Michael', 56],
        ['Kelsey', 37],
        ['Chris', 49],
        ['Jonathan', 28],
        ['Anthony', 26],
        ['Esther', 48],
        ['Rachel', 52],
        ['Joseph', 56],
        ['Richard', 49],
        ]
  
columns = ['Name', 'Age']
df_pyspark = spark_session.createDataFrame(rows, columns)
  
# printing the DataFrame
df_pyspark.show()
  
# COnvert PySpark dataframe to pandas
# dataframe
df_pandas = df_pyspark.toPandas()
  
# Convert the dataframe into
# dictionary
result = df_pandas.to_dict(orient='list')
  
# Print the dictionary
print(result)

输出 ：

方法3：通过遍历一列字典

遍历列并生成一个字典，使得键是列，值是列中的值列表。

为此，我们需要先将 PySpark DataFrame 转换为 Pandas DataFrame

Python

# importing pyspark
# make sure you have installed the pyspark library
import pyspark
  
# Importing and creating a SparkSession to work on 
# DataFrames The session name is 'Practice_Session'
from pyspark.sql import SparkSession
spark_session = SparkSession.builder.appName(
  'Practice_Session').getOrCreate()
  
# Creating a DataFrame using createDataFrame()
# method, with hard coded data.
rows = [['John', 54],
        ['Adam', 65],
        ['Michael', 56],
        ['Kelsey', 37],
        ['Chris', 49],
        ['Jonathan', 28],
        ['Anthony', 26],
        ['Esther', 48],
        ['Rachel', 52],
        ['Joseph', 56],
        ['Richard', 49],
        ]
  
columns = ['Name', 'Age']
df_pyspark = spark_session.createDataFrame(rows, columns)
  
# printing the DataFrame
df_pyspark.show()
  
result = {}
  
# Convert PySpark DataFrame to Pandas
# DataFrame
df_pandas = df_pyspark.toPandas()
  
# Traverse through each column
for column in df_pandas.columns:
  
    # Add key as column_name and
    # value as list of column values
    result[column] = df_pandas[column].values.tolist()
  
# Print the dictionary
print(result)

输出 ：