您需要重做字典并构建行以正确推断模式。

import datetime
from pyspark.sql import Row

data_dict = {
    'prathameshsalap@gmail.com': {
        'Date': datetime.date(2019, 10, 21),
        'idle_time': datetime.datetime(2019, 10, 21, 1, 50)
    },
    'vaishusawant143@gmail.com': {
        'Date': datetime.date(2019, 10, 21),
        'idle_time': datetime.datetime(2019, 10, 21, 1, 35)
    },
    'you@example.com': {
        'Date': datetime.date(2019, 10, 21),
        'idle_time': datetime.datetime(2019, 10, 21, 1, 55)
    }
}

data_as_rows = [Row(**{'user_name': k,**v}) for k,v in data_dict.items()]
data_df = spark.createDataFrame(data_as_rows).select('user_name', 'Date', 'idle_time')

data_df.show(truncate=False)

>>>
+-------------------------+----------+-------------------+
|user_name                |Date      |idle_time          |
+-------------------------+----------+-------------------+
|prathameshsalap@gmail.com|2019-10-21|2019-10-21 01:50:00|
|vaishusawant143@gmail.com|2019-10-21|2019-10-21 01:35:00|
|you@example.com          |2019-10-21|2019-10-21 01:55:00|
+-------------------------+----------+-------------------+

注意：如果您已经准备好了模式，并且不需要推断，那么只需将模式提供给createdataframe函数：

import pyspark.sql.types as T

schema = T.StructType([
    T.StructField('user_name', T.StringType(), False),
    T.StructField('Date', T.DateType(), False),
    T.StructField('idle_time', T.TimestampType(), False)
])
data_as_tuples = [(k, v['Date'], v['idle_time']) for k,v in data_dict.items()]

data_df = spark.createDataFrame(data_as_tuples, schema=schema)

data_df.show(truncate=False)

>>>
+-------------------------+----------+-------------------+
|user_name                |Date      |idle_time          |
+-------------------------+----------+-------------------+
|prathameshsalap@gmail.com|2019-10-21|2019-10-21 01:50:00|
|vaishusawant143@gmail.com|2019-10-21|2019-10-21 01:35:00|
|you@example.com          |2019-10-21|2019-10-21 01:55:00|
+-------------------------+----------+-------------------+

将字典转换为元组列表，然后每个元组将成为spark dataframe中的一行：

rows = []
for key, value in data.items():
    row = (key,value['Date'], value['idle_time'])
    rows.append(row)

为数据定义架构：

from pyspark.sql.types import *

sch = StructType([
    StructField('user_name', StringType()),
    StructField('date', DateType()),
    StructField('idle_time', TimestampType())
])

创建sparkDataframe：

df = spark.createDataFrame(rows, sch)

df.show()
+--------------------+----------+-------------------+
|           user_name|      date|          idle_time|
+--------------------+----------+-------------------+
|prathameshsalap@g...|2019-10-21|2019-10-21 01:50:00|
|vaishusawant143@g...|2019-10-21|2019-10-21 01:35:00|
|     you@example.com|2019-10-21|2019-10-21 01:55:00|
+--------------------+----------+-------------------+