CODEWITHSUNDEEP
apache-sparkdictionarypysparkapache-spark-sql
Shivam Gupta
Shivam Gupta

Reputation: 193

Pyspark: create spark data frame from nested dictionary

How to create a spark data frame from a nested dictionary? I'm new to spark. I do not want to use the pandas data frame.

My dictionary look like:-

{'[email protected]': {'Date': datetime.date(2019, 10, 21),'idle_time': datetime.datetime(2019, 10, 21, 1, 50)},
 '[email protected]': {'Date': datetime.date(2019, 10, 21),'idle_time': datetime.datetime(2019, 10, 21, 1, 35)},
 '[email protected]': {'Date': datetime.date(2019, 10, 21),'idle_time': datetime.datetime(2019, 10, 21, 1, 55)}
}

I want to convert this dict to spark data frame using pyspark data frame.

My expected output:-

                                Date    idle_time
    user_name       
[email protected]   2019-10-21  2019-10-21 01:50:00
[email protected]   2019-10-21  2019-10-21 01:35:00
[email protected]             2019-10-21  2019-10-21 01:55:00

Upvotes: 2

Views: 6906

Answers (2)

David Vrba
David Vrba

Reputation: 3344

Convert the dictionary to a list of tuples, each tuple will then become a row in Spark DataFrame:

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

Define schema for your data:

from pyspark.sql.types import *

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

Create the Spark DataFrame:

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|
|     [email protected]|2019-10-21|2019-10-21 01:55:00|
+--------------------+----------+-------------------+

Upvotes: 2

Mike Souder
Mike Souder

Reputation: 541

You need to redo your dictionary and build rows to properly infer the schema.

import datetime
from pyspark.sql import Row

data_dict = {
    '[email protected]': {
        'Date': datetime.date(2019, 10, 21),
        'idle_time': datetime.datetime(2019, 10, 21, 1, 50)
    },
    '[email protected]': {
        'Date': datetime.date(2019, 10, 21),
        'idle_time': datetime.datetime(2019, 10, 21, 1, 35)
    },
    '[email protected]': {
        '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          |
+-------------------------+----------+-------------------+
|[email protected]|2019-10-21|2019-10-21 01:50:00|
|[email protected]|2019-10-21|2019-10-21 01:35:00|
|[email protected]          |2019-10-21|2019-10-21 01:55:00|
+-------------------------+----------+-------------------+

Note: if you already have the schema prepared and don't need to infer, you can just supply the schema to the createDataFrame function:

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          |
+-------------------------+----------+-------------------+
|[email protected]|2019-10-21|2019-10-21 01:50:00|
|[email protected]|2019-10-21|2019-10-21 01:35:00|
|[email protected]          |2019-10-21|2019-10-21 01:55:00|
+-------------------------+----------+-------------------+

Upvotes: 4

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