import pyspark
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext
from pyspark.sql.functions import struct
from pyspark.sql import functions as F
from pyspark.sql.types import StructType, StructField, IntegerType,
StringType, DateType
spark = SparkSession.builder.appName("testme").getOrCreate()
sc = spark.sparkContext
# Set the log level to ERROR to reduce verbosity
sc.setLogLevel("ERROR")
# Define the schema
schema = StructType([
StructField("code", IntegerType(), True),
StructField("doc_type", StringType(), True),
StructField("amount", IntegerType(), True),
StructField("load_date", StringType(), True)
])
# Create the DataFrame
data = [
[1, 'AB', 12, '2022-01-01'],
[1, 'AA', 22, '2022-01-10'],
[1, 'AC', 11, '2022-01-11'],
[2, 'AB', 22, '2022-02-01'],
[2, 'AA', 28, '2022-02-10'],
[2, 'AC', 25, '2022-02-22']
]
df = spark.createDataFrame(data, schema=schema)
df = df.withColumn('load_date', F.to_date('load_date'))
grouped_df = df.groupBy('code')
pivot_aggs = [
F.sum(F.when(F.col('doc_type') == doc_type,
F.col('amount'))).alias(f'{doc_type}_amnt')
for doc_type in ['AB', 'AA', 'AC'] # Dynamically define pivot columns
]
non_pivot_aggs = [
F.first('load_date').alias('load_date') # Or any other aggregation
like min, max...
]
all_aggs = pivot_aggs + non_pivot_aggs
df = grouped_df.agg(*all_aggs)
df.printSchema()
df.show(20, False)
Output
root
|-- code: integer (nullable = true)
|-- AB_amnt: long (nullable = true)
|-- AA_amnt: long (nullable = true)
|-- AC_amnt: long (nullable = true)
|-- load_date: date (nullable = true)
+----+-------+-------+-------+----------+
|code|AB_amnt|AA_amnt|AC_amnt|load_date |
+----+-------+-------+-------+----------+
|1 |12 |22 |11 |2022-01-01|
|2 |22 |28 |25 |2022-02-01|
+----+-------+-------+-------+----------+
HTH
Dr Mich Talebzadeh,
Architect | Data Science | Financial Crime | Forensic Analysis | GDPR
view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
On Sun, 9 Mar 2025 at 17:23, Dhruv Singla <dvsingla...@gmail.com> wrote:
> Yes, this is it. I want to form this using a simple short command. The way
> I mentioned is a lengthy one.
>
> On Sun, Mar 9, 2025 at 10:16 PM Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>> Is this what you are expecting?
>>
>> root
>> |-- code: integer (nullable = true)
>> |-- AB_amnt: long (nullable = true)
>> |-- AA_amnt: long (nullable = true)
>> |-- AC_amnt: long (nullable = true)
>> |-- load_date: date (nullable = true)
>>
>> +----+-------+-------+-------+----------+
>> |code|AB_amnt|AA_amnt|AC_amnt|load_date |
>> +----+-------+-------+-------+----------+
>> |1 |12 |22 |11 |2022-01-01|
>> |2 |22 |28 |25 |2022-02-01|
>> +----+-------+-------+-------+----------+
>>
>> Dr Mich Talebzadeh,
>> Architect | Data Science | Financial Crime | Forensic Analysis | GDPR
>>
>> view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>>
>>
>> On Sun, 9 Mar 2025 at 14:12, Dhruv Singla <dvsingla...@gmail.com> wrote:
>>
>>> Hi Everyone
>>>
>>> Hope you are doing well
>>>
>>> I have the following dataframe.
>>>
>>> df = spark.createDataFrame(
>>> [
>>> [1, 'AB', 12, '2022-01-01']
>>> , [1, 'AA', 22, '2022-01-10']
>>> , [1, 'AC', 11, '2022-01-11']
>>> , [2, 'AB', 22, '2022-02-01']
>>> , [2, 'AA', 28, '2022-02-10']
>>> , [2, 'AC', 25, '2022-02-22']
>>> ]
>>> , 'code: int, doc_type: string, amount: int, load_date: string'
>>> )
>>> df = df.withColumn('load_date', F.to_date('load_date'))
>>>
>>> I want to pivot the amount but just want the first value from the date.
>>> This is what I tried and it is not giving me the desried results.
>>>
>>> (
>>> df.groupBy('code')
>>> .pivot('doc_type', ['AB', 'AA', 'AC'])
>>> .agg(F.sum('amount').alias('amnt'),
>>> F.first('load_date').alias('ldt'))
>>> .show()
>>> )
>>>
>>> +----+-------+----------+-------+----------+-------+----------+
>>> |code|AB_amnt| AB_ldt|AA_amnt| AA_ldt|AC_amnt| AC_ldt|
>>> +----+-------+----------+-------+----------+-------+----------+
>>> | 1| 12|2022-01-01| 22|2022-01-10| 11|2022-01-11|
>>> | 2| 22|2022-02-01| 28|2022-02-10| 25|2022-02-22|
>>> +----+-------+----------+-------+----------+-------+----------+
>>>
>>> This is what I want.
>>>
>>> (
>>> df.groupBy('code')
>>> .agg(
>>> F.sum(F.when(F.col('doc_type') == 'AB',
>>> F.col('amount'))).alias('AB_amnt')
>>> , F.sum(F.when(F.col('doc_type') == 'AA',
>>> F.col('amount'))).alias('AA_amnt')
>>> , F.sum(F.when(F.col('doc_type') == 'AC',
>>> F.col('amount'))).alias('AC_amnt')
>>> , F.first('load_date').alias('load_date')
>>> )
>>> .show()
>>> )
>>>
>>> +----+-------+-------+-------+----------+
>>> |code|AB_amnt|AA_amnt|AC_amnt| load_date|
>>> +----+-------+-------+-------+----------+
>>> | 1| 12| 22| 11|2022-01-01|
>>> | 2| 22| 28| 25|2022-02-01|
>>> +----+-------+-------+-------+----------+
>>>
>>> Is there any simpler way to do it? I have more than one column to put
>>> into pivot and also to put into non pivot.
>>>
>>> I am using Databricks 14.3 LTS with Spark 3.5.0
>>>
>>> Thanks & Regards
>>> Dhruv
>>>
>>
