My code works but looking for higher efficent method instead of udf as my dataframe is huge and this udf may make it less efficient.
from pyspark.sql import functions as F
df = spark.createDataFrame(
[(1, 'a', 'A'),
(2, 'a', 'B'),
(3, 'a', 'C'),
(4, 'b', 'A'),
(5, 'b', 'B'),
(6, 'b', 'C'),
(7, 'c', 'A'),
(8, 'c', 'B'),
(9, 'c', 'C')],
['id', 'c1', 'c2']
)
from itertools import chain
from collections import defaultdict
custom_dict = {'c': 0,'a':1}
# Define the custom order as a defaultdict
my_Order = defaultdict(lambda: float('inf'))
my_Order.update(custom_dict)
# Create a UDF to get the custom order value for each value in the column
get_order = F.udf(lambda x: my_Order[x])
# Add a new column with the custom order value
df = df.withColumn("order_value", get_order(F.col("c1")))
# Order the DataFrame based on the custom order value
df = df.orderBy("order_value")
df.show()
I also tried using below instead of udf but threw error as getItem cant be used with defaultdict
df = df.withColumn("order_value", F.when(F.col("c1").isin(list(my_order.keys())), my_Order.getItem(F.col("c1")).otherwise(float('inf'))))
Transform the custom_dict into a case statement with an else part for the default value.
stmt = "case c1 "
for k,v in custom_dict.items():
stmt = stmt + f" when '{k}' then {v}"
stmt = stmt + " else cast('Infinity' as double) end"
Result:
case c1 when 'c' then 0 when 'a' then 1 else cast('Infinity' as double) end
Then add a column using this statement and order the dataframe
df.withColumn("order_value", F.expr(stmt))\
.orderBy("order_value") \
.show()
Output:
+---+---+---+-----------+
| id| c1| c2|order_value|
+---+---+---+-----------+
| 7| c| A| 0.0|
| 9| c| C| 0.0|
| 8| c| B| 0.0|
| 1| a| A| 1.0|
| 3| a| C| 1.0|
| 2| a| B| 1.0|
| 4| b| A| Infinity|
| 5| b| B| Infinity|
| 6| b| C| Infinity|
+---+---+---+-----------+