With a dataframe as follows:
from pyspark.sql.functions import avg, first
rdd = sc.parallelize(
[
(0, "A", 223,"201603", "PORT"),
(0, "A", 22,"201602", "PORT"),
(0, "A", 422,"201601", "DOCK"),
(1,"B", 3213,"201602", "DOCK"),
(1,"B", 3213,"201601", "PORT"),
(2,"C", 2321,"201601", "DOCK")
]
)
df_data = sqlContext.createDataFrame(rdd, ["id","type", "cost", "date", "ship"])
df_data.show()
I do a pivot on it,
df_data.groupby(df_data.id, df_data.type).pivot("date").agg(avg("cost"), first("ship")).show()
+---+----+----------------+--------------------+----------------+--------------------+----------------+--------------------+
| id|type|201601_avg(cost)|201601_first(ship)()|201602_avg(cost)|201602_first(ship)()|201603_avg(cost)|201603_first(ship)()|
+---+----+----------------+--------------------+----------------+--------------------+----------------+--------------------+
| 2| C| 2321.0| DOCK| null| null| null| null|
| 0| A| 422.0| DOCK| 22.0| PORT| 223.0| PORT|
| 1| B| 3213.0| PORT| 3213.0| DOCK| null| null|
+---+----+----------------+--------------------+----------------+--------------------+----------------+--------------------+
But I get these really complicated names for the columns. Applying alias
on the aggregation usually works, but because of the pivot
in this case the names are even worse:
+---+----+--------------------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------+------------------------------------------------------------------+
| id|type|201601_(avg(cost),mode=Complete,isDistinct=false) AS cost#1619|201601_(first(ship)(),mode=Complete,isDistinct=false) AS ship#1620|201602_(avg(cost),mode=Complete,isDistinct=false) AS cost#1619|201602_(first(ship)(),mode=Complete,isDistinct=false) AS ship#1620|201603_(avg(cost),mode=Complete,isDistinct=false) AS cost#1619|201603_(first(ship)(),mode=Complete,isDistinct=false) AS ship#1620|
+---+----+--------------------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------+------------------------------------------------------------------+
| 2| C| 2321.0| DOCK| null| null| null| null|
| 0| A| 422.0| DOCK| 22.0| PORT| 223.0| PORT|
| 1| B| 3213.0| PORT| 3213.0| DOCK| null| null|
+---+----+--------------------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------+------------------------------------------------------------------+--------------------------------------------------------------+------------------------------------------------------------------+
Is there a way to rename the column names on the fly on the pivot and aggregation?
Spark has a withColumnRenamed() function on DataFrame to change a column name. This is the most straight forward approach; this function takes two parameters; the first is your existing column name and the second is the new column name you wish for. Returns a new DataFrame (Dataset[Row]) with a column renamed.
A simple regular expression should do the trick:
import re
def clean_names(df):
p = re.compile("^(\w+?)_([a-z]+)\((\w+)\)(?:\(\))?")
return df.toDF(*[p.sub(r"\1_\3", c) for c in df.columns])
pivoted = df_data.groupby(...).pivot(...).agg(...)
clean_names(pivoted).printSchema()
## root
## |-- id: long (nullable = true)
## |-- type: string (nullable = true)
## |-- 201601_cost: double (nullable = true)
## |-- 201601_ship: string (nullable = true)
## |-- 201602_cost: double (nullable = true)
## |-- 201602_ship: string (nullable = true)
## |-- 201603_cost: double (nullable = true)
## |-- 201603_ship: string (nullable = true)
If you want to preserve function name you change substitution pattern to for example \1_\2_\3
.
A simple approach will be using alias after the aggregate function. I start with the df_data spark dataFrame you created.
df_data.groupby(df_data.id, df_data.type).pivot("date").agg(avg("cost").alias("avg_cost"), first("ship").alias("first_ship")).show()
+---+----+---------------+-----------------+---------------+-----------------+---------------+-----------------+
| id|type|201601_avg_cost|201601_first_ship|201602_avg_cost|201602_first_ship|201603_avg_cost|201603_first_ship|
+---+----+---------------+-----------------+---------------+-----------------+---------------+-----------------+
| 1| B| 3213.0| PORT| 3213.0| DOCK| null| null|
| 2| C| 2321.0| DOCK| null| null| null| null|
| 0| A| 422.0| DOCK| 22.0| PORT| 223.0| PORT|
+---+----+---------------+-----------------+---------------+-----------------+---------------+-----------------+
column names will be the form of "original_column_name_aliased_column_name". For your case, original_column_name will be 201601, aliased_column_name will be avg_cost, and the column name is 201601_avg_cost(linked by underscore "_").
You can alias the aggregations directly:
pivoted = df_data \
.groupby(df_data.id, df_data.type) \
.pivot("date") \
.agg(
avg('cost').alias('cost'),
first("ship").alias('ship')
)
pivoted.printSchema()
##root
##|-- id: long (nullable = true)
##|-- type: string (nullable = true)
##|-- 201601_cost: double (nullable = true)
##|-- 201601_ship: string (nullable = true)
##|-- 201602_cost: double (nullable = true)
##|-- 201602_ship: string (nullable = true)
##|-- 201603_cost: double (nullable = true)
##|-- 201603_ship: string (nullable = true)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With