You need a rolling window on date with window ranging from past 30 days to previous day. Since interval functions are not available for window, you can convert the dates into long values and use the days long value to create window range.
from pyspark.sql.functions
import *
days = lambda i: i * 86400
partition = Window.partitionBy("card_uid").orderBy(col("date").cast("timestamp").cast("long")).rangeBetween(days(-30), days(-1))
df_cum_sum = data.withColumn("duration_cum_sum", sum(col('amount_local')).over(partition))\
.fillna(0, subset = ['duration_cum_sum'])
df_cum_sum.show()In order to calculate cumulative sum of column in pyspark we will be using sum function and partitionBy. To calculate cumulative sum of a group in pyspark we will be using sum function and also we mention the group on which we want to partitionBy lets get clarity with an example.,Calculate cumulative sum of the column by group in pyspark using sum() function and partitionby() function,Sum() function and partitionBy() is used to calculate the cumulative sum of column in pyspark.,Calculate cumulative sum of column in pyspark using sum() function
Sum() function and partitionBy() is used to calculate the cumulative sum of column in pyspark.
import sys
from pyspark.sql.window
import Window
import pyspark.sql.functions as f
cum_sum = df_basket1.withColumn('cumsum', f.sum(df_basket1.Price).over(Window.partitionBy().orderBy().rowsBetween(-sys.maxsize, 0)))
cum_sum.show()Sum() function and partitionBy a column name is used to calculate the cumulative sum of the “Price” column by group (“Item_group”) in pyspark
import sys
from pyspark.sql.window
import Window
import pyspark.sql.functions as f
cum_sum = df_basket1.withColumn('cumsum', f.sum(df_basket1.Price).over(Window.partitionBy('Item_group').orderBy().rowsBetween(-sys.maxsize, 0)))
cum_sum.show()At First we will be replacing the missing and NaN values with 0, using fill.na(0) ; then will use Sum() function and partitionBy a column name is used to calculate the cumulative sum of the “Price” column
# # # # cumulative sum with NA and missing
import sys
from pyspark.sql.window
import Window
import pyspark.sql.functions as f
df_basket2 = df_basket2.fillna(0)
cum_sum = df_basket2.withColumn('cumsum', f.sum(df_basket2.Price).over(Window.partitionBy().orderBy().rowsBetween(-sys.maxsize, 0)))
cum_sum.show()1 week ago Using the sum () method, we can get the total value from the column. To use this method, we have to import it from pyspark.sql.functions module, and finally, we can use the collect () … , If we want to return the total value from multiple columns, we must use the sum () method inside the select () method by specifying the column name separated by a comma. This example will get the total value from the height column in the PySpark dataframe. The total value (sum) from the height column is returned in the above example. , rowsBetween (-sys.maxsize, 0) along with sum function is used to create cumulative sum of the column and it is named as cumsum Sum () function and partitionBy a column name is used to calculate the cumulative sum of the “Price” column by group (“Item_group”) in pyspark , 2 days ago I need to have a cumulative sum of column b, that resets the count to zero if i encounter a reset condition. The problem is I do not know when the reset (s) will occur ( It is not a fixed …
+ -- - + -- -- + -- - + | A | B | C | + -- - + -- -- + -- - + | 0 | null | 1 | | 1 | 3.0 | 0 | | 2 | 7.0 | 0 | | 3 | null | 1 | | 4 | 4.0 | 0 | | 5 | 3.0 | 0 | | 6 | null | 1 | | 7 | null | 1 | | 8 | null | 1 | | 9 | 5.0 | 0 | | 10 | 2.0 | 0 | | 11 | null | 1 | + -- - + -- -- + -- - +import pyspark.sql.functions as f from pyspark.sql
import Window x.withColumn("grp", f.sum((f.col("C") == 0).cast("int")).over(Window.orderBy("A"))).withColumn("D", f.sum(f.col("C")).over(Window.partitionBy("grp").orderBy("A"))).drop("grp").show() # + -- - + -- -- + -- - + -- - +# | A | B | C | D | # + -- - + -- -- + -- - + -- - +# | 0 | null | 1 | 1 | # | 1 | 3.0 | 0 | 0 | # | 2 | 7.0 | 0 | 0 | # | 3 | null | 1 | 1 | # | 4 | 4.0 | 0 | 0 | # | 5 | 3.0 | 0 | 0 | # | 6 | null | 1 | 1 | # | 7 | null | 1 | 2 | # | 8 | null | 1 | 3 | # | 9 | 5.0 | 0 | 0 | # | 10 | 2.0 | 0 | 0 | # | 11 | null | 1 | 1 | # + -- - + -- -- + -- - + -- - ++ -- -- -- + -- -- -- -- -- -- -- -- -- -- + | Flag | value | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + | 1 | 5 | | 1 | 4 | | 1 | 3 | | 1 | 5 | | 1 | 6 | | 1 | 4 | | 1 | 7 | | 1 | 5 | | 1 | 2 | | 1 | 3 | | 1 | 2 | | 1 | 6 | | 1 | 9 | + -- -- -- + -- -- -- -- -- -- -- -- -- -- +
+ -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + | Flag | value | cumsum | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + | 1 | 5 | 5 | | 1 | 4 | 9 | | 1 | 3 | 12 | | 1 | 5 | 17 | | 1 | 6 | 23 | | 1 | 4 | 27 | | 1 | 7 | 34 | | 1 | 5 | 39 | | 1 | 2 | 41 | | 1 | 3 | 44 | | 1 | 2 | 46 | | 1 | 6 | 52 | | 1 | 9 | 61 | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- +
+ -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + -- -- -- -- - + | Flag | value | cumsum | expected | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + -- -- -- -- - + | 1 | 5 | 5 | 5 | | 1 | 4 | 9 | 9 | | 1 | 3 | 12 | 12 | | 1 | 5 | 17 | 17 | | 1 | 6 | 23 | 23 | | 1 | 4 | 27 | 4 | < -- -- - reset | 1 | 7 | 34 | 11 | | 1 | 5 | 39 | 16 | | 1 | 2 | 41 | 18 | | 1 | 3 | 44 | 21 | | 1 | 2 | 46 | 2 | < -- -- - reset | 1 | 6 | 52 | 8 | | 1 | 9 | 61 | 17 | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + -- -- -- -- - +
win_counter = Window.partitionBy("flag") df_partitioned = df_partitioned.withColumn('cumsum',F.sum(F.col('value')).over(win_counter)) from pyspark.sql.window
import Window
import pyspark.sql.functions as f df = spark.createDataFrame([(1, 5), (1, 4), (1, 3), (1, 5), (1, 6), (1, 4), (1, 7), (1, 5), (1, 2), (1, 3), (1, 2), (1, 6), (1, 9)], schema = 'Flag int, value int') w = (Window.partitionBy('flag').orderBy(f.monotonically_increasing_id()).rowsBetween(Window.unboundedPreceding, Window.currentRow)) df = df.withColumn('values', f.collect_list('value').over(w)) expr = "AGGREGATE(values, 0, (acc, el) ->IF(acc <20, acc + el, el))"
df = df.select('Flag', 'value', f.expr(expr).alias('cumsum')) df.show(truncate = False)df = spark.createDataFrame([(1, 5), (1, 4), (1, 3), (1, 5), (1, 6), (1, 4), (1, 7), (1, 5), (1, 2), (1, 3), (1, 2), (1, 6), (1, 9)], schema = 'Flag int, value int') def cumsum_by_flag(rows): cumsum, reset = 0, False
for row in rows: if reset: cumsum = row.valuereset = Falseelse: cumsum += row.valuereset = cumsum > 20 yield row.value, cumsum def unpack(value): flag = value[0] value, cumsum = value[1]
return flag, value, cumsum rdd = df.rdd.keyBy(lambda row: row.Flag) rdd = (rdd.groupByKey().flatMapValues(cumsum_by_flag).map(unpack)) df = rdd.toDF('Flag int, value int, cumsum int') df.show(truncate = False)cume_dist() window function is used to get the cumulative distribution of values within a window partition.,ntile() window function returns the relative rank of result rows within a window partition. In below example we have used 2 as an argument to ntile hence it returns ranking between 2 values (1 and 2),In this tutorial, you have learned what are PySpark SQL Window functions their syntax and how to use them with aggregate function along with several examples in Scala.,row_number() window function is used to give the sequential row number starting from 1 to the result of each window partition.
Before we start with an example, first let’s create a PySpark DataFrame to work with.
spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
simpleData = (("James", "Sales", 3000), \
("Michael", "Sales", 4600), \
("Robert", "Sales", 4100), \
("Maria", "Finance", 3000), \
("James", "Sales", 3000), \
("Scott", "Finance", 3300), \
("Jen", "Finance", 3900), \
("Jeff", "Marketing", 3000), \
("Kumar", "Marketing", 2000), \
("Saif", "Sales", 4100)\
)
columns = ["employee_name", "department", "salary"]
df = spark.createDataFrame(data = simpleData, schema = columns)
df.printSchema()
df.show(truncate = False)Yields below output
root
|
--employee_name: string(nullable = true) |
--department: string(nullable = true) |
--salary: long(nullable = true)
+
-- -- -- -- -- -- - + -- -- -- -- -- + -- -- -- +
|
employee_name | department | salary |
+ -- -- -- -- -- -- - + -- -- -- -- -- + -- -- -- +
|
James | Sales | 3000 |
|
Michael | Sales | 4600 |
|
Robert | Sales | 4100 |
|
Maria | Finance | 3000 |
|
James | Sales | 3000 |
|
Scott | Finance | 3300 |
|
Jen | Finance | 3900 |
|
Jeff | Marketing | 3000 |
|
Kumar | Marketing | 2000 |
|
Saif | Sales | 4100 |
+ -- -- -- -- -- -- - + -- -- -- -- -- + -- -- -- +row_number() window function is used to give the sequential row number starting from 1 to the result of each window partition.
from pyspark.sql.window
import Window
from pyspark.sql.functions
import row_number
windowSpec = Window.partitionBy("department").orderBy("salary")
df.withColumn("row_number", row_number().over(windowSpec))\
.show(truncate = False)rank() window function is used to provide a rank to the result within a window partition. This function leaves gaps in rank when there are ties.
""
"rank"
""
from pyspark.sql.functions
import rank
df.withColumn("rank", rank().over(windowSpec))\
.show()Yields below output.
+ -- -- -- -- -- -- - + -- -- -- -- -- + -- -- -- + -- -- + | employee_name | department | salary | rank | + -- -- -- -- -- -- - + -- -- -- -- -- + -- -- -- + -- -- + | James | Sales | 3000 | 1 | | James | Sales | 3000 | 1 | | Robert | Sales | 4100 | 3 | | Saif | Sales | 4100 | 3 | | Michael | Sales | 4600 | 5 | | Maria | Finance | 3000 | 1 | | Scott | Finance | 3300 | 2 | | Jen | Finance | 3900 | 3 | | Kumar | Marketing | 2000 | 1 | | Jeff | Marketing | 3000 | 2 | + -- -- -- -- -- -- - + -- -- -- -- -- + -- -- -- + -- -- +
We have dataframe like below :,This is how we are calculating the anycodings_pyspark cumulative sum.,Now what we want is for cumsum to reset when anycodings_pyspark specific condition is set for ex. when it anycodings_pyspark crosses 20.,Extracting data from xml with similar tag name using beautiful soup
We have dataframe like below :
+ -- -- -- + -- -- -- -- -- -- -- -- -- -- + | Flag | value | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + | 1 | 5 | | 1 | 4 | | 1 | 3 | | 1 | 5 | | 1 | 6 | | 1 | 4 | | 1 | 7 | | 1 | 5 | | 1 | 2 | | 1 | 3 | | 1 | 2 | | 1 | 6 | | 1 | 9 | + -- -- -- + -- -- -- -- -- -- -- -- -- -- +
After normal cumsum we get this.
+ -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + | Flag | value | cumsum | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + | 1 | 5 | 5 | | 1 | 4 | 9 | | 1 | 3 | 12 | | 1 | 5 | 17 | | 1 | 6 | 23 | | 1 | 4 | 27 | | 1 | 7 | 34 | | 1 | 5 | 39 | | 1 | 2 | 41 | | 1 | 3 | 44 | | 1 | 2 | 46 | | 1 | 6 | 52 | | 1 | 9 | 61 | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- +
Below is expected output:
+ -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + -- -- -- -- - + | Flag | value | cumsum | expected | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + -- -- -- -- - + | 1 | 5 | 5 | 5 | | 1 | 4 | 9 | 9 | | 1 | 3 | 12 | 12 | | 1 | 5 | 17 | 17 | | 1 | 6 | 23 | 23 | | 1 | 4 | 27 | 4 | < -- -- - reset | 1 | 7 | 34 | 11 | | 1 | 5 | 39 | 16 | | 1 | 2 | 41 | 18 | | 1 | 3 | 44 | 21 | | 1 | 2 | 46 | 2 | < -- -- - reset | 1 | 6 | 52 | 8 | | 1 | 9 | 61 | 17 | + -- -- -- + -- -- -- -- -- -- -- -- -- -- + -- -- -- -- -- + -- -- -- -- - +
Dataframe
from pyspark.sql.window
import Window
import pyspark.sql.functions as f
df = spark.createDataFrame([
(1, 5), (1, 4), (1, 3), (1, 5), (1, 6), (1, 4),
(1, 7), (1, 5), (1, 2), (1, 3), (1, 2), (1, 6), (1, 9)
], schema = 'Flag int, value int')
w = (Window
.partitionBy('flag')
.orderBy(f.monotonically_increasing_id())
.rowsBetween(Window.unboundedPreceding, Window.currentRow))
df = df.withColumn('values', f.collect_list('value').over(w))
expr = "AGGREGATE(values, 0, (acc, el) -> IF(acc < 20, acc + el, el))"
df = df.select('Flag', 'value', f.expr(expr).alias('cumsum'))
df.show(truncate = False)RDD
df = spark.createDataFrame([
(1, 5), (1, 4), (1, 3), (1, 5), (1, 6), (1, 4),
(1, 7), (1, 5), (1, 2), (1, 3), (1, 2), (1, 6), (1, 9)
], schema = 'Flag int, value int')
def cumsum_by_flag(rows):
cumsum, reset = 0, False
for row in rows:
if reset:
cumsum = row.value
reset = False
else:
cumsum += row.value
reset = cumsum > 20
yield row.value, cumsum
def unpack(value):
flag = value[0]
value, cumsum = value[1]
return flag, value, cumsum
rdd = df.rdd.keyBy(lambda row: row.Flag)
rdd = (rdd
.groupByKey()
.flatMapValues(cumsum_by_flag)
.map(unpack))
df = rdd.toDF('Flag int, value int, cumsum int')
df.show(truncate = False)Output:
+ -- -- + -- -- - + -- -- -- + | Flag | value | cumsum | + -- -- + -- -- - + -- -- -- + | 1 | 5 | 5 | | 1 | 4 | 9 | | 1 | 3 | 12 | | 1 | 5 | 17 | | 1 | 6 | 23 | | 1 | 4 | 4 | | 1 | 7 | 11 | | 1 | 5 | 16 | | 1 | 2 | 18 | | 1 | 3 | 21 | | 1 | 2 | 2 | | 1 | 6 | 8 | | 1 | 9 | 17 | + -- -- + -- -- - + -- -- -- +
It's probably best to do with pandas_udf anycodings_apache-spark-sql here.
from pyspark.sql.functions
import pandas_udf, PandasUDFType
pdf = pd.DataFrame({
'flag': [1] * 13,
'id': range(13),
'value': [5, 4, 3, 5, 6, 4, 7, 5, 2, 3, 2, 6, 9]
})
df = spark.createDataFrame(pdf)
df = df.withColumn('cumsum', F.lit(math.inf))
@pandas_udf(df.schema, PandasUDFType.GROUPED_MAP)
def _calc_cumsum(pdf):
pdf.sort_values(by = ['id'], inplace = True, ascending = True)
cumsums = []
prev = None
reset = False
for v in pdf['value'].values:
if prev is None:
cumsums.append(v)
prev = v
else:
prev = prev + v
if not reset
else v
cumsums.append(prev)
reset = True
if prev >= 20
else False
pdf['cumsum'] = cumsums
return pdf
df = df.groupby('flag').apply(_calc_cumsum)
df.show()the results:
+ -- -- + -- - + -- -- - + -- -- -- + | flag | id | value | cumsum | + -- -- + -- - + -- -- - + -- -- -- + | 1 | 0 | 5 | 5.0 | | 1 | 1 | 4 | 9.0 | | 1 | 2 | 3 | 12.0 | | 1 | 3 | 5 | 17.0 | | 1 | 4 | 6 | 23.0 | | 1 | 5 | 4 | 4.0 | | 1 | 6 | 7 | 11.0 | | 1 | 7 | 5 | 16.0 | | 1 | 8 | 2 | 18.0 | | 1 | 9 | 3 | 21.0 | | 1 | 10 | 2 | 2.0 | | 1 | 11 | 6 | 8.0 | | 1 | 12 | 9 | 17.0 | + -- -- + -- - + -- -- - + -- -- -- +
cumulative sum function in pyspark grouping on multiple columns based on condition,How to create new columns in pandas depending on multiple conditions and by grouping to calculate previous costs and cumulative cost?,Perhaps you should calculate df = df[df.delta>=40] before running the groupby- I'm not sure if that matters.,How to calculate group by cumulative sum for multiple columns in python
First let's create the dataframe. Note that you can also directly load it as a dataframe from a csv:
df = spark.createDataFrame(
sc.parallelize(
[
[1, 20, 30, 40, 1, 1],
[1, 20, 30, 40, 2, 1],
[1, 20, 30, 40, 3, 1],
[1, 20, 30, 40, 4, 1],
[1, 20, 30, 40, 45, 2],
[1, 20, 30, 40, 1, 2],
[1, 30, 30, 40, 2, 1],
[1, 30, 30, 40, 3, 1],
[1, 30, 30, 40, 4, 1],
[1, 30, 30, 40, 5, 1]
]
),
["v_id", "d_id", "ip", "l_id", "delta", "event_id"]
)You have an implicit ordering in your table, we need to create a monotonically increasing id so that we don't end up shuffling it around:
import pyspark.sql.functions as psf
df = df.withColumn(
"rn",
psf.monotonically_increasing_id()
) +
-- -- + -- -- + -- - + -- -- + -- -- - + -- -- -- -- + -- -- -- -- -- +
|
v_id | d_id | ip | l_id | delta | event_id | rn |
+ -- -- + -- -- + -- - + -- -- + -- -- - + -- -- -- -- + -- -- -- -- -- +
|
1 | 20 | 30 | 40 | 1 | 1 | 0 |
|
1 | 20 | 30 | 40 | 2 | 1 | 1 |
|
1 | 20 | 30 | 40 | 3 | 1 | 2 |
|
1 | 20 | 30 | 40 | 4 | 1 | 3 |
|
1 | 20 | 30 | 40 | 45 | 2 | 4 |
|
1 | 20 | 30 | 40 | 1 | 2 | 8589934592 |
|
1 | 30 | 30 | 40 | 2 | 1 | 8589934593 |
|
1 | 30 | 30 | 40 | 3 | 1 | 8589934594 |
|
1 | 30 | 30 | 40 | 4 | 1 | 8589934595 |
|
1 | 30 | 30 | 40 | 5 | 1 | 8589934596 |
+ -- -- + -- -- + -- - + -- -- + -- -- - + -- -- -- -- + -- -- -- -- -- +Now to compute event_id and last_event_flag:
from pyspark.sql
import Window
w1 = Window.partitionBy("v_id", "d_id", "l_id", "ip").orderBy("rn")
w2 = Window.partitionBy("v_id", "d_id", "l_id", "ip").orderBy(psf.desc("rn"))
df.withColumn(
"event_id",
psf.sum((df.delta >= 40).cast("int")).over(w1) + 1
).withColumn(
"last_event_flag",
psf.row_number().over(w2) == 1
).drop("rn")
+
-- -- + -- -- + -- - + -- -- + -- -- - + -- -- -- -- + -- -- -- -- -- -- -- - +
|
v_id | d_id | ip | l_id | delta | event_id | last_event_flag |
+ -- -- + -- -- + -- - + -- -- + -- -- - + -- -- -- -- + -- -- -- -- -- -- -- - +
|
1 | 20 | 30 | 40 | 1 | 1 | false |
|
1 | 20 | 30 | 40 | 2 | 1 | false |
|
1 | 20 | 30 | 40 | 3 | 1 | false |
|
1 | 20 | 30 | 40 | 4 | 1 | false |
|
1 | 20 | 30 | 40 | 45 | 2 | false |
|
1 | 20 | 30 | 40 | 1 | 2 | true |
|
1 | 30 | 30 | 40 | 2 | 1 | false |
|
1 | 30 | 30 | 40 | 3 | 1 | false |
|
1 | 30 | 30 | 40 | 4 | 1 | false |
|
1 | 30 | 30 | 40 | 5 | 1 | true |
+ -- -- + -- -- + -- - + -- -- + -- -- - + -- -- -- -- + -- -- -- -- -- -- -- - +Create a temporary column (grp) that increments a counter each time column C is equal to 0 (the reset condition) and use this as a partitioning column for your cumulative sum.,Wow: (f.col("C") == 0).cast("int") - making a boolean and then casting it to 1, so that it can be summed to a partition. Is this actually necessary for some sort of performance, or is it just "clever"? - Stephen ,@stephen I don't remember to be honest but I think it was necessary because sum needs numeric types and won't make the implicit conversion. Maybe the latest version of spark handles it differently. If you try it out, feel free to make a clarifying update to this answer. - pault , Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque rem aperiam.
I have this dataframe
+ -- - + -- -- + -- - +
|
A | B | C |
+ -- - + -- -- + -- - +
|
0 | null | 1 |
|
1 | 3.0 | 0 |
|
2 | 7.0 | 0 |
|
3 | null | 1 |
|
4 | 4.0 | 0 |
|
5 | 3.0 | 0 |
|
6 | null | 1 |
|
7 | null | 1 |
|
8 | null | 1 |
|
9 | 5.0 | 0 |
|
10 | 2.0 | 0 |
|
11 | null | 1 |
+ -- - + -- -- + -- - +Expected output:
+ -- - + -- -- + -- - + -- -- +
|
A | B | C | D |
+ -- - + -- -- + -- - + -- -- +
|
0 | null | 1 | 1 |
|
1 | 3.0 | 0 | 0 |
|
2 | 7.0 | 0 | 0 |
|
3 | null | 1 | 1 |
|
4 | 4.0 | 0 | 0 |
|
5 | 3.0 | 0 | 0 |
|
6 | null | 1 | 1 |
|
7 | null | 1 | 2 |
|
8 | null | 1 | 3 |
|
9 | 5.0 | 0 | 0 |
|
10 | 2.0 | 0 | 0 |
|
11 | null | 1 | 1 |
+ -- - + -- -- + -- - + -- -- +To reproduce dataframe:
from pyspark.shell
import sc
from pyspark.sql
import Window
from pyspark.sql.functions
import lag, when, sum
x = sc.parallelize([
[0, None],
[1, 3.],
[2, 7.],
[3, None],
[4, 4.],
[5, 3.],
[6, None],
[7, None],
[8, None],
[9, 5.],
[10, 2.],
[11, None]
])
x = x.toDF(['A', 'B'])
# Transform null values into "1"
x = x.withColumn('C', when(x.B.isNull(), 1).otherwise(0))