Group by value within range in Azure Databricks

Question

Consider following data:

EventDate,Value
1.1.2019,11
1.2.2019,5
1.3.2019,6
1.4.2019,-15
1.5.2019,-20
1.6.2019,-30
1.7.2019,12
1.8.2019,20

I want to create groups of when these values are within thresholds:

 1. > 10
 2. <=10 >=-10
 3. >-10

The result should be with start and end of values in a certain state:

1.1.2019, 1.1.2019, [11]
1.2.2019, 1.3.2019, [5, 6]
1.4.2019, 1.6.2019, [-15, -20, -30]
1.7.2019, 1.8.2018, [12, 20]

I believe the answer is within the window function, but I am fairly new to databricks and I can't understand how to use it (yet).

Here is a working (python) solution based on looping through the dataframe as a list, however I would prefer a solution that works directly on the dataframe for performance.

from pyspark.sql.functions import *
import pandas as pd
STATETHRESHOLDCHARGE = 10
list = [{"eventDateTime":x["EventDate"], "value":x["Value"]} for x in dataframe.sort(dfArrayOneCast.EventDate).rdd.collect()]
cycles = []
previous = None
for row in list:
  currentState = 'charge'
  if row["value"] < STATETHRESHOLDCHARGE and row["value"] > (STATETHRESHOLDCHARGE * -1):
    currentState = 'idle'
  if row["value"] <= (STATETHRESHOLDCHARGE * -1):
    currentState = 'discharge'

  eventDateTime = row["eventDateTime"]
  if previous is None or previous["state"] != currentState:
    previous = {"start":row["eventDateTime"], "end":row["eventDateTime"], "values":[row["value"]], "timestamps":[row["eventDateTime"]], "state":currentState}
    cycles.append(previous)
  else:
    previous["end"] = row["eventDateTime"]
    previous["values"].append(row["value"])
    previous["timestamps"].append(row["eventDateTime"])

display(cycles)

Answer 1

Assuming you have above data in df data frame, let's take this piece by piece

from pyspark.sql.functions import col, last, lag, udf, when, collect_list
from pyspark.sql.types import StringType
value = 'value'
date = 'EventDate'
valueBag = 'valueBag'

def bagTransform(v):
  if v > 10:
    return 'charging'
  elif v < -10:
    return 'discharging'
  else:
    return 'idle'

bagTransformUDF = udf(bagTransform, StringType())  

withBaggedValue = df.withColumn(valueBag, bagTransformUDF(col(value)))

So first we bagged values into ranges as you declared, now we can use lag to move a window over previous value:

from pyspark.sql import Window
windowSpec = Window.orderBy(date)
prevValueBag = 'prevValueBag'
bagBeginning = 'bagBeginning'

withLag = (withBaggedValue
  .withColumn(prevValueBag, lag(withBaggedValue[valueBag]).over(windowSpec)))

Now the fun part starts: we detect change points and temporarily assign there current event date or null:

withInitialBeginnings = withLag.withColumn(bagBeginning, when((col(prevValueBag) != col(valueBag)) | col(prevValueBag).isNull(), col(date)).otherwise(None))

and fill them in using last found value

withFilledBeginnings = (withInitialBeginnings.withColumn(bagBeginning, 
                 last(col(bagBeginning), ignorenulls=True)
                 .over(windowSpec)))
display(withFilledBeginnings)

with that set we can simply aggregate over starting point

aggregate = withFilledBeginnings.groupby(col(bagBeginning)).agg(collect_list(value))

display(aggregate)

If you also need the end date you can do similar preprocessing using pyspark.sql.functions.lead which works symmetrically to last but in forward direction.

Answer 2

I created a csv file for testing my sample code with Pandas in Python, the content of the test.csv file as below.

A,B
1.1.2019,11
1.2.2019,5
1.3.2019,6
1.4.2019,-15
1.5.2019,-20
1.6.2019,-30
1.7.2019,12
1.8.2019,20

Due to there is a limits that can not construct the pandas.IntervalIndex with some pandas.Intervals with different closed values like right, left, both, neither, so I converted your defined groups with thresholds to the equivalent groups with right closed, as below.

    Your defined groups        |   The equivalent groups
 1. > 10       : (10,inf]      | >10       :  (10, inf]
 2. <=10 >=-10 : [-10,10]      | <=10 >-11 :  (-11,10]
 3. <-10       : (-inf,-10)    | <=-11     :  (-inf, -11]

Here is my sample code.

import pandas as pd
import numpy as np

df = pd.read_csv('test.csv')
# print(df)
bins = np.array([-np.inf, -11, 10, np.inf])
"""
# Can not construct IntervalIndex with Intervals with different closed
>>> left = pd.Interval(left=-np.Inf, right=-10, closed='neither')
>>> center = pd.Interval(left=-10, right=10, closed='both')
>>> right = pd.Interval(left=10, right=np.Inf, closed='neither')
>>> pd.IntervalIndex([left, center, right])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "\lib\site-packages\pandas\core\indexes\interval.py", line 151, in __new__
    verify_integrity=verify_integrity)
  File "\lib\site-packages\pandas\core\arrays\interval.py", line 157, in __new__
    data, validate_closed=closed is None)
  File "pandas\_libs\interval.pyx", line 483, in pandas._libs.interval.intervals_to_interval_bounds
ValueError: intervals must all be closed on the same side
"""
# print(bins)
labels = ['left', 'center', 'right']
by = pd.cut(df['B'], bins, labels=labels)
# print(by)
groupby = df.groupby(by)
result = pd.DataFrame([(groupby['A'].min()[label], groupby['A'].max()[label], df['B'].take(groupby.indices.get(label)).get_values()) for label in labels], index=['<-10','>=-10 <=10','>10'], columns=['min_A', 'max_A', 'Bs'])
print(result)

The result is as below.

               min_A     max_A               Bs
<-10        1.4.2019  1.6.2019  [-15, -20, -30]
>=-10 <=10  1.2.2019  1.3.2019           [5, 6]
>10         1.1.2019  1.8.2019     [11, 12, 20]