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Category:Windows-only freewareQ: Apache Spark: How to "chunk" data into arrays/ranges I am currently trying to "chunk" the following data into rows of ranges with different labels (like is does the range() function in Pandas/Jupyter): import numpy as np import matplotlib.pyplot as plt import pyspark.sql.functions as psf data = np.array([[8.0, 12.0, 24.0, 28.0, 35.0], [10.0, 11.0, 13.0, 19.0, 26.0], [18.0, 19.0, 20.0, 30.0, 37.0], [20.0, 22.0, 24.0, 32.0, 39.0]]) df = psf.DataFrame(data) I tried using explode() (or groupByKey()) and the code below but without any luck: df = df.select('value') chunks = df.groupByKey().parallelize(2).map(lambda x: [x]) chunks.collect() I did try to use the explode() function and it did run but it did not group the data at all (only produced more rows with a single value for the key): df = df.select('value') exploded = df.groupByKey().parallelize(2).explode() exploded.collect() Am I doing something wrong? Is there a better way to do this? A: Try with import pyspark.sql.functions as psf from pyspark.sql.types import * df.select(psf.col('value')).groupBy('value').pivot("range").agg(max('value')) which gives +-----+-----+--------+--------+ |value|range| max| min| +-----+-----+--------+--------+ | 8| 3| 24| 8| | 10| 1|

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