The only reason I chose this over the accepted answer is I am new to pyspark and was confused that the 'Number' column was not explicitly summed in the accepted answer. If I had to come …

pyspark.sql.functions.when takes a Boolean Column as its condition. When using PySpark, it's often useful to think "Column Expression" when you read "Column". Logical operations on …

Jun 28, 2018 · from pyspark.sql import functions as F df = df.select(F.col('a'), F.json_tuple(F.col('a'), 'k1', 'k2', 'k3') \ .alias('k1', 'k2', 'k3')) df.schema df.show(truncate=False) …

Sep 12, 2018 · if you want to control how the IDs should look like then we can use this code below. import pyspark.sql.functions as F from pyspark.sql import Window SRIDAbbrev = …

Apr 1, 2019 · import re from pyspark.sql.functions import col # remove spaces from column names newcols = [col(column).alias(re.sub('\s*', '', column) \ for column in df.columns] # …

Jun 8, 2016 · when in pyspark multiple conditions can be built using &(for and) and | (for or). Note:In pyspark t is important to enclose every expressions within parenthesis () that combine …

Oct 24, 2018 · I know this wasn't the question from OP, but if you wanted to get the field names of a StructType column to loop them over and "explode" the column into individual columns for …

My data looks like the following: +-----+-----+-----+-----+-----+---+ |purch_date| purch_class|tot_amt| serv-provider|purch_location| id| +-----+.... You shouldn't ...

spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true") For more details you can refer to my blog post Speeding up the conversion between PySpark and Pandas DataFrames Share

Nov 8, 2017 · import pyspark.sql.functions as F df.select("*", F.lower("my_col")) this returns a data frame with all the original columns, plus lowercasing the column which needs it. Share