from pyspark.sql.functions import udf from pyspark.sql.types import StringType def array_to_string(my_list): return '[' + ','.join([str(elem) for elem in my_list]) + ']' array_to_string_udf = udf(array_to_string, StringType()) df = df.withColumn('column_as_str', array_to_...
pyspark.sql.windowmodule provides a set of functions like row_number(), rank(), and dense_rank() to add a column with row number. Therow_number()assigns unique sequential numbers to rows within specified partitions and orderings,rank()provides a ranking with tied values receiving the same r...
while the dept DataFrame contains the “dept_id” column with unique values. Additionally, the “emp_dept_id” from “emp” refers to the “dept_id” in the “dept” dataset.
A quick reference guide to the most commonly used patterns and functions in PySpark SQL. Table of Contents Quickstart Basics Common Patterns Importing Functions & Types Filtering Joins Column Operations Casting & Coalescing Null Values & Duplicates String Operations String Filters String Functions Numbe...
,<值n+3>,…,<值2n>)ONDUPLICATEKEYUPDATE<字段名1>=VALUES(<字段名1 >),<字段名2>=VALUES(<字段名2>),<字段名3>=VALUES(<字段名3>),…,<字段名n>=VAL UES(<字段名n>);或insertinto?[`<架构名称>`.]`<表名>`(<主键字段名>,<字段名1>,<字段名2 ...
('N/A')))# Drop duplicate rows in a dataset (distinct)df=df.dropDuplicates()# ordf=df.distinct()# Drop duplicate rows, but consider only specific columnsdf=df.dropDuplicates(['name','height'])# Replace empty strings with null (leave out subset keyword arg to replace in all columns)...