python - Spark Equivalent of IF Then ELSE - Stack Overflow
python apache-spark pyspark apache-spark-sql edited Dec 10, 2017 at 1:43 Community Bot 1 1
PySpark: multiple conditions in when clause - Stack Overflow
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 …
pyspark - How to use AND or OR condition in when in Spark
107 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 …
Comparison operator in PySpark (not equal/ !=) - Stack Overflow
Aug 24, 2016 · Comparison operator in PySpark (not equal/ !=) Asked 9 years, 2 months ago Modified 1 year, 8 months ago Viewed 164k times
spark dataframe drop duplicates and keep first - Stack Overflow
Aug 1, 2016 · 2 I just did something perhaps similar to what you guys need, using drop_duplicates pyspark. Situation is this. I have 2 dataframes (coming from 2 files) which are exactly same …
How to change dataframe column names in PySpark?
I come from pandas background and am used to reading data from CSV files into a dataframe and then simply changing the column names to something useful using the simple command: …
pyspark: rolling average using timeseries data - Stack Overflow
Aug 22, 2017 · pyspark: rolling average using timeseries data Asked 8 years, 2 months ago Modified 6 years, 2 months ago Viewed 77k times
Pyspark: display a spark data frame in a table format
Pyspark: display a spark data frame in a table format Asked 9 years, 2 months ago Modified 2 years, 2 months ago Viewed 411k times
python - Convert pyspark string to date format - Stack Overflow
Jun 28, 2016 · Convert pyspark string to date format Asked 9 years, 4 months ago Modified 2 years, 2 months ago Viewed 521k times
Pyspark replace strings in Spark dataframe column
For Spark 1.5 or later, you can use the functions package: from pyspark.sql.functions import regexp_replace newDf = df.withColumn('address', regexp_replace('address', 'lane', 'ln')) Quick …
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