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2. python - Spark Equivalent of IF Then ELSE - Stack Overflow

   stackoverflow.com/questions/39048229

   There are different ways you can achieve if-then-else. Using when function in DataFrame API. You can specify the list of conditions in when and also can specify otherwise what value you need.

3. python - Concatenate two PySpark dataframes - Stack Overflow

   stackoverflow.com/questions/37332434

   To make it more generic of keeping both columns in df1 and df2:. import pyspark.sql.functions as F # Keep all columns in either df1 or df2 def outter_union(df1, df2): # Add missing columns to df1 left_df = df1 for column in set(df2.columns) - set(df1.columns): left_df = left_df.withColumn(column, F.lit(None)) # Add missing columns to df2 right_df = df2 for column in set(df1.columns) - set(df2 ...

4. Pyspark: Filter dataframe based on multiple conditions

   stackoverflow.com/questions/49301373

   79. I want to filter dataframe according to the following conditions firstly (d<5) and secondly (value of col2 not equal its counterpart in col4 if value in col1 equal its counterpart in col3). If the original dataframe DF is as follows: The desired Dataframe is: Code I have tried that did not work as expected:

5. Pyspark: Convert column to lowercase - Stack Overflow

   stackoverflow.com/questions/47179745

   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

6. pyspark - How to use AND or OR condition in when in Spark - Stack...

   stackoverflow.com/questions/40686934

   When using PySpark, it's often useful to think "Column Expression" when you read "Column". Logical operations on PySpark columns use the bitwise operators: & for and. | for or. ~ for not. When combining these with comparison operators such as <, parenthesis are often needed. In your case, the correct statement is:

7. Comparison operator in PySpark (not equal/ !=) - Stack Overflow

   stackoverflow.com/questions/39120934

   The selected correct answer does not address the question, and the other answers are all wrong for pyspark. There is no "!=" operator equivalent in pyspark for this solution. The correct answer is to use "==" and the "~" negation operator, like this:

8. Pyspark: show histogram of a data frame column - Stack Overflow

   stackoverflow.com/questions/39154325

   import pyspark.sql.functions as F import pyspark.sql as SQL win = SQL.Window.partitionBy('column_of_values') Then all you need it to use count aggregation partitioned by the window: df.select(F.count('column_of_values').over(win).alias('histogram'))

9. cannot resolve column due to data type mismatch PySpark

   stackoverflow.com/questions/60646254

   As you are accessing array of structs we need to give which element from array we need to access i.e 0,1,2..etc.

10. PySpark: How to Append Dataframes in For Loop - Stack Overflow

    stackoverflow.com/questions/56363561

    You should add, in your answer, the lines from functools import reduce from pyspark.sql import DataFrame So people don't have to look further up. – Laurent Commented Dec 2, 2021 at 13:09

11. Python Spark Cumulative Sum by Group Using DataFrame

    stackoverflow.com/questions/45946349

    This can be done using a combination of a window function and the Window.unboundedPreceding value in the window's range as follows: from pyspark.sql import Window. from pyspark.sql import functions as F. windowval = (Window.partitionBy('class').orderBy('time') .rangeBetween(Window.unboundedPreceding, 0))