I’m trying to collect groups of rows into sliding windows represented as vectors. Given the example input: An expected output would be: My latest attempt produces tumbling windows without padding. Here’s my code: I tried looking for variations of this, maybe by performing a SQL query like in this case or with some built-in SQL function such as ROWS N
Get the most consecutive day from Date column with PySpark
Original dataframe: member_id AccessDate 111111 2020-02-03 111111 2022-03-05 222222 2015-03-04 333333 2021-11-23 333333 2021-11-24 333333 2021-11-25 333333 2022-10-11 333333 2022-10-12 333333 2022-10-13 333333 2022-07-07 444444 2019-01-21 444444 2019-04-21 444444 2019-04-22 444444 2019-04-23 444444 2019-04-24 444444 2019-05-05 444444 2019-05-06 444444 2019-05-07 Result dataframe: member_id Most_Consecutive_AccessDate total 111111 2022-03-05 1 222222 2015-03-04 1 333333 2022-10-11, 2022-10-12, 2022-10-13 3 444444 2019-04-21, 2019-04-22, 2019-04-23,
PySpark sum all the values of Map column into a new column
I have a dataframe which looks like this I want to sum of all the row wise decimal values and store into a new column My approach This is not working as it says, it can be applied only to int Answer Since, your values are of float type, the initial value passed within the aggregate should match the type
How to write this pandas logic for pyspark.sql.dataframe.DataFrame without using pandas on spark API?
I’m totally new to Pyspark, as Pyspark doesn’t have loc feature how can we write this logic. I tried by specifying conditions but couldn’t get the desirable result, any help would be greatly appreciated! Answer For a data like the following You’re actually updating total column in each statement, not in an if-then-else way. Your code can be replicated (as
Not able to perform operations on resulting dataframe after “join” operation in PySpark
Here I have created three dataframes: df,rule_df and query_df. I’ve performed inner join on rule_df and query_df, and stored the resulting dataframe in join_df. However, when I try to simply print the columns of the join_df dataframe, I get the following error- The resultant dataframe is not behaving as one, I’m not able to perform any dataframe operations on it.
Groupby column and create lists for other columns, preserving order
I have a PySpark dataframe which looks like this: I want to group by or partition by ID column and then the lists for col1 and col2 should be created based on the order of timestamp. My approach: But this is not returning list of col1 and col2. Answer I don’t think the order can be reliably preserved using groupBy
Counting consecutive occurrences of a specific value in PySpark
I have a column named info defined as well: I would like to count the consecutive occurrences of 1s and insert 0 otherwise. The final column would be: I tried using the following function, but it didn’t work. Answer From Adding a column counting cumulative pervious repeating values, credits to @blackbishop
Debugging PySpark udf (lambda function using datetime)
I came across- the below lambda code line in PySpark while browsing a long python Jupyter notebook, I am trying to understand this piece of line. Can you explain what it does in a best possible way? Answer udf in PySpark assigns a Python function which is run for every row of Spark df. Creates a user defined function (UDF).
how to avoid row number in read_sql output
When I use pandas read_sql to read from mysql, it returns rows with row number as first column as given below. Is this possible to avoid row numbers? Answer You can use False as the second parameter to exclude indexing. Example or Use this function to guide you You can read more about this here -> Pandas DataFrame: to_csv() function
Is there a more efficient way to write code for bin values in Databricks SQL?
I am using Databricks SQL, and want to understand if I can make my code lighter: Instead of writing each line, is there a cool way to state that all of these columns starting with “age_” need to be null in 1 or 2 lines of code? Answer If each bin is a column then you probably are going to