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
Tag: pyspark
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
Unable to load S3-hosted CSV into Spark Dataframe on Jupyter Notebook
Unable to load S3-hosted CSV into Spark Dataframe on Jupyter Notebook. I believe I uploaded the 2 required packages with the os.environ line below. If I did it incorrectly please show me how to correctly install it. The Jupyter Notebook is hosted on an EC2 instance, which is why I’m trying to pull the CSV from a S3 bucket. Here
PySpark – Combine a list of filtering conditions
For starters, let me define a sample dataframe and import the sql functions: This returns the following dataframe: Now lets say I have a list of filtering conditions, for example, a list of filtering conditions detailing that columns A and B shall be equal to 1 I can combine these two conditions as follows and then filter the dataframe, obtaining
I want to groupby id and count the unique grade and return max
I have this data and try to solve the following question. DataFrame_from_Scratch = spark.createDataFrame(values, columns) DataFrame_from_Scratch.show() groupby id and count unique grade what is the maximum groupby id and date and how many unique date is there Answer Your implementation for the 1st question is correct. Not sure what exactly your question is seeking as an answer But nevertheless, below
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).
Dataframe – Find sum of all values from dictionary column (row-wise) and then create new column for that Sum
My pyspark Dataframe which has two columns, ID and count, count column is a dict/Map<str,int>. I want to create another column which is the total of all values of count I have I want something like, Sum of all the values of count column My approach But I am getting grouped by individual Key and then aggregating which is incorrect.
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
using map/reduce on lists of lists
I have a very large list of lists, and I want to use map/reduce techniques (in Python/PySpark), in an efficient way, to calculate the PageRank of the network made of the elements in the list of lists that sharing a list means a link between them. I have no clue how to deal with the elements in the lists because
Caching a PySpark Dataframe
Suppose we have a PySpark dataframe df with ~10M rows. Also let the columns be [col_a, col_b]. Which would be faster: or Would caching df_test make sense here? Answer It won’t make much difference. it is just one loop where you can skip cache like below Here spark is loading Data once in memory. If you want to use df_sample