I have a PySpark dataframe that looks like this: Values Column {[0.0, 54.04, 48…. Sector A {[0.0, 55.4800000… Sector A If I show the first element of the column ‘Values’ without truncating the data, it looks like this: {[0.0, 54.04, 48.19, 68.59, 61.81, 54.730000000000004, 48.51, 57.03, 59.49, 55.44, 60.56, 52.52, 51.44, 55.06, 55.27, 54.61, 55.89, 56.5, 45.4, 68.63, 63.88, 48.25,
Tag: apache-spark
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
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
Apache Spark unable to recognize columns in UTF-16 csv file
Question: Why I am getting following error on the last line of the code below, how the issue can be resolved? AttributeError: ‘DataFrame’ object has no attribute ‘OrderID’ CSV File encoding: UTF-16 LE BOM Number of columns: 150 Rows: 5000 Language etc.: Python, Apache Spark, Azure-Databricks MySampleDataFile.txt: Code sample: Output of display(df.limit(4)) It successfully displays the content of df in
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
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
Double assignment of same variable in one expression in Python – does it have any purpose?
I was going through delta lake documentation page. There is a line like this : In the last line, we see a double assignment of same variable (spark). Does this do something different compared to : In general, in python, is there a meaning to double assignment of same variable ? Answer Short answer: It’s meaningless. Aside from a few
Rename a redshift SQL table within PySpark Databricks
I want to rename a redshift table within a Python Databricks notebook. Currently I have a query that pulls in data and creates a table: I want to take this table I created and rename it. I referenced this doc but find it hard to follow. I want to run this SQL command alter table public.test rename to test_table_to_be_dropped in