👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-14

apache-spark dataframe groupby python

I need some guidance on I'm maintaining legacy code that I'm trying to implement I'm sure I'm missing something obvious here, but I'm currently using Spark 3.4.1 and facing an issue with the way DataFrames behave during group by operations..... I have a DataFrame with sales data that includes columns for `product_id`, `region`, and `sales_amount`. My goal is to group the data by `product_id` and `region`, and then calculate the total `sales_amount` for each group. However, I noticed that the results seem inconsistent when I run the aggregation. Here’s how I set up my DataFrame: ```python from pyspark.sql import SparkSession from pyspark.sql import functions as F spark = SparkSession.builder.appName('SalesAggregation').getOrCreate() data = [ (1, 'North', 100), (1, 'North', 150), (2, 'South', 200), (2, 'South', 300), (1, 'South', 50) ] columns = ['product_id', 'region', 'sales_amount'] sales_df = spark.createDataFrame(data, columns) ``` After that, I executed the following aggregation: ```python result_df = sales_df.groupBy('product_id', 'region').agg(F.sum('sales_amount').alias('total_sales')) result_df.show() ``` The output I'm getting is: ``` +----------+------+-----------+ |product_id|region|total_sales| +----------+------+-----------+ | 1| North| 250| | 1| South| 50| | 2| South| 500| +----------+------+-----------+ ``` I was expecting the total sales for `product_id 2` in the `South` region to be `500`, but that seems correct. However, I can’t wrap my head around why the `total_sales` for `product_id 1` in `North` isn't aggregating properly if I run the same aggregation logic after adding another transformation, like filtering out some regions: ```python filtered_df = sales_df.filter(sales_df.region != 'South') result_filtered_df = filtered_df.groupBy('product_id', 'region').agg(F.sum('sales_amount').alias('total_sales')) result_filtered_df.show() ``` This yields: ``` +----------+------+-----------+ |product_id|region|total_sales| +----------+------+-----------+ | 1| North| 250| +----------+------+-----------+ ``` The filtering seems to be working correctly, but I’m not able to explain the unexpected results when I run the aggregation on the full DataFrame directly. I’ve checked the data types and everything seems fine. I’m looking for any insights or suggestions on why I might be getting these results or if there's a better approach to perform this aggregation. Any help would be appreciated! For context: I'm using Python on macOS. Any help would be greatly appreciated! Any ideas what could be causing this? I recently upgraded to Python 3.11. Thanks in advance! I'm on Linux using the latest version of Python.