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Is there a way to compare two dataframes and report which column is different in Pyspark?

I'm using df1.subtract(df2).rdd.isEmpty() to compare two dataframes (assuming the schema of these two df are the same, or at least we expect them to be the same), but if one of the column doesn't match, I can't tell from the output logs, and it takes long time for me to find out the issue in the data (and it's exhausting when the datasets are quite big)
Is there a way that we can compare two df and return which column doesn't match with Pyspark? Thanks a lot.

You could use the chispa library, it is a great tool for comparing data frames.

Iterating through big data with pandas, large and small dataframes

This is my first post here and it’s based upon an issue I’ve created and tried to solve at work. I’ll try to precisely summarize my issue as I’m having trouble wrapping my head around a preferred solution. #3 is a real stumper for me.
Grab a large data file based on a parquet - no problem
Select 5 columns from the parquet and create a dataframe - no problem
import pandas
df = pd.read_parquet(’/Users/marmicha/Downloads/sample.parquet’,
columns=["ts", "session_id", "event", "duration", "sample_data"])
But here is where it gets a bit tricky for me. One column(a key column) is called "session_id" . Many values are unique. Many duplicate values(of session_id) exist and have multiple associated entry rows of data. I wish to iterate through the master dataframe, create a unique dataframe per session_id. Each of these unique (sub) dataframes would have a calculation done that simply gets the SUM of the "duration" column per session_id. Again that SUM would be unique per unique session_id, so each sub dataframe would have it's own SUM with a row added with that total listed along with the session_id I'm thinking there is a nested loop formula that will work for me but every effort has been a mess to date.
Ultimately, I'd like to have a final dataframe that is a collection of these unique sub dataframes. I guess I'd need to define this final dataframe, and append it with each new sub dataframe as I iterate through the data. I should be able to do that simply
Finally, write this final df to a new parquet file. Should be simple enough so I won't need help with that.
But that is my challenge in a nutshell. The main design I’d need help with is #3. I’ve played with interuples and iterows

I think the groupby function will work:
df.groupby('session_id')['duration'].sum()
More info here:
https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html

Creating Groupby-like dataframe

I want to make a daily table of team stats and want to create a data frame where every team would have a row on every date. I tried doing it with counting and then repetitive joining and it gets all messy so I was wondering if there is a simpler solution like a function.

Not sure if that is what you want, but it seens like pandas.melt() is the case:
reference: https://www.geeksforgeeks.org/python-pandas-melt/

General Approach to Working with Data in DataFrames

Question for experienced Pandas users on approach to working with Dataframe data.
Invariably we want to use Pandas to explore relationships among data elements. Sometimes we use groupby type functions to get summary level data on subsets of the data. Sometimes we use plots and charts to compare one column of data against another. I'm sure there are other application I haven't thought of.
When I speak with other fairly novice users like myself, they generally try to extract portions of a "large" dataframe into smaller dfs that are sorted or formatted properly to run applications or plot. This approach certainly has disadvantages in that if you strip out a subset of data into a smaller df and then want to run an analysis against a column of data you left in the bigger df, you have to go back and recut stuff.
My question is - is best practices for more experienced users to leave the large dataframe and try to syntactically pull out the data in such a way that the effect is the same or similar to cutting out a smaller df? Or is it best to actually cut out smaller dfs to work with?
Thanks in advance.

How can I manipulate tabular data with python?

I have column-based data in a CSV file and I would like to manipulate it in several ways. People have pointed me to R because it gives you easy access to both rows and columns, but I am already familiar with python and rather use it.
For example, I want to be able to delete all the rows that have a certain value in one of the columns. Or I want to change all the values of one column (i.e., trim the string). I also want to be able to aggregate rows based on common values (like a SQL GROUP BY).
Is there a way to do this in python without having to write a loop to iterate over all of the rows each time?

Look at the pandas library. It provides a DataFrame type similar to R's dataframe that lets you do the kind of thing you're talking about.