I’m encountering a couple of issues while working with Dask DataFrames in my Python project.

1. TypeError during Concatenation:
   When I try to concatenate multiple Dask DataFrames using pd.concat, I get the following error:

TypeError: cannot concatenate object of type '<class 'dask_expr._collection.DataFrame'>'; only Series and DataFrame objs are valid

It seems that the objects I am trying to concatenate are not recognized as valid DataFrames. How can I properly concatenate Dask DataFrames?

2. ValueError during Resampling:
   While attempting to resample a Dask DataFrame with the following code:

ddf_cleaned_monthly = ddf.resample('M').mean()

I encounter this error:

ValueError: Can only resample dataframes with known divisions

The error suggests that my DataFrame does not have known divisions. How should I set up my DataFrame or adjust my approach to handle resampling with Dask?

Additional Information:
I’m using Python 3.12 and the Dask library for handling large datasets.
The Dask DataFrames I am working with are created from a set of .nc and csv files and include complex operations.
I’ve tried to follow the Dask documentation for best practices, but I’m still running into issues.

Any guidance or suggestions on how to resolve these errors would be greatly appreciated!

Additional Context:
I am working on a project where I need to develop an algorithm that uses data from different sensors. Specifically, I have datasets from two sources:
NetCDF Files (.nc): Containing soil moisture data.
CSV Files: Containing weather data with columns like temperature, humidity, rainfall, etc.

My goal is to merge these two datasets, but before merging, I need to:
Clean both datasets.
Combine them into a single DataFrame so that I can create features and targets for my algorithm. then many more, but i got stuck on it