@sanderlenselink you create a list of data frames. You could append them in you loop or add these lines afterwards. You will have to reset the index because KNIME does not allow duplicate indexes when bringing the data back from Python.
# create an initial data frame to export
df_export = dfs
# add the other data frames below
for i in range(1, len(dfs)):
df_export = pd.concat([df_export, dfs[i]], axis=0)
# reset the index
output_table_1 = df_export.copy()
Just for fun you could also export the data frames as single parquet files and bring them back later into KNIME in one step. You would have to clean the column names (the one with the date time names) since Parquet would not like that.
@mlauber71 . . . thnx for you feedback. A very interesting solution (fun ).
Now I understand also much better @Daniel_Weikert his remark.
However . . . I try to execute your script/example (import some finance data with Python etc 47320).
I didn’t know the Conda Environment Propagation node and not fully understand it but I get it running.
In the Python Script (Labs) you import Skimpy but that produces an error. I searched at anaconda.org but there’s only a link for Linux and OSX-64. In your configuration I see you use the channel “pypi”.
I use u Windows system
My question . . . what is the command to implement Skimpy in Conda? See the 2nd screenshot the command I used . . neither “conda install -c weilandtd skimpy” or.“conda install -c pypi skimpy” works.
If I delete (comment out) the Skimpy lines in Python Labs including "clean columns"etc your script runs smoothly