I’ve written something to read the location of some lake files dynamically provided by a list called partition_paths:

dfs = [spark.read.parquet(f'{l}') for l in partition_paths]

I will then combine all these dfs into one in the next line:

df = reduce(DataFrame.unionAll, dfs)

But it maybe possible that the partition_paths are either built up incorrectly, or the location in the lake simply doesn’t exist, so I need to error handle the first line of code. How can I do that so it won’t just stop and would continue getting all the dfs?