[Python] saving a dataframe to the same partitioned location silently doubles the data

When a user saves a dataframe:

df1.to_parquet('/tmp/table', partition_cols=['col_a'], engine='pyarrow')

it will create sub-directories named "a=val1", "a=val2" in /tmp/table. Each of them will contain one (or more?) parquet files with random filenames.

If a user runs the same command again, the code will use the existing sub-directories, but with different (random) filenames. As a result, any data loaded from this folder will be wrong - each row will be present twice.

For example, when using

df1.to_parquet('/tmp/table', partition_cols=['col_a'], engine='pyarrow')  # second time

df2 = pd.read_parquet('/tmp/table', engine='pyarrow')
assert len(df1) == len(df2)  # raise an error

This is a subtle change in the data that can pass unnoticed.

I would expect that the code will prevent the user from using an non-empty destination as partitioned target. an overwrite flag can also be useful.