Open sandorkertesz opened 1 year ago
how about:
df = pdbufr.read_bufr(...., missing_values="ignore")
I think we should use as policies (identified by strings), but the key does not need to be verbose to include _policy
Yes, the shorter the better!
Maybe missing_values=None?
In the case where the user wants to extract five variables from the data and just one of them is missing for a given row, would missing_values="ignore"
remove the row, or would it only remove the row if all five variables are missing? And so, do we need an option to disambiguate this?
Maybe missing_values=None?
What other values than None could we specify for missing_values
? What would they mean?
I prefer option 2
Currently
read_bufr
does not offer control over missing values during the extraction and we have to filter the resulting Pandas dataframe to remove them.Option 1
Add option
missing_value_policy
with the following values: "include", ignore" (default="include")Option 2
Add option
skip_missing
as a bool (default=False)Option 3
Add option
skip_na_values
as a bool (default=False)