Possible to add missing columns?

[aodj]

As far as I can see from the documentation the opposite behavior exists (filtering out columns not specified in the schema) but I can't see if there's a way to add in columns that should exist but aren't.

Is there a way to accomplish this? I was wondering if it might be possible by adding to the strict flag, an option to add in missing columns, such that you could do strict: "add+filter" allowing default values to be supplied AND filtering out unnecessary columns:

schema = DataFrameSchema(
    {"this_is_a_column": Column(str)},
    {"another_column": Column(str, default="na")},
 strict='add+filter')

df = pd.DataFrame({
    "unneeded_column": ["drop", "me"],
    "this_is_a_column": ["keep", "me"]},
)

validated_df = schema.validate(df)
print(validated_df)

   this_is_a_column  another_column
0              keep              na
1                me              na

Might this be possible with the DataFrame checks?

[aodj]

Feels similar to #687

[cosmicBboy]

Hi @aodj looking at the two issues, it does indeed look like they're semantically the same!

The only difference is in the other issue there's an additional behavior that it's a nullable column.

I'm gonna go ahead and close this issue so we can continue the discussion in #687. Thanks!