support SchemaPipelines

[cosmicBboy]

Schema Pipeline

This would be a convenience class for using an in-memory object that contains a registry of transformations that are expected to occur to a data frame. The example above would look something like:

schema_pipeline = (
    SchemaPipeline(
        base_schema=DataFrameSchema({
            "col1": Column(Int, Check(lambda s: s >= 0)),
            "col2": Column(Int, Check(lambda s: s >= 0)),
    })
    .pipe("combine", lambda schema: schema.add_columns({
        "col3": Column(Int, Check(lambda s: s >= 0))
    }))
    .pipe("remove", lambda schema: schema.remove_columns(["col1"]))
)

df = pd.DataFrame({
    "col1": [1, 2, 3],
    "col2": [1, 2, 3],
})
df = schema_pipeline["base_schema"].validate(df)

df["col3"] = df["col1"] + df["col2"]
df = schema_pipeline["combine"].validate(df)

del df["col1"]
df = schema_pipeline["remove"].validate(df)

[cosmicBboy]

closing this issue for now, can re-visit later if it still seems like a good idea