MAINT: obligatory no longer forces columns to exist

CHANGELOG.md

@@ -8,10 +8,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added ###
 - TableImporter now accepts a `existing_columns` argument which demands that certain columns exist
-  although they may have missing values
 
 ### Changed ###
 - The converters and datatype arguments of TableImporter now may have keys for nonexisting columns
+- The obligatory key word no longer enforces that the columns exist. Since all rows are still
+  checked whether this column has a non-NULL value, this is deemed a non breaking change.
 
 ### Deprecated ###
 

src/caosadvancedtools/table_importer.py

@@ -221,7 +221,7 @@ class TableImporter():
           value check is not necessary.
 
         obligatory_columns : list, optional
-          List of column names, each listed column must exist and must not have missing values.
+          List of column names that must not have missing values.
 
         unique_keys : list, optional
           List of column names that in combination must be unique: each row has a unique
@@ -272,7 +272,7 @@ class TableImporter():
 
         """
 
-        for col in self.obligatory_columns+self.existing_columns:
+        for col in self.existing_columns:
             if col not in df.columns:
                 errmsg = "Column '{}' missing in ".format(col)
                 errmsg += ("\n{}.\n".format(filename) if filename

unittests/test_table_importer.py

@@ -153,10 +153,6 @@ class TableImporterTest(unittest.TestCase):
             [['a', 1, 2.0, 'yes', np.nan]], columns=['a', 'b', 'c', 'd', 'e'])
 
     def test_missing_col(self):
-        # check missing from obligatory
-        df = pd.DataFrame(columns=['a', 'e'])
-        importer = TableImporter(**self.importer_kwargs)
-        self.assertRaises(ValueError, importer.check_columns, df)
         # check missing from existing
         df = pd.DataFrame(columns=['a', 'b'])
         importer = TableImporter(**self.importer_kwargs)