TableConverter now converts int to float and vice versa to match the desired dtype.

Summary

TableConverter now converts int to float and vice versa to match the desired dtype.

Focus

Does the implemented code match the desired behavior?

Test Environment

make unittest

Check List for the Author

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• All automated tests pass
• Reference related issues caosdb/customers/glaz_awi/management#226
• Up-to-date CHANGELOG.md (or not necessary)
• Annotations in code (Gitlab comments)
  • Intent of new code
  • Problems with old code
  • Why this implementation?

Check List for the Reviewer

• I understand the intent of this MR
• All automated tests pass
• Up-to-date CHANGELOG.md (or not necessary)
• The test environment setup works and the intended behavior is reproducible in the test environment
• In-code documentation and comments are up-to-date.
• Check: Are there specifications? Are they satisfied?

For further good practices have a look at our review guidelines.

src/caosadvancedtools/table_importer.py

@@ -312,8 +312,8 @@ class TableImporter():
 
         .. note::
 
-          If columns are float, but should be integer or vice versa, this method converts the
-          respective columns in place.
+          If columns are integer, but should be float, this method converts the respective columns
+          in place.
 
         Parameters
         ----------
@@ -323,14 +323,13 @@ class TableImporter():
 
         """
         for key, datatype in self.datatypes.items():
-            # Check for castable numeric types first: We unconditionally cast float to int and vice
-            # versa, because CaosDB does not have different sizes anyway.
+            # Check for castable numeric types first: We unconditionally cast int to the default
+            # float, because CaosDB does not have different sizes anyway.
             col_dtype = df.dtypes[key]
             if not strict and not np.issubdtype(col_dtype, datatype):
                 issub = np.issubdtype
                 #  These special cases should be fine.
-                if ((issub(col_dtype, np.integer) and issub(datatype, np.floating))
-                        or (issub(col_dtype, np.floating) and issub(datatype, np.integer))):
+                if issub(col_dtype, np.integer) and issub(datatype, np.floating):
                     df[key] = df[key].astype(datatype)
 
             # Now check each element