ENH: allow to provide required columns explicitly

Summary

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

Test Environment

unit test

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src/caosadvancedtools/table_importer.py

@@ -210,7 +210,7 @@ class TableImporter():
     """
 
     def __init__(self, converters, obligatory_columns=None, unique_keys=None,
-                 datatypes=None):
+                 datatypes=None, existing_columns=None):
         """
         Parameters
         ----------
@@ -221,7 +221,7 @@ class TableImporter():
           value check is not necessary.
 
         obligatory_columns : list, optional
-          List of column names, each listed column must not have missing values.
+          List of column names that (if they exist) must not have missing values.
 
         unique_keys : list, optional
           List of column names that in combination must be unique: each row has a unique
@@ -232,22 +232,31 @@ class TableImporter():
           checked whether they have the provided datatype.  This dict also defines what columns are
           required to exist throught the existing keys.
 
+        existing_columns : list, optional
+          List of column names that must exist but may have missing (NULL) values
         """
 
         if converters is None:
             converters = {}
+        self.converters = converters
+
+        if obligatory_columns is None:
+            obligatory_columns = []
+        self.obligatory_columns = obligatory_columns
+
+        if unique_keys is None:
+            unique_keys = []
+        self.unique_keys = unique_keys
 
         if datatypes is None:
             datatypes = {}
+        self.datatypes = datatypes
+
+        if existing_columns is None:
+            existing_columns = []
+        self.existing_columns = existing_columns
 
         self.sup = SuppressKnown()
-        self.required_columns = list(converters.keys())+list(datatypes.keys())
-        self.obligatory_columns = ([]
-                                   if obligatory_columns is None
-                                   else obligatory_columns)
-        self.unique_keys = [] if unique_keys is None else unique_keys
-        self.converters = converters
-        self.datatypes = datatypes
 
     def read_file(self, filename, **kwargs):
         raise NotImplementedError()
@@ -263,7 +272,7 @@ class TableImporter():
 
         """
 
-        for col in self.required_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
@@ -323,6 +332,8 @@ class TableImporter():
 
         """
         for key, datatype in self.datatypes.items():
+            if key not in df.columns:
+                continue
             # 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]
@@ -333,8 +344,7 @@ class TableImporter():
                     df[key] = df[key].astype(datatype)
 
             # Now check each element
-            for idx, val in df.loc[
-                    pd.notnull(df.loc[:, key]), key].items():
+            for idx, val in df.loc[pd.notnull(df.loc[:, key]), key].items():
 
                 if not isinstance(val, datatype):
                     msg = (
@@ -363,22 +373,20 @@ class TableImporter():
 
         for index, row in df.iterrows():
             # if none of the relevant information is given, skip
-
-            if np.array([pd.isnull(row.loc[key]) for key in
-                         self.obligatory_columns]).all():
-
+            if pd.isnull(row.loc[[key for key in self.obligatory_columns if key in df.columns]]).all():
                 df = df.drop(index)
 
                 continue
 
             # if any of the relevant information is missing, report it
-
             i = 0
             okay = True
 
             while okay and i < len(self.obligatory_columns):
                 key = self.obligatory_columns[i]
                 i += 1
+                if key not in df.columns:
+                    continue
 
                 if pd.isnull(row.loc[key]):
                     errmsg = (
@@ -449,7 +457,10 @@ class XLSImporter(TableImporter):
                 "All but the first are being ignored.".format(filename))
 
         try:
-            df = xls_file.parse(converters=self.converters, **kwargs)
+            tmpdf = xls_file.parse(**kwargs)
+            applicable_converters = {k: v for k, v in self.converters.items()
+                                     if k in tmpdf.columns}
+            df = xls_file.parse(converters=applicable_converters, **kwargs)
         except Exception as e:
             logger.warning(
                 "Cannot parse {}.\n{}".format(filename, e),
@@ -465,7 +476,11 @@ class XLSImporter(TableImporter):
 class CSVImporter(TableImporter):
     def read_file(self, filename, sep=",", **kwargs):
         try:
-            df = pd.read_csv(filename, sep=sep, converters=self.converters,
+            tmpdf = pd.read_csv(filename, sep=sep, converters=self.converters,
+                                **kwargs)
+            applicable_converters = {k: v for k, v in self.converters.items()
+                                     if k in tmpdf.columns}
+            df = pd.read_csv(filename, sep=sep, converters=applicable_converters,
                              **kwargs)
         except ValueError as ve:
             logger.warning(
@@ -482,6 +497,10 @@ class CSVImporter(TableImporter):
 class TSVImporter(TableImporter):
     def read_file(self, filename, **kwargs):
         try:
+            tmpdf = pd.read_csv(filename, sep="\t", converters=self.converters,
+                                **kwargs)
+            applicable_converters = {k: v for k, v in self.converters.items()
+                                     if k in tmpdf.columns}
             df = pd.read_csv(filename, sep="\t", converters=self.converters,
                              **kwargs)
         except ValueError as ve: