csv_metadata_quality/app.py: rework exclude/skip

Instead of processing the excludes inside the for column loop we do
it once before and then only need to check if the current column is
in the list.

csv_metadata_quality/app.py

@@ -76,19 +76,19 @@ def run(argv):
     # Read all fields as strings so dates don't get converted from 1998 to 1998.0
     df = pd.read_csv(args.input_file, dtype=str)
 
-    for column in df.columns:
-        # Check if the user requested to skip any fields
-        if args.exclude_fields:
-            skip = False
-            # Split the list of excludes on ',' so we can test exact matches
-            # rather than fuzzy matches with regexes or "if word in string"
-            for exclude in args.exclude_fields.split(","):
-                if column == exclude and skip is False:
-                    skip = True
-            if skip:
-                print(f"{Fore.YELLOW}Skipping {Fore.RESET}{column}")
+    # Check if the user requested to skip any fields
+    if args.exclude_fields:
+        # Split the list of excluded fields on ',' into a list. Note that the
+        # user should be careful to no include spaces here.
+        exclude = args.exclude_fields.split(",")
+    else:
+        exclude = list()
 
-                continue
+    for column in df.columns:
+        if column in exclude:
+            print(f"{Fore.YELLOW}Skipping {Fore.RESET}{column}")
+
+            continue
 
         # Fix: whitespace
         df[column] = df[column].apply(fix.whitespace, field_name=column)