Use category dtype

This is more memory efficient for columns with many duplicate values,
which can be expected in most use cases.

¹ <https://pandas.pydata.org/pandas-docs/version/1.5/user_guide/scale.html#use-efficient-datatypes>

augur/filter/_run.py

@@ -175,7 +175,7 @@ def run(args):
         columns=useful_metadata_columns,
         id_columns=[metadata_object.id_column],
         chunk_size=args.metadata_chunk_size,
-        dtype="string",
+        dtype={col: 'category' for col in useful_metadata_columns},
     )
     for metadata in metadata_reader:
         duplicate_strains = (
@@ -297,7 +297,7 @@ def run(args):
             columns=useful_metadata_columns,
             id_columns=args.metadata_id_columns,
             chunk_size=args.metadata_chunk_size,
-            dtype="string",
+            dtype={col: 'category' for col in useful_metadata_columns},
         )
         for metadata in metadata_reader:
             # Recalculate groups for subsampling as we loop through the

tests/functional/filter/cram/filter-query-errors.t

@@ -22,7 +22,7 @@ Some error messages from Pandas may be useful, so they are exposed:
   >  --query "region >= 0.50" \
   >  --output-strains filtered_strains.txt > /dev/null
   ERROR: Internal Pandas error when applying query:
-  	'>=' not supported between instances of 'str' and 'float'
+  	Unordered Categoricals can only compare equality or not
   Ensure the syntax is valid per <https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-query>.
   [2]
 

tests/functional/filter/cram/filter-query-numerical.t

@@ -30,7 +30,7 @@ The 'category' column will fail when used with a numerical comparison.
   >  --query "category >= 0.95" \
   >  --output-strains filtered_strains.txt
   ERROR: Internal Pandas error when applying query:
-  	'>=' not supported between instances of 'str' and 'float'
+  	Unordered Categoricals can only compare equality or not
   Ensure the syntax is valid per <https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-query>.
   [2]