fix mypy

splink/profile_data.py

@@ -1,9 +1,11 @@
 import logging
 import re
 from copy import deepcopy
+from typing import List, Optional, Union
 
 from .charts import altair_or_json, load_chart_definition
 from .column_expression import ColumnExpression
+from .database_api import DatabaseAPI
 from .misc import ensure_is_list
 from .pipeline import CTEPipeline
 from .vertically_concatenate import vertically_concatenate_sql
@@ -197,7 +199,11 @@ def _add_100_percentile_to_df_percentiles(percentile_rows):
 
 
 def profile_columns(
-    table_or_tables, db_api, column_expressions=None, top_n=10, bottom_n=10
+    table_or_tables,
+    db_api: DatabaseAPI,
+    column_expressions: Optional[List[Union[str, ColumnExpression]]] = None,
+    top_n=10,
+    bottom_n=10,
 ):
     """
     Profiles the specified columns of the dataframe initiated with the linker.
@@ -263,7 +269,7 @@ def profile_columns(
         for ce in column_expressions_raw
     ]
 
-    column_expressions = expressions_to_sql(column_expressions_raw)
+    column_expressions_as_sql = expressions_to_sql(column_expressions_raw)
 
     sql = _col_or_expr_frequencies_raw_data_sql(
         column_expressions_raw, "__splink__df_concat"
@@ -274,27 +280,26 @@ def profile_columns(
 
     pipeline = CTEPipeline(input_dataframes=[df_raw])
     sqls = _get_df_percentiles()
-    for sql in sqls:
-        pipeline.enqueue_sql(sql["sql"], sql["output_table_name"])
+    pipeline.enqueue_list_of_sqls(sqls)
 
     df_percentiles = db_api.sql_pipeline_to_splink_dataframe(pipeline)
     percentile_rows_all = df_percentiles.as_record_dict()
 
     pipeline = CTEPipeline(input_dataframes=[df_raw])
-    sql = _get_df_top_bottom_n(column_expressions, top_n, "desc")
+    sql = _get_df_top_bottom_n(column_expressions_as_sql, top_n, "desc")
     pipeline.enqueue_sql(sql, "__splink__df_top_n")
     df_top_n = db_api.sql_pipeline_to_splink_dataframe(pipeline)
     top_n_rows_all = df_top_n.as_record_dict()
 
     pipeline = CTEPipeline(input_dataframes=[df_raw])
-    sql = _get_df_top_bottom_n(column_expressions, bottom_n, "asc")
+    sql = _get_df_top_bottom_n(column_expressions_as_sql, bottom_n, "asc")
     pipeline.enqueue_sql(sql, "__splink__df_bottom_n")
     df_bottom_n = db_api.sql_pipeline_to_splink_dataframe(pipeline)
     bottom_n_rows_all = df_bottom_n.as_record_dict()
 
     inner_charts = []
 
-    for expression in column_expressions:
+    for expression in column_expressions_as_sql:
         percentile_rows = [
             p for p in percentile_rows_all if p["group_name"] == _group_name(expression)
         ]