Use typing Dict/List for 3.8 support

Author: stuartquin

dataqa/column_mapping.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, List
 
 from dataclasses import dataclass
 
@@ -21,11 +21,11 @@ class PredictionColumn:
 
 @dataclass
 class ColumnMapping:
-    numerical_columns: Optional[list[str]] = None
-    categorical_columns: Optional[list[str]] = None
-    text_columns: Optional[list[str]] = None
-    time_columns: Optional[list[str]] = None
-    prediction_columns: Optional[list[PredictionColumn]] = None
+    numerical_columns: Optional[List[str]] = None
+    categorical_columns: Optional[List[str]] = None
+    text_columns: Optional[List[str]] = None
+    time_columns: Optional[List[str]] = None
+    prediction_columns: Optional[List[PredictionColumn]] = None
 
 
 class ColumnType:

dataqa/infer_schema.py

@@ -1,5 +1,5 @@
 from collections import defaultdict
-from typing import Any, Optional, Union
+from typing import Any, Dict, Optional, Union, List
 
 import numpy as np
 import pandas as pd
@@ -25,11 +25,11 @@ def is_column_categorical(values: pd.Series) -> bool:
 
 def infer_schema(
     df: pd.DataFrame,
-    numerical_columns: Optional[list[str]] = None,
-    categorical_columns: Optional[list[str]] = None,
-    text_columns: Optional[list[str]] = None,
-    time_columns: Optional[list[str]] = None,
-    prediction_columns: Optional[list[PredictionColumn]] = None,
+    numerical_columns: Optional[List[str]] = None,
+    categorical_columns: Optional[List[str]] = None,
+    text_columns: Optional[List[str]] = None,
+    time_columns: Optional[List[str]] = None,
+    prediction_columns: Optional[List[PredictionColumn]] = None,
 ) -> ColumnMapping:
     """
     The keyword arguments will take precedence over the inferred schema.
@@ -159,8 +159,8 @@ def check_all_columns_in_df(df: pd.DataFrame, column_mapping: ColumnMapping):
 
 
 def check_categorical_columns(
-    df: pd.DataFrame, categorical_columns: list[str]
-) -> dict[str, list[Union[str, np.number]]]:
+    df: pd.DataFrame, categorical_columns: List[str]
+) -> Dict[str, List[Union[str, np.number]]]:
     """
     Make sure the dtype is numeric or string (not mixed) and that unique categories <= MAX_CATEGORICAL_UNIQUE
     """
@@ -184,33 +184,33 @@ def check_categorical_columns(
     return column_to_categories
 
 
-def check_numerical_columns(df: pd.DataFrame, numerical_columns: list[str]):
+def check_numerical_columns(df: pd.DataFrame, numerical_columns: List[str]):
     for column in numerical_columns:
         if not pd.api.types.is_numeric_dtype(df[column].dtype):
             raise Exception(f"Column {column} is not of type numerical.")
 
 
-def check_text_columns(df: pd.DataFrame, text_columns: list[str]):
+def check_text_columns(df: pd.DataFrame, text_columns: List[str]):
     for column in text_columns:
         if not pd.api.types.infer_dtype(df[column], skipna=True) == "string":
             raise Exception(f"Text column {column} is not of type string.")
 
 
-def check_time_columns(df: pd.DataFrame, time_columns: list[str]):
+def check_time_columns(df: pd.DataFrame, time_columns: List[str]):
     for column in time_columns:
         try:
             _ = pd.to_datetime(df[column], errors="raise")
         except:
             raise Exception(f"Column {column} cannot be cast to a datetime.")
 
 
-def is_subset(list1: list[Any], list2: list[Any]) -> bool:
+def is_subset(list1: List[Any], list2: List[Any]) -> bool:
     return len(set(list1).difference(set(list2))) == 0
 
 
 def check_prediction_columns(
     column_mapping: ColumnMapping,
-    column_to_categories: dict[str, list[Union[str, np.number]]],
+    column_to_categories: Dict[str, List[Union[str, np.number]]],
 ) -> dict:
     schema_dict = dict(
         (column, {"type": ColumnType.CATEGORICAL})
@@ -297,8 +297,8 @@ def check_prediction_columns(
 def format_validated_schema(
     df: pd.DataFrame,
     schema_dict: dict,
-    prediction_columns: list[PredictionColumn],
-    column_to_categories: dict[str, list[Union[str, np.number]]],
+    prediction_columns: List[PredictionColumn],
+    column_to_categories: Dict[str, List[Union[str, np.number]]],
 ) -> dict:
     new_schema = []
     prediction_columns_dict = {

dataqa/publish.py

@@ -1,7 +1,7 @@
 from getpass import getpass
 import json
 import requests
-from typing import Optional
+from typing import Optional, List, Dict
 
 import pandas as pd
 
@@ -30,7 +30,7 @@ def login(self):
 
         self.auth_token = response.json()["token"]
 
-    def create_release(self, project_id: str, column_mapping: list[dict]) -> str:
+    def create_release(self, project_id: str, column_mapping: List[Dict]) -> str:
         response = requests.post(
             self.api_url + "/api/v1/release/",
             headers={

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "dataqa"
-version = "2.0.1"
+version = "2.0.3"
 description = "Python Client library for DataQA"
 authors = ["Maria Mestre <maria@dataqa.ai>","Stuart Quin <stuart@dataqa.ai>"]
 readme = "README.md"