strtobool should not fail

app/__init__.py

@@ -23,25 +23,3 @@
     ],
 )
 logging.debug(f"Logging to {log_path}")
-
-
-def strtobool(val) -> bool:
-    """
-    Replaces deprecated https://docs.python.org/3.9/distutils/apiref.html#distutils.util.strtobool
-    The deprecation recommendation is to re-implement the function https://peps.python.org/pep-0632/
-
-    ------------------------------------------------------------
-
-    Convert a string representation of truth to true (1) or false (0).
-
-    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
-    are 'n', 'no', 'f', 'false', 'off', and '0'.  Raises ValueError if
-    'val' is anything else.
-    """
-    val_str = str(val).lower()
-    if val_str in ("y", "yes", "t", "true", "on", "1"):
-        return True
-    elif val_str in ("n", "no", "f", "false", "off", "0"):
-        return False
-    else:
-        raise ValueError(f"invalid truth value {val!r}")

app/main.py

@@ -25,7 +25,7 @@
 from plotly.graph_objs import Layout
 from sqlalchemy.exc import ProgrammingError
 
-from app import __version__, db, models, strtobool
+from app import __version__, db, models
 from app.downloads import daily
 
 logger = logging.getLogger(__name__)
@@ -212,10 +212,14 @@ def _summarize_visits(interval="5min") -> Response:
         df["end"] = df["start"] + pd.to_timedelta(interval)
         df["start"] = df["start"].dt.strftime("%Y-%m-%d %H:%M")
         df["end"] = df["end"].dt.strftime("%Y-%m-%d %H:%M")
-        df["is_docker"] = df["is_docker"].apply(lambda val: strtobool(val) if val else False)
-        df["is_singularity"] = df["is_singularity"].apply(lambda val: strtobool(val) if val else False)
-        df["is_conda"] = df["is_conda"].apply(lambda val: strtobool(val) if val else False)
-        df["is_ci"] = df["is_ci"].apply(lambda val: strtobool(val) if val else False)
+
+        def strtobool(val) -> bool:
+            return str(val).lower() in ("y", "yes", "t", "true", "on", "1")
+
+        df["is_docker"] = df["is_docker"].apply(strtobool)
+        df["is_singularity"] = df["is_singularity"].apply(strtobool)
+        df["is_conda"] = df["is_conda"].apply(strtobool)
+        df["is_ci"] = df["is_ci"].apply(strtobool)
         df = df.drop(columns=["timestamp"])
 
         # Summarize visits per user per time interval