TYP: mypy upgrade (#188) (#1109)

Co-authored-by: Mike Lync <[email protected]>

Author: mikelync

python/rateslib/_spec_loader.py

@@ -41,7 +41,7 @@ def _map_true_false(v: str) -> bool | None:
             # TODO (low): clean this up when setting a minimum pandas version at 2.1.0
             df[df["dtype"] == "bool"] = df[df["dtype"] == "bool"].map(_map_true_false)
         else:
-            df[df["dtype"] == "bool"] = df[df["dtype"] == "bool"].applymap(_map_true_false)  # type: ignore[operator]
+            df[df["dtype"] == "bool"] = df[df["dtype"] == "bool"].applymap(_map_true_false)  # type: ignore[operator, index]
         return df
 
     path = "data/__instrument_spec.csv"

python/rateslib/data/fixings.py

@@ -933,7 +933,7 @@ def __init__(
         self._accrual_end = accrual_end
         self._fixing_method = _get_float_fixing_method(fixing_method)
         self._method_param = method_param
-        self._populated = Series(index=[], data=[], dtype=float)
+        self._populated = Series(index=[], data=[], dtype=float)  # type: ignore[assignment]
 
     @property
     def fixing_method(self) -> FloatFixingMethod:

python/rateslib/fx_volatility/utils.py

@@ -510,7 +510,7 @@ def _validate_weights(
         w[s:e] = (  # type: ignore[misc]
             w[s:e] * days / w[s:e].sum()  # type: ignore[misc]
         )  # scale the weights to allocate the correct time between nodes.
-    w[eval_date] = 0.0
+    w[eval_date] = 0.0  # type: ignore[call-overload]
     return w
 
 

python/rateslib/instruments/bonds/bond_future.py

@@ -559,20 +559,20 @@ def dlv(
             ],
             index=range(len(basket)),
         )
-        df["Price"] = prices
-        df["YTM"] = [bond.ytm(prices[i], settlement, dirty=dirty) for i, bond in enumerate(basket)]
-        df["C.Factor"] = self.cfs
-        df["Gross Basis"] = self.gross_basis(future_price, prices, settlement, dirty=dirty)
-        df["Implied Repo"] = self.implied_repo(
+        df["Price"] = prices  # type: ignore[assignment]
+        df["YTM"] = [bond.ytm(prices[i], settlement, dirty=dirty) for i, bond in enumerate(basket)]  # type: ignore[assignment]
+        df["C.Factor"] = self.cfs  # type: ignore[assignment]
+        df["Gross Basis"] = self.gross_basis(future_price, prices, settlement, dirty=dirty)  # type: ignore[assignment]
+        df["Implied Repo"] = self.implied_repo(  # type: ignore[assignment]
             future_price,
             prices,
             settlement,
             delivery,
             convention,
             dirty=dirty,
         )
-        df["Actual Repo"] = r_
-        df["Net Basis"] = self.net_basis(
+        df["Actual Repo"] = r_  # type: ignore[assignment]
+        df["Net Basis"] = self.net_basis(  # type: ignore[assignment]
             future_price,
             prices,
             r_,

python/rateslib/solver.py

@@ -1747,7 +1747,7 @@ def delta(
             df.loc[indexes[key[0]], (key[1], key[2])] = array
 
         if not isinstance(base, NoInput):
-            df.loc[r_idx, ("all", base)] = df.loc[r_idx, (slice(None), base)].sum(axis=1)  # type: ignore[index]
+            df.loc[r_idx, ("all", base)] = df.loc[r_idx, (slice(None), base)].sum(axis=1)
 
         sorted_cols = df.columns.sort_values()
         ret: DataFrame = df.loc[:, sorted_cols].astype("float64")
@@ -2291,7 +2291,7 @@ def exo_delta(
             df.loc[indexes[key[0]], (key[1], key[2])] = array
 
         if not isinstance(base, NoInput):
-            df.loc[r_idx, ("all", base)] = df.loc[r_idx, (slice(None), base)].sum(axis=1)  # type: ignore[index]
+            df.loc[r_idx, ("all", base)] = df.loc[r_idx, (slice(None), base)].sum(axis=1)
 
         sorted_cols = df.columns.sort_values()
         _: DataFrame = df.loc[:, sorted_cols].astype("float64")