Adding a special case for floats that are passed the strings "nan" or "inf"

dacite/core.py

@@ -97,6 +97,8 @@ def _build_value(type_: Type, data: Any, config: Config) -> Any:
         data = _build_value_for_collection(collection=type_, data=data, config=config)
     elif cache(is_dataclass)(type_) and isinstance(data, Mapping):
         data = from_dict(data_class=type_, data=data, config=config)
+    elif type_ == float and isinstance(data, str) and data.lower() in ["nan", "inf"]:
+        return type_(data)
     for cast_type in config.cast:
         if is_subclass(type_, cast_type):
             if is_generic_collection(type_):

dacite/types.py

@@ -99,6 +99,8 @@ def is_instance(value: Any, type_: Type) -> bool:
         # As described in PEP 484 - section: "The numeric tower"
         if (type_ in [float, complex] and isinstance(value, (int, float))) or isinstance(value, type_):
             return True
+        if type_ == float and isinstance(value, str) and value.lower() in ["nan", "inf"]:
+            return True
     except TypeError:
         pass
     if type_ == Any:

tests/core/test_base.py

@@ -1,3 +1,4 @@
+import math
 from dataclasses import dataclass, field
 from typing import Any, NewType, Optional, List
 
@@ -204,3 +205,18 @@ class A:
     a2 = from_dict(A, {"name": "a2"})
 
     assert a1.items is not a2.items
+
+
+def test_translate_nan_to_float():
+    @dataclass
+    class X:
+        i: float
+        n: float
+
+    result_1 = from_dict(X, {"i": "inf", "n": "nan"})
+    result_2 = from_dict(X, {"i": "INF", "n": "NaN"})
+
+    assert math.isnan(result_1.n)
+    assert math.isnan(result_2.n)
+    assert math.isinf(result_1.i)
+    assert math.isinf(result_2.i)