CLN: one less try/except in Block methods (pandas-dev#27606)

pandas/core/internals/blocks.py

@@ -644,7 +644,9 @@ def _astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
                 if isinstance(values, np.ndarray):
                     values = values.reshape(self.shape)
 
-            except Exception:  # noqa: E722
+            except Exception:
+                # e.g. astype_nansafe can fail on object-dtype of strings
+                #  trying to convert to float
                 if errors == "raise":
                     raise
                 newb = self.copy() if copy else self
@@ -877,9 +879,17 @@ def setitem(self, indexer, value):
 
         # coerce if block dtype can store value
         values = self.values
-        try:
+        if self._can_hold_element(value):
             value = self._try_coerce_args(value)
-        except (TypeError, ValueError):
+
+            values = self._coerce_values(values)
+            # can keep its own dtype
+            if hasattr(value, "dtype") and is_dtype_equal(values.dtype, value.dtype):
+                dtype = self.dtype
+            else:
+                dtype = "infer"
+
+        else:
             # current dtype cannot store value, coerce to common dtype
             find_dtype = False
 
@@ -902,13 +912,6 @@ def setitem(self, indexer, value):
                 if not is_dtype_equal(self.dtype, dtype):
                     b = self.astype(dtype)
                     return b.setitem(indexer, value)
-        else:
-            values = self._coerce_values(values)
-            # can keep its own dtype
-            if hasattr(value, "dtype") and is_dtype_equal(values.dtype, value.dtype):
-                dtype = self.dtype
-            else:
-                dtype = "infer"
 
         # value must be storeable at this moment
         arr_value = np.array(value)
@@ -938,7 +941,7 @@ def setitem(self, indexer, value):
         elif (
             len(arr_value.shape)
             and arr_value.shape[0] == values.shape[0]
-            and np.prod(arr_value.shape) == np.prod(values.shape)
+            and arr_value.size == values.size
         ):
             values[indexer] = value
             try:
@@ -1134,9 +1137,7 @@ def coerce_to_target_dtype(self, other):
         try:
             return self.astype(dtype)
         except (ValueError, TypeError, OverflowError):
-            pass
-
-        return self.astype(object)
+            return self.astype(object)
 
     def interpolate(
         self,

pandas/core/internals/construction.py

@@ -213,7 +213,7 @@ def init_dict(data, index, columns, dtype=None):
         arrays = Series(data, index=columns, dtype=object)
         data_names = arrays.index
 
-        missing = arrays.isnull()
+        missing = arrays.isna()
         if index is None:
             # GH10856
             # raise ValueError if only scalars in dict

pandas/core/series.py

@@ -112,7 +112,7 @@ def remove_na(arr):
     """
 
     warnings.warn(
-        "remove_na is deprecated and is a private " "function. Do not use.",
+        "remove_na is deprecated and is a private function. Do not use.",
         FutureWarning,
         stacklevel=2,
     )
@@ -127,7 +127,7 @@ def _coerce_method(converter):
     def wrapper(self):
         if len(self) == 1:
             return converter(self.iloc[0])
-        raise TypeError("cannot convert the series to " "{0}".format(str(converter)))
+        raise TypeError("cannot convert the series to {0}".format(str(converter)))
 
     wrapper.__name__ = "__{name}__".format(name=converter.__name__)
     return wrapper
@@ -226,7 +226,7 @@ def __init__(
 
             if isinstance(data, MultiIndex):
                 raise NotImplementedError(
-                    "initializing a Series from a " "MultiIndex is not supported"
+                    "initializing a Series from a MultiIndex is not supported"
                 )
             elif isinstance(data, Index):
                 if name is None:
@@ -275,7 +275,7 @@ def __init__(
                 pass
             elif isinstance(data, (set, frozenset)):
                 raise TypeError(
-                    "{0!r} type is unordered" "".format(data.__class__.__name__)
+                    "{0!r} type is unordered".format(data.__class__.__name__)
                 )
             elif isinstance(data, ABCSparseArray):
                 # handle sparse passed here (and force conversion)
@@ -604,7 +604,7 @@ def asobject(self):
         *this is an internal non-public method*
         """
         warnings.warn(
-            "'asobject' is deprecated. Use 'astype(object)'" " instead",
+            "'asobject' is deprecated. Use 'astype(object)' instead",
             FutureWarning,
             stacklevel=2,
         )
@@ -710,7 +710,7 @@ def put(self, *args, **kwargs):
         numpy.ndarray.put
         """
         warnings.warn(
-            "`put` has been deprecated and will be removed in a" "future version.",
+            "`put` has been deprecated and will be removed in a future version.",
             FutureWarning,
             stacklevel=2,
         )
@@ -955,7 +955,7 @@ def real(self):
         .. deprecated 0.25.0
         """
         warnings.warn(
-            "`real` has be deprecated and will be removed in a " "future verison",
+            "`real` has be deprecated and will be removed in a future version",
             FutureWarning,
             stacklevel=2,
         )
@@ -973,7 +973,7 @@ def imag(self):
         .. deprecated 0.25.0
         """
         warnings.warn(
-            "`imag` has be deprecated and will be removed in a " "future verison",
+            "`imag` has be deprecated and will be removed in a future version",
             FutureWarning,
             stacklevel=2,
         )
@@ -1561,7 +1561,7 @@ def reset_index(self, level=None, drop=False, name=None, inplace=False):
                 ).__finalize__(self)
         elif inplace:
             raise TypeError(
-                "Cannot reset_index inplace on a Series " "to create a DataFrame"
+                "Cannot reset_index inplace on a Series to create a DataFrame"
             )
         else:
             df = self.to_frame(name)
@@ -1813,7 +1813,7 @@ def to_sparse(self, kind="block", fill_value=None):
         """
 
         warnings.warn(
-            "Series.to_sparse is deprecated and will be removed " "in a future version",
+            "Series.to_sparse is deprecated and will be removed in a future version",
             FutureWarning,
             stacklevel=2,
         )
@@ -4055,7 +4055,7 @@ def _reduce(
         elif isinstance(delegate, np.ndarray):
             if numeric_only:
                 raise NotImplementedError(
-                    "Series.{0} does not implement " "numeric_only.".format(name)
+                    "Series.{0} does not implement numeric_only.".format(name)
                 )
             with np.errstate(all="ignore"):
                 return op(delegate, skipna=skipna, **kwds)