Replaced numpy type aliases; fix CI failure (#34835)

Author: WillAyd

asv_bench/benchmarks/pandas_vb_common.py

@@ -33,7 +33,7 @@
     np.uint8,
 ]
 datetime_dtypes = [np.datetime64, np.timedelta64]
-string_dtypes = [np.object]
+string_dtypes = [object]
 try:
     extension_dtypes = [
         pd.Int8Dtype,

asv_bench/benchmarks/series_methods.py

@@ -58,17 +58,15 @@ def time_isin_nan_values(self):
 
 class IsInForObjects:
     def setup(self):
-        self.s_nans = Series(np.full(10 ** 4, np.nan)).astype(np.object)
-        self.vals_nans = np.full(10 ** 4, np.nan).astype(np.object)
-        self.s_short = Series(np.arange(2)).astype(np.object)
-        self.s_long = Series(np.arange(10 ** 5)).astype(np.object)
-        self.vals_short = np.arange(2).astype(np.object)
-        self.vals_long = np.arange(10 ** 5).astype(np.object)
+        self.s_nans = Series(np.full(10 ** 4, np.nan)).astype(object)
+        self.vals_nans = np.full(10 ** 4, np.nan).astype(object)
+        self.s_short = Series(np.arange(2)).astype(object)
+        self.s_long = Series(np.arange(10 ** 5)).astype(object)
+        self.vals_short = np.arange(2).astype(object)
+        self.vals_long = np.arange(10 ** 5).astype(object)
         # because of nans floats are special:
-        self.s_long_floats = Series(np.arange(10 ** 5, dtype=np.float)).astype(
-            np.object
-        )
-        self.vals_long_floats = np.arange(10 ** 5, dtype=np.float).astype(np.object)
+        self.s_long_floats = Series(np.arange(10 ** 5, dtype=np.float)).astype(object)
+        self.vals_long_floats = np.arange(10 ** 5, dtype=np.float).astype(object)
 
     def time_isin_nans(self):
         # if nan-objects are different objects,

asv_bench/benchmarks/sparse.py

@@ -32,7 +32,7 @@ def time_series_to_frame(self):
 
 class SparseArrayConstructor:
 
-    params = ([0.1, 0.01], [0, np.nan], [np.int64, np.float64, np.object])
+    params = ([0.1, 0.01], [0, np.nan], [np.int64, np.float64, object])
     param_names = ["dense_proportion", "fill_value", "dtype"]
 
     def setup(self, dense_proportion, fill_value, dtype):

doc/source/user_guide/io.rst

@@ -1884,7 +1884,7 @@ Fallback behavior
 If the JSON serializer cannot handle the container contents directly it will
 fall back in the following manner:
 
-* if the dtype is unsupported (e.g. ``np.complex``) then the ``default_handler``, if provided, will be called
+* if the dtype is unsupported (e.g. ``np.complex_``) then the ``default_handler``, if provided, will be called
   for each value, otherwise an exception is raised.
 
 * if an object is unsupported it will attempt the following:

pandas/_libs/hashtable_class_helper.pxi.in

@@ -178,7 +178,7 @@ cdef class StringVector:
             Py_ssize_t n
             object val
 
-        ao = np.empty(self.data.n, dtype=np.object)
+        ao = np.empty(self.data.n, dtype=object)
         for i in range(self.data.n):
             val = self.data.data[i]
             ao[i] = val

pandas/_libs/hashtable_func_helper.pxi.in

@@ -94,7 +94,7 @@ cpdef value_count_{{dtype}}({{c_type}}[:] values, bint dropna):
     build_count_table_{{dtype}}(values, table, dropna)
     {{endif}}
 
-    result_keys = np.empty(table.n_occupied, dtype=np.{{dtype}})
+    result_keys = np.empty(table.n_occupied, '{{dtype}}')
     result_counts = np.zeros(table.n_occupied, dtype=np.int64)
 
     {{if dtype == 'object'}}

pandas/_libs/parsers.pyx

@@ -2037,7 +2037,7 @@ def _concatenate_chunks(list chunks):
         numpy_dtypes = {x for x in dtypes if not is_categorical_dtype(x)}
         if len(numpy_dtypes) > 1:
             common_type = np.find_common_type(numpy_dtypes, [])
-            if common_type == np.object:
+            if common_type == object:
                 warning_columns.append(str(name))
 
         dtype = dtypes.pop()

pandas/_libs/sparse.pyx

@@ -791,4 +791,4 @@ def make_mask_object_ndarray(ndarray[object, ndim=1] arr, object fill_value):
         if value == fill_value and type(value) == type(fill_value):
             mask[i] = 0
 
-    return mask.view(dtype=np.bool)
+    return mask.view(dtype=bool)

pandas/_libs/testing.pyx

@@ -11,7 +11,7 @@ cdef NUMERIC_TYPES = (
     bool,
     int,
     float,
-    np.bool,
+    np.bool_,
     np.int8,
     np.int16,
     np.int32,

pandas/core/algorithms.py

@@ -171,7 +171,7 @@ def _ensure_data(
         return values, dtype
 
     # we have failed, return object
-    values = np.asarray(values, dtype=np.object)
+    values = np.asarray(values, dtype=object)
     return ensure_object(values), np.dtype("object")