replace numpy aliases with builtin types (#2236)

examples/many_pairwise_correlations.py

@@ -21,7 +21,7 @@
 corr = d.corr()
 
 # Generate a mask for the upper triangle
-mask = np.triu(np.ones_like(corr, dtype=np.bool))
+mask = np.triu(np.ones_like(corr, dtype=bool))
 
 # Set up the matplotlib figure
 f, ax = plt.subplots(figsize=(11, 9))

seaborn/axisgrid.py

@@ -337,7 +337,7 @@ def __init__(
 
         # Make a boolean mask that is True anywhere there is an NA
         # value in one of the faceting variables, but only if dropna is True
-        none_na = np.zeros(len(data), np.bool)
+        none_na = np.zeros(len(data), bool)
         if dropna:
             row_na = none_na if row is None else data[row].isnull()
             col_na = none_na if col is None else data[col].isnull()
@@ -1483,8 +1483,8 @@ def map_diag(self, func, **kwargs):
                 for ax in diag_axes[1:]:
                     group.join(ax, diag_axes[0])
 
-            self.diag_vars = np.array(diag_vars, np.object)
-            self.diag_axes = np.array(diag_axes, np.object)
+            self.diag_vars = np.array(diag_vars, np.object_)
+            self.diag_axes = np.array(diag_axes, np.object_)
 
         if "hue" not in signature(func).parameters:
             return self._map_diag_iter_hue(func, **kwargs)
@@ -1629,7 +1629,7 @@ def _plot_bivariate_iter_hue(self, x_var, y_var, ax, func, **kwargs):
                 data_k = hue_grouped.get_group(label_k)
             except KeyError:
                 data_k = pd.DataFrame(columns=axes_vars,
-                                      dtype=np.float)
+                                      dtype=float)
 
             if self._dropna:
                 data_k = data_k[axes_vars].dropna()

seaborn/categorical.py

@@ -79,7 +79,7 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
 
                 # Convert to a list of arrays, the common representation
                 iter_data = plot_data.iteritems()
-                plot_data = [np.asarray(s, np.float) for k, s in iter_data]
+                plot_data = [np.asarray(s, float) for k, s in iter_data]
 
             # Option 1b:
             # The input data is an array or list
@@ -125,7 +125,7 @@ def establish_variables(self, x=None, y=None, hue=None, data=None,
                     plot_data = data
 
                 # Convert to a list of arrays, the common representation
-                plot_data = [np.asarray(d, np.float) for d in plot_data]
+                plot_data = [np.asarray(d, float) for d in plot_data]
 
                 # The group names will just be numeric indices
                 group_names = list(range((len(plot_data))))
@@ -1058,7 +1058,7 @@ def point_colors(self):
         for i, group_data in enumerate(self.plot_data):
 
             # Initialize the array for this group level
-            group_colors = np.empty(group_data.size, np.int)
+            group_colors = np.empty(group_data.size, int)
             if isinstance(group_data, pd.Series):
                 group_colors = pd.Series(group_colors, group_data.index)
 
@@ -1118,10 +1118,10 @@ def draw_stripplot(self, ax, kws):
             if self.plot_hues is None or not self.dodge:
 
                 if self.hue_names is None:
-                    hue_mask = np.ones(group_data.size, np.bool)
+                    hue_mask = np.ones(group_data.size, bool)
                 else:
                     hue_mask = np.array([h in self.hue_names
-                                         for h in self.plot_hues[i]], np.bool)
+                                         for h in self.plot_hues[i]], bool)
                     # Broken on older numpys
                     # hue_mask = np.in1d(self.plot_hues[i], self.hue_names)
 
@@ -1354,10 +1354,10 @@ def draw_swarmplot(self, ax, kws):
                 width = self.width
 
                 if self.hue_names is None:
-                    hue_mask = np.ones(group_data.size, np.bool)
+                    hue_mask = np.ones(group_data.size, bool)
                 else:
                     hue_mask = np.array([h in self.hue_names
-                                         for h in self.plot_hues[i]], np.bool)
+                                         for h in self.plot_hues[i]], bool)
                     # Broken on older numpys
                     # hue_mask = np.in1d(self.plot_hues[i], self.hue_names)
 

seaborn/distributions.py

@@ -2553,7 +2553,7 @@ def distplot(a=None, bins=None, hist=True, kde=True, rug=False, fit=None,
         a = x
 
     # Make a a 1-d float array
-    a = np.asarray(a, np.float)
+    a = np.asarray(a, float)
     if a.ndim > 1:
         a = a.squeeze()
 

seaborn/matrix.py

@@ -66,7 +66,7 @@ def _matrix_mask(data, mask):
 
     """
     if mask is None:
-        mask = np.zeros(data.shape, np.bool)
+        mask = np.zeros(data.shape, bool)
 
     if isinstance(mask, np.ndarray):
         # For array masks, ensure that shape matches data then convert
@@ -76,7 +76,7 @@ def _matrix_mask(data, mask):
         mask = pd.DataFrame(mask,
                             index=data.index,
                             columns=data.columns,
-                            dtype=np.bool)
+                            dtype=bool)
 
     elif isinstance(mask, pd.DataFrame):
         # For DataFrame masks, ensure that semantic labels match data
@@ -196,7 +196,7 @@ def _determine_cmap_params(self, plot_data, vmin, vmax,
         """Use some heuristics to set good defaults for colorbar and range."""
 
         # plot_data is a np.ma.array instance
-        calc_data = plot_data.astype(np.float).filled(np.nan)
+        calc_data = plot_data.astype(float).filled(np.nan)
         if vmin is None:
             if robust:
                 vmin = np.nanpercentile(calc_data, 2)

seaborn/relational.py

@@ -384,7 +384,7 @@ def aggregate(self, vals, grouper, units=None):
         seed = self.seed
 
         # Define a "null" CI for when we only have one value
-        null_ci = pd.Series(index=["low", "high"], dtype=np.float)
+        null_ci = pd.Series(index=["low", "high"], dtype=float)
 
         # Function to bootstrap in the context of a pandas group by
         def bootstrapped_cis(vals):

seaborn/tests/test_axisgrid.py

@@ -622,7 +622,7 @@ def test_hue_kws(self):
     def test_dropna(self):
 
         df = self.df.copy()
-        hasna = pd.Series(np.tile(np.arange(6), 10), dtype=np.float)
+        hasna = pd.Series(np.tile(np.arange(6), 10), dtype=float)
         hasna[hasna == 5] = np.nan
         df["hasna"] = hasna
         g = ag.FacetGrid(df, dropna=False, row="hasna")

seaborn/tests/test_matrix.py

@@ -87,7 +87,7 @@ def test_df_multindex_input(self):
         npt.assert_array_equal(p.xticklabels, combined_tick_labels)
         nt.assert_equal(p.xlabel, "letter-number")
 
-    @pytest.mark.parametrize("dtype", [np.float, np.int64, np.object])
+    @pytest.mark.parametrize("dtype", [float, np.int64, object])
     def test_mask_input(self, dtype):
         kws = self.default_kws.copy()
 
@@ -443,7 +443,7 @@ def test_mask_validation(self):
 
     def test_missing_data_mask(self):
 
-        data = pd.DataFrame(np.arange(4, dtype=np.float).reshape(2, 2))
+        data = pd.DataFrame(np.arange(4, dtype=float).reshape(2, 2))
         data.loc[0, 0] = np.nan
         mask = mat._matrix_mask(data, None)
         npt.assert_array_equal(mask, [[True, False], [False, False]])