MAINT: More Future and Deprecations

Author: bashtage

statsmodels/graphics/mosaicplot.py

@@ -326,7 +326,7 @@ def _normalize_dataframe(dataframe, index):
     #groupby the given keys, extract the same columns and count the element
     # then collapse them with a mean
     data = dataframe[index].dropna()
-    grouped = data.groupby(index, sort=False)
+    grouped = data.groupby(index, sort=False, observed=False)
     counted = grouped[index].count()
     averaged = counted.mean(axis=1)
     # Fill empty missing with 0, see GH5639

statsmodels/iolib/summary2.py

@@ -595,7 +595,10 @@ def _df_to_simpletable(df, align='r', float_format="%.4f", header=True,
                        index=True, table_dec_above='-', table_dec_below=None,
                        header_dec_below='-', pad_col=0, pad_index=0):
     dat = df.copy()
-    dat = dat.applymap(lambda x: _formatter(x, float_format))
+    try:
+        dat = dat.map(lambda x: _formatter(x, float_format))
+    except AttributeError:
+        dat = dat.applymap(lambda x: _formatter(x, float_format))
     if header:
         headers = [str(x) for x in dat.columns.tolist()]
     else:

statsmodels/nonparametric/kde.py

@@ -215,7 +215,7 @@ def cdf(self):
             a, b = kern.domain
 
         def func(x, s):
-            return kern.density(s, x)
+            return np.squeeze(kern.density(s, x))
 
         support = self.support
         support = np.r_[a, support]

statsmodels/robust/norms.py

@@ -1059,7 +1059,7 @@ def estimate_location(a, scale, norm=None, axis=0, initial=None,
     for _ in range(maxiter):
         W = norm.weights((a-mu)/scale)
         nmu = np.sum(W*a, axis) / np.sum(W, axis)
-        if np.alltrue(np.less(np.abs(mu - nmu), scale * tol)):
+        if np.all(np.less(np.abs(mu - nmu), scale * tol)):
             return nmu
         else:
             mu = nmu

statsmodels/sandbox/bspline.py

@@ -482,7 +482,7 @@ def fit(self, y, x=None, weights=None, pen=0.):
 
         # throw out rows with zeros (this happens at boundary points!)
 
-        mask = np.flatnonzero(1 - np.alltrue(np.equal(bt, 0), axis=0))
+        mask = np.flatnonzero(1 - np.all(np.equal(bt, 0), axis=0))
 
         bt = bt[:,mask]
         y = y[mask]

statsmodels/sandbox/distributions/extras.py

@@ -817,7 +817,10 @@ def _sf(self, x, *args, **kwargs):
             return 1.0 - self._cdf(x, *args, **kwargs)
 
     def _munp(self, n, *args, **kwargs):
-        return self._mom0_sc(n, *args)
+        out = np.squeeze(self._mom0_sc(n, *args))
+        if np.isscalar(out):
+            return float(out)
+        return out
 
 
 # ppf might not be possible in general case?

statsmodels/sandbox/distributions/tests/test_transf.py

@@ -8,7 +8,7 @@
 
 todo:
 change moment calculation, (currently uses default _ppf method - I think)
->>> lognormalg.moment(4)
+# >>> lognormalg.moment(4)
 Warning: The algorithm does not converge.  Roundoff error is detected
   in the extrapolation table.  It is assumed that the requested tolerance
   cannot be achieved, and that the returned result (if full_output = 1) is

statsmodels/stats/libqsturng/qsturng_.py

@@ -839,7 +839,10 @@ def opt_func(p, r, v):
         soln = 1. - fminbound(opt_func, .1, .999, args=(r,v))
         return np.atleast_1d(soln)
 
-_vpsturng = np.vectorize(_psturng)
+def _psturng_scalar(q, r, v):
+    return np.squeeze(_psturng(q, r, v))
+
+_vpsturng = np.vectorize(_psturng_scalar)
 _vpsturng.__doc__ = """vector version of psturng"""
 
 def psturng(q, r, v):

statsmodels/tsa/statespace/dynamic_factor_mq.py

@@ -1766,8 +1766,10 @@ def summary(self, truncate_endog_names=None):
         data = self.factor_block_orders.reset_index()
         data['block'] = data['block'].map(
             lambda factor_names: ', '.join(factor_names))
-        data[['order']] = (
-            data[['order']].applymap(str))
+        try:
+            data[['order']] = data[['order']].map(str)
+        except AttributeError:
+            data[['order']] = data[['order']].applymap(str)
 
         params_data = data.values
         params_header = data.columns.map(str).tolist()
@@ -4221,7 +4223,10 @@ def summary(self, alpha=.05, start=None, title=None, model_name=None,
             data = pd.DataFrame(
                 self.filter_results.design[:, mod._s['factors_L1'], 0],
                 index=endog_names, columns=mod.factor_names)
-            data = data.applymap(lambda s: '%.2f' % s)
+            try:
+                data = data.map(lambda s: '%.2f' % s)
+            except AttributeError:
+                data = data.applymap(lambda s: '%.2f' % s)
 
             # Idiosyncratic terms
             # data['   '] = '   '
@@ -4231,8 +4236,12 @@ def summary(self, alpha=.05, start=None, title=None, model_name=None,
                     self.params[mod._p['idiosyncratic_ar1']])
                 k_idio += 1
             data['var.'] = self.params[mod._p['idiosyncratic_var']]
-            data.iloc[:, -k_idio:] = data.iloc[:, -k_idio:].applymap(
-                lambda s: '%.2f' % s)
+            try:
+                data.iloc[:, -k_idio:] = data.iloc[:, -k_idio:].map(
+                    lambda s: f'{s:.2f}')
+            except AttributeError:
+                data.iloc[:, -k_idio:] = data.iloc[:, -k_idio:].applymap(
+                    lambda s: f'{s:.2f}')
 
             data.index.name = 'Factor loadings:'
 
@@ -4273,7 +4282,10 @@ def summary(self, alpha=.05, start=None, title=None, model_name=None,
                                     index=block.factor_names,
                                     columns=lag_names)
                 data.index.name = ''
-                data = data.applymap(lambda s: '%.2f' % s)
+                try:
+                    data = data.map(lambda s: '%.2f' % s)
+                except AttributeError:
+                    data = data.applymap(lambda s: '%.2f' % s)
 
                 Q = self.filter_results.state_cov
                 # data[' '] = ''
@@ -4283,9 +4295,15 @@ def summary(self, alpha=.05, start=None, title=None, model_name=None,
                     data['   error covariance'] = block.factor_names
                     for j in range(block.k_factors):
                         data[block.factor_names[j]] = Q[ix1:ix2, ix1 + j]
-                data.iloc[:, -block.k_factors:] = (
-                    data.iloc[:, -block.k_factors:].applymap(
-                        lambda s: '%.2f' % s))
+                try:
+                    formatted_vals = data.iloc[:, -block.k_factors:].map(
+                        lambda s: f'{s:.2f}'
+                    )
+                except AttributeError:
+                    formatted_vals = data.iloc[:, -block.k_factors:].applymap(
+                        lambda s: f'{s:.2f}'
+                    )
+                data.iloc[:, -block.k_factors:] = formatted_vals
 
                 data = data.reset_index()