Remove useless precompilation, update new images, adapt new pandas parameters

Author: Jacobluke-

dabest/_dabest_object.py

@@ -112,7 +112,7 @@ def __init__(
         # Determine the kind of estimation plot we need to produce.
         if all([isinstance(i, (str, int, float)) for i in idx]):
             # flatten out idx.
-            all_plot_groups = pd.unique([t for t in idx]).tolist()
+            all_plot_groups = pd.unique(pd.Series(idx)).tolist()
             if len(idx) > len(all_plot_groups):
                 err0 = "`idx` contains duplicated groups. Please remove any duplicates and try again."
                 raise ValueError(err0)
@@ -122,7 +122,7 @@ def __init__(
             self.__idx = (idx,)
 
         elif all([isinstance(i, (tuple, list)) for i in idx]):
-            all_plot_groups = pd.unique([tt for t in idx for tt in t]).tolist()
+            all_plot_groups = pd.unique(pd.Series([tt for t in idx for tt in t])).tolist()
 
             actual_groups_given = sum([len(i) for i in idx])
 

dabest/_delta_objects.py

@@ -388,7 +388,7 @@ def __init__(self, effectsizedataframe, permutation_count,
         # compute the variances of each control group and each test group
         control_var=[]
         test_var=[]
-        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar)}
+        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}
         for j, current_tuple in enumerate(idx):
             cname = current_tuple[0]
             control = grouped_data[cname]

dabest/_effsize_objects.py

@@ -731,7 +731,7 @@ def __pre_calc(self):
         out = []
         reprs = []
 
-        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar)}
+        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}
         if self.__delta2:
             mixed_data = []
             for j, current_tuple in enumerate(idx):

nbs/API/dabest_object.ipynb

@@ -180,7 +180,7 @@
     "        # Determine the kind of estimation plot we need to produce.\n",
     "        if all([isinstance(i, (str, int, float)) for i in idx]):\n",
     "            # flatten out idx.\n",
-    "            all_plot_groups = pd.unique([t for t in idx]).tolist()\n",
+    "            all_plot_groups = pd.unique(pd.Series(idx)).tolist()\n",
     "            if len(idx) > len(all_plot_groups):\n",
     "                err0 = \"`idx` contains duplicated groups. Please remove any duplicates and try again.\"\n",
     "                raise ValueError(err0)\n",
@@ -190,7 +190,7 @@
     "            self.__idx = (idx,)\n",
     "\n",
     "        elif all([isinstance(i, (tuple, list)) for i in idx]):\n",
-    "            all_plot_groups = pd.unique([tt for t in idx for tt in t]).tolist()\n",
+    "            all_plot_groups = pd.unique(pd.Series([tt for t in idx for tt in t])).tolist()\n",
     "\n",
     "            actual_groups_given = sum([len(i) for i in idx])\n",
     "\n",

nbs/API/delta_objects.ipynb

@@ -528,7 +528,7 @@
     "        # compute the variances of each control group and each test group\n",
     "        control_var=[]\n",
     "        test_var=[]\n",
-    "        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar)}\n",
+    "        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}\n",
     "        for j, current_tuple in enumerate(idx):\n",
     "            cname = current_tuple[0]\n",
     "            control = grouped_data[cname]\n",

nbs/API/effsize_objects.ipynb

@@ -890,7 +890,7 @@
     "        out = []\n",
     "        reprs = []\n",
     "\n",
-    "        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar)}\n",
+    "        grouped_data = {name: group[yvar].copy() for name, group in dat.groupby(xvar, observed=False)}\n",
     "        if self.__delta2:\n",
     "            mixed_data = []\n",
     "            for j, current_tuple in enumerate(idx):\n",