working on 3.9

rowers/views/analysisviews.py

@@ -414,11 +414,26 @@ def trendflexdata(workouts, options, userid=0):
         groups = datadf.groupby(pd.cut(datadf['days ago'], bins,
                                        labels=False))
 
-    xvalues = groups.mean()[xparam]
-    yvalues = groups.mean()[yparam]
-    xerror = groups.std()[xparam]
-    yerror = groups.std()[yparam]
-    groupsize = groups.count()[xparam]
+    xvalues = []
+    yvalues = []
+    xerror = []
+    yerror = []
+    groupsize = []
+    groupval = []
+    for key, item in groups:
+        xvalues.append(groups.get_group(key)[xparam].mean())
+        yvalues.append(groups.get_group(key)[yparam].mean())
+        xerror.append(groups.get_group(key)[xparam].std())
+        yerror.append(groups.get_group(key)[yparam].std())
+        groupsize.append(len(groups.get_group(key)[xparam]))
+        groupval.append(groups.get_group(key)[groupby].mean())
+
+
+    xvalues = pd.Series(xvalues)
+    yvalues = pd.Series(yvalues)
+    xerror = pd.Series(xerror)
+    yerror = pd.Series(yerror)
+    groupsize = pd.Series(groupsize)
 
     mask = groupsize <= min([0.01*groupsize.sum(), 0.2*groupsize.mean()])
     xvalues.loc[mask] = np.nan
@@ -458,7 +473,7 @@ def trendflexdata(workouts, options, userid=0):
 
     if groupby != 'date':
         try:
-            df['groupval'] = groups.mean()[groupby]
+            df['groupval'] = pd.Series(groupval)
             df.loc[mask, 'groupval'] = np.nan
 
             groupcols = df['groupval']