Add hist_boxplot to ClassPOV

notes_tools/tools/evaluation.py

@@ -6,6 +6,7 @@ import numpy as np
 from notes_tools.tools.marks_plottings import (pie_pivot_table,
         parallel_on,
         radar_on,
+        hist_boxplot
         )
 
 import seaborn as sns
@@ -187,6 +188,16 @@ class Classe(object):
         self.exo_df = exo_df
         self.eval_df = eval_df
 
+    @property
+    def desription(self):
+        """ Desribe on marks """
+        # TODO: not working... |ven. mars 31 18:48:17 EAT 2017
+        try:
+            self._description
+        except AttributeError:
+            self._description = self.eval_df["Mark"].describe()
+        return self._description
+
     @property
     def marks_tabular(self):
         """ Latex tabular with marks of students"""
@@ -197,6 +208,15 @@ class Classe(object):
             self._marks_tabular.columns = ["Élèves", "Note"]
         return self._marks_tabular.to_latex()
 
+    @property
+    def hist_boxplot(self):
+        """ Marks histogram and associed box plot """
+        try:
+            self._hist_boxplot
+        except AttributeError:
+            self._hist_boxplot = hist_boxplot(self.eval_df)
+        return self._hist_boxplot
+
     @property
     def level_heatmap(self):
         """ Heapmap on acheivement level """

notes_tools/tools/marks_plottings.py

@@ -67,6 +67,27 @@ def marks_hist(df, **kwargs):
 
     return ax
 
+def hist_boxplot(df, kwargs_hist=[], kwargs_box=[]):
+    f, (ax_hist, ax_box) = plt.subplots(2, sharex=True, 
+            gridspec_kw={"height_ratios": (.85, .15)})
+
+    marks_hist(df, ax = ax_hist, rwidth=0.9)
+
+    ev_desc = df["Mark"].describe()
+    m = ev_desc["mean"]
+
+    ax_hist.plot([m,m], ax_hist.get_ylim())
+    ax_hist.annotate(round(ev_desc["mean"],1),
+            xy=(ev_desc["mean"] + 0.2, ax_hist.get_ylim()[1]-0.2))
+
+    df["Mark"].plot.box(ax = ax_box, vert=False, widths = 0.6)
+    ax_box.set_yticklabels("")
+    for e in ["min", "25%", "50%", "75%", "max"]:
+        ax_box.annotate(ev_desc[e], 
+                xy=(ev_desc[e] - 0.2, ax_box.get_ylim()[1]))
+
+    return f, (ax_hist, ax_box)
+
 def parallel_on(df, index, student=None):
     """ Plot parallel one line by student