Built composite overall district rating.

main/district_score.py

@@ -0,0 +1,58 @@
+#! /usr/bin/env 
+
+import pandas as pd
+import math
+
+# helper to calculate an overall district rating
+def get_overall_rating(df: pd.DataFrame):
+    '''
+    Returns a DataFrame that contains the overall district scores.
+        Parameters:
+                df (DataFrame): 
+        Returns:
+                overall (DataFrame): 
+    '''
+    weights = {
+        'e': 0.3,
+        'm': 0.2,
+        'h': 0.5
+    }
+    levels = weights.keys()
+    empty_stats = {
+        'e_ave': 0,
+        'm_ave': 0,
+        'h_ave': 0,
+        'weighted_ave': 0    
+    }
+    overall = pd.DataFrame()
+
+    districts = list(df.index.unique(level=0))
+    for district in districts:
+        scores = {}
+        stats = empty_stats
+        weighted_ave = 0
+        schools = list(df.loc[district].index.unique(level=0))
+        if len(schools) <= 1:
+            continue
+        for level in levels:
+            scores[level] = []
+            for school in schools:
+                this_school = df.loc[[(district,school)]]
+                codes = this_school['level-codes'].iloc[0]
+                if level in codes:
+                    rating = float(this_school['rating'].iloc[0])
+                    if not math.isnan(rating):
+                        scores[level].append(this_school['rating'].iloc[0])
+            #print(district, scores)
+            try:
+                stats[level+'_ave'] = round(sum(scores[level])/len(scores[level]),2)
+            except:
+                continue
+        for weight in weights.keys():
+            weighted_ave += weights[weight]*stats[weight+'_ave']
+        stats['weighted_ave'] = round(weighted_ave,2)
+        district_dict = {'district-name': district}
+        district_dict = {**district_dict, **stats}
+        overall = pd.DataFrame(district_dict)
+        print(overall)
+        return