added percentage indicator in legend of PIE chart(s), regenerated too

itzmeanjan · Sep 12, 2019 · 3f7f1f5 · 3f7f1f5
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diff --git a/plots/mca_andaman_21042018_company_category.png b/plots/mca_andaman_21042018_company_category.png
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diff --git a/plots/mca_gujarat_21042018_company_category.png b/plots/mca_gujarat_21042018_company_category.png
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diff --git a/plots/mca_haryana_21042018_company_category.png b/plots/mca_haryana_21042018_company_category.png
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diff --git a/plots/mca_himachalpradesh_21042018_company_category.png b/plots/mca_himachalpradesh_21042018_company_category.png
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diff --git a/plots/mca_jammuandkashmir_21042018_company_category.png b/plots/mca_jammuandkashmir_21042018_company_category.png
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diff --git a/plots/mca_karnataka_21042018_company_category.png b/plots/mca_karnataka_21042018_company_category.png
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diff --git a/plots/mca_kerala_21042018_company_category.png b/plots/mca_kerala_21042018_company_category.png
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diff --git a/plots/mca_meghalaya_21042018_company_category.png b/plots/mca_meghalaya_21042018_company_category.png
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diff --git a/plots/mca_uttarakhand_21042018_company_principalBusinessActivity.png b/plots/mca_uttarakhand_21042018_company_principalBusinessActivity.png
diff --git a/plots/mca_uttarakhand_21042018_company_status.png b/plots/mca_uttarakhand_21042018_company_status.png
diff --git a/plots/mca_uttarakhand_21042018_company_subCategory.png b/plots/mca_uttarakhand_21042018_company_subCategory.png
diff --git a/plots/mca_uttarpradesh_21042018_company_category.png b/plots/mca_uttarpradesh_21042018_company_category.png
diff --git a/plots/mca_uttarpradesh_21042018_company_class.png b/plots/mca_uttarpradesh_21042018_company_class.png
diff --git a/plots/mca_uttarpradesh_21042018_company_principalBusinessActivity.png b/plots/mca_uttarpradesh_21042018_company_principalBusinessActivity.png
diff --git a/plots/mca_uttarpradesh_21042018_company_status.png b/plots/mca_uttarpradesh_21042018_company_status.png
diff --git a/plots/mca_uttarpradesh_21042018_company_subCategory.png b/plots/mca_uttarpradesh_21042018_company_subCategory.png
diff --git a/plots/mca_westbengal_21042018_company_category.png b/plots/mca_westbengal_21042018_company_category.png
diff --git a/plots/mca_westbengal_21042018_company_class.png b/plots/mca_westbengal_21042018_company_class.png
diff --git a/plots/mca_westbengal_21042018_company_principalBusinessActivity.png b/plots/mca_westbengal_21042018_company_principalBusinessActivity.png
diff --git a/plots/mca_westbengal_21042018_company_status.png b/plots/mca_westbengal_21042018_company_status.png
diff --git a/plots/mca_westbengal_21042018_company_subCategory.png b/plots/mca_westbengal_21042018_company_subCategory.png
diff --git a/util.py b/util.py
@@ -12,7 +12,7 @@
     exit(1)
 
 '''
-    Takes a Dict[str, int], which is generated by function(s) defined below 
+    Takes a Dict[str, int], which is generated by function(s) defined below
     ( categorizes company dataset, for a certain State in India, using various parameters )
     & a targetPath on local file system ( an image of `*.png` form ),
     where to store this generated PIE chart.
@@ -30,10 +30,15 @@ def plotCategorizedCompanyDataForACertainState(dataSet, targetPath, title):
             'weight': 'normal',
             'size': 12
         }
-        labels = sorted(dataSet, key=lambda elem:
-                        dataSet[elem], reverse=True)
+        # calculating total # of companies we're considering here, for a certain state
+        total = sum([dataSet[i] for i in dataSet])
+        _tmpLabels = sorted(dataSet, key=lambda e:
+                            dataSet[e], reverse=True)
+        # now PIE chart's labels will include a percentage field too, in its legend
+        labels = ['{} ( {:.4f} % )'.format(
+            i, dataSet[i]*100/total) for i in _tmpLabels]
         # this is the actual data to be plotted
-        data = [dataSet[i] for i in labels]
+        data = [dataSet[i] for i in _tmpLabels]
         # figure on which pie chart to be drawn ( of size 2400x1200 )
         plt.figure(figsize=(24, 12), dpi=100)
         patches, _ = plt.pie(data)  # plotting pie chart