add the get_commits_per_region function

utils/plot.py

@@ -1,8 +1,10 @@
 __copyright__ = "Copyright (C) 2023 Ali Mustapha"
 __license__ = "GPL-3.0-or-later"
 
-import plotly.graph_objects as go
+import pandas as pd
+import plotly.express as px
 from plotly.subplots import make_subplots
+import plotly.graph_objects as go
 
 def get_commits_per_gender(gender_counts):
     gender_counts=gender_counts[gender_counts["Predicted_Gender"]!="Unknown"]
@@ -38,4 +40,44 @@ def get_gender_percentage(df):
     counts = df['Predicted_Gender'].value_counts()
     colors = ["blue", "pink", "gray"]
     Gender_Percentage_plot = go.Figure(data=[go.Pie(labels=df['Predicted_Gender'].unique(), values=counts, marker=dict(colors=colors))])
-    return Gender_Percentage_plot
+    return Gender_Percentage_plot
+
+def get_commits_per_region(df,url):
+    Country_to_region=pd.read_csv("utils/CodeToRegion.csv")
+    Country_to_region=Country_to_region.rename(columns={"sub-region":"sub-region-prediction"})
+    
+    # Group by Year and sub-region-prediction, then count unique sub-regions
+    sub_region_counts = df.groupby(['Year', "sub-region-prediction"])["sub-region-prediction"].count().reset_index(name='Count')
+    # Merge the sub_region_counts DataFrame with the Country_to_region DataFrame
+    merged_df = sub_region_counts.merge(Country_to_region, on="sub-region-prediction", how="left")
+    color_max=df["sub-region-prediction"].value_counts().max()
+    # Create a choropleth map using plotly.express
+    fig = go.Figure(data=px.choropleth(
+        merged_df,
+        locations="code3",
+        color="Count",
+        hover_name="sub-region-prediction",
+        # color_continuous_scale=px.colors.qualitative.Set3,  # Choose your color scale
+        color_continuous_scale="Greens",
+        animation_frame="Year",
+        title="Counts by Sub-Region"
+    ))
+    
+    fig.update_layout(
+        title_text='Commits Counts by Sub-Region for '+url  
+        geo=dict(
+            showframe=False,
+            showcoastlines=False,
+            projection_type='equirectangular'
+        ),
+        annotations = [dict(
+            x=0.55,
+            y=0.1,
+            xref='paper',
+            yref='paper',
+            text='Source: <a href="https://huggingface.co/spaces/AliMustapha/Geo-GenderStudy">\
+                Geo-GenderStudy</a>',
+            showarrow = False
+        )]
+    )
+    return fig