UP my solution

pandas_questions.py

@@ -8,16 +8,17 @@
 To do that, you will load the data as pandas.DataFrame, merge the info and
 aggregate them by regions and finally plot them on a map using `geopandas`.
 """
+
 import pandas as pd
 import geopandas as gpd
 import matplotlib.pyplot as plt
 
 
 def load_data():
-    """Load data from the CSV files referundum/regions/departments."""
-    referendum = pd.DataFrame({})
-    regions = pd.DataFrame({})
-    departments = pd.DataFrame({})
+    """Load data from the CSV files referendum/regions/departments."""
+    referendum = pd.read_csv('data/referendum.csv', sep=';')
+    regions = pd.read_csv('data/regions.csv')
+    departments = pd.read_csv('data/departments.csv')
 
     return referendum, regions, departments
 
@@ -28,18 +29,35 @@ def merge_regions_and_departments(regions, departments):
     The columns in the final DataFrame should be:
     ['code_reg', 'name_reg', 'code_dep', 'name_dep']
     """
+    regions_merge = regions[['code', 'name']]
+    departments_merge = departments[['region_code', 'code', 'name']]
+
+    regions_merge = regions_merge.rename(columns={'code': 'code_reg',
+                                                  'name': 'name_reg'})
+    departments_merge = departments_merge.rename(
+        columns={'region_code': 'code_reg',
+                 'code': 'code_dep', 'name': 'name_dep'})
+    regions_and_departments = pd.merge(
+        regions_merge, departments_merge, on='code_reg', how='left')
 
-    return pd.DataFrame({})
+    return regions_and_departments
 
 
 def merge_referendum_and_areas(referendum, regions_and_departments):
     """Merge referendum and regions_and_departments in one DataFrame.
 
-    You can drop the lines relative to DOM-TOM-COM departments, and the
-    french living abroad.
+    You can drop the lines relative to DOM-TOM-COM departments,
+    and the French living abroad.
     """
+    regions_and_departments["code_dep"] = (
+        regions_and_departments["code_dep"].str.lstrip('0')
+    )
+    referendum = referendum[referendum['Department code'].str[0] != 'Z']
+    referendum_and_areas = referendum.merge(
+        regions_and_departments, how='inner', left_on='Department code',
+        right_on='code_dep')
 
-    return pd.DataFrame({})
+    return referendum_and_areas
 
 
 def compute_referendum_result_by_regions(referendum_and_areas):
@@ -48,36 +66,59 @@ def compute_referendum_result_by_regions(referendum_and_areas):
     The return DataFrame should be indexed by `code_reg` and have columns:
     ['name_reg', 'Registered', 'Abstentions', 'Null', 'Choice A', 'Choice B']
     """
-
-    return pd.DataFrame({})
+    count_referendum_regions = referendum_and_areas[['name_reg',
+                                                     'Registered',
+                                                     'Abstentions',
+                                                     'Null', 'Choice A',
+                                                     'Choice B', 'code_reg']]
+    return count_referendum_regions.groupby('code_reg').agg({
+        'Registered': 'sum',
+        'Abstentions': 'sum',
+        'Null': 'sum',
+        'Choice A': 'sum',
+        'Choice B': 'sum',
+        'name_reg': 'first'
+    })
 
 
 def plot_referendum_map(referendum_result_by_regions):
     """Plot a map with the results from the referendum.
 
     * Load the geographic data with geopandas from `regions.geojson`.
     * Merge these info into `referendum_result_by_regions`.
-    * Use the method `GeoDataFrame.plot` to display the result map. The results
-      should display the rate of 'Choice A' over all expressed ballots.
+    * Use the method `GeoDataFrame.plot` to display the result map.
+    * The results should display the rate of 'Choice A' over all
+    expressed ballots.
     * Return a gpd.GeoDataFrame with a column 'ratio' containing the results.
     """
+    regions_geo = gpd.read_file('data/regions.geojson')
 
-    return gpd.GeoDataFrame({})
+    referendum_result_by_regions['ratio'] = (
+        referendum_result_by_regions['Choice A'] /
+        (referendum_result_by_regions['Choice A'] +
+         referendum_result_by_regions['Choice B'])
+    )
 
+    regions_ratio = regions_geo.merge(
+        referendum_result_by_regions, how='inner', left_on='nom',
+        right_on='name_reg')
+
+    regions_ratio.plot(column='ratio')
+
+    return regions_ratio
 
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     referendum, df_reg, df_dep = load_data()
-    regions_and_departments = merge_regions_and_departments(
-        df_reg, df_dep
-    )
+
+    regions_and_departments = merge_regions_and_departments(df_reg, df_dep)
     referendum_and_areas = merge_referendum_and_areas(
-        referendum, regions_and_departments
-    )
+        referendum, regions_and_departments)
     referendum_results = compute_referendum_result_by_regions(
-        referendum_and_areas
-    )
+        referendum_and_areas)
+
     print(referendum_results)
 
     plot_referendum_map(referendum_results)
+
     plt.show()