milestone2

Author: AnnaelleMyriam

milestones/milestone-2/create_emdat_clean.py

@@ -7,32 +7,26 @@
 import pandas as pd
 from pathlib import Path
 
-# Paths
 DATA_DIR = Path('docs/data')
 INPUT_FILE = DATA_DIR / 'public_emdat_incl_hist_2026-03-17.xlsx'
 OUTPUT_FILE = DATA_DIR / 'emdat_clean.csv'
 
-# Configuration
 SHEET_NAME = 'EM-DAT Data'
 YEAR_START = 1975
 YEAR_END = 2025
 
-print('Creating emdat_clean.csv for website...')
 print(f'Reading: {INPUT_FILE}')
 
-# Load the Excel file
 df_full = pd.read_excel(INPUT_FILE, sheet_name=SHEET_NAME)
 print(f'Loaded {len(df_full):,} total records from Excel')
 
-# Filter to analysis period and natural disasters only
 df = df_full[
     (df_full['Start Year'].between(YEAR_START, YEAR_END, inclusive='both')) &
     (df_full['Disaster Group'] == 'Natural')
 ].copy()
 
 print(f'Filtered to {YEAR_START}-{YEAR_END}, Natural disasters: {len(df):,} records')
 
-# Select and rename columns for website
 df_clean = df[[
     'Start Year', 'ISO', 'Country', 'Region', 'Subregion',
     'Disaster Type', 'Disaster Subtype', 'Event Name',
@@ -48,7 +42,6 @@
     'lat', 'lon'
 ]
 
-# Map disaster types to website format (lowercase, simplified)
 type_mapping = {
     'Flood': 'flood',
     'Storm': 'storm',
@@ -57,10 +50,10 @@
     'Earthquake': 'earthquake',
     'Volcanic activity': 'volcano',
     'Landslide': 'landslide',
-    'Extreme temperature': 'drought',  # Map to drought category
-    'Mass movement (dry)': 'landslide',  # Map to landslide category
-    'Glacial lake outburst': 'flood',  # Map to flood category
-    'Fog': 'storm',  # Map to storm category
+    'Extreme temperature': 'drought',  
+    'Mass movement (dry)': 'landslide', 
+    'Glacial lake outburst': 'flood',  
+    'Fog': 'storm', 
 }
 
 df_clean['type'] = df_clean['type'].map(type_mapping)
@@ -77,18 +70,16 @@
 df_clean['affected'] = df_clean['affected'].fillna(0)
 df_clean['damage_usd_thousands'] = df_clean['damage_usd_thousands'].fillna(0)
 
-# Save to CSV
 df_clean.to_csv(OUTPUT_FILE, index=False)
 
 print(f'\n✓ Saved: {OUTPUT_FILE}')
 print(f'✓ Records: {len(df_clean):,}')
 print(f'✓ Year range: {int(df_clean["year"].min())}-{int(df_clean["year"].max())}')
 print(f'✓ Countries: {df_clean["iso"].nunique()}')
 
-# Show disaster type distribution
 print('\nDisaster type distribution:')
 type_counts = df_clean['type'].value_counts()
 for dtype, count in type_counts.items():
     print(f'  {dtype}: {count:,} ({count/len(df_clean)*100:.1f}%)')
 
-print('\n✅ Done! The website can now load the data from emdat_clean.csv')
+print('\n Done! The website can now load the data from emdat_clean.csv')