Adding cleaning script and resulting csv

DataProcessing/CleanSightingsData.py

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+import pandas as pd
+import re
+
+# Load the CSV file
+file_path = "Species_Sightings_1km_Block_16Oct2024.csv"
+df = pd.read_csv(file_path)
+
+# Function to extract species, count, and date
+def parse_species_data(entry):
+    observations = entry.split(", ")
+    parsed_entries = []
+    for obs in observations:
+        match = re.match(r"(.+) \((\d+)\) (\d{4}-\d{2}-\d{2})", obs)
+        if match:
+            species, count, date = match.groups()
+            parsed_entries.append((species, int(count), date))
+    return parsed_entries
+
+# Expand rows for multiple species observations
+expanded_rows = []
+for _, row in df.iterrows():
+    parsed_entries = parse_species_data(row['Aggregated_Species_Data'])
+    for species, count, date in parsed_entries:
+        expanded_rows.append({
+            'Latitude': row['Latitude'],
+            'Longitude': row['Longitude'],
+            'Species': species,
+            'Count': count,
+            'Date': date
+        })
+
+# Create a new DataFrame with structured data
+structured_df = pd.DataFrame(expanded_rows)
+
+# Save the structured data
+structured_df.to_csv("Structured_Bird_Data.csv", index=False)
+
+print("Structured data saved as 'Structured_Bird_Data.csv'")

DataProcessing/README.md

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+# Data Processing
+
+1. Run CleanSightingsData.py to generate a new CSV called Structured_Bird_Data.csv 
+   1. This should hopefully be better formatted than the original
+2. Open QGIS
+   1. To Download
+   2. https://qgis.org/download/
+3. Click New Project
+4. Click Layer > Add Layer > Add Delimited Text Layer 
+5. Select the Cleaned CSV to use
+6. Update the projection and make sure the X values are Longitude and Y values are Latitude 
+7. Select OK
+8. Right-Click the newly generated layer and select Export > Save Feature As
+9.  Select GeoJson 
+10. Save to the DataProcessing folder