Merge pull request #27 from torreyma/master

added pandas multiprocessing example in examples/ directory

Author: ishiland

examples/pandas-multiprocessing-example.py

@@ -0,0 +1,72 @@
+# from: https://gist.github.com/ishiland/824ddd386fcd0b90fc55aea573a28b22
+# written by ishiland: https://github.com/ishiland
+# Minor edits by torreyma: https://github.com/torreyma
+#
+from geosupport import Geosupport, GeosupportError
+import pandas as pd
+from multiprocessing import Pool, cpu_count
+from functools import partial
+import numpy as np
+
+"""
+Example of how to use python-geosupport, Pandas and Multiprocessing to speed up geocoding workflows. 
+"""
+
+# For Windows:
+g = Geosupport(geosupport_path="C:\\Program Files (x86)\\Geosupport Desktop Edition")
+# On linux, geosupport location is set in environment variables GEOFILES and LD_LIBRARY_PATH.
+
+cpus = cpu_count()
+
+
+def geo_by_address(row):
+    """
+    Geocodes a pandas row containing address atributes.
+
+    :param row: Pandas Series
+    :return: Pandas Series with lat, lon & Geosupport message.
+    """
+    try:
+        result = g.address(house_number=row['PHN'], street_name=row['STREET'], zip=row['ZIP_CODE']) # Adjust these to match your data column names
+        lat = result.get("Latitude")
+        lon = result.get('Longitude')
+        msg = result.get('Message')
+    except GeosupportError as ge:
+        lat = "Error"
+        lon = "Error"
+        msg = str(ge)
+        pass
+    return pd.Series([lat, lon, msg])
+  
+  
+def parallelize(data, func, num_of_processes=cpus):
+    data_split = np.array_split(data, num_of_processes)
+    pool = Pool(num_of_processes)
+    data = pd.concat(pool.map(func, data_split))
+    pool.close()
+    pool.join()
+    return data
+
+
+def run_on_subset(func, data_subset):
+    return data_subset.apply(func, axis=1)
+
+
+def parallelize_on_rows(data, func, num_of_processes=cpus):
+    return parallelize(data, partial(run_on_subset, func), num_of_processes)
+
+
+if __name__ == '__main__':
+  
+    # read in csv
+    df = pd.read_csv('INPUT.csv')
+    
+    # add 3 Geosupport columns - Latitude, Longitude and Geosupport message
+    df[['lat', 'lon', 'msg']] = parallelize_on_rows(df, geo_by_address)
+
+    # output to csv with the 3 new columns.
+    df.to_csv('OUTPUT.csv')
+
+
+
+