@@ -103,3 +103,35 @@ import pandas as pd
103103df = pd.DataFrame(all_places)
104104df.to_csv(' results.csv' , index = None )
105105```
106+
107+ ## Example 6: Get Places Information From a list of Place IDs (Multithreading)
108+
109+ ``` python
110+ from functools import partial
111+ from multiprocessing.pool import ThreadPool
112+
113+ place_ids = [
114+ ' ChIJNw4_-cWXyFYRF_4GTtujVsw' ,
115+ ' ChIJ39fGAcGXyFYRNdHIXy-W5BA' ,
116+ ' ChIJVVVl-cWXyFYRQYBCEkX0W5Y' ,
117+ ' ChIJScUP1R6XyFYR0sY1UwNzq-c' ,
118+ ' ChIJmeiNBMeXyFYRzQrnMMDV8Jc' ,
119+ ' ChIJifOTBMeXyFYRmu3EGp_QBuY' ,
120+ ' ChIJ1fwt-cWXyFYR2cjoDAGs9UI' ,
121+ ' ChIJ5zQrTzSXyFYRuiY31iE7M1s' ,
122+ ' ChIJQSyf4huXyFYRpP9W4rtBelA' ,
123+ ' ChIJRWK5W2-byFYRiaF9vVgzZA4'
124+ ]
125+
126+ # fast download in 4 threads
127+ pool = ThreadPool(4 )
128+ results = pool.map(partial(api_client.google_maps_search_v2, limit = 500 , language = ' en' , region = ' US' ), place_ids)
129+
130+ # combine places from all queries
131+ all_places = []
132+ for query_places in results:
133+ if query_places and query_places[0 ]:
134+ all_places.append(query_places[0 ][0 ])
135+ else :
136+ all_places.append({})
137+ ```
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