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Python Geopy Nominatim 请求太多

转载 作者:行者123 更新时间:2023-12-05 08:31:10 38 4
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以下脚本可以完美处理包含 2 行的文件,但是当我尝试 2500 行文件时,出现 429 个异常。所以,我将查询时间增加到 5 秒。我还填写了用户代理。尝试失败后,我连接到 VPN 以更改“新鲜”,但我再次收到 429 错误。我在这里缺少什么吗? Nominatim 政策规定连接数不超过每秒 1 个,我每 5 秒做一个...任何帮助都会有帮助!

from geopy.geocoders import Nominatim
import pandas
from functools import partial

from geopy.extra.rate_limiter import RateLimiter

nom = Nominatim(user_agent="xxx@gmail.com")
geocode = RateLimiter(nom.geocode, min_delay_seconds=5)


df=pandas.read_csv('Book1.csv', engine='python')
df["ALL"] = df['Address'].apply(partial(nom.geocode, timeout=1000, language='en'))
df["Latitude"] = df["ALL"].apply(lambda x: x.latitude if x != None else None)
df["Longitude"] = df["ALL"].apply(lambda x: x.longitude if x != None else None)

writer = pandas.ExcelWriter('Book1.xlsx')
df.to_excel(writer, 'new_sheet')
writer.save()

错误信息:

Traceback (most recent call last):
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\site-packages\geopy\geocoders\base.py", line 355, in _call_geocoder
page = requester(req, timeout=timeout, **kwargs)
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 531, in open
response = meth(req, response)
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 641, in http_response
'http', request, response, code, msg, hdrs)
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 569, in error
return self._call_chain(*args)
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 503, in _call_chain
result = func(*args)
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\urllib\request.py", line 649, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 429: Too Many Requests

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:/Users/u6022697/Documents/python work/Multiple GPS Nom Pandas.py", line 14, in <module>
df["ALL"] = df['Address'].apply(partial(nom.geocode, timeout=1000, language='en'))
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\series.py", line 3849, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "pandas\_libs\lib.pyx", line 2327, in pandas._libs.lib.map_infer
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\site-packages\geopy\geocoders\osm.py", line 406, in geocode
self._call_geocoder(url, timeout=timeout), exactly_one
File "C:\Users\u6022697\AppData\Local\Programs\Python\Python37\lib\site-packages\geopy\geocoders\base.py", line 373, in _call_geocoder
raise ERROR_CODE_MAP[code](message)
geopy.exc.GeocoderQuotaExceeded: HTTP Error 429: Too Many Requests

最佳答案

我在不到一天的时间内完成了大约 10K 种不同经纬度组合的反向地理编码。 Nominatim 不喜欢批量查询,所以这个想法是为了防止看起来像一个。这是我的建议:

  1. 确保您只查询唯一 项。我发现重复查询同一经纬度组合会被 Nominatim 阻止。地址也是如此。您可以使用 unq_address = df['address'].unique() 然后使用该系列进行查询。您甚至可以得到更少的地址。

  2. 查询之间的时间应该是随机的。我还设置 user_agent 每次都有一个随机数。就我而言,我使用以下代码:

    from time import sleep
    from random import randint
    from geopy.geocoders import Nominatim
    from geopy.exc import GeocoderTimedOut, GeocoderServiceError

    user_agent = 'user_me_{}'.format(randint(10000,99999))
    geolocator = Nominatim(user_agent=user_agent)
    def reverse_geocode(geolocator, latlon, sleep_sec):
    try:
    return geolocator.reverse(latlon)
    except GeocoderTimedOut:
    logging.info('TIMED OUT: GeocoderTimedOut: Retrying...')
    sleep(randint(1*100,sleep_sec*100)/100)
    return reverse_geocode(geolocator, latlon, sleep_sec)
    except GeocoderServiceError as e:
    logging.info('CONNECTION REFUSED: GeocoderServiceError encountered.')
    logging.error(e)
    return None
    except Exception as e:
    logging.info('ERROR: Terminating due to exception {}'.format(e))
    return None

我发现 sleep(randint(1*100,sleep_sec*100)/100) 这行对我有用。

关于Python Geopy Nominatim 请求太多,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/60083187/

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