python - 增加请求/第二个 Python Google map 地理编码器

Question

我在增加使用 Google map 地理编码器每秒发出的请求量时遇到困难。我使用的是付费帐户(按 0.50 美元/1000 个请求)，因此根据 Google Geocoder API，我每秒应该能够发出最多 50 个请求。

我有一个包含 15k 个地址的列表，我正在尝试获取其 GPS 坐标。我将它们存储为 Pandas Dataframe 并循环它们。为了确保这不是由于循环缓慢造成的，我测试了 15k 的循环速度，结果只花了 1.5 秒。但我每秒只能发出少于 1 个请求。我意识到这可能是由于我的互联网连接速度较慢，因此我启动了一个具有明显快速互联网的 Windows Google Cloud 虚拟机。我能够将请求速度加快到大约 1.5 个请求/秒，但仍然比理论上可能的速度慢得多。

我认为这可能是由于使用了 python 库 Geocoder，所以我尝试直接使用 python requests 发出请求，但这也没有加快速度。

这与我没有使用服务器有关吗？我认为这并不重要，因为我使用的是 Google Cloud VM。另外，我知道这与多线程无关，因为它已经可以使用 1 个核心以极快的速度迭代循环。预先感谢您的任何想法。

import geocoder
import pandas as pd
import time
import requests


startTime = time.time()
#Read File Name with all transactions up to October 4th
input_filename = "C:/Users/username/Downloads/transaction-export 10-04-2017.csv"
df = pd.read_csv(input_filename, header=0, error_bad_lines=False)
#Only look at customer addresses
df = df['Customer Address']
#Drop duplicates and NAs
df = df.drop_duplicates(keep='first')
df = df.dropna()
#convert dataframe to string
addresses = df.tolist()
#Google Api Key
api_key = 'my_api_key'
#create empty array
address_gps = []
#google api address
url = 'https://maps.googleapis.com/maps/api/geocode/json'
#For each address return its geocoded latlng coordinates
for int, val in enumerate(addresses):
    ''' Direct way to make call without geocoder
    params = {'sensor': 'false', 'address': address, 'key': api_key}
    r = requests.get(url, params=params)
    results = r.json()['results']
    location = results[0]['geometry']['location']
    print location['lat'], location['lng']
    num_address = num_address+1;
    '''
    endTime = time.time()
    g = geocoder.google(val, key=api_key,  exactly_one=True)
    print "Address,", (val), "Number,", int, "Total,", len(addresses), "Time,", endTime-startTime

    if g.ok:
        address_gps.append(g.latlng)
        print g.latlng
    else:
        address_gps.append(0)
        print("Error")
    #save every 100 iterations
    if int%100==0:
        # save as csv
        df1 = pd.DataFrame({'Address GPS': address_gps})
        df1.to_csv('C:/Users/username/Downloads/AllCustomerAddressAsGPS.csv')


# save as csv
df1 = pd.DataFrame({'Address GPS': address_gps})
df1.to_csv('C:/Users/username/Downloads/AllCustomerAddressAsGPS.csv')

Answer 1

提高速度的一种方法是维护与 Google 的请求 session ，而不是为每个请求创建一个新 session 。 geocoder documentation 中建议这样做.

您修改后的代码将是:

import requests

#Google Api Key
api_key = 'my_api_key'
#create empty array
address_gps = []
#google api address
url = 'https://maps.googleapis.com/maps/api/geocode/json'
#For each address return its geocoded latlng coordinates
with requests.Session() as session:
    for int, val in enumerate(addresses):
        ''' Direct way to make call without geocoder
        params = {'sensor': 'false', 'address': address, 'key': api_key}
        r = requests.get(url, params=params)
        results = r.json()['results']
        location = results[0]['geometry']['location']
        print location['lat'], location['lng']
        num_address = num_address+1;
        '''
        endTime = time.time()
        g = geocoder.google(val, key=api_key,  exactly_one=True, session=session)
        print "Address,", (val), "Number,", int, "Total,", len(addresses), "Time,", endTime-startTime

        if g.ok:
            address_gps.append(g.latlng)
            print g.latlng
        else:
            address_gps.append(0)
            print("Error")
        #save every 100 iterations
        if int%100==0:
            # save as csv
            df1 = pd.DataFrame({'Address GPS': address_gps})
            df1.to_csv('C:/Users/username/Downloads/AllCustomerAddressAsGPS.csv')

# save as csv
df1 = pd.DataFrame({'Address GPS': address_gps})
df1.to_csv('C:/Users/username/Downloads/AllCustomerAddressAsGPS.csv')