我正在尝试从Google API地理编码服务创建一个解析了地理编码数据信息的.csv文件。我想将地址信息解析为单独的列。我的脚本运行正常，直到我得到Type错误的位置数据。任何人都可以帮助我并修改我的脚本，以便我可以将这些数据包含在我的表中吗？
import pandas as pd
import requests
import geocoder
import time
import json
df = pd.read_csv('/Users/albertgonzalobautista/Desktop/workingbook.csv') # define CSV to be read to be geocdoed
# create new columns for the output CSV
df['geocode_data'] = ''
df['address']=''
df['street_number']=''
df['street_name']=''
df['postalcode']=''
df['city']=''
df['st_pr_mn']=''
df['country']=''
df['location_lat']=''
df['location_lon']=''
# Create function that handles the geocoding requests
average = 0
def reverseGeocode(latlng): #defines reverse geocoding function
#Set parameters
start = time.time()
result = {} #create empty list
url = 'https://maps.googleapis.com/maps/api/geocode/json?latlng={0}&key={1}' #Access URL for Google Geocoder API
apikey = 'XXX' # Set you API Key taken from Google API website and your Google Developers Account
request = url.format(latlng, apikey)
#delays responses so that it does not over
data = json.loads(requests.get(request).text)
if len(data['results']) > 0:
result = data['results'][0]
#global average #if not work delete first char(uncomment)
average = time.time() - start
return result
for i, row in df.iterrows():
if average < 0.3 : time.sleep(0.3 - average) #0.3 is period time (min= 0.2 max = free)
df['geocode_data'][i] = reverseGeocode(df['lat'][i].astype(str) + ',' + df['lon'][i].astype(str))
for i, row in df.iterrows():
if 'address_components' in row['geocode_data']:
for component in row['geocode_data']['address_components']:
df['address'][i] = row['geocode_data']['formatted_address']
for component in row['geocode_data']['address_components']:
if 'street_number' in component['types']:
df['street_number'][i] = component['long_name']
for component in row['geocode_data']['address_components']:
if 'route' in component ['types']:
df['street_name'][i] = component['long_name']
break
for component in row['geocode_data']['address_components']:
if 'route' in component ['types']:
df['street_name'][i] = component['long_name']
for component in row['geocode_data']['address_components']:
if 'postal_code' in component ['types']:
df['postalcode'][i] = component['short_name']
break
for component in row['geocode_data']['address_components']:
if 'locality' in component ['types']:
df['city'][i]= component['short_name']
break
for component in row['geocode_data']['address_components']:
if 'administrative_area_level_1' in component ['types']:
df['st_pr_mn'][i] = component ['long_name']
break
for component in row['geocode_data']['address_components']:
if 'country' in component ['types']:
df['country'][i] = component ['long_name']
break
for component in row['geocode_data']['geometry']:
if component['location']:
df['location_lng'][i] = int(component['location']['lng'])
df['location_lat'][i] = int(component['location']['lat'])
df.to_csv('test10.csv', encoding='utf-8', index=False)
{＆＃39;几何＆＃39;：{＆＃39;视口＆＃39;：{＆＃39;西南＆＃39;：{＆＃39; lng＆＃39;：4.947849719708499，＆＃39; lat＆ ＃39;：52.36571761970851}，＆＃39;东北＆＃39;：{＆＃39; lng＆＃39;：4.950547680291502，＆＃39; lat＆＃39;：52.3684155802915}}，＆＃39; location＆＃39;： {＆＃39; lng＆＃39;：4.9491987，＆＃39; lat＆＃39;：52.3670666}，＆＃39; location_type＆＃39;：＆＃39; ROOFTOP＆＃39;}，＆＃39; address_components＆＃39 ;：[{＆＃39; long_name＆＃39;：＆＃39; 114＆＃39;，＆＃39; types＆＃39;：[＆＃39; street_number＆＃39;]，＆＃39; short_name＆＃39; ：＆＃39; 114＆＃39;}，{＆＃39; long_name＆＃39;：＆＃39; Zeeburgerpad＆＃39;，＆＃39; types＆＃39;：[＆＃39; route＆＃39;]， ＆＃39; short_name＆＃39;：＆＃39; Zeeburgerpad＆＃39;}，{＆＃39; long_name＆＃39;：＆＃39; Amsterdam-Oost＆＃39;，＆＃39; types＆＃39;：[ ＆＃39; sublocality_level_1＆＃39;，＆＃39; sublocality＆＃39;，＆＃39; political＆＃39;]，＆＃39; short_name＆＃39;：＆＃39; Amsterdam-Oost＆＃39;}，{ ＆＃39; long_name＆＃39;：＆＃39;阿姆斯特丹＆＃39;，＆＃39;类型＆＃39;：[＆＃39;地点＆＃39;，＆＃39;政治＆＃39;]，＆＃39; ; short_name＆＃39;：＆＃39;阿姆斯特丹＆＃39;}，{＆＃39; long_name＆＃39;：＆＃39; Amsterda m＆＃39;，＆＃39;类型＆＃39;：[＆＃39; administrative_area_level_2＆＃39;，＆＃39;政治＆＃39;]，＆＃39; short_name＆＃39;：＆＃39;阿姆斯特丹＆＃39; ;}，{＆＃39; long_name＆＃39;：＆＃39; Noord-Holland＆＃39;，＆＃39; types＆＃39;：[＆＃39; administrative_area_level_1＆＃39;，＆＃39; political＆＃39; ;]，＆＃39; short_name＆＃39;：＆＃39; NH＆＃39;}，{＆＃39; long_name＆＃39;：＆＃39;荷兰＆＃39;＆＃39;类型＆＃39;： [＆＃39; country＆＃39;，＆＃39; political＆＃39;]，＆＃39; short_name＆＃39;：＆＃39; NL＆＃39;}，{＆＃39; long_name＆＃39;：＆ ＃39; 1019 AE＆＃39;，＆＃39;类型＆＃39;：[＆＃39; postal_code＆＃39;]，＆＃39; short_name＆＃39;：＆＃39; 1019 AE＆＃39;}]， ＆＃39; place_id＆＃39;：＆＃39; ChIJD14pyz8JxkcRF1Kpg8opql4＆＃39;，＆＃39; formatted_address＆＃39;：＆＃39; Zeeburgerpad 114,1019 AE Amsterdam，Netherlands＆＃39;，＆＃39; types＆＃39 ;：[＆＃39; street_address＆＃39;]}
答案 0 :(得分：0)
在您的代码中发现可能的拼写错误：
public ActionResult Save(EditableBaseModel editableBaseModel) {
var baseModel = new BaseModel() {
Content = editableBaseModel.Content
// ...
};
myDbContextInstance.BaseModels.Add(baseModel);
}
我相信你想在那里寻找组件['lng']。
如果这不能解决问题，你可以打印出df ['location_lng']，这样我们就可以确切地看到它是什么类型了吗？
答案 1 :(得分：0)
您的“位置”路径似乎不正确。试试这个：
for component in row['geocode_data']['geometry']:
if component['location']: # Note: NOT: if 'location' in component ['...']
df['location_lng'][i] = component['location']['lng']
df['location_lat'][i] = component['location']['lat']
更新： 看来实际问题是输出'数组'正在使用。 您正在分配以下输出：
df['location_lat']=''
然后你分配：
df['location_lat'][i]=...
实际上是：
''[i] = ...
将导致注明Type错误。 [我得到“KeyError：'location_lat'”，但是嘿......]
也许尝试类似的事情：
results = []
...
for ...
df = {}
...
df['location_lng'] = component['location']['lng']
...
results.append(df)
更新更新： 没有使用过Pandas，我不确定如何反映df csv格式;不过，我将推测，如果您删除“为输出CSV创建新列”部分，您的DataFrame结构将不会被破坏。 即：删除：
df['geocode_data'] = ''
df['address']=''
等
我可能会在错误的树上吠叫。
另一个更新： 我的简单测试用例是：
js = ... (json string as above)
data = json.loads(js)
component = data['geometry']
if component['location']:
val = component['location']['lat']
print val
适用于提取纬度。 所以问题不应该是'if'部分。
更新（再次......） 好的 - 再试一次：而不是：
df['geocode_data'][i] = reverseGeocode(...)
直接分配给字典变量进行数据提取。即：
data = reverseGeocode(...)
然后如上所述提取数据并根据需要分配给您的DataFrame。