为什么我在解析数据帧时会收到错误，但当它是单行时却没有？

Question

python 新手。我在 python 中使用 pygeocodio 库

API_KEY = "myapikey"

from geocodio import GeocodioClient

client = GeocodioClient(API_KEY)


addresses = client.geocode("21236 Birchwood Loop, 99567, AK")
addresses.best_match.get("accuracy")
Out[61]: 1

addresses.best_match.get("accuracy_type")
Out[62]: 'rooftop'

但是，如果我想遍历数据框（example.csv）：

import pandas as pd
customers = pd.read_csv("example.csv")

for row in customers.iterrows():
    addresses = client.geocode(row)
    addresses.best_match.get("accuracy")

我收到一个错误：

  File "C:\Users\jtharian\AppData\Local\Continuum\anaconda3\lib\site-packages\geocodio\client.py", line 58, in error_response
    raise exceptions.GeocodioDataError(response.json()["error"])

GeocodioDataError: Could not geocode address. Postal code or city required.

example.csv 的代表：

21236 Birchwood Loop, 99567, AK
1731 Bragaw St, 99508, AK
300 E Fireweed Ln, 99503, AK
4360 Snider Dr, 99654, AK
1921 W Dimond Blvd 108, 99515, AK
2702 Peger Rd, 99709, AK
1651 College Rd, 99709, AK
898 Ballaine Rd, 99709, AK
23819 Immelman Circle, 99567, AK
9750 W Parks Hwy, 99652, AK
7205 Shorewood Dr, 99645, AK

为什么我会收到此错误？

Answer 1

查看api docs，您需要一个字符串来代表您的各个地址组件列中的地址，如下所示：

location = client.geocode("1109 N Highland St, Arlington VA")

因此，要在您的 df 中获得类似的列，您可以将每个向量映射到一个字符串，然后使用简单的字符串连接来生成一个字符串，然后将其插入到您的 df 中的新系列中：

import pandas as pd

customers = pd.read_csv("example.csv", header=None)
customers['address_string'] = customers[0].map(str) + ' ' + customers[1].map(str) + customers[2].map(str)

制作：

# >>> customers['address_string']
# 0       21236 Birchwood Loop 99567 AK
# 1             1731 Bragaw St 99508 AK
# 2          300 E Fireweed Ln 99503 AK
# 3             4360 Snider Dr 99654 AK
# 4     1921 W Dimond Blvd 108 99515 AK

然后您可以遍历地址字符串系列的值并将准确性存储在一个列表中，该列表可以插入您的df：

geocoded_acuracy = []
geocoded_acuracy_type = []

for address in customers['address_string'].values:
    geocoded_address = client.geocode(address)
    accuracy = geocoded_address.best_match.get("accuracy")
    accuracy_type = geocoded_address.best_match.get("accuracy_type")

    geocoded_acuracy.append(accuracy)
    geocoded_acuracy_type.append(accuracy_type)

customers['accuracy'] = geocoded_acuracy
customers['accuracy_type'] = geocoded_acuracy_type

results = customers[['address_string', 'accuracy', 'accuracy_type']]

df 的结果将如下所示：

# >>> results
#                      address_string  accuracy        accuracy_type
# 0     21236 Birchwood Loop 99567 AK      1.00              rooftop
# 1           1731 Bragaw St 99508 AK      1.00              rooftop
# 2        300 E Fireweed Ln 99503 AK      1.00              rooftop
# 3           4360 Snider Dr 99654 AK      1.00  range_interpolation
# 4   1921 W Dimond Blvd 108 99515 AK      1.00              rooftop
# 5            2702 Peger Rd 99709 AK      1.00              rooftop
# 6          1651 College Rd 99709 AK      1.00              rooftop
# 7          898 Ballaine Rd 99709 AK      1.00              rooftop
# 8    23819 Immelman Circle 99567 AK      1.00              rooftop
# 9         9750 W Parks Hwy 99652 AK      0.33                place
# 10       7205 Shorewood Dr 99645 AK      1.00  range_interpolation

然后将结果df写入.csv：

results.to_csv('results.csv')

将所有这些放在一起产生以下代码：

import pandas as pd
from geocodio import GeocodioClient

API_KEY = 'insert_your_key_here'

client = GeocodioClient(API_KEY)

customers = pd.read_csv("example.csv", header=None)
customers['address_string'] = customers[0].map(str) + ' ' + customers[1].map(str) + customers[2].map(str)

geocoded_acuracy = []
geocoded_acuracy_type = []

for address in customers['address_string'].values:
    geocoded_address = client.geocode(address)
    accuracy = geocoded_address.best_match.get("accuracy")
    accuracy_type = geocoded_address.best_match.get("accuracy_type")

    geocoded_acuracy.append(accuracy)
    geocoded_acuracy_type.append(accuracy_type)

customers['accuracy'] = geocoded_acuracy
customers['accuracy_type'] = geocoded_acuracy_type

results = customers[['address_string', 'accuracy', 'accuracy_type']]

results.to_csv('results.csv')

Answer 2

我会使用apply 和特定的异常等，但现在我想虽然新的只是专注于有效的方法和错误。但是，当您熟悉 pandas 和 python 时，一定要研究这些主题。

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html https://geek-university.com/python/catch-specific-exceptions/

errors, address_list, accuracy_list, accuracy_type_list = [], [], [], []
for index, row in customers.iterrows():
    try:
        addresses = client.geocode(row.values[0])
        accuracy = addresses.best_match.get("accuracy")
        accuracy_type = addresses.best_match.get("accuracy_type")

        address_list.append(addresses)
        accuracy_list.append(accuracy)
        accuracy_type_list.append(accuracy_type)
    except Exception as e:
        address_list.append(None)
        accuracy_list.append(None)
        accuracy_type_list.append(None)
        errors.append(f"failure {e.args[0]} at index {index}")

我在做什么？ iterrows 提供索引和行的元组。所以我对每个行项目进行地理编码。如果它有效，我将结果添加到 address_list。与准确性相同。但是当它失败时，我会在错误列表中添加一条消息，以指示数据帧中发生错误的位置；即索引。但我还需要地址列表中的占位符，所以我只添加无。所以现在我可以做

customers['addresses'] = address_list
customers['accuracy'] = accuracy_list
customers['accuracy_type'] = accuracy_type_list

如果需要，保存我的数据框。 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html