python：將類型 requests.models.Response 導入數據幀時出錯

Question

我是 python 的新手，我正在嘗試使用人口普查地理編碼服務 API 對地址進行地理編碼，然后將輸出轉換為數據幀。 我已經能夠讀入我的地址文件並且我可以看到輸出，但我似乎無法弄清楚如何將它導入到數據幀中。 我提供了我在下面使用的代碼以及地址文件的內容。

輸出似乎不是 JSON 格式，而是 CSV。 我試圖像導入 CSV 文件一樣導入輸出，但是我無法像導入 CSV 文件一樣弄清楚如何導入變量，而且我無法弄清楚如何將輸出導出到我可以的 CSV 文件進口。

描述 API 的 URL 是https://geocoding.geo.census.gov/geocode...es_API.pdf

import requests
import pandas as pd
import json
url = 'https://geocoding.geo.census.gov/geocoder/geographies/addressbatch'
payload = {'benchmark':'Public_AR_Current','vintage':'Current_Current'}
files = {'addressFile': ('C:\PYTHON_CLASS\CSV\ADDRESS_SAMPLE.csv', open('C:\PYTHON_CLASS\CSV\ADDRESS_SAMPLE.csv', 'rb'), 'text/csv')}
response = requests.post(url, files=files, data = payload)
type(response)
print(response.text)

- 我嘗試了下面的代碼（在許多其他版本中），這是我通常導入 CSV 文件的方式，但它生成一條錯誤消息“文件路徑或緩沖區對象類型無效：<class 'requests.models.Response'>”

df = pd.read_csv(response)

我用來生成地理編碼的地址文件的內容是：

id,地址,城市,州,郵政編碼 1,1600 Pennsylvania Avenue NW, Washington,DC,20500 2,4 S Market St,Boston,MA,02109 3,1200 Getty Center Drive,Los Angeles,CA,90049 4,1800 Congress Ave,Austin,TX,78701 5,One Caesars Palace Drive,Las Vegas,NV,89109 6,1060 West Addison,Chicago,IL,60613 7,One East 161st Street,Bronx,NY,10451 8,201 E Jefferson St,Phoenix, AZ,85004 9,600 N 1st Ave,Minneapolis,MN,55403 10,400 W Church St,Orlando,FL,32801

輸出如下所示：

print(response.text)

"1","1600 賓夕法尼亞大道 NW, 華盛頓特區, 20500","Match","Non_Exact","1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20006","-77.03535,38.898754","73","5 L","11","001","006202","1031" "2","4 S Market St, Boston, MA, 02109","Match","Exact","4 S MARKET ST, BOSTON , MA, 02109","-71.05566,42.359936","85723841","R","25","025","030300","2017" "3","1200 Getty Center Drive, Los Angeles, CA , 90049","Match","Exact","1200 GETTY CENTER DR, LOS ANGELES, CA, 90049","-118.47564,34.08857","142816014","L","06","037" 262302","1005" "4","1800 Congress Ave, Austin, TX, 78701","Match","Exact","1800 CONGRESS AVE, AUSTIN, TX, 78701","-97.73847,30.279745" 63946318","L","48","453","000700","1007" "5","One Caesars Palace Drive, Las Vegas, NV, 89109","No_Match" "6","1060 West Addison, Chicago, IL, 60613","Match","Non_Exact","1060 W ADDISON ST, CHICAGO, IL, 60613","-87.65581,41.947227","111863716","R","17" 031","061100","1014","7","One East 161st Street, Bronx, NY, 10451","No_Match""8","201 E Jefferson St, Phoenix, AZ, 85004","Match","Exact","201 E JEFFERSON ST, PHOENIX, AZ, 85004","-112.07113,33.44675","128300920","L","04"," 013","114100","1058" "9","600 N 1st Ave, Minneapolis, MN, 55403","No_Match" "id","address, city, state, zipcode","No_Match" "10" ,"400 W Church St, Orlando, FL, 32801","Match","Exact","400 W CHURCH ST, ORLANDO, FL, 32801","-81.38436,28.540176","94416807","L", “12”、“095”、“010500”、“1002”

response.text的輸出是：

'"1","1600 賓夕法尼亞大道 NW, 華盛頓特區, 20500","Match","Non_Exact","1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20006","-77.03535,38.8987254","8136" "L","11","001","006202","1031"\\n"2","4 S Market St, Boston, MA, 02109","Match","Exact","4 S MARKET ST, BOSTON, MA, 02109","-71.05566,42.359936","85723841","R","25","025","030300","2017"\\n"3","1200 Getty Center Drive , Los Angeles, CA, 90049","Match","Exact","1200 GETTY CENTER DR, LOS ANGELES, CA, 90049","-118.47564,34.08857","142816014","L","06" "037","262302","1005"\\n"4","1800 Congress Ave, Austin, TX, 78701","Match","Exact","1800 CONGRESS AVE, AUSTIN, TX, 78701"," -97.73847,30.279745","63946318","L","48","453","000700","1007"\\n"5","One Caesars Palace Drive, Las Vegas, NV, 89109"," No_Match"\\n"6","1060 West Addison, Chicago, IL, 60613","Match","Non_Exact","1060 W ADDISON ST, CHICAGO, IL, 60613","-87.65581,41.947218","3716 ","R","17","031","061100","1014"\\n"7","One East 161st Street, Bronx, NY, 10451","No_Match"\\n"8","第201章弗森街, 鳳凰城, AZ, 85004","Match","Exact","201 E JEFFERSON ST, PHOENIX, AZ, 85004","-112.07113,33.44675","128300920","L","04" "013","114100","1058"\\n"9","600 N 1st Ave, Minneapolis, MN, 55403","No_Match"\\n"id","address, city, state, zipcode"," No_Match"\\n"10","400 W Church St, Orlando, FL, 32801","Match","Exact","400 W CHURCH ST, ORLANDO, FL, 32801","-81.38436,28.540176"," 94416807","L","12","095","010500","1002"\\n'

當我嘗試

df = pd.read_csv(io.StringIO(response), sep=',', header=None, quoting=csv.QUOTE_ALL)

我收到錯誤信息

TypeError                                 Traceback (most recent call last)
<ipython-input-60-55e6c5ac54af> in <module>
----> 1 df = pd.read_csv(io.StringIO(response), sep=',', header=None, quoting=csv.QUOTE_ALL)

TypeError: initial_value must be str or None, not Response

當我嘗試

df = pd.read_csv(io.StringIO(response.replace('" "', '"\n"')), sep=',', header=None, quoting=csv.QUOTE_ALL)

我有

AttributeError                            Traceback (most recent call last)
<ipython-input-61-a92a7ffcf170> in <module>
----> 1 df = pd.read_csv(io.StringIO(response.replace('" "', '"\n"')), sep=',', header=None, quoting=csv.QUOTE_ALL)

AttributeError: 'Response' object has no attribute 'replace'

Answer 1

如果response.text看起來像下面的s字符串，即有換行符分隔行，您可以嘗試：

>>> import csv
>>> import io
>>> import pandas as pd 
>>>
>>> # let's pretend s is response.text
>>> s = '''"1","1600 Pennsylvania Avenue NW, Washington, DC, 20500","Match","Non_Exact","1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20006","-77.03535,38.898754","76225813","L","11","001","006202","1031"
... "2","4 S Market St, Boston, MA, 02109","Match","Exact","4 S MARKET ST, BOSTON, MA, 02109","-71.05566,42.359936","85723841","R","25","025","030300","2017"
... "3","1200 Getty Center Drive, Los Angeles, CA, 90049","Match","Exact","1200 GETTY CENTER DR, LOS ANGELES, CA, 90049","-118.47564,34.08857","142816014","L","06","037","262302","1005"
... "4","1800 Congress Ave, Austin, TX, 78701","Match","Exact","1800 CONGRESS AVE, AUSTIN, TX, 78701","-97.73847,30.279745","63946318","L","48","453","000700","1007"'''
>>>
>>> df = pd.read_csv(io.StringIO(s), sep=',', header=None, quoting=csv.QUOTE_ALL)
>>> 
>>> with pd.option_context(
...       'display.width', None, 
...       'display.max_columns', None,
...       'display.max_colwidth', -1,
...       'display.colheader_justify', 'left'):
...     print(df)
... 
   0 1                                                   2      3          4                                                 \
0  1  1600 Pennsylvania Avenue NW, Washington, DC, 20500  Match  Non_Exact  1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20006   
1  2  4 S Market St, Boston, MA, 02109                    Match  Exact      4 S MARKET ST, BOSTON, MA, 02109                  
2  3  1200 Getty Center Drive, Los Angeles, CA, 90049     Match  Exact      1200 GETTY CENTER DR, LOS ANGELES, CA, 90049      
3  4  1800 Congress Ave, Austin, TX, 78701                Match  Exact      1800 CONGRESS AVE, AUSTIN, TX, 78701              

  5                     6         7   8   9    10      11    
0  -77.03535,38.898754  76225813   L  11  1    6202    1031  
1  -71.05566,42.359936  85723841   R  25  25   30300   2017  
2  -118.47564,34.08857  142816014  L  6   37   262302  1005  
3  -97.73847,30.279745  63946318   L  48  453  700     1007  

如果response.text沒有分隔行的換行符（但在描述中有空格），則需要將io.StringIO(s)替換為io.StringIO(s.replace('" "', '"\\n"')) 。

Answer 2

尼古拉斯給出了答案。 我的最終代碼是

import requests
import pandas as pd
import io
import csv
url = 'https://geocoding.geo.census.gov/geocoder/geographies/addressbatch'
payload = {'benchmark':'Public_AR_Current','vintage':'Current_Current'}
files = {'addressFile': ('C:\PYTHON_CLASS\CSV\ADDRESS_SAMPLE.csv', open('C:\PYTHON_CLASS\CSV\ADDRESS_SAMPLE.csv', 'rb'), 'text/csv')}
s = requests.post(url, files=files, data = payload)
df = pd.read_csv(io.StringIO(s.text), sep=',', header=None, quoting=csv.QUOTE_ALL)
with pd.option_context(
    'display.width', None, 
    'display.max_columns', None,
    'display.max_colwidth', -1,
    'display.colheader_justify', 'left'):
    print(df)

謝謝尼古拉斯！