blocks|key|336547|text|这应有助于：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|336548|import+csv
import+requests

CSV_URL+=+'http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv'


with+requests.Session()+as+s:
++++download+=+s.get(CSV_URL)

++++decoded_content+=+download.content.decode('utf-8')

++++cr+=+csv.reader(decoded_content.splitlines(),+delimiter=',')
++++my_list+=+list(cr)
++++for+row+in+my_list:
++++++++print(row)|code-block|syntax|javascript|336549|输出样本：|336550|['street',+'city',+'zip',+'state',+'beds',+'baths',+'sq__ft',+'type',+'sale_date',+'price',+'latitude',+'longitude']
['3526+HIGH+ST',+'SACRAMENTO',+'95838',+'CA',+'2',+'1',+'836',+'Residential',+'Wed+May+21+00:00:00+EDT+2008',+'59222',+'38.631913',+'-121.434879']
['51+OMAHA+CT',+'SACRAMENTO',+'95823',+'CA',+'3',+'1',+'1167',+'Residential',+'Wed+May+21+00:00:00+EDT+2008',+'68212',+'38.478902',+'-121.431028']
['2796+BRANCH+ST',+'SACRAMENTO',+'95815',+'CA',+'2',+'1',+'796',+'Residential',+'Wed+May+21+00:00:00+EDT+2008',+'68880',+'38.618305',+'-121.443839']
['2805+JANETTE+WAY',+'SACRAMENTO',+'95815',+'CA',+'2',+'1',+'852',+'Residential',+'Wed+May+21+00:00:00+EDT+2008',+'69307',+'38.616835',+'-121.439146']
[...]|336551|相关问题及答案：https://stackoverflow.com/a/33079644/295246|offset|length|336552|编辑:如果您需要下载大型文件(即stream=True)，其他答案是有用的。|style|CODE|336553|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/33079644/295246^0|0|0|0|0|8|17|0|0|G|B|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|14|8|@]|9|@$M|15|N|16|1|17]]|A|$]]|$1|O|3|P|5|6|7|18|8|@$M|19|N|1A|Q|R]]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]]]

This should help:

<pre><code>import csv
import requests

CSV_URL = 'http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv'


with requests.Session() as s:
 download = s.get(CSV_URL)

 decoded_content = download.content.decode('utf-8')

 cr = csv.reader(decoded_content.splitlines(), delimiter=',')
 my_list = list(cr)
 for row in my_list:
 print(row)
</code></pre>

Ouput sample:

<pre><code>['street', 'city', 'zip', 'state', 'beds', 'baths', 'sq__ft', 'type', 'sale_date', 'price', 'latitude', 'longitude']
['3526 HIGH ST', 'SACRAMENTO', '95838', 'CA', '2', '1', '836', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '59222', '38.631913', '-121.434879']
['51 OMAHA CT', 'SACRAMENTO', '95823', 'CA', '3', '1', '1167', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '68212', '38.478902', '-121.431028']
['2796 BRANCH ST', 'SACRAMENTO', '95815', 'CA', '2', '1', '796', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '68880', '38.618305', '-121.443839']
['2805 JANETTE WAY', 'SACRAMENTO', '95815', 'CA', '2', '1', '852', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '69307', '38.616835', '-121.439146']
[...]
</code></pre>

Related question with answer: <a href="https://stackoverflow.com/a/33079644/295246">https://stackoverflow.com/a/33079644/295246</a>

<hr>

Edit: Other answers are useful if you need to download large files (i.e. <code>stream=True</code>).

blocks|key|2364108|text|从一个小小的搜索中，我知道文件应该以通用换行符模式打开，这是您不能直接使用响应内容的(我猜)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2364109|要完成任务，可以将下载的内容保存到临时文件中，也可以在内存中处理。|2364110|保存为文件：|2364111|import+requests
import+csv
import+os

temp_file_name+=+'temp_csv.csv'
url+=+'http://url.to/file.csv'
download+=+requests.get(url)

with+open(temp_file_name,+'w')+as+temp_file:
++++temp_file.writelines(download.content)

with+open(temp_file_name,+'rU')+as+temp_file:
++++csv_reader+=+csv.reader(temp_file,+dialect=csv.excel_tab)
++++for+line+in+csv_reader:
++++++++print+line

#+delete+the+temp+file+after+process
os.remove(temp_file_name)|code-block|syntax|javascript|2364112|记忆中：|2364113|(待更新)|2364114|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|R|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|S|8|@]|9|@]|A|$]]|$1|F|3|G|5|H|7|T|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|V|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

From a little search, that I understand the file should be opened in universal newline mode, which you cannot directly do with a response content (I guess).

To finish the task, you can either save the downloaded content to a temporary file, or process it in memory.

Save as file:

<pre><code>import requests
import csv
import os

temp_file_name = 'temp_csv.csv'
url = 'http://url.to/file.csv'
download = requests.get(url)

with open(temp_file_name, 'w') as temp_file:
 temp_file.writelines(download.content)

with open(temp_file_name, 'rU') as temp_file:
 csv_reader = csv.reader(temp_file, dialect=csv.excel_tab)
 for line in csv_reader:
 print line

# delete the temp file after process
os.remove(temp_file_name)
</code></pre>

In memory:

(To be updated)

blocks|key|2364127|text|如果文件非常大，可以使用请求的iter_lines方法更新已接受的答案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2364128|import+csv
import+requests

CSV_URL+=+'http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv'

with+requests.Session()+as+s:
++++download+=+s.get(CSV_URL)

++++line_iterator+=+(x.decode('utf-8')+for+x+in+download.iter_lines(decode_unicode=True))

++++cr+=+csv.reader(line_iterator,+delimiter=',')
++++my_list+=+list(cr)
++++for+row+in+my_list:
++++++++print(row)|code-block|syntax|javascript|2364129|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can update the accepted answer with the iter_lines method of requests if the file is very large

<pre><code>import csv
import requests

CSV_URL = 'http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv'

with requests.Session() as s:
 download = s.get(CSV_URL)

 line_iterator = (x.decode('utf-8') for x in download.iter_lines(decode_unicode=True))

 cr = csv.reader(line_iterator, delimiter=',')
 my_list = list(cr)
 for row in my_list:
 print(row)
</code></pre>

blocks|key|2364174|text|为了简化这些答案，并在下载大文件时提高性能，下面的工作可能会更高效一些。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2364175|import+requests
from+contextlib+import+closing
import+csv
from+codecs+import+iterdecode

url+=+"http://download-and-process-csv-efficiently/python.csv"

with+closing(requests.get(url,+stream=True))+as+r:
++++reader+=+iterdecode(csv.reader(r.iter_lines(),+'utf-8'),+
++++++++++++++++++++++++delimiter=',',+
++++++++++++++++++++++++quotechar='"')
++++for+row+in+reader:
++++++++print(row)|code-block|syntax|javascript|2364176|通过在GET请求中设置stream=True，当我们将r.iter_lines()传递给csv.reader()时，就是将生成器传递给csv.reader()。通过这样做，我们使csv.reader()能够懒洋洋地遍历for+row+in+reader响应中的每一行。|offset|length|style|CODE|2364177|这避免了在我们开始处理文件之前将整个文件加载到内存中，从而大大减少了大型文件的内存开销。|2364178|entityMap|0|LINK|mutability|MUTABLE|url|https://wiki.python.org/moin/Generators^0|0|0|B|B|R|E|31|H|1P|3|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@$I|Z|J|10|K|L]|$I|11|J|12|K|L]|$I|13|J|14|K|L]]|9|@$I|15|J|16|1|17]]|A|$]]|$1|M|3|N|5|6|7|18|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]]]

To simplify these answers, and increase performance when downloading a large file, the below may work a bit more efficiently.
<pre><code>import requests
from contextlib import closing
import csv
from codecs import iterdecode

url = &quot;http://download-and-process-csv-efficiently/python.csv&quot;

with closing(requests.get(url, stream=True)) as r:
 reader = iterdecode(csv.reader(r.iter_lines(), 'utf-8'), 
 delimiter=',', 
 quotechar='&quot;')
 for row in reader:
 print(row)
</code></pre>
By setting <code>stream=True</code> in the GET request, when we pass <code>r.iter_lines()</code> to csv.reader(), we are passing a <a href="https://wiki.python.org/moin/Generators" rel="nofollow noreferrer">generator</a> to csv.reader(). By doing so, we enable csv.reader() to lazily iterate over each line in the response with <code>for row in reader</code>.
This avoids loading the entire file into memory before we start processing it, drastically reducing memory overhead for large files.

blocks|key|336650|text|您还可以使用DictReader来迭代{'columnname':+'value',+...}的字典。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|336651|import+csv
import+requests

response+=+requests.get('http://example.test/foo.csv')
reader+=+csv.DictReader(response.iter_lines())
for+record+in+reader:
++++print(record)|code-block|syntax|javascript|336652|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/3/library/csv.html#csv.DictReader^0|6|A|J|S|6|A|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|H|7|10|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|11|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

You can also use the <a href="https://docs.python.org/3/library/csv.html#csv.DictReader" rel="noreferrer"><code>DictReader</code></a> to iterate dictionaries of <code>{'columnname': 'value', ...}</code>

<pre><code>import csv
import requests

response = requests.get('http://example.test/foo.csv')
reader = csv.DictReader(response.iter_lines())
for record in reader:
 print(record)
</code></pre>

blocks|key|336723|text|我喜欢艾芬恩和阿卡托的答案。我只能通过缩短一些，删除多余的部分，使用真正的数据源，使其与2.x+&3.x兼容，并保持其他地方看到的高内存效率来改进它们：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|336724|import+csv
import+requests

CSV_URL+=+'http://web.cs.wpi.edu/~cs1004/a16/Resources/SacramentoRealEstateTransactions.csv'

with+requests.get(CSV_URL,+stream=True)+as+r:
++++lines+=+(line.decode('utf-8')+for+line+in+r.iter_lines())
++++for+row+in+csv.reader(lines):
++++++++print(row)|code-block|syntax|javascript|336725|遗憾的是，3.x没有那么灵活，因为迭代器必须发出Unicode字符串(而requests做bytes)，而2.x版本的for+row+in+csv.reader(r.iter_lines()):-is则更Pythonic+(更短、更容易阅读)。无论如何，请注意，上面的2.x/3.x解决方案无法处理OP描述的情况，即在读取的数据中未引用NEWLINE。|style|CODE|336726|对于OP中关于下载(与处理)实际CSV文件有关的部分，下面是另一个脚本，它与2.x+&3.x兼容、最小、可读性和内存效率：|336727|import+os
import+requests

CSV_URL+=+'http://web.cs.wpi.edu/~cs1004/a16/Resources/SacramentoRealEstateTransactions.csv'

with+open(os.path.split(CSV_URL)[1],+'wb')+as+f,+\
++++++++requests.get(CSV_URL,+stream=True)+as+r:
++++for+line+in+r.iter_lines():
++++++++f.write(line%2B'\n'.encode())|336728|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/users/3923962|1|https://stackoverflow.com/users/1259640^0|3|3|0|7|3|1|0|0|10|8|19|5|1N|12|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@$A|11|B|12|1|13]|$A|14|B|15|1|16]]|C|$]]|$1|D|3|E|5|F|7|17|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|18|8|@$A|19|B|1A|K|L]|$A|1B|B|1C|K|L]|$A|1D|B|1E|K|L]]|9|@]|C|$]]|$1|M|3|N|5|6|7|1F|8|@]|9|@]|C|$]]|$1|O|3|P|5|F|7|1G|8|@]|9|@]|C|$G|H]]|$1|Q|3|-4|5|6|7|1H|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|T|U|V|C|$W|Z]]]]

I like the answers from <a href="https://stackoverflow.com/users/3923962">The Aelfinn</a> and <a href="https://stackoverflow.com/users/1259640">aheld</a>. I can improve them only by shortening a bit more, removing superfluous pieces, using a real data source, making it 2.x &amp; 3.x-compatible, and maintaining the high-level of memory-efficiency seen elsewhere:
<pre><code>import csv
import requests

CSV_URL = 'http://web.cs.wpi.edu/~cs1004/a16/Resources/SacramentoRealEstateTransactions.csv'

with requests.get(CSV_URL, stream=True) as r:
 lines = (line.decode('utf-8') for line in r.iter_lines())
 for row in csv.reader(lines):
 print(row)
</code></pre>
Too bad 3.x is less flexible CSV-wise because the iterator must emit Unicode strings (while <code>requests</code> does <code>bytes</code>) while the 2.x-only version—<code>for row in csv.reader(r.iter_lines()):</code>—is more Pythonic (shorter and easier-to-read). Anyhow, note the 2.x/3.x solution above won't handle the situation described by the OP where a NEWLINE is found unquoted in the data read.
For the part of the OP's question regarding downloading (vs. processing) the actual CSV file, here's another script that does that, 2.x &amp; 3.x-compatible, minimal, readable, and memory-efficient:
<pre><code>import os
import requests

CSV_URL = 'http://web.cs.wpi.edu/~cs1004/a16/Resources/SacramentoRealEstateTransactions.csv'

with open(os.path.split(CSV_URL)[1], 'wb') as f, \
 requests.get(CSV_URL, stream=True) as r:
 for line in r.iter_lines():
 f.write(line+'\n'.encode())
</code></pre>

blocks|key|196393|text|下面的方法对我很管用。我也不需要使用csv.reader()或csv.writer()函数，我觉得这可以使代码更简洁。代码与Python2和Python3兼容。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|196394|from+six.moves+import+urllib

DOWNLOAD_URL+=+"https://raw.githubusercontent.com/gjreda/gregreda.com/master/content/notebooks/data/city-of-chicago-salaries.csv"
DOWNLOAD_PATH+="datasets\city-of-chicago-salaries.csv"+
urllib.request.urlretrieve(URL,DOWNLOAD_PATH)|code-block|syntax|javascript|196395|注-6是一个帮助编写与Python2和Python3兼容的代码的包。|196396|entityMap^0|I|C|V|C|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|V|8|@]|D|@]|E|$]]]|N|$]]

The following approach worked well for me. I also did not need to use <code>csv.reader()</code> or <code>csv.writer()</code> functions, which I feel makes the code cleaner. The code is compatible with Python2 and Python 3.

<pre><code>from six.moves import urllib

DOWNLOAD_URL = "https://raw.githubusercontent.com/gjreda/gregreda.com/master/content/notebooks/data/city-of-chicago-salaries.csv"
DOWNLOAD_PATH ="datasets\city-of-chicago-salaries.csv" 
urllib.request.urlretrieve(URL,DOWNLOAD_PATH)
</code></pre>

Note - six is a package that helps in writing code that is compatible with both Python 2 and Python 3. For additional details regarding six see - <a href="https://stackoverflow.com/questions/34989206/what-does-from-six-moves-import-urllib-do-in-python">What does <code>from six.moves import urllib</code> do in Python?</a>

blocks|key|195767|text|我使用以下代码(我使用Python+3)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|195768|import+csv
import+io
import+requests

url+=+"http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv"
r+=+requests.get(url)
r.encoding+=+'utf-8'++#+useful+if+encoding+is+not+sent+(or+not+sent+properly)+by+the+server
csvio+=+io.StringIO(r.text,+newline="")
data+=+[]
for+row+in+csv.DictReader(csvio):
++++data.append(row)|code-block|syntax|javascript|195769|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I use this code (I use Python 3):

<pre class="lang-py prettyprint-override"><code>import csv
import io
import requests

url = "http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv"
r = requests.get(url)
r.encoding = 'utf-8' # useful if encoding is not sent (or not sent properly) by the server
csvio = io.StringIO(r.text, newline="")
data = []
for row in csv.DictReader(csvio):
 data.append(row)
</code></pre>

blocks|key|336879|text|Python3支持码|type|unstyled|depth|inlineStyleRanges|entityRanges|data|336880|++++with+closing(requests.get(PHISHTANK_URL,+stream=True}))+as+r:
++++++++reader+=+csv.reader(codecs.iterdecode(r.iter_lines(),+'utf-8'),+delimiter=',',+quotechar='"')
++++++++for+record+in+reader:
+++++++++++print+(record)|code-block|syntax|javascript|336881|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Python3 Supported Code
<pre><code> with closing(requests.get(PHISHTANK_URL, stream=True})) as r:
 reader = csv.reader(codecs.iterdecode(r.iter_lines(), 'utf-8'), delimiter=',', quotechar='&quot;')
 for record in reader:
 print (record)
</code></pre>

blocks|key|2364380|text|这件事对我来说很管用：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2364381|from+csv+import+DictReader

f+=+requests.get('https://somedomain.com/file').content.decode('utf-8')
reader+=+DictReader(f.split('\n'))
csv_dict_list+=+list(reader)|code-block|syntax|javascript|2364382|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

this worked nicely for me:
<pre><code>from csv import DictReader

f = requests.get('https://somedomain.com/file').content.decode('utf-8')
reader = DictReader(f.split('\n'))
csv_dict_list = list(reader)
</code></pre>

blocks|key|195789|text|要转换为Pandas+DataFrame：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|195790|from+io+import+StringIO
text=StringIO(download.content.decode('utf-8'))
df=pd.read_csv(text)|code-block|syntax|javascript|195791|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

To convert to Pandas DataFrame:
<pre><code>from io import StringIO
text=StringIO(download.content.decode('utf-8'))
df=pd.read_csv(text)
</code></pre>

blocks|key|196455|text|我使用了下面的解决方案(不幸的是，其他人没有为我工作)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|196456|import+pandas+as+pd+
df+=+pd.read_csv('http://.../file.csv')+|code-block|syntax|javascript|196457|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I used below solution (Unfortunately others did not work for me):
<pre><code>import pandas as pd 
df = pd.read_csv('http://.../file.csv') 
</code></pre>

Here is my code: 

<pre><code>import csv
import requests
with requests.Session() as s:
 s.post(url, data=payload)
 download = s.get('url that directly download a csv report')
</code></pre>

This gives me the access to the csv file. I tried different method to deal with the download:

This will give the the csv file in one string:

<pre><code>print download.content
</code></pre>

This print the first row and return error: _csv.Error: new-line character seen in unquoted field

<pre><code>cr = csv.reader(download, dialect=csv.excel_tab)
for row in cr:
 print row
</code></pre>

This will print a letter in each row and it won't print the whole thing:

<pre><code>cr = csv.reader(download.content, dialect=csv.excel_tab)
for row in cr:
 print row
</code></pre>

My question is: what's the most efficient way to read a csv file in this situation. 
And how to download it.

thanks

Use python requests to download CSV

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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这是我的代码：import csvimport requestswith requests.Session() as s:    s.post(url, data=payload)    download = s.get('url that directly download a csv report')这使我可以访问...

问使用python请求下载CSV
EN

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