Here is my 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')
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:
print download.content
This print the first row and return error: _csv.Error: new-line character seen in unquoted field
cr = csv.reader(download, dialect=csv.excel_tab)
for row in cr:
print row
This will print a letter in each row and it won't print the whole thing:
cr = csv.reader(download.content, dialect=csv.excel_tab)
for row in cr:
print row
My question is: what's the most efficient way to read a csv file in this situation. And how to download it.
thanks
This should help:
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)
Ouput sample:
['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']
[...]
Related question with answer: https://stackoverflow.com/a/33079644/295246
Edit: Other answers are useful if you need to download large files (i.e. stream=True
).
To simplify these answers, and increase performance when downloading a large file, the below may work a bit more efficiently.
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)
By setting stream=True
in the GET request, when we pass r.iter_lines()
to csv.reader(), we are passing a generator to csv.reader(). By doing so, we enable csv.reader() to lazily iterate over each line in the response with for row in reader
.
This avoids loading the entire file into memory before we start processing it, drastically reducing memory overhead for large files.
I like the answers from The Aelfinn and aheld. I can improve them only by shortening a bit more, removing superfluous pieces, using a real data source, making it 2.x & 3.x-compatible, and maintaining the high-level of memory-efficiency seen elsewhere:
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)
Too bad 3.x is less flexible CSV-wise because the iterator must emit Unicode strings (while requests
does bytes
) while the 2.x-only version—for row in csv.reader(r.iter_lines()):
—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 & 3.x-compatible, minimal, readable, and memory-efficient:
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())
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