Convert text data from requests object to dataframe with pandas
Question:
Using requests I am creating an object which is in .csv format. How can I then write that object to a DataFrame with pandas?
To get the requests object in text format:
import requests
import pandas as pd
url = r'http://test.url'
r = requests.get(url)
r.text #this will return the data as text in csv format
I tried (doesn’t work):
pd.read_csv(r.text)
pd.DataFrame.from_csv(r.text)
Answers:
I think you can use read_csv
with url
:
pd.read_csv(url)
filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)
The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.csv
import pandas as pd
import io
import requests
url = r'http://...'
r = requests.get(url)
df = pd.read_csv(io.StringIO(r))
If it doesnt work, try update last line:
import pandas as pd
import io
import requests
url = r'http://...'
r = requests.get(url)
df = pd.read_csv(io.StringIO(r.text))
if the url has no authentication then you can directly use read_csv(url)
if you have authentication you can use request to get it un-pickel and print the csv and make sure the result is CSV and use panda.
You can directly use importing
import csv
Try this
import requests
import pandas as pd
import io
urlData = requests.get(url).content
rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8')))
Using “read_csv with url” worked:
import requests, csv
import pandas as pd
url = 'https://arte.folha.uol.com.br/ciencia/2020/coronavirus/csv/mundo/dados-bra.csv'
corona_bra = pd.read_csv(url)
print(corona_bra.head())
Using requests I am creating an object which is in .csv format. How can I then write that object to a DataFrame with pandas?
To get the requests object in text format:
import requests
import pandas as pd
url = r'http://test.url'
r = requests.get(url)
r.text #this will return the data as text in csv format
I tried (doesn’t work):
pd.read_csv(r.text)
pd.DataFrame.from_csv(r.text)
I think you can use read_csv
with url
:
pd.read_csv(url)
filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)
The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.csv
import pandas as pd
import io
import requests
url = r'http://...'
r = requests.get(url)
df = pd.read_csv(io.StringIO(r))
If it doesnt work, try update last line:
import pandas as pd
import io
import requests
url = r'http://...'
r = requests.get(url)
df = pd.read_csv(io.StringIO(r.text))
if the url has no authentication then you can directly use read_csv(url)
if you have authentication you can use request to get it un-pickel and print the csv and make sure the result is CSV and use panda.
You can directly use importing
import csv
Try this
import requests
import pandas as pd
import io
urlData = requests.get(url).content
rawData = pd.read_csv(io.StringIO(urlData.decode('utf-8')))
Using “read_csv with url” worked:
import requests, csv
import pandas as pd
url = 'https://arte.folha.uol.com.br/ciencia/2020/coronavirus/csv/mundo/dados-bra.csv'
corona_bra = pd.read_csv(url)
print(corona_bra.head())