Import Kaggle csv from download url to pandas DataFrame - csv

Question

I've been trying different methods to import the SpaceX missions csv file on Kaggle directly into a pandas DataFrame, without any success.
I'd need to send requests to login. This is what I have so far:
import requests
import pandas as pd
from io import StringIO
# Link to the Kaggle data set & name of zip file
login_url = 'http://www.kaggle.com/account/login?ReturnUrl=/spacex/spacex-missions/downloads/database.csv'
# Kaggle Username and Password
kaggle_info = {'UserName': "user", 'Password': "pwd"}
# Login to Kaggle and retrieve the data.
r = requests.post(login_url, data=kaggle_info, stream=True)
df = pd.read_csv(StringIO(r.text))
r is returning the html content of the page.
df = pd.read_csv(url) gives a CParser error:
CParserError: Error tokenizing data. C error: Expected 1 fields in line 13, saw 6
I've searched for a solution, but so far nothing I've tried worked.

Answer 1

You are creating a stream and passing it directly to pandas. I think you need to pass a file like object to pandas. Take a look at this answer for a possible solution (using post and not get in the request though).
Also i think the login url with redirect that you use is not working as it is. I know i suggested that here. But i ended up not using is because the post request call did not handle the redirect (i suspect).
The code i ended up using in my project was this:
def from_kaggle(data_sets, competition):
"""Fetches data from Kaggle
Parameters
----------
data_sets : (array)
list of dataset filenames on kaggle. (e.g. train.csv.zip)
competition : (string)
name of kaggle competition as it appears in url
(e.g. 'rossmann-store-sales')
"""
kaggle_dataset_url = "https://www.kaggle.com/c/{}/download/".format(competition)
KAGGLE_INFO = {'UserName': config.kaggle_username,
'Password': config.kaggle_password}
for data_set in data_sets:
data_url = path.join(kaggle_dataset_url, data_set)
data_output = path.join(config.raw_data_dir, data_set)
# Attempts to download the CSV file. Gets rejected because we are not logged in.
r = requests.get(data_url)
# Login to Kaggle and retrieve the data.
r = requests.post(r.url, data=KAGGLE_INFO, stream=True)
# Writes the data to a local file one chunk at a time.
with open(data_output, 'wb') as f:
# Reads 512KB at a time into memory
for chunk in r.iter_content(chunk_size=(512 * 1024)):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
Example use:
sets = ['train.csv.zip',
'test.csv.zip',
'store.csv.zip',
'sample_submission.csv.zip',]
from_kaggle(sets, 'rossmann-store-sales')
You might need to unzip the files.
def _unzip_folder(destination):
"""Unzip without regards to the folder structure.
Parameters
----------
destination : (str)
Local path and filename where file is should be stored.
"""
with zipfile.ZipFile(destination, "r") as z:
z.extractall(config.raw_data_dir)
So i never really directly loaded it into the DataFrame, but rather stored it to disk first. But you could modify it to use a temp directory and just delete the files after you read them.