Import Kaggle Csv From Download Url To Pandas Dataframe
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
Solution 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:
deffrom_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.withopen(data_output, 'wb') as f:
# Reads 512KB at a time into memoryfor 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.
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