csv - 将 Kaggle csv 从下载 url 导入到 Pandas DataFrame

Question

我一直在尝试不同的方法来导入 SpaceX 任务 csv file在 Kaggle直接进入一个pandas DataFrame，没有任何成功。

我需要发送登录请求。这是我到目前为止:

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 正在返回页面的 html 内容。 df = pd.read_csv(url)给出 CParser 错误: CParserError: Error tokenizing data. C error: Expected 1 fields in line 13, saw 6
我一直在寻找解决方案，但到目前为止我没有尝试过任何工作。

Answer 1

您正在创建一个流并将其直接传递给 Pandas 。我认为您需要将像对象这样的文件传递给 Pandas 。看看this answer对于可能的解决方案(使用 post 而不是在请求中)。

此外，我认为您使用的带有重定向的登录 url 无法正常工作。 I know i suggested that here .但我最终没有使用是因为 post 请求调用没有处理重定向(我怀疑)。

我最终在我的项目中使用的代码是这样的:

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)

使用示例:

sets = ['train.csv.zip',
        'test.csv.zip',
        'store.csv.zip',
        'sample_submission.csv.zip',]
from_kaggle(sets, 'rossmann-store-sales')

您可能需要解压缩文件。

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)

所以我从来没有真正将它直接加载到 DataFrame 中，而是先将它存储到磁盘中。但是您可以修改它以使用临时目录，并在读取文件后删除它们。