将 Kaggle csv 从下载网址导入到 pandas DataFrame

我一直在尝试不同的方法来导入 SpaceX 任务csv file https://www.kaggle.com/spacex/spacex-missions/downloads/database.csv on Kaggle https://www.kaggle.com/spacex/spacex-missions直接进入 pandas DataFrame，没有任何成功。

我需要发送登录请求。这是我到目前为止所拥有的：

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，但到目前为止我尝试过的都没有效果。

您正在创建一个流并将其直接传递给 pandas。我认为你需要将一个类似文件的对象传递给 pandas。看一眼这个答案 https://stackoverflow.com/a/32400969/900271寻找可能的解决方案（使用 post 而不是进入请求）。

另外，我认为您使用的带有重定向的登录网址无法正常工作。我知道我在这里建议了 http://ramhiser.com/2012/11/23/how-to-download-kaggle-data-with-python-and-requests-dot-py/。但我最终没有使用 is 因为发布请求调用没有处理重定向（我怀疑）。

我最终在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其直接加载到 DataFrame 中，而是先将其存储到磁盘中。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。