python - 从 Google Cloud 存储读取 csv 到 pandas 数据框

Question

我正在尝试将 Google Cloud Storage 存储桶上的 csv 文件读取到 panda 数据帧中。

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from io import BytesIO

from google.cloud import storage

storage_client = storage.Client()
bucket = storage_client.get_bucket('createbucket123')
blob = bucket.blob('my.csv')
path = "gs://createbucket123/my.csv"
df = pd.read_csv(path)

它显示了这个错误信息:

FileNotFoundError: File b'gs://createbucket123/my.csv' does not exist

我做错了什么，我找不到任何不涉及 google datalab 的解决方案？

Answer 1

更新

从 pandas 0.24 版开始，read_csv 支持直接从 Google Cloud Storage 读取。只需像这样提供指向存储桶的链接:

df = pd.read_csv('gs://bucket/your_path.csv')

read_csv 然后将使用 gcsfs 模块来读取 Dataframe，这意味着它必须被安装(否则你会得到一个指向缺少依赖项的异常)。

为了完整起见，我留下了其他三个选项。

• 自制代码
• gcsfs
• 黎明

我将在下面介绍它们。

困难的方法:自己动手编写代码

我编写了一些方便的函数来从 Google 存储中读取数据。为了使其更具可读性，我添加了类型注释。如果您碰巧在 Python 2 上，只需删除这些代码即可。

假设您已获得授权，它同样适用于公共(public)和私有(private)数据集。在这种方法中，您无需先将数据下载到本地驱动器。

使用方法:

fileobj = get_byte_fileobj('my-project', 'my-bucket', 'my-path')
df = pd.read_csv(fileobj)

代码:

from io import BytesIO, StringIO
from google.cloud import storage
from google.oauth2 import service_account

def get_byte_fileobj(project: str,
                     bucket: str,
                     path: str,
                     service_account_credentials_path: str = None) -> BytesIO:
    """
    Retrieve data from a given blob on Google Storage and pass it as a file object.
    :param path: path within the bucket
    :param project: name of the project
    :param bucket_name: name of the bucket
    :param service_account_credentials_path: path to credentials.
           TIP: can be stored as env variable, e.g. os.getenv('GOOGLE_APPLICATION_CREDENTIALS_DSPLATFORM')
    :return: file object (BytesIO)
    """
    blob = _get_blob(bucket, path, project, service_account_credentials_path)
    byte_stream = BytesIO()
    blob.download_to_file(byte_stream)
    byte_stream.seek(0)
    return byte_stream

def get_bytestring(project: str,
                   bucket: str,
                   path: str,
                   service_account_credentials_path: str = None) -> bytes:
    """
    Retrieve data from a given blob on Google Storage and pass it as a byte-string.
    :param path: path within the bucket
    :param project: name of the project
    :param bucket_name: name of the bucket
    :param service_account_credentials_path: path to credentials.
           TIP: can be stored as env variable, e.g. os.getenv('GOOGLE_APPLICATION_CREDENTIALS_DSPLATFORM')
    :return: byte-string (needs to be decoded)
    """
    blob = _get_blob(bucket, path, project, service_account_credentials_path)
    s = blob.download_as_string()
    return s


def _get_blob(bucket_name, path, project, service_account_credentials_path):
    credentials = service_account.Credentials.from_service_account_file(
        service_account_credentials_path) if service_account_credentials_path else None
    storage_client = storage.Client(project=project, credentials=credentials)
    bucket = storage_client.get_bucket(bucket_name)
    blob = bucket.blob(path)
    return blob

gcsfs

gcsfs是“用于 Google 云存储的 Pythonic 文件系统”。

使用方法:

import pandas as pd
import gcsfs

fs = gcsfs.GCSFileSystem(project='my-project')
with fs.open('bucket/path.csv') as f:
    df = pd.read_csv(f)

黎明

Dask “为分析提供高级并行性，为您喜爱的工具实现大规模性能”。当您需要在 Python 中处理大量数据时，它非常棒。 Dask 尝试模仿 pandas API 的大部分内容，使其易于新手使用。

这里是 read_csv

使用方法:

import dask.dataframe as dd

df = dd.read_csv('gs://bucket/data.csv')
df2 = dd.read_csv('gs://bucket/path/*.csv') # nice!

# df is now Dask dataframe, ready for distributed processing
# If you want to have the pandas version, simply:
df_pd = df.compute()