python – 从谷歌云存储中读取csv到pandas数据帧

我正在尝试将Google Cloud Storage存储桶中的csv文件读取到熊猫数据框中.

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)

它显示以下错误消息：

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谷歌datalab的解决方案？

UPDATE

截至0.24版本的pandas,read_csv支持直接从Google云端存储中读取.只需提供链接到这样的桶：

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

为了完整起见,我还留下了其他三个选项.

>自制代码
> gcsfs
> dask

我将在下面介绍它们.

艰难的方法：自己动手做代码

我已经写了一些便利功能来从Google存储中读取.为了使其更具可读性,我添加了类型注释.如果你碰巧在Python 2上,只需删除它们,代码将完全相同.

假设您获得授权,它在公共和私人数据集上同样有效.在此方法中,您无需先将数据下载到本地驱动器.

如何使用它：

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云端存储的Pythonic文件系统”.

如何使用它：

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)

DASK

Dask“为分析提供高级并行性,为您喜爱的工具提供大规模性能”.当您需要在Python中处理大量数据时,它非常棒. Dask尝试模仿大部分的pandas API,使其易于用于新手.

这是read_csv

如何使用它：

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()