Question

我正在尝试使用以下代码将位于AWS S3存储桶中的CSV文件作为pandas数据帧读入内存：

import pandas as pd
import boto

data = pd.read_csv('s3:/example_bucket.s3-website-ap-southeast-2.amazonaws.com/data_1.csv')

为了提供完整的访问权限，我在S3存储桶上设置了存储桶策略，如下所示：

{
"Version": "2012-10-17",
"Id": "statement1",
"Statement": [
    {
        "Sid": "statement1",
        "Effect": "Allow",
        "Principal": "*",
        "Action": "s3:*",
        "Resource": "arn:aws:s3:::example_bucket"
    }
]

}

不幸的是我仍然在python中遇到以下错误：

boto.exception.S3ResponseError: S3ResponseError: 405 Method Not Allowed

想知道是否有人可以帮助解释如何在AWS S3中正确设置权限或正确配置pandas以导入文件。谢谢！

Answer 1

使用pandas 0.20.3

import os
import boto3
import pandas as pd
import sys

if sys.version_info[0] < 3: 
    from StringIO import StringIO # Python 2.x
else:
    from io import StringIO # Python 3.x

# get your credentials from environment variables
aws_id = os.environ['AWS_ID']
aws_secret = os.environ['AWS_SECRET']

client = boto3.client('s3', aws_access_key_id=aws_id,
        aws_secret_access_key=aws_secret)

bucket_name = 'my_bucket'

object_key = 'my_file.csv'
csv_obj = client.get_object(Bucket=bucket_name, Key=object_key)
body = csv_obj['Body']
csv_string = body.read().decode('utf-8')

df = pd.read_csv(StringIO(csv_string))

Answer 2

你不需要pandas ..你可以使用python的默认csv库

def read_file(bucket_name,region, remote_file_name, aws_access_key_id, aws_secret_access_key):
    # reads a csv from AWS

    # first you stablish connection with your passwords and region id

    conn = boto.s3.connect_to_region(
        region,
        aws_access_key_id=aws_access_key_id,
        aws_secret_access_key=aws_secret_access_key)

    # next you obtain the key of the csv you want to read
    # you will need the bucket name and the csv file name

    bucket = conn.get_bucket(bucket_name, validate=False)
    key = Key(bucket)
    key.key = remote_file_name
    data = key.get_contents_as_string()
    key.close()

    # you store it into a string, therefore you will need to split it
    # usually the split characters are '\r\n' if not just read the file normally 
    # and find out what they are 

    reader = csv.reader(data.split('\r\n'))
    data = []
    header = next(reader)
    for row in reader:
        data.append(row)

    return data

希望它能解决你的问题，祝好运！：）

Answer 3

我最终意识到您还需要为存储桶中的每个单独对象设置权限，以便使用以下代码将其解压缩：

from boto.s3.key import Key
k = Key(bucket)
k.key = 'data_1.csv'
k.set_canned_acl('public-read')

我还必须在pd.read_csv命令中修改存储桶的地址，如下所示：

data = pd.read_csv('https://s3-ap-southeast-2.amazonaws.com/example_bucket/data_1.csv')

Answer 4

基于this answer的建议，建议使用smart_open来读取S3，这就是我将其与Pandas结合使用的方式：

import os
import pandas as pd
from smart_open import smart_open

aws_key = os.environ['AWS_ACCESS_KEY']
aws_secret = os.environ['AWS_SECRET_ACCESS_KEY']

bucket_name = 'my_bucket'
object_key = 'my_file.csv'

path = 's3://{}:{}@{}/{}'.format(aws_key, aws_secret, bucket_name, object_key)

df = pd.read_csv(smart_open(path))

Answer 5

您也可以尝试使用熊猫read_sql和pyathena：

from pyathena import connect
import pandas as pd

conn = connect(s3_staging_dir='s3://bucket/folder',region_name='region')
df = pd.read_sql('select * from database.table', conn) #don't change the "database.table"

如何使用Python中的Pandas从s3存储桶读取csv文件

5 个答案: