Simplify use of AWS resources in your code with aws-python-utils
Project description
aws-python-utils
Simplify use of AWS resources in your code with aws-python-utils
Install
pip install aws-python-util
AwsSecretManager
get_secret()
Use ENVIRONMENT variable "AWS_SECRET_MANAGER_SECRET" or pass in secret_key name.
Example Usage
Via ENVIRONMENT variable
In [1]: import os
In [2]: import secretmanager
In [3]: os.environ["AWS_SECRET_MANAGER_SECRET"] = "mysecret"
In [4]: secretmanager = secretmanager.AwsSecretManager()
In [5]: secretmanager.get_secret()
Out[5]: 'supersecretpass'
Or pass in secret_key name ..
In [1]: import os
In [2]: import secretmanager
In [3]: secretmanager = secretmanager.AwsSecretManager()
In [4]: secretmanager.get_secret(secret_key="mysecret")
Out[4]: 'supersecretpass'
S3 Util
- Streams an s3 object directly into a pandas DataFrame to avoid writing to disk and then loading from disk
- Uploads a DataFrame directly to s3
Example Usage
from aws_python_utils import s3
from io import BytesIO
import pandas as pd
import numpy as np
bucket,key = s3.get_bucket_and_key_from_s3_path("s3://my-bucket/mypath/to/object")
print("bucket = " + bucket) # my-bucket
print("key = " + key) # mypath/to/object
# download a tab separated file schema: id val1 val2
df = s3.download_s3_file(s3_path, header=0, sep='\t', index='id')
df2 = pd.DataFrame(np.random.randint(low=0, high=10, size=(5, 5)), columns=['a', 'b', 'c', 'd', 'e'])
io_buffer = BytesIO()
df2.to_csv(io_buffer, columns=['a', 'c', 'e'], sep='\t', index=False)
s3.upload_to_s3("s3://your-bucket/path/to/object.tsv", io_buffer)
Project details
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aws_python_utils-0.0.3.tar.gz
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