将 pickle 文件写入 AWS 中的 s3 存储桶

Question

I'm trying to write a pandas dataframe as a pickle file into an s3 bucket in AWS. I know that I can write dataframe new_df as a csv to an s3 bucket as follows:

bucket='mybucket'
key='path'

csv_buffer = StringIO()
s3_resource = boto3.resource('s3')

new_df.to_csv(csv_buffer, index=False)
s3_resource.Object(bucket,path).put(Body=csv_buffer.getvalue())

I've tried using the same code as above with to_pickle() but with no success.

Answer 1

Further to you answer, you don't need to convert to csv. pickle.dumps method returns a byte obj. see here: https://docs.python.org/3/library/pickle.html

import boto3
import pickle

bucket='your_bucket_name'
key='your_pickle_filename.pkl'
pickle_byte_obj = pickle.dumps([var1, var2, ..., varn]) 
s3_resource = boto3.resource('s3')
s3_resource.Object(bucket,key).put(Body=pickle_byte_obj)

Answer 2

I've found the solution, need to call BytesIO into the buffer for pickle files instead of StringIO (which are for CSV files).

import io
import boto3

pickle_buffer = io.BytesIO()
s3_resource = boto3.resource('s3')

new_df.to_pickle(pickle_buffer)
s3_resource.Object(bucket, key).put(Body=pickle_buffer.getvalue())

Answer 3

this worked for me with pandas 0.23.4 and boto3 1.7.80 :

bucket='your_bucket_name'
key='your_pickle_filename.pkl'
new_df.to_pickle(key)
s3_resource.Object(bucket, key).put(Body=open(key, 'rb'))

Answer 4

This solution (using s3fs) worked perfectly and elegantly for my team:

import s3fs
from pickle import dump

fs = s3fs.S3FileSystem(anon=False)

bucket = 'bucket1'
key = 'your_pickle_filename.pkl'

dump(data, fs.open(f's3://{bucket}/{key}', 'wb'))

Answer 5

This adds some clarification to a previous answer:

import pandas as pd
import boto3

# make df
df = pd.DataFrame({'col1:': [1,2,3]})

# bucket name
str_bucket = 'bucket_name'
# filename
str_key_file = 'df.pkl'
# bucket path
str_key_bucket = dir_1/dir2/{str_key_file}'

# write df to local pkl file
df.to_pickle(str_key_file)

# put object into s3
boto3.resource('s3').Object(str_bucket, str_key_bucket).put(Body=open(str_key_file, 'rb'))

Answer 6

From the just-released book 'Time Series Analysis with Python' by Tarek Atwan, I learned this method:

            import pandas as pd
            df = pd.DataFrame(...)

            df.to_pickle('s3://mybucket/pklfile.bz2',
                   storage_options={
                       'key': AWS_ACCESS_KEY,
                       'secret': AWS_SECRET_KEY
                   }
            )

which I believe is more pythonic. I add that this works for me most of the time, but sometimes it throws a "PermissionError: Access Denied" exception that I cannot explain (as I am new to AWS/S3, I must be missing something in the setup).

Answer 7

I've found the best solution - just upgrade pandas and also install s3fs:

pip install s3fs==2022.8.2
pip install install pandas==1.1.5


bucket,key='mybucket','path'


df.to_pickle(f"{bucket}{key}.pkl.gz", compression='gzip')