How to read a list of parquet files from S3 as a pandas dataframe using pyarrow?

Question

I have a hacky way of achieving this using boto3 (1.4.4), pyarrow (0.4.1) and pandas (0.20.3).

First, I can read a single parq

Answer 1

Thanks! Your question actually tell me a lot. This is how I do it now with pandas (0.21.1), which will call pyarrow, and boto3 (1.3.1).

import boto3
import io
import pandas as pd

# Read single parquet file from S3
def pd_read_s3_parquet(key, bucket, s3_client=None, **args):
    if s3_client is None:
        s3_client = boto3.client('s3')
    obj = s3_client.get_object(Bucket=bucket, Key=key)
    return pd.read_parquet(io.BytesIO(obj['Body'].read()), **args)

# Read multiple parquets from a folder on S3 generated by spark
def pd_read_s3_multiple_parquets(filepath, bucket, s3=None, 
                                 s3_client=None, verbose=False, **args):
    if not filepath.endswith('/'):
        filepath = filepath + '/'  # Add '/' to the end
    if s3_client is None:
        s3_client = boto3.client('s3')
    if s3 is None:
        s3 = boto3.resource('s3')
    s3_keys = [item.key for item in s3.Bucket(bucket).objects.filter(Prefix=filepath)
               if item.key.endswith('.parquet')]
    if not s3_keys:
        print('No parquet found in', bucket, filepath)
    elif verbose:
        print('Load parquets:')
        for p in s3_keys: 
            print(p)
    dfs = [pd_read_s3_parquet(key, bucket=bucket, s3_client=s3_client, **args) 
           for key in s3_keys]
    return pd.concat(dfs, ignore_index=True)

Then you can read multiple parquets under a folder from S3 by

df = pd_read_s3_multiple_parquets('path/to/folder', 'my_bucket')

(One can simplify this code a lot I guess.)

Answer 2

You should use the s3fs module as proposed by yjk21. However as result of calling ParquetDataset you'll get a pyarrow.parquet.ParquetDataset object. To get the Pandas DataFrame you'll rather want to apply .read_pandas().to_pandas() to it:

import pyarrow.parquet as pq
import s3fs
s3 = s3fs.S3FileSystem()

pandas_dataframe = pq.ParquetDataset('s3://your-bucket/', filesystem=s3).read_pandas().to_pandas()

Answer 3

If you are open to also use AWS Data Wrangler.

import awswrangler as wr

df = wr.s3.read_parquet(path="s3://...")

Answer 4

Probably the easiest way to read parquet data on the cloud into dataframes is to use dask.dataframe in this way:

import dask.dataframe as dd
df = dd.read_parquet('s3://bucket/path/to/data-*.parq')

dask.dataframe can read from Google Cloud Storage, Amazon S3, Hadoop file system and more!

Answer 5

You can use s3fs from dask which implements a filesystem interface for s3. Then you can use the filesystem argument of ParquetDataset like so:

import s3fs
s3 = s3fs.S3FileSystem()
dataset = pq.ParquetDataset('s3n://dsn/to/my/bucket', filesystem=s3)

Answer 6

Provided you have the right package setup

$ pip install pandas==1.1.0 pyarrow==1.0.0 s3fs==0.4.2

and your AWS shared config and credentials files configured appropriately

you can use pandas right away:

import pandas as pd

df = pd.read_parquet("s3://bucket/key.parquet")

---

In case of having multiple AWS profiles you may also need to set

$ export AWS_DEFAULT_PROFILE=profile_under_which_the_bucket_is_accessible

so you can access your bucket.