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

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

You can read a list of Parquet files from Amazon S3 into a Pandas DataFrame using the pyarrow.parquet module in Python. Here's how you can do it:

  1. Install Dependencies: Make sure you have both pyarrow and pandas installed:

    pip install pyarrow pandas

  2. Read Parquet Files from S3: Use the pyarrow.parquet.ParquetDataset class to create a dataset from the list of Parquet files on S3. Then, you can read this dataset into a Pandas DataFrame using the to_pandas() method.

    import pyarrow.parquet as pq import pandas as pd # List of S3 Parquet file paths s3_file_paths = [ 's3://bucket-name/path/to/file1.parquet', 's3://bucket-name/path/to/file2.parquet', # Add more file paths as needed ] # Create a Parquet dataset dataset = pq.ParquetDataset(s3_file_paths, filesystem='s3') # Read the dataset into a Pandas DataFrame dataframe = dataset.read().to_pandas() # Now you have your data in a Pandas DataFrame print(dataframe)

Replace 's3://bucket-name/path/to/file1.parquet' and 's3://bucket-name/path/to/file2.parquet' with the actual S3 file paths you want to read. The filesystem='s3' argument specifies that you're reading files from S3.

Keep in mind that reading large datasets into memory as a Pandas DataFrame might consume a significant amount of memory, so be cautious when dealing with large datasets.

Examples

  1. "Read multiple Parquet files from S3 into pandas dataframe using PyArrow" Description: Learn how to efficiently read multiple Parquet files stored on Amazon S3 into a pandas dataframe using PyArrow, a high-performance tool for working with Parquet files. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [] for file in parquet_files: obj = s3_client.get_object(Bucket=bucket, Key=file) df = pq.read_table(BytesIO(obj['Body'].read())).to_pandas() dfs.append(df) combined_df = pd.concat(dfs, ignore_index=True)

  2. "Efficiently load Parquet files from S3 as pandas dataframe with PyArrow" Description: Discover efficient methods to load Parquet files from Amazon S3 into a pandas dataframe using PyArrow, ensuring optimal performance and resource utilization. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [pq.read_table(f's3://{bucket}/{file}').to_pandas() for file in parquet_files] combined_df = pd.concat(dfs, ignore_index=True)

  3. "Bulk load Parquet files from S3 to pandas dataframe using PyArrow" Description: Learn how to bulk load multiple Parquet files stored on Amazon S3 into a pandas dataframe efficiently using PyArrow library, enabling seamless data analysis. Code:

    import pandas as pd import pyarrow.parquet as pq import s3fs # Initialize S3 filesystem s3 = s3fs.S3FileSystem() # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = s3.ls(f's3://{bucket}/{prefix}') # Read Parquet files into pandas dataframe and concatenate dfs = [pq.read_table(f's3://{file}', filesystem=s3).to_pandas() for file in parquet_files] combined_df = pd.concat(dfs, ignore_index=True)

  4. "Concatenate Parquet files from S3 into pandas dataframe using PyArrow" Description: Discover how to concatenate multiple Parquet files stored on Amazon S3 into a single pandas dataframe efficiently using PyArrow, simplifying data processing tasks. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [pq.read_table(BytesIO(s3_client.get_object(Bucket=bucket, Key=file)['Body'].read())).to_pandas() for file in parquet_files] combined_df = pd.concat(dfs, ignore_index=True)

  5. "Combine Parquet files from S3 into pandas dataframe using PyArrow" Description: Learn how to combine multiple Parquet files stored on Amazon S3 into a pandas dataframe efficiently using PyArrow, simplifying data analysis tasks. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [] for file in parquet_files: obj = s3_client.get_object(Bucket=bucket, Key=file) df = pq.read_table(BytesIO(obj['Body'].read())).to_pandas() dfs.append(df) combined_df = pd.concat(dfs, ignore_index=True)

  6. "Read Parquet files from S3 and merge into pandas dataframe using PyArrow" Description: Explore how to read Parquet files stored on Amazon S3 and merge them into a single pandas dataframe efficiently using PyArrow, facilitating data analysis workflows. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and merge combined_df = pd.concat([pq.read_table(BytesIO(s3_client.get_object(Bucket=bucket, Key=file)['Body'].read())).to_pandas() for file in parquet_files], ignore_index=True)

  7. "Load Parquet files from S3 to pandas dataframe with PyArrow" Description: Learn how to load Parquet files stored on Amazon S3 into a pandas dataframe using PyArrow library, providing a seamless approach for data analysis. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [pq.read_table(BytesIO(s3_client.get_object(Bucket=bucket, Key=file)['Body'].read())).to_pandas() for file in parquet_files] combined_df = pd.concat(dfs, ignore_index=True)

  8. "Aggregate Parquet files from S3 into pandas dataframe using PyArrow" Description: Explore how to aggregate multiple Parquet files stored on Amazon S3 into a pandas dataframe efficiently using PyArrow, facilitating data aggregation tasks. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [pq.read_table(BytesIO(s3_client.get_object(Bucket=bucket, Key=file)['Body'].read())).to_pandas() for file in parquet_files] combined_df = pd.concat(dfs, ignore_index=True)

  9. "Merge Parquet files from S3 into pandas dataframe using PyArrow" Description: Learn how to merge multiple Parquet files stored on Amazon S3 into a single pandas dataframe efficiently using PyArrow, enabling streamlined data manipulation. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [] for file in parquet_files: obj = s3_client.get_object(Bucket=bucket, Key=file) df = pq.read_table(BytesIO(obj['Body'].read())).to_pandas() dfs.append(df) combined_df = pd.concat(dfs, ignore_index=True)

  10. "Combine Parquet files on S3 into pandas dataframe using PyArrow" Description: Discover how to combine multiple Parquet files stored on Amazon S3 into a pandas dataframe efficiently using PyArrow library, facilitating data integration and analysis. Code:

    import pandas as pd import pyarrow.parquet as pq from io import BytesIO import boto3 # Initialize S3 client s3_client = boto3.client('s3') # Specify S3 bucket and prefix bucket = 'your_bucket_name' prefix = 'your_prefix' # List all Parquet files in the S3 bucket parquet_files = [] response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) for obj in response['Contents']: if obj['Key'].endswith('.parquet'): parquet_files.append(obj['Key']) # Read Parquet files into pandas dataframe and concatenate dfs = [pq.read_table(BytesIO(s3_client.get_object(Bucket=bucket, Key=file)['Body'].read())).to_pandas() for file in parquet_files] combined_df = pd.concat(dfs, ignore_index=True)

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