binaryrain-helper-data-processing 0.0.2

Aims to simplify and help with commonly used functions in the data processing areas.

Binary Rain Helper Toolkit: Data Processing

binaryrain_helper_data_processing is a python package that aims to simplify and help with common functions data processing areas. It builds on top of the pandas library and provides additional functionality to make data processing easier, reduces boilerplate code and provides clear error messages.

Supported File Formats

• PARQUET: For efficient columnar storage
• CSV: For common tabular data
• JSON: For structured data exchange
• DICT: For Python dictionary data

Key Functions

• create_dataframe() simplifies creating pandas DataFrames from various formats:

    from binaryrain_helper_data_processing import FileFormat, create_dataframe
  
    # Create from CSV bytes
    df = create_dataframe(csv_bytes, FileFormat.CSV)
  
    # Create with custom options
    df = create_dataframe(parquet_bytes, FileFormat.PARQUET,
    file_format_options={'engine': 'pyarrow'})
  

• convert_dataframe_to_type(): handles converting DataFrames to different formats:

    from binaryrain_helper_data_processing import FileFormat, convert_dataframe_to_type
  
    # ....df is a pandas DataFrame
  
    # Convert to CSV bytes
    csv_bytes = convert_dataframe_to_type(df, FileFormat.CSV)
  
    # Convert with custom options
    parquet_bytes = convert_dataframe_to_type(df, FileFormat.PARQUET,
    file_format_options={'engine': 'pyarrow'})
  

• merge_dataframes(): provides a simple way to merge multiple DataFrames:

    from binaryrain_helper_data_processing import merge_dataframes
  
    # ....df1 and df2 are pandas DataFrames
  
    # Merge DataFrames
    merged_df = merge_dataframes(df1, df2, sort=True)
  

Benefits

• Consistent interface for different file formats
• Simplified error handling with clear messages
• Optional format-specific configurations
• Built on pandas for robust data processing
• Type hints for better IDE support