Format django queryset as a table in the terminal

django_query_set_as_table.py

def query_set_as_table(queryset, many_to_many_fields=None, foreign_key_fields=None, exclude_fields=None, prefetch_related=False):
    from decimal import Decimal
    if many_to_many_fields is None:
        many_to_many_fields = []
    if foreign_key_fields is None:
        foreign_key_fields = {}
    if exclude_fields is None:
        exclude_fields = []
    # Prefetch related data for the ManyToMany fields and select related ForeignKey fields
    if prefetch_related:
        queryset = queryset.prefetch_related(*many_to_many_fields).select_related(*foreign_key_fields.keys())
    data = []
    for obj in queryset:
        # Collect field values using the field names, excluding fields listed in exclude_fields
        row_data = {
            field.name: getattr(obj, field.name)
            for field in obj._meta.fields
            if field.name not in exclude_fields
        }
        # Add ForeignKey field values, excluding fields listed in exclude_fields
        for fk_field, fk_attr in foreign_key_fields.items():
            if fk_field not in exclude_fields:
                related_obj = getattr(obj, fk_field)
                # Use the 'key.field' format for the label and add the value
                row_data[f"{fk_field}.{fk_attr}"] = getattr(related_obj, fk_attr, None) if related_obj else None
        # Add ManyToMany field values, excluding fields listed in exclude_fields
        for m2m_field in many_to_many_fields:
            if m2m_field not in exclude_fields:
                related_objects = getattr(obj, m2m_field).all()
                # Join related objects' string representation into a comma-separated string
                row_data[m2m_field] = ", ".join(str(rel_obj) for rel_obj in related_objects)
        data.append(row_data)
    # Check if data is empty
    if not data:
        print("No data to display.")
        return
    # Extract headers, excluding fields listed in exclude_fields
    headers = data[0].keys()
    # Calculate initial column widths based on data
    col_widths = [max(len(str(item)) for item in col) for col in zip(*[d.values() for d in data])]
    # Adjust column widths to ensure they are at least as wide as the headers
    col_widths = [max(width, len(header)) for width, header in zip(col_widths, headers)]
    # Print headers
    header_row = " | ".join(f"{header:<{width}}" for header, width in zip(headers, col_widths))
    print(header_row)
    print("-" * len(header_row)) 
    # Print data rows
    for row in data:
        print(" | ".join(f"{str(value):<{width}}" for value, width in zip(row.values(), col_widths)))  
    # Print row count
    row_count = len(data)
    print("\nSummary:")
    print(f"Total Rows: {row_count}")
    # Calculate and print summary data for numeric columns
    summary = {}
    for header in headers:
        # Extract numeric values (including Decimal)
        values = [row[header] for row in data if isinstance(row[header], (int, float, Decimal))]
        if values:
            total_sum = sum(values)
            # Ensure proper formatting for Decimal types
            formatted_sum = f"{total_sum:.2f}" if any(isinstance(v, Decimal) for v in values) else f"{total_sum}"
            summary[header] = {
                'count': len(values),
                'sum': formatted_sum
            }    
    for header, stats in summary.items():
        print(f"{header}: Count = {stats['count']}, Sum = {stats['sum']}")