file-ripper 1.4.1

No project description provided

file-ripper

A small lightweight library to parse your flat files and deliver the data inside.

Install

file-ripper is available for download on PyPI.

FileDefinition and FieldDefinition

file-ripper provides multiple ways for you to parse your files. Using file-ripper's FileDefinition and FieldDefinition contracts, you decide how to persist your file configurations:

FieldDefinition fields:

• field_name: str - required - the name of the field in the result set
• start_position: int - required for fixed width files - the start position of the field in its row
• field_length: int - required for fixed width files - the length of the field
• xml_node_name: str - optional, name is used if missing - the xml node containing the data
• position_in_row: int - required for delimited files - the position of the field in the delimited row

FileDefinition fields:

• file_type: str - required - the type of the file. DELIMITED, FIXED, and XML are currently supported
• field_definitions: List[FieldDefinition] - required - list of FieldDefinition objects to define data fields
• has_header: bool - optional - whether the file has a header row to skip or not
• delimiter: str - required for DELIMITED files - character or string of characters that delimit fields
• record_element_name: str - required for xml files - name of the xml node that represents a full record
• file_mask: str - required for finding files - a glob pattern to be used in matching file names for look up
• input_directory: str - required for finding files - the absolute path where the files reside
• completed_directory: str - optional - the absolute path to move files to once they are ripped

from file_ripper import FieldDefinition, FileDefinition, file_constants as fc


def build_delimited_file_definition():
    field_definitions = [
        FieldDefinition('name', fc.DELIMITED),
        FieldDefinition('age', fc.DELIMITED),
        FieldDefinition('dob', fc.DELIMITED)
    ]
    
    return FileDefinition(fc.DELIMITED, field_definitions)


def build_fixed_file_definition():
    field_definitions = [
        FieldDefinition('name', fc.FIXED, start_position=0, field_length=20),
        FieldDefinition('age', fc.FIXED, start_position=20, field_length=5),
        FieldDefinition('dob', fc.FIXED, start_position=25, field_length=10)
    ]
    
    return FileDefinition(fc.FIXED, field_definitions)


def build_xml_file_definition():
    field_definitions = [
        FieldDefinition('name', fc.XML, xml_node_name='name'),
        FieldDefinition('age', fc.XML, xml_node_name='age'),
        FieldDefinition('dob', fc.XML, xml_node_name='dateOfBirth')
    ]

    return FileDefinition(fc.FIXED, field_definitions)

Ripping Files

file-ripper provides easy access to your file data. The rip_file function takes a file-like object and a FileDefinition. It returns a tuple containing your file's name and your records as a list of dicts

from file_ripper import rip_file, FileInstance, FileDefinition, FieldDefinition, file_constants as fc

field_definitions = [
    FieldDefinition('name', fc.DELIMITED),
    FieldDefinition('age', fc.DELIMITED),
    FieldDefinition('dob', fc.DELIMITED)
]
    
file_definition = FileDefinition(fc.DELIMITED, field_definitions)
with open('path/to/file.txt', 'r') as file:
    file_instance: FileInstance = rip_file(file, file_definition)    

The FileRipper class also supports ripping multiple files using the rip_files function.

from file_ripper import rip_files, FileInstance, FileDefinition, FieldDefinition, file_constants as fc
from typing import List

field_definitions = [
    FieldDefinition('name', fc.DELIMITED),
    FieldDefinition('age', fc.DELIMITED),
    FieldDefinition('dob', fc.DELIMITED)
]
    
file_definition = FileDefinition(fc.DELIMITED, field_definitions)
with open('path/to/file.txt', 'r') as file:
    file_results: List[FileInstance] = rip_files([file], file_definition) 

Finding And Ripping Files

This is a new feature for version 1.1.0 of file-ripper. It now supports finding and ripping your files based on a provided file mask (using glob pattern matching) and an input directory. An optional completed directory can be specified if you wish for the file to be moved after they are ripped.

In the following example, any file in /usr/bin matching whose name matches the glob pattern Valid-*.txt will be ripped and have its data added to the result set. Those files will then be moved to /usr/bin/completed so that they will not be ripped if that directory is processed a second time

from file_ripper import find_and_rip_files, FileDefinition, FieldDefinition, FileInstance, file_constants as fc
from typing import List

field_definitions = [
    FieldDefinition('name', fc.DELIMITED),
    FieldDefinition('age', fc.DELIMITED),
    FieldDefinition('dob', fc.DELIMITED)
]
    
file_definition = FileDefinition(fc.DELIMITED, field_definitions, file_mask='Valid-*.txt', input_directory='/usr/bin',
                                 completed_directory='/usr/bin/completed')
file_results: List[FileInstance] = find_and_rip_files(file_definition)

FileInstance and FileRow

file-ripper provides your data via the FileInstance and FileRow classes. FileInstance provides all the metadata associated with the file as well as a list of FileRow instances representing your data. FileRow exposes the fields that make up the file as a dictionary. The keys to this dictionary are the field names you supplied in your input FieldDefinition objects.

FileInstance and FileRow are also both iterable directly. You don't need to access the file_rows and/or fields properties to access their data using an iterator or loop.

FileInstance fields:

• file_name: str - the name of the file associated with this instance
• file_rows: List[FileRow] - objects holding the data ripped from the file

FileRow fields:

• fields: Dict[str, str] the data associated with a row from the file

from file_ripper import rip_file, FileDefinition, FileInstance, file_constants as fc
from typing import IO

def process_file(file: IO, file_definition: FileDefinition):
    file_instance: FileInstance = rip_file(file, file_definition)
    print(f'ripped file {file_instance.file_name}')
    index = 1
    for row in file_instance:
        print(f'row {index} contains the following')
        index += 1
        for field_name in row:
            print(f'{field_name} : {row[field_name]}')