task3 1.0.4

xSV file parser CLI and library

Parsing xSV with Regex in Python.

Module 2: Python. Task 3A+B.

       

Preface

This project contains a solution to one of the tasks of the EPAM DevOps Initial Internal Training Course #7 in 2023. Detailed information about the course, as well as reports on each of the completed tasks (including this one) can be found here .
As mentioned above, the project contains a solution to task #3A+B as part of module #2 of learning the Python programming language. Below you will find a detailed description of the task, as well as a brief description of the implementation.

Table of Contents

• Part A condition
• Part B condition
• Implementation
  • Structure
• Installation
• Usage
  • Library
  • CLI
  • Pipes and files
  • xSV example file generator
  • Showcase
• General Provisions

Part A conditions

Work with Regex in Python and make a bunch of regex:

• IPv4 address (special address: private network, CIDR notation);
• IPv6 address (special address: private network, CIDR notation);
• IP mask (any length, given length);
• MAC address (format: general, Linux, Windows, Cisco);
• domain address (only TLD, first DL, second);
• email (get login, get domain);
• URI;
• URL;
• SSH Key (private, public);
• card number;
• UUID.

Part B conditions

The xSV parsing CLI is supposed to provide the following features:

• It should work with the canonic file formats CSV, TSV, DSV.
• Implements import/export data from those file formats:
  • has or doesn't have a header row;
  • filtering:
    • in all rows;
    • in a specific row field by regular expression;
    • in certain rows by particular regular expression;
  • Output file fragment:
    • by row number or row number range
    • by column index or column index range
  • has different string value format:
    • single string
    • multi-string
  • processing files in case of breach of the canonical format (try to get the content as completely as possible)
• Implements all of the above:
  • by pure python string, module re (regular expression);
  • by using the Python module CSV.
• Convert CSV to JSON with Python by pure Python module CSV and JSON.

The CSV file must contain the following columns:

• MAC address
• Hostname
• IPv4
• IPv6
• Netmask (xxx.xxx.xxx.xxx or xx (CIDR))
• User login (in epam format)
• Full username
• Email
• SSH private key
• SSH public key
• Host description
• Installed app list
• UUID

Implementation

Parse_xSV is a Python package that could be added to your global or virtual environment by preferable package manager pip, pipenv, poetry, etc. The project itself was managed and built using the Poetry library, so if you intend to clone this repo and make some changes for your own purposes, please install Poetry or migrate to your preferred package management library.

Based on the need to build and the possibility of using both the library and the CLI, the code was split into a library for importing and a script for execution via the command line. Additionally, the package contains a showcase that demonstrates all use cases when run through the command line.

To enhance the command line's functionality and expand showcase capabilities, the Questionary library is used and will be installed through a dependency link upon package installation.

In order to help users generate sample xSV files, the Faker library is used and will be installed through a dependency link upon package installation.

Structure

task3/
├── README.md (You are here now)
├── pyproject.toml # Poetry package management file
└── parse_xsv/ 
    ├── __init__.py # library entry point
    ├── __main__.py # CLI entry point
    ├── __version__.py 
    ├── parse_xsv.py # library implementation
    ├── cli/
    │   ├── __init__.py
    │   ├── __main__.py
    │   └── cli.py # CLI code implementation
    ├── sample_gen/
    │   ├── __init__.py
    │   ├── __main__.py
    │   └── sample_gen.py # CLI for generation of sample xSV files
    └── showcase/
            ├── __init__.py 
            ├── __main__.py # showcase entry point when using python -m parse_xsv.showcase
            ├── sample.csv # a sample CSV file
            ├── sample.tsv # a sample TSV file
            ├── corrupted_sample.csv # an ill-formed format CSV file
            └── showcase.py # showcase implementation

Installation

Use your preferred installation method via different package installation managers to install Parse_xSV.

Pip

To install Parse_xSV packet to your environment using pip manager invoke pip install task3.

$ pip install task3
Collecting task3
  Using cached task3-0.1.N-py3-none-any.whl (62 kB)
Collecting faker<20.0.0,>=19.2.0 (from task3)
  Downloading Faker-19.2.0-py3-none-any.whl (1.7 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB N.N MB/s eta 0:00:00
Collecting questionary<2.0.0,>=1.10.0 (from task3)
  Downloading questionary-1.10.0-py3-none-any.whl (31 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 31.1/31.1 kB N.N MB/s eta 0:00:00
Collecting python-dateutil>=2.4 (from faker<20.0.0,>=19.2.0->task3)
  Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 247.2/247.2 kB N.N MB/s eta 0:00:00     
Collecting prompt_toolkit<4.0,>=2.0 (from questionary<2.0.0,>=1.10.0->task3)
  Downloading prompt_toolkit-3.0.39-py3-none-any.whl (385 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 385.2/385.2 kB N.N MB/s eta 0:00:00
Collecting wcwidth (from prompt_toolkit<4.0,>=2.0->questionary<2.0.0,>=1.10.0->task3)
  Downloading wcwidth-0.2.6-py2.py3-none-any.whl (29 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 29.2/29.2 kB N.N MB/s eta 0:00:00
Collecting six>=1.5 (from python-dateutil>=2.4->faker<20.0.0,>=19.2.0->task3)
  Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 11.8/11.8 kB N.N MB/s eta 0:00:00       
Installing collected packages: wcwidth, six, prompt_toolkit, questionary, python-dateutil, faker, task3
Successfully installed faker-19.2.0 prompt_toolkit-3.0.39 python-dateutil-2.8.2 questionary-1.10.0 six-1.16.0 task3-0.1.4 wcwidth-0.2.6

To uninstall Pwgen from your environment invoke pip uninstall task3. It's important to note that the pip manager does not uninstall dependent packages. Therefore, if you wish to remove them, you'll need to take the initiative and perform the task yourself. You can do this by using the commands pip uninstall questionary prompt-toolkit faker python-dateutil wcwidth six.

Poetry

To install Pwgen packet to your environment using poetry manager invoke poetry add task3.

$ poetry add task3
Using version ^0.1.N for task3

Updating dependencies
Resolving dependencies...

Package operations: 6 installs, 0 updates, 0 removals

  • Installing six (1.16.0)
  • Installing wcwidth (0.2.6)
  • Installing prompt-toolkit (3.0.39)
  • Installing python-dateutil (2.8.2)
  • Installing faker (19.2.0)
  • Installing questionary (1.10.0)

Writing lock file

By taking this action, a new dependency line will be added to your pyproject.toml file.

[tool.poetry.dependencies]
task3 = "^0.1.N"

To uninstall Pwgen from your environment invoke poetry remove task3. One of the benefits of utilizing Poetry is that it allows for the removal of all dependent packages with a single command.

Usage

There are various ways to use this library, as mentioned earlier.

• Utilize it like a library you can just import it into your .py file and use ReaderXSV class within your code.
• Utilize CLI via the command shell, either as a Python module or as a standalone command.
• Utilize CLI command in a pipe by passing stdout of other commands to the stdin of the parse_xsv command, writing stdout and stderr to files, or passing them to following commands.
• The library contains CLI for generation of sample xSV files that allows to make sample files for a test purposes.
• The library also contains rich showcase command that allows you to test all the use cases and even perform them in batches.

Library

Below is a code snippet that demonstrates how to be able to use the pwgen library in your code.

import logging
from task3.parse_xsv import ReaderXSV

with open('sample.csv', 'r') as f:
    output = list(ReaderXSV(file_name,
                            no_header=self.arguments.no_header,
                            search_string=self.arguments.search,
                            search_regex=s_regex,
                            column_regex=c_regex,
                            rows=self.arguments.rows,
                            columns=self.arguments.columns,
                            restkey='rest',
                            delimiter=self.arguments.delimiter,
                            dialect=self.arguments.dialect,
                            quoting=QuotingTypes[self.arguments.quoting].value,
                            json=self.arguments.json,
                            strict=not self.arguments.force))

    output_file = [','.join([quoting_value(el) for el in ln]) for ln in output]
    output_file = '\n'.join(output_file)

    print(output_file)

CLI

The CLI interface has a single command called "parse_xsv". It can be invoked using two methods: python3 -m parse_xsv or simply parse_xsv. Parse_xsv accepts various arguments, which are described below.

parse_xsv [-h] [--version] [-d] [-D] [-j] [-p] [-q] [--no-header] [--force] [-v]
          [-c [INDEXES ...]] [-r [INDEXES ...]]
          [-C [REGEX ...] | -s SEARCH | -S REGEX] [file_name]

positional arguments:
file_name             An input filename

optional arguments:
-h, --help            Show this help message and exit
--version             Show program's version number and exit
-d , --delimiter      Specifies values separator (default: ",")
-D , --dialect        The xsv dialect type. Possible values: excel,excel-tab,unix
-j, --json            Show output in JSON format. If filename is passed
-q , --quoting        How values are quoted in the CSV file (default: QUOTE_MINIMAL).
                      Possible values could be: QUOTE_NONE, QUOTE_NONNUMERIC,
                      QUOTE_MINIMAL, QUOTE_ALL
--no-header           Whether parsing CSV file(s) contains a header or not
--force               Forcibly process an ill-formed format input file
-v                    Increase verbosity level (add more v)
-c [INDEXES ...], --columns [INDEXES ...]
                      The column range from the xCV file to be output. 
                      You can pass values in the following formats:
                      particular indexes: index1 index2 ...
                      indexN range of indexes: index1-index2
                      from the beginning up to index: -index
                      from index to the end: index-                      
-r [INDEXES ...], --rows [INDEXES ...]
                      The row range from the xCV file to be output. 
                      You can pass values in the following formats: 
                      particular indexes: index1 index2 ...
                      indexN range of indexes: index1-index2 
                      from the beginning up to index: -index
                      from index to the end: index-                      
-C [REGEX ...], --col-regex [REGEX ...]
                      Find rows based on a given regex string in specific columns.
                      You can pass values in the following formats:
                      particular indexes: index1,regex1 index2,regex2 ... indexN,
                      regexN range of indexes: index1-index2,regex 
                      from the beginning up to index: -index,regex
                      from index to the end: index-,regex
                      Possible values could be: IP4, IP4_PRIVATE, IP4_CIDR, IP6,
                      IP6_PRIVATE, IP6_CIDR, MASK, MAC, MAC_LINUX, MAC_WINDOWS,
                      MAC_CISCO, DOMAIN, TLD, DOMAIN_SECOND, EMAIL, URI, URL, SSH_KEY,
                      SSH_PRIVATE, SSH_PUBLIC, CARD, UUID                      
-s, --search          Find rows based on a given search string
-S REGEX, --search-regex REGEX
                      Find rows based on a given regex. Possible values could be:
                      IP4, IP4_PRIVATE, IP4_CIDR, IP6, IP6_PRIVATE, IP6_CIDR, MASK,
                      MAC, MAC_LINUX, MAC_WINDOWS, MAC_CISCO, DOMAIN, TLD, DOMAIN_SECOND,
                      EMAIL, URI, URL, SSH_KEY, SSH_PRIVATE, SSH_PUBLIC, CARD, UUID

There are some rules for argument handling:

• if you pass argument -j, the output will be in JSON format. This will prevent further use of this command in a pipe with the following parse_xsv commands. Without this argument the output will be in the xSV format.
• the -C, -s, -S arguments are mutual. These arguments cannot be used together, they are mutually exclusive.

To handle the arguments, the argparse module is used. If you are already acquainted with it, you will have no difficulty in passing the arguments along with their values and comprehending their behavior.

The examples in a more convenient form you could find in the showcase.

Pipes and files

The parse_xsv command could be used inside the pipe of the BASH commands. It can be used in various ways within a pipeline:

• receiving input
• direct output to a file
• direct logging also to a file
• direct output to the next command in the pipeline
• using a sequence of the parse_xsv command mainly to put on different filters in a row on the same file

xSV example file generator

In order to create sample xSV files for a test purposes of parse_xsv CLI the xsv_sample_gen CLI was created. The CLI interface has a single command called "xsv_sample_gen". It can be invoked using two methods: python3 -m xsv_sample_gen or simply xsv_sample_gen. Parse_xsv accepts various arguments, which are described below. The common way of using this command is to pass the list (separated by space) of column types with a header which will be used to create the xSV file structure. To fill this file with mock values the external Faker library is used.

sample_gen [-h] [--version] [-d] [-D] [-q] [--no-header] [-i] [-v] [-r] structure [structure ...]

positional arguments:
  structure          The list of column names and column types to be used as output file structure.
                     You can pass values in the following formats: 
                     just a type (column name will be index number): type1 type2 ... typeN
                     column name + type: column_name1,typeN column_name2,typeN ...
                     column_nameN,typeN.
                     Possible type values could be: address, administrative_unit, bothify,
                     building_number, city, city_prefix, city_suffix, country, country_code,
                     current_country, current_country_code, hexify, language_code, lexify,
                     locale, military_apo, military_dpo, military_ship, military_state,
                     numerify, postalcode, postalcode_in_state, postalcode_plus4, postcode,
                     postcode_in_state, random_choices, random_digit, random_digit_above_two,
                     random_digit_not_null, random_digit_not_null_or_empty, random_digit_or_empty,
                     random_element, random_elements, random_int, random_letter, random_letters,
                     random_lowercase_letter, random_number, random_sample,
                     random_uppercase_letter, randomize_nb_elements, secondary_address, state,
                     state_abbr, street_address, street_name, street_suffix, zipcode,
                     zipcode_in_state, zipcode_plus4, license_plate, vin, aba, bank_country,
                     bban, iban, swift, swift11, swift8, ean, ean13, ean8, localized_ean,
                     localized_ean13, localized_ean8, upc_a, upc_e, color, color_name, hex_color,
                     rgb_color, rgb_css_color, safe_color_name, safe_hex_color, bs, catch_phrase,
                     company, company_suffix, credit_card_expire, credit_card_full, credit_card_number,
                     credit_card_provider, credit_card_security_code, cryptocurrency,
                     cryptocurrency_code, cryptocurrency_name, currency, currency_code,        
                     currency_name, currency_symbol, pricetag, am_pm, century, date, date_between,
                     date_between_dates, date_object, date_of_birth, date_this_century, date_this_decade,
                     date_this_month, date_this_year, date_time, date_time_ad, date_time_between,
                     date_time_between_dates, date_time_this_century, date_time_this_decade,
                     date_time_this_month, date_time_this_year, day_of_month, day_of_week, future_date,
                     future_datetime, iso8601, month, month_name, past_date, past_datetime, pytimezone,
                     time, time_delta, time_object, time_series, timezone, unix_time, year, emoji,
                     file_extension, file_name, file_path, mime_type, unix_device, unix_partition,  
                     coordinate, latitude, latlng, local_latlng, location_on_land, longitude,
                     ascii_company_email, ascii_email, ascii_free_email, ascii_safe_email, company_email,
                     dga, domain_name, domain_word, email, free_email, free_email_domain, hostname,
                     http_method, iana_id, image_url, ipv4, ipv4_network_class, ipv4_private, ipv4_public,
                     ipv6, mac_address, nic_handle, nic_handles, port_number, ripe_id,
                     safe_domain_name, safe_email, slug, tld, uri, uri_extension, uri_page, uri_path, url, user_name, isbn10, isbn13, job, paragraph,
                     paragraphs, sentence, sentences, text, texts, word, words, binary, boolean, csv, dsv, fixed_width, json, json_bytes, md5, null_boolean,    
                     password, psv, sha1, sha256, tar, tsv, uuid4, zip, passport_dates, passport_dob, passport_full, passport_gender, passport_number,
                     passport_owner, first_name, first_name_female, first_name_male, first_name_nonbinary, language_name, last_name, last_name_female,
                     last_name_male, last_name_nonbinary, name, name_female, name_male, name_nonbinary, prefix, prefix_female, prefix_male, prefix_nonbinary,   
                     suffix, suffix_female, suffix_male, suffix_nonbinary, basic_phone_number, country_calling_code, msisdn, phone_number, profile,
                     simple_profile, enum, pybool, pydecimal, pydict, pyfloat, pyint, pyiterable, pylist, pyobject, pyset, pystr, pystr_format, pystruct,       
                     pytuple, sbn9, ein, invalid_ssn, itin, ssn, android_platform_token, chrome, firefox, internet_explorer, ios_platform_token,
                     linux_platform_token, linux_processor, mac_platform_token, mac_processor, opera, safari, user_agent, windows_platform_token

optional arguments:
  -h, --help         show this help message and exit
  --version          show program's version number and exit
  -d , --delimiter   Specifies values separator (default: ",")
  -D , --dialect     The xsv dialect type. Possible values: excel,excel-tab,unix,faker-csv
  -q , --quoting     How values are quoted in the CSV file (default: QUOTE_MINIMAL). Possible values could be: QUOTE_NONE, QUOTE_NONNUMERIC, QUOTE_MINIMAL,     
                     QUOTE_ALL
  --no-header        Whether generated CSV file contains a header or not
  -i, --ill-formed   Forcibly generate an ill-formed format file
  -v                 Increase verbosity level (add more v)
  -r , --rows        Number of rows to be generated

This generator generates an xSV file an print it to stdout.
Mainly it will be displayed on your monitor screen.
If you want ot save generated data to file, please use > operator or pipe |.

Showcase

To showcase the behavior of the parse_xsv library, an interactive command called "parse_xsv_showcase" has been created. This command utilizes both the parse_xsv CLI and the parse_xsv library. It's an interactive command you can invoke via parse_xsv_showcase or python3 -m parse_xsv.showcase. It has an optional flag that allows you to view all use cases at once without any interaction. You can use the command parse_xsv_showcase --all to activate this feature. There are also ready-made xSV files present - sample.csv, sample.tsv' and corrupted_sample.csv`, but you can use the хsv_sample_gen command for this purpose.

General provisions

All materials provided and/or made available contain EPAM’s proprietary and confidential information and must not to be copied, reproduced or disclosed to any third party or to any other person, other than those persons who have a bona fide need to review it for the purpose of participation in the online courses being provided by EPAM. The intellectual property rights in all materials (including any trademarks) are owned by EPAM Systems Inc or its associated companies, and a limited license, terminable at the discretion of EPAM without notice, is hereby granted to you solely for the purpose of participating in the online courses being provided by EPAM. Neither you nor any other party shall acquire any intellectual property rights of any kind in such materials.