Skip to main content

Preprocessing required data for customer service purpose

Project description

preprocessing_pgp

PyPI Python License Downloads linting: pylint

preprocessing_pgp -- The Preprocessing library for any kind of data -- is a suit of open source Python modules, preprocessing techniques supporting research and development in Machine Learning. preprocessing_pgp requires Python version 3.6, 3.7, 3.8, 3.9, 3.10


Installation

To install the current release:

pip install preprocessing-pgp

To install the release with specific version (e.g. 0.1.3):

pip install preprocessing-pgp==0.1.3

To upgrade package to latest version:

pip install --upgrade preprocessing-pgp

Features

1. Vietnamese Naming Functions

1.1. Preprocessing Names

python
>>> import preprocessing_pgp as pgp
>>> pgp.preprocess.basic_preprocess_name('Phan Thị    Thúy    Hằng *$%!@#')
Phan Thị Thúy Hằng

1.2. Enrich Vietnamese Names (New Features)

python
>>> import pandas as pd
>>> from preprocessing_pgp.name.enrich_name import process_enrich
>>> data = pd.read_parquet('/path/to/data.parquet')
>>> enrich_data, _ = process_enrich(data, name_col='name')
Basic pre-processing names...
100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 19669.68it/s]



--------------------
0 names have been clean!
--------------------




Filling diacritics to names...
100%|███████████████████████████████████████| 1000/1000 [01:29<00:00, 11.23it/s]

AVG prediction time : 0.0890703010559082s



Applying rule-based postprocess...
100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 38292.26it/s]

AVG rb time : 2.671933174133301e-05s


>>> enrich_data.columns
Index(['name', 'predict', 'final'], dtype='object')

2. Extracting Vietnamese Phones

python
>>> import pandas as pd
>>> from preprocessing_pgp.phone.extractor import extract_valid_phone
>>> data = pd.read_parquet('/path/to/data.parquet')
>>> extracted_data = extract_valid_phone(phones=data, phone_col='phone')
# OF PHONE CLEANED : 0

Sample of non-clean phones:
Empty DataFrame
Columns: [id, phone, clean_phone]
Index: []

100%|██████████| ####/#### [00:00<00:00, ####it/s]

# OF PHONE 10 NUM VALID : ####


# OF PHONE 11 NUM VALID : ####


0it [00:00, ?it/s]

# OF OLD PHONE CONVERTED : ####


# OF OLD LANDLINE PHONE : ####

100%|██████████| ####/#### [00:00<00:00, ####it/s]

# OF VALID PHONE : ####

# OF INVALID PHONE : ####

Sample of invalid phones:
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
|      |      id |       phone | is_phone_valid   | is_mobi   | is_new_mobi   | is_old_mobi   | is_new_landline   | is_old_landline   | phone_convert   |
+======+=========+=============+==================+===========+===============+===============+===================+===================+=================+
|   47 | ####### |   083###### | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
|  317 | ####### |   098###### | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
|  398 | ####### | 039######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
|  503 | ####### | 093######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
| 1261 | ####### | 096######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
| 1370 | ####### | 097######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
| 1554 | ####### | 098######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
| 2469 | ####### | 032######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
| 2609 | ####### | 086######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+
| 2750 | ####### | 078######## | False            | False     | False         | False         | False             | False             |                 |
+------+---------+-------------+------------------+-----------+---------------+---------------+-------------------+-------------------+-----------------+

3. Verify Vietnamese Card IDs

python
>>> import pandas as pd
>>> from preprocessing_pgp.card.validation import verify_card
>>> data = pd.read_parquet('/path/to/data.parquet')
>>> verified_data = verify_card(data, card_col='card_id')

##### CLEANSING #####


# NAN CARD ID: ####


# CARD ID CONTAINS NON-DIGIT CHARACTERS: ####


SAMPLE OF CARDS WITH NON-DIGIT CHARACTERS:
              card_id  is_valid  is_personal_id
#######      B#######     False           False
#######      C#######     False           False
#######       G######     False           False
#######     A########     False           False
#######  ###########k     False           False
#######  ###########k     False           False
#######      C#######     False           False
#######      B#######     False           False
#######  PT AR#######     False           False
#######     E########     False           False



# CARD OF LENGTH 9 OR 12: #######
STATISTIC:
True     ######
False     #####
Name: is_valid, dtype: int64




# CARD OF LENGTH 8 OR 11: ###
STATISTIC:
True     ######
False     #####
Name: is_valid, dtype: int64



# CARD WITH OTHER LENGTH: ####
# PASSPORT FOUND: ####


SAMPLE OF PASSPORT:
          card_id  is_valid  card_length clean_card_id  is_passport
#######  B#######      True            8      B#######         True
#######  C#######      True            8      C#######         True
#######  C#######      True            8      C#######         True
#######  B#######      True            8      B#######         True
#######  B#######      True            8      B#######         True
#######  B#######      True            8      B#######         True
#######  C#######      True            8      C#######         True
#######  B#######      True            8      B#######         True
#######  B#######      True            8      B#######         True
#######  B#######      True            8      B#######         True




# DRIVER LICENSE FOUND: 41461


SAMPLE OF DRIVER LICENSE:
          card_id  is_valid  is_personal_id  ...  clean_card_id is_passport  is_driver_license
47   0###########      True           False  ...   0###########       False               True
74   0###########      True           False  ...   0###########       False               True
170  0###########      True           False  ...   0###########       False               True
179  0###########      True           False  ...   0###########       False               True
206  0###########      True           False  ...   0###########       False               True
282  0###########      True           False  ...   0###########       False               True
295  0###########      True           False  ...   0###########       False               True
616  0###########      True           False  ...   0###########       False               True
663  0###########      True           False  ...   0###########       False               True
671  0###########      True           False  ...   0###########       False               True


##### GENERAL CARD ID REPORT #####

COHORT SIZE: #######
STATISTIC:
True     ######
False     #####
PASSPORT: ####
DRIVER LICENSE: ####

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

preprocessing-pgp-0.1.12.tar.gz (6.0 MB view details)

Uploaded Source

Built Distribution

preprocessing_pgp-0.1.12-py3-none-any.whl (6.0 MB view details)

Uploaded Python 3

File details

Details for the file preprocessing-pgp-0.1.12.tar.gz.

File metadata

  • Download URL: preprocessing-pgp-0.1.12.tar.gz
  • Upload date:
  • Size: 6.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for preprocessing-pgp-0.1.12.tar.gz
Algorithm Hash digest
SHA256 cb8c03842fc7c3dd1b5e60ab54b33eb64f2f63d0d6315997a361c6434e5ca193
MD5 97043ba381dc7c253bf551e3b3fa69df
BLAKE2b-256 e14d2021c165ec4f2862eb9ebccd85fc6d9a6ccdda717040d90812eafc6cbaf0

See more details on using hashes here.

File details

Details for the file preprocessing_pgp-0.1.12-py3-none-any.whl.

File metadata

File hashes

Hashes for preprocessing_pgp-0.1.12-py3-none-any.whl
Algorithm Hash digest
SHA256 71eda57e423677bfc1b6c049ab05d9b363e820bb53cf56bc91c349ca84bf4459
MD5 b859dbad89d471b3ed411f6f1aed5308
BLAKE2b-256 68bdd2c04a083c2037d75ceb86e775c82c838774db56eaa59e27721565b4bedb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page