preprocessing-pgp 0.1.30

Preprocessing required data for customer service purpose

preprocessing_pgp

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

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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

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Features

1. Vietnamese Naming Functions

1.1. Preprocessing Names

python

>>> import preprocessing_pgp.name.preprocess import basic_preprocess_name
>>> 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')


Cleansing Takes 0m0s


Enrich names takes 5m10s

>>> 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', print_info=True)
# 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', print_info=True)

##### 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: ####

4. Extract Information in Vietnamese Address

All the region codes traced are retrieve from Đơn Vị Hành Chính Việt Nam

Apart from original columns of dataframe, we also generate columns with specific meanings:

• cleaned_<address_col> : The cleaned address retrieve from the raw address column
• level 1 : The raw city extracted from the cleaned address
• best level 1 : The beautified city traced from extracted raw city
• level 1 code : The generated city code
• level 2 : The raw district extracted from the cleaned address
• best level 2 : The beautified district traced from extracted raw district
• level 2 code : The generated district code
• level 3 : The raw ward extracted from the cleaned address
• best level 3 : The beautified ward traced from extracted raw ward
• level 3 code : The generated ward code
• remained address : The remaining address not being extracted

python

>>> import pandas as pd
>>> from preprocessing_pgp.address.extractor import extract_vi_address
>>> data = pd.read_parquet('/path/to/data.parquet')
>>> extracted_data = extract_vi_address(data, address_col='address')
Cleansing takes 0m0s


Extracting takes 0m22s


Code generation takes 0m3s

>>> extracted_data.columns
Index(['address', 'cleaned_address', 'level 1', 'best level 1', 'level 2',
       'best level 2', 'level 3', 'best level 3', 'remained address',
       'level 1 code', 'level 2 code', 'level 3 code'],
      dtype='object')

5. Validate email address

A valid email is consist of:

1. Large company email's address (@gmail, @yahoo, @outlook, etc.)
2. Common email address (contains at least a alphabet character in email's name)
3. Education email (can start with a number)
4. Not auto-email

Apart from original columns of dataframe, we also generate columns with specific meanings:

• is_email_valid : indicator of whether the email is valid or not

python

>>> import pandas as pd
>>> from preprocessing_pgp.email.validator import process_validate_email
>>> data = pd.read_parquet('/path/to/data.parquet')
>>> validated_data = process_validate_email(data, email_col='email')
Cleansing email takes 0m0s


Validating email takes 0m22s