# Anonymizer
_Anonymizer_ is a Python package that generates fake data for you. It internally makes use of the [Faker](https://github.com/joke2k/faker) package, and allows you to keep track of the mapping between your original and fake data. This will be especially useful when you are anonymizing data in pandas data frames.
```
_____ .__
/ _ \ ____ ____ ____ ___.__. _____ |__|________ ____ _______
/ /_\ \ / \ / _ \ / \< | | / \ | |\___ /_/ __ \\_ __ \
/ | \| | \( <_> )| | \\___ || Y Y \| | / / \ ___/ | | \/
\____|__ /|___| / \____/ |___| // ____||__|_| /|__|/_____ \ \___ >|__|
\/ \/ \/ \/ \/ \/ \/
```
## Basic Usage
### Initialization
```
names = ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']
anonymizer = Anonymizer()
```
### Get Anonymized Name
```
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg')
# 'Catherine Parker'
```
### Get Original Name
```
anonymizer.get_original_name('Catherine Parker')
# 'Ghajinikanth Zuckerberg'
```
### Get Anonymized Name for Same Name
```
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # First Call
# 'Catherine Parker'
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # Second Call
# 'Catherine Parker'
```
### Fetch list of Anonymized Names
```
anonymizer.get_anonymized_names(names)
# ['Leslie Adams', 'Michelle Burke', 'Annette Maxwell']
```
### Fetch list of Original Names
```
anonymizer.get_original_names(anonymizedNames)
# ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']
```
### Get Anonymized Data for a different Faker Type
```
address_anonymizer = Anonymizer(faker_type=FakerType.ADDRESS)
address_anonymizer.get_anonymized_name('74437 Alexandra Well\nSouth Jade, CT 40282')
# 'USNS Hernandez\nFPO AA 32353'
```
## Acknowledgements
- [Faker](https://github.com/joke2k/faker)
_Anonymizer_ is a Python package that generates fake data for you. It internally makes use of the [Faker](https://github.com/joke2k/faker) package, and allows you to keep track of the mapping between your original and fake data. This will be especially useful when you are anonymizing data in pandas data frames.
```
_____ .__
/ _ \ ____ ____ ____ ___.__. _____ |__|________ ____ _______
/ /_\ \ / \ / _ \ / \< | | / \ | |\___ /_/ __ \\_ __ \
/ | \| | \( <_> )| | \\___ || Y Y \| | / / \ ___/ | | \/
\____|__ /|___| / \____/ |___| // ____||__|_| /|__|/_____ \ \___ >|__|
\/ \/ \/ \/ \/ \/ \/
```
## Basic Usage
### Initialization
```
names = ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']
anonymizer = Anonymizer()
```
### Get Anonymized Name
```
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg')
# 'Catherine Parker'
```
### Get Original Name
```
anonymizer.get_original_name('Catherine Parker')
# 'Ghajinikanth Zuckerberg'
```
### Get Anonymized Name for Same Name
```
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # First Call
# 'Catherine Parker'
anonymizer.get_anonymized_name('Ghajinikanth Zuckerberg') # Second Call
# 'Catherine Parker'
```
### Fetch list of Anonymized Names
```
anonymizer.get_anonymized_names(names)
# ['Leslie Adams', 'Michelle Burke', 'Annette Maxwell']
```
### Fetch list of Original Names
```
anonymizer.get_original_names(anonymizedNames)
# ['Kevin Bell', 'Ricky Sheppard', 'James Hill MD']
```
### Get Anonymized Data for a different Faker Type
```
address_anonymizer = Anonymizer(faker_type=FakerType.ADDRESS)
address_anonymizer.get_anonymized_name('74437 Alexandra Well\nSouth Jade, CT 40282')
# 'USNS Hernandez\nFPO AA 32353'
```
## Acknowledgements
- [Faker](https://github.com/joke2k/faker)
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