Anonymize df: a convenient way to anonymize your data for analytics

What is it?

Anonymize df is a package that helps you quickly and easily generate realistic fake data from a Pandas DataFrame.

What are the expected use cases / why was this made?

• You're hiring consultants to work on your data but need to anonymize it first
• You're a consultant and created something great that you want to make into a template

Installation

You can install anonymizedf using pip:

pip install anonymizedf

This will also try downloading the tableau hyper api and pandas packages if you don't have them already.

If you don't want to use pip you can also download this repository and execute:

python setup.py install

Example usage

import pandas as pd
from anonymizedf.anonymizedf import anonymize

# Import the data
df = pd.read_csv("https://query.data.world/s/shcktxndtu3ojonm46tb5udlz7sp3e")

# Prepare the data to be anonymized
an = anonymize(df)

# Select what data you want to anonymize and your preferred style

# Example 1 - just updates df
an.fake_names("Customer Name")
an.fake_ids("Customer ID")
an.fake_whole_numbers("Loyalty Reward Points")
an.fake_categories("Segment")
an.fake_dates("Date")
an.fake_decimal_numbers("Fraction")

# Example 2 - method chaining
fake_df = (
    an
    .fake_names("Customer Name", chaining=True)
    .fake_ids("Customer ID", chaining=True)
    .fake_whole_numbers("Loyalty Reward Points", chaining=True)
    .fake_categories("Segment", chaining=True)
    .fake_dates("Date", chaining=True)
    .fake_decimal_numbers("Fraction", chaining=True)
    .show_data_frame()
)

# Example 3 - multiple assignments
fake_df = an.fake_names("Customer Name")
fake_df = an.fake_ids("Customer ID")
fake_df = an.fake_whole_numbers("Loyalty Reward Points")
fake_df = an.fake_categories("Segment")
fake_df = an.fake_dates("Date")
fake_df = an.fake_decimal_numbers("Fraction")

fake_df.to_csv("fake_customers.csv", index=False)

# One thing to note is that you can't directly pass in a list of columns.
# If you want to apply the same function to multiple columns there are many ways to do that.

# Example 4 - for multiple columns

for column in column_list:
    an.fake_categories(column)

Example output

	Customer ID	Customer Name	Loyalty Reward Points	Segment	Date	Fraction	Fake_Customer Name	Fake_Customer ID	Fake_Loyalty Reward Points	Fake_Segment	Fake_Date	Fake_Fraction
0	AA-10315	Alex Avila	76	Consumer	01/01/2000	7.6	Christian Metcalfe-Reid	YEJP71011502726136	558	Segment 1	1978-11-09	29.96
1	AA-10375	Allen Armold	369	Consumer	02/01/2000	36.9	Helen Taylor	XWOB83170110594048	286	Segment 1	1989-12-29	72.50
2	AA-10480	Andrew Allen	162	Consumer	03/01/2000	16.2	Joanne Price	VVCJ28547588747677	742	Segment 1	1982-09-23	79.77
3	AA-10645	Anna Andreadi	803	Consumer	04/01/2000	80.3	Rhys Jones	OXCI12190813836802	206	Segment 1	2000-10-14	7.15
4	AB-10015	Aaron Bergman	935	Consumer	05/01/2000	93.5	Nigel Baldwin-Cook	JOXS05799252235987	914	Segment 1	2018-01-30	40.66

Dependencies

• Pandas
• Faker