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Description:

anonymizedf 1.0.1

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