anonymizedf 1.0.1
a convenient way to anonymize your data for analytics
License: BSD
Keywords: faker pandas anonymize fake mock data

# Anonymize df: a convenient way to anonymize your data for analytics
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## 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:
```bash
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:
```bash
python setup.py install
```

## Example usage

```python
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

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

## Dependencies
- [Pandas](https://pandas.pydata.org)
- [Faker](https://github.com/joke2k/faker)