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https://github.com/alexandercbooth/decoy
https://github.com/alexandercbooth/decoy
Last synced: 23 days ago
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- Host: GitHub
- URL: https://github.com/alexandercbooth/decoy
- Owner: alexandercbooth
- Created: 2016-05-12T19:30:50.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2016-06-15T02:19:00.000Z (over 8 years ago)
- Last Synced: 2024-11-30T00:28:20.128Z (25 days ago)
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
Awesome Lists containing this project
README
# DECOY :squirrel:
[](https://travis-ci.com/alexandercbooth/decoy)
Have proprietary data and want to still run tests on a continuous integration platform? Decoy fabricates your data's structure and returns a pandas dataframe with random data in the same structure
```
In [1]: from decoy import make_decoy_data
In [2]: from sklearn.datasets import load_iris
In [3]: import pandas as pd
In [4]: iris = load_iris()
In [5]: train = pd.DataFrame(iris.data)
In [6]: train['target'] = iris.target
In [7]: df = make_decoy_data(train, .5)
In [8]: train.info()
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
0 150 non-null float64
1 150 non-null float64
2 150 non-null float64
3 150 non-null float64
target 150 non-null int64
dtypes: float64(4), int64(1)
memory usage: 5.9 KB
In [9]: df.info()
RangeIndex: 75 entries, 0 to 74
Data columns (total 5 columns):
0 75 non-null float64
1 75 non-null float64
2 75 non-null float64
3 75 non-null float64
4 75 non-null int64
dtypes: float64(4), int64(1)
memory usage: 3.0 KB
```
## Install from github
```
pip install git+https://github.com/alexandercbooth/decoy.git
```