changepy
Changepoint detection in time series in pure python
Install
pip install changepy
Examples
>>> from changepy import pelt
>>> from changepy.costs import normal_mean
>>> size = 100
>>> mean_a = 0.0
>>> mean_b = 10.0
>>> var = 0.1
>>> data_a = np.random.normal(mean_a, var, size)
>>> data_b = np.random.normal(mean_b, var, size)
>>> data = np.append(data_a, data_b)
>>> pelt(normal_mean(data, var), len(data))
[0, 100] # since data is random, sometimes it might be different, but most of the time there will be at most a couple more values around 100
For more examples see pelf_test.py
Reference
Currently there is only one algorithm for changepoint evaluation, the PELT algorithm [1].
The PERL algorithm requires a cost function. Currently there are three functions available through this library. However, you could implement your own, for your specific needs. Those functions are:

normal_mean
, which expects normal distributed data, with changing mean 
normal_var
, which expects normal distributed data, with changing variance 
normal_meanvar
, which expects normal distributed data, with changing mean and variance 
poisson
, which expect poisson distributed data, with changing mean 
exponential
, which expect exponential distributed data, with changing mean
Test with
python test_pelt.py
Other implementations
This is mostly a port from other libraries, most of all from STORi's changepoint package for julia and rkillick cpt package for r
[1]: Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost, JASA 107(500), 15901598