You can build NumPy with only a C compiler, but it won't have accelerated BLAS/LAPACK. However, lifetimes requires SciPy, which in turn requires Fortran. This is a common requirement with a lot of the scientific-computing stack, so you may as well choose a complete solution such as Anaconda.
That said, if you just need a few packages, then Christoph Gohlke provides an extensive collection of wheels [1]. For example, I have a directory with the following wheels downloaded from Christoph's site:
C:\>dir /b Z:\Python\wheel
matplotlib-1.5.0-cp35-none-win_amd64.whl
numpy-1.10.2+mkl-cp35-none-win_amd64.whl
pandas-0.17.1-cp35-none-win_amd64.whl
scipy-0.16.1-cp35-none-win_amd64.whl
I'll test installing lifetimes and matplotlib in a virtual environment:
C:\>py -3 -m venv --symlinks C:\Temp\env35
C:\>C:\Temp\env35\Scripts\activate.bat
The command-line option "-f directory" makes pip look for packages in a local directory:
(env35) C:\>pip install -f Z:\Python\wheel lifetimes matplotlib
Collecting lifetimes
Using cached Lifetimes-0.1.6.3.tar.gz
Collecting matplotlib
Collecting numpy (from lifetimes)
Collecting scipy (from lifetimes)
Collecting pandas>=0.14 (from lifetimes)
Collecting pyparsing!=2.0.4,>=1.5.6 (from matplotlib)
Downloading pyparsing-2.0.7-py2.py3-none-any.whl
Collecting pytz (from matplotlib)
Using cached pytz-2015.7-py2.py3-none-any.whl
Collecting python-dateutil (from matplotlib)
Using cached python_dateutil-2.4.2-py2.py3-none-any.whl
Collecting cycler (from matplotlib)
Downloading cycler-0.9.0-py2.py3-none-any.whl
Collecting six>=1.5 (from python-dateutil->matplotlib)
Using cached six-1.10.0-py2.py3-none-any.whl
Installing collected packages: numpy, scipy, six, python-dateutil,
pytz, pandas, lifetimes, pyparsing, cycler, matplotlib
Running setup.py install for lifetimes
Successfully installed cycler-0.9.0 lifetimes-0.1.6.3
matplotlib-1.5.0 numpy-1.10.2 pandas-0.17.1 pyparsing-2.0.7
python-dateutil-2.4.2 pytz-2015.7 scipy-0.16.1 six-1.10.0
[1]: http://www.lfd.uci.edu/~gohlke/pythonlibs
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