A question I would like to answer. Perhaps this is the place to start: https://docs.python.org/3/library/unittest.mock.html
Tag Archives: python
Can I use unittest.mock in python?
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Filed under software engineering
Two ways to do categorical data in Python
Pandas has it: http://pandas-docs.github.io/pandas-docs-travis/categorical.html
Python has it, too: https://docs.python.org/3/library/enum.html
Will this make my life easier?
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Scientific Constants
A fun pointer I picked up at the eScience institute recently is where to find constants in python: scipy.constants http://docs.scipy.org/doc/scipy/reference/constants.html
or, if you fancy units, astropy.constants http://docs.astropy.org/en/stable/constants/index.html
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Filed under disease modeling
What style should I use for my docstrings?
The numpy docstring style should be just fine: http://sphinxcontrib-napoleon.readthedocs.org/en/latest/example_numpy.html
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SciPy2014 Plotting Contest
I helped judge a plotting contest for the Scientific Python conference last summer. Who won? I don’t know, and a short web searching binge didn’t find out. A lovely plot took 3rd place, and every entry is here (with sourcecode). Good stuff for seeing how different groups do different tricks, and for checking what still doesn’t work in mpld3.
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Filed under dataviz
Styling Excel with Pandas
I had a bunch of stylish tables to make once long ago, and I thought, “why don’t I do that automatically?” It would take longer the first time, but it would be faster in future iterations. Unfortunately, there never were any future iterations, but fortunately, it was more fun to research automatic generation of stylish tables than do what I needed to get done.
The seeds I planted have started to sprout a little bit, though, and the latest pandas now supports openpyxl2 which supports a lot of style. So here is a start on the stylish table writing feature.
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Python Pandas Intros
I’m going to give a Python Pandas guest lecture in the Python Science class next week, and I thought I’d take a look at the Pandas intros that are out there. There are a lot now! Here are some that I flipped through:
http://pandas.pydata.org/pandas-docs/stable/10min.html
http://nbviewer.ipython.org/gist/fonnesbeck/5850375
http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
http://www.gregreda.com/2013/10/26/working-with-pandas-dataframes/
http://www.gregreda.com/2013/10/26/using-pandas-on-the-movielens-dataset/
http://synesthesiam.com/posts/an-introduction-to-pandas.html
https://www.youtube.com/watch?v=p8hle-ni-DM
http://www.datarobot.com/blog/introduction-to-python-for-statistical-learning/
Click to access Python_introduction.pdf
Its fun being a teacher in the age of information.
Filed under education
MCMC in Python: sim and fit with same model
Here is a github issue and solution that I saw the other day. I think it’s a nice pattern.
def generate_model(values={'mu': true_param, 'm': None}):
#prior
mu = pymc.Uniform("mu", lower=-10, upper=10, value=values['mu'],
observed=(values['mu'] is not None))
# likelihood function
m = pymc.Normal("m", mu=mu, tau=tau, value=values['m'],
observed=(values['m'] is not None))
return locals()
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Filed under statistics
Tabular Data in Python: Getting just the columns I want from pandas.DataFrame.describe
The Python Pandas DataFrame object has become the mainstay of my data manipulation work over the last two years. One thing that I like about it is the `.describe()` method, that computes lots of interesting things about columns of a table. I often want those results stratified, and `.groupby(col)` + `.describe()` is a powerful combination for doing that.
*But* today, and many days, I don’t want all of the things that `.describe()` describes. And the ones that I do want, I want as columns. Here is the recipe for that:
import pandas as pd
df = pd.DataFrame({'A': [0,0,0,0,1,1],
'B': [1,2,3,4,5,6],
'C': [8,9,10,11,12,13]})
df.groupby('A').describe().unstack()\
.loc[:,(slice(None),['count','mean']),]
and out comes just what I wanted:
B C
count mean count mean
A
0 4 2.5 4 9.5
1 2 5.5 2 12.5
It took me a while to figure this out, and these docs helped:
http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-by-stacking-and-unstacking
http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-xs
Here it is as a ipython notebook.
(Note: this requires Pandas version at least 0.14.)
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MCMC in Python: a bake-off
While I’m on a microblogging spree, I’ve been meaning to link to this informative comparison of pymc, emcee, and pystan: http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
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Filed under statistics
Healthy Algorithms 