Tag Archives: python

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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Filed under software engineering

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

http://www.datarobot.com/blog/introduction-to-python-for-statistical-learning/

http://www.kevinsheppard.com/images/0/09/Python_introduction.pdf

http://blog.kaggle.com/2013/01/17/getting-started-with-pandas-predicting-sat-scores-for-new-york-city-schools/

Its fun being a teacher in the age of information.

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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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Filed under software engineering

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

MCMC in Python: Estimating failure rates from observed data

A question and answer on CrossValidated, which make me reflect on the danger of knowing enough statistics to be dangerous.

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Filed under statistics