stan_example.ipynb

stan_example.py

# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>

# <codecell>

from stan.transcompile import transcompile
import stan_magic
from pandas import DataFrame
import numpy as np
import pkgutil

# <codecell>

import stan.proc_functions as proc_func

mod_name = ["from stan.proc_functions import %s" % name for _, name, _ in pkgutil.iter_modules(proc_func.__path__)] 
exec("\n".join(mod_name))

# <codecell>

# create an example data frame 
df = DataFrame(np.random.randn(10, 5), columns = ['a','b','c','d','e'])
df

# <codecell>

%%stan
data test;
set df (drop = a);
run;

# <codecell>

exec(_)
test

# <markdowncell>

# `if` statements combined with `do` `end` statements were difficult to implement. Here is my current
# implementation of if-then-else control flow, (I'll have to revisit `if` and `do` `end` statements in the future...)

# <codecell>

%%stan
data df_if;
    set df;
    x = if b < 0.3 then 0 else if b < 0.6 then 1 else 2;
run;

# <codecell>

exec(_)
df_if

# <markdowncell>

# ---

# <codecell>

# procs can be added manually they can be thought of as python functions
# you can define your own, though I need to work on the parser
# to get it "smooth"

df1 = DataFrame({'a' : [1, 0, 1], 'b' : [0, 1, 1] }, dtype=bool)
df1

# <codecell>

%%stan
proc describe data = df1 out = df2;
by a;
run;

# <codecell>

exec(_)
df2

# <markdowncell>

# The proc actually isn't difficult to write. So for the above code it is actually just this:
# 
# 
#     def describe(data, by):
#         return data.groupby(by).describe()  
# 
# This functionality allow you to handle most of the `by` and `retain` cases. For languages
# like Python and R, the normal way to handle data is through the split-apply-combine methodology.
# 
# Merges can be achieved in a similar way, by creating a `proc`:

# <codecell>

%%stan
proc merge out = df2;
dt_left left;
dt_right right;
on = 'key';
run;

# <codecell>

left = DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
right = DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})

exec(_)
df2

# <markdowncell>

# heres an example showing how you can define your own function and run it (not a function 
# that came with the package)

# <codecell>

def sum_mean_by(data, by):
    return data.groupby(by).agg([np.sum, np.mean]) 

# <codecell>

%%stan
proc sum_mean_by data = df_if out = df_sum;
by x;
run;

# <codecell>

exec(_)
df_sum