Jim Crist-Harif jcrist

## reddit_to_castra.py
import ujson
from datetime import datetime
import pandas as pd
from toolz import partition_all, dissoc
from castra import Castra


def to_json(line):
    blob = ujson.loads(line)
    date = blob['created_utc']

## airline_to_castra.py
from __future__ import print_function
import pandas as pd
from castra import Castra
from toolz import peek

def make_frame(year):
    path = '{0}.csv'.format(year)
    cols = ['year', 'month', 'day_of_month', 'day_of_week', 'deptime',
            'crs_deptime', 'arrtime', 'crs_arrtime', 'unique_carrier',
            'flight_num', 'tail_num', 'actual_elapsed_time',

## bytecode.ipynb

      
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                jcrist
                / bytecode.ipynb
            
            
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              December 11, 2015 18:51
            
              
                Manipulating Python Bytecode with Codetransformer
              
          
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## taxi.ipynb

      
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                jcrist
                / taxi.ipynb
            
            
              Created
              January 11, 2016 07:19
            
              
                Demonstration of basic transfer functions
              
          
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## Example_use.md

      
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                jcrist
                / Example_use.md
            
            
              Created
              January 19, 2016 00:43
            
              
                Generated functions in numba
              
          
    In [1]: from generated import generated

In [2]: import numba as nb

In [3]: @generated(nopython=True)
   ...: def foo(x, y):
   ...:     if isinstance(x, nb.types.Integer):
   ...:         return lambda x, y: x + y
 ...: else:

  
## dask_interface.ipynb

      
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                jcrist
                / dask_interface.ipynb
            
            
              Created
              April 8, 2016 19:10
            
              
                Dask Interface
              
          
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## binning.ipynb

      
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                jcrist
                / binning.ipynb
            
            
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              April 22, 2016 18:41
            
              
                Binning Implementations. Best viewed at nbviewer: http://nbviewer.jupyter.org/gist/jcrist/3dfea5a77cbffbc5f6a381f991dd439e
              
          
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## dominion.ipynb

      
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                jcrist
                / dominion.ipynb
            
            
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              May 5, 2016 21:36
            
              
                Logistic Regression in Dask
              
          
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## dominion.html
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>dominion</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!

## logistic_regression_lmbfgs_dask.py
from __future__ import print_function, absolute_import, division

import numbers
import warnings

import dask.array as da
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
from sklearn import linear_model
from sklearn.preprocessing import LabelEncoder
	import ujson
	from datetime import datetime
	import pandas as pd
	from toolz import partition_all, dissoc
	from castra import Castra


	def to_json(line):
	blob = ujson.loads(line)
	date = blob['created_utc']
	from __future__ import print_function
	import pandas as pd
	from castra import Castra
	from toolz import peek

	def make_frame(year):
	path = '{0}.csv'.format(year)
	cols = ['year', 'month', 'day_of_month', 'day_of_week', 'deptime',
	'crs_deptime', 'arrtime', 'crs_arrtime', 'unique_carrier',
	'flight_num', 'tail_num', 'actual_elapsed_time',
	<!DOCTYPE html>
	<html>
	<head><meta charset="utf-8" />
	<title>dominion</title>

	<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

	<style type="text/css">
	/*!
	from __future__ import print_function, absolute_import, division

	import numbers
	import warnings

	import dask.array as da
	import numpy as np
	from scipy.optimize import fmin_l_bfgs_b
	from sklearn import linear_model
	from sklearn.preprocessing import LabelEncoder