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from collections import OrderedDict
import pandas as pd
from dateutil import parser


def forecast_ts_model(res, start, end):
    """
    Take a statsmodels.tsa.arima_model.ARIMAResults object
    and predict (forecast) the timeseries values
    between the "start" and "end" times, which

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import json

from plotly import __version__
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
from plotly import tools, utils
from plotly.graph_objs import Scatter, Marker, Layout, XAxis, YAxis

def get_json_for_js(data):
    figure = tools.return_figure_from_figure_or_data(data, True)

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def plot_table(features, targets):

    for idx, feature in enumerate(features.columns):
        grouped = loans.groupby(targets)

        if idx % 2==0:
            fig = plt.figure(figsize=(16,4))
        
        plt.subplot(1, 2, idx % 2 + 1)

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;; From: 
;; http://frenchy64.github.io/typed/clojure,/core.typed,/clojure/2013/09/03/core-typed-repl.html
(ns example.typed
  (:require [clojure.core.typed :as t]))

(t/cf (t/ann my-inc [Number -> Number]))
(t/cf (defn my-inc [a] (inc a)))

(t/cf (my-inc 1))

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def conditional_rate(df):
    """
    Given a dataframe whose columns are all boolean
    valued, return a data frame of size:
    n_columns * n_columns
    where the ith row and the jth column represents
    the rate at which the jth variable is true
    given that the ith variable is true.
    """
    

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Data and Modeling

Boiler plate for manipulating data and modeling using python libraries.

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from __future__ import division

%matplotlib inline

import numpy as np

import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)