For the code below, I get an error on the line:

pd.set_option('mode.use_inf_as_null',True)  

Accessing pd.set_option raised an AttributeError. No attributes with a similar name were found.

Any idea why?

from sklearn import cluster, covariance  
import numpy as np  
from collections import defaultdict  
from scipy import stats  
import pandas as pd  
pd.set_option('mode.use_inf_as_null',True)

# based on the example at:  
# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html

update_period = 5*390 # update clusters at this period in minutes

def initialize(context):  
    c = context  
    # Nasdaq 100 from https://www.quantopian.com/posts/list-of-nasdaq-100-sids-to-use-in-your-algo  
    c.sids = [sid(24),    sid(114),   sid(122),   sid(630)  , sid(67),  
              sid(20680), sid(328),   sid(14328), sid(368),   sid(16841),  
              sid(9883),  sid(337),   sid(38650), sid(739),   sid(27533),  
              sid(3806),  sid(18529), sid(1209),  sid(40207), sid(1419),  
              sid(15101), sid(17632), sid(39095), sid(1637),  sid(1900),  
              sid(32301), sid(18870), sid(14014), sid(25317), sid(36930),  
              sid(12652), sid(26111), sid(24819), sid(24482), sid(2618),  
              sid(2663),  sid(27543), sid(1787) , sid(2696),  sid(42950),  
              sid(20208), sid(2853),  sid(8816),  sid(12213),  sid(3212),  
              sid(9736),  sid(23906), sid(26578), sid(22316), sid(13862),  
              sid(3951),  sid(8655),  sid(25339), sid(4246),  sid(43405),  
              sid(27357), sid(32046), sid(4485),  sid(43919), sid(4668),  
              sid(8677),  sid(22802), sid(3450),  sid(5061),  sid(5121),  
              sid(5149),  sid(5166),  sid(23709), sid(13905), sid(19926),  
              sid(19725), sid(8857),  sid(5767),  sid(5787),  sid(19917),  
              sid(6295),  sid(6413),  sid(6546),  sid(20281), sid(6683),  
              sid(26169), sid(6872),  sid(11901), sid(13940), sid(7061),  
              sid(15581), sid(24518), sid(7272),  sid(39840), sid(7671),  
              sid(27872), sid(8017),  sid(38817), sid(8045),  sid(8132),  
              sid(8158),  sid(24124), sid(8344),  sid(8352),  sid(14848)]  
    context.elapsed_minutes = 0  
def stock_cluster(attribute,context):  
    c = context  
    # tell it we're looking for a graph structure  
    edge_model = covariance.GraphLassoCV()  
    X = attribute.values.copy()  
    X_zscore = stats.zscore(X, axis=0, ddof=1)  
    if np.any(pd.isnull(X_zscore)):  
        print 'null found in X_zscore'  
        return None  
    edge_model.fit(X_zscore)  
    # now process into clusters based on co-fluctuation  
    _, labels = cluster.affinity_propagation(edge_model.covariance_)  
    log.debug("Found {0} groups from {1} complete histories".format(max(labels)+1,len(attribute)))  
    # filter the sids into groups, in order they appear in c.sids  
    groups = defaultdict(lambda: [])  
    for i, grp_idx in enumerate(labels):  
        # groups[grp_idx].append( c.sids[i] )  
        groups[grp_idx].append( c.sids[i].symbol )  
    return groups  
def handle_data(context, data):  
    if context.elapsed_minutes % update_period != 0.0:  
        context.elapsed_minutes += 1  
        return  
    else:  
        context.elapsed_minutes += 1  
    prices_open = history(31, '1d', 'open_price',ffill=False).dropna()[:-1]  
    prices_close = history(31, '1d', 'close_price',ffill=False).dropna()[:-1]  
    prices_delta = prices_close - prices_open  
    c = context  
    groups = stock_cluster(prices_delta,c)  
    result = '------------------\n'  
    if groups is not None:  
        # display stock sids that co-fluctuate:  
        for i, g in groups.iteritems():  
            result = result + 'Cluster %i: %s\n' % ((i + 1), ", ".join([str(s) for s in g]))  
        print result