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Accessing dataframe of CSV from quandl

I am trying to get access to the fama french factors from quandl

Using the code below the df seems to preview ok and I can print that days data with "print data['FAMA_FRENCH']" and I can see the various factors.

But if I try:

data['FAMA_FRENCH'].HML #attribute error
data['FAMA_FRENCH']['HML'] #attribute error
getattr(data['FAMA_FRENCH'],'HML') # Not allowed

My aim would be to have a dataframe of the daily factors prior to this day (for some lookback period).

How is best to do this? Am I just going about it the wrong way?

def initialize(context):  
                  symbol = 'FAMA_FRENCH',  
              date_column = 'Month',  
              date_format = '%Y-%m-%d',  

def preview(df):  
    return df  
# Will be called on every trade event for the securities you specify.  
def handle_data(context, data):  
    print data['FAMA_FRENCH']  
    order(sid(24), 0) # It seems to skip handle_data if you don't have an order function  
2 responses

The problem is that those attributes are not properly present on the first day. The easiest way to fix this is to check that the attributes exist before you try to access them. Sorry, there should be a better way of managing this in the future!

Clone Algorithm
Backtest from to with initial capital
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 5592e4696e04621246b15856
There was a runtime error.

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Hey I'm new to quantopian and trying to build an algo model . Im having some trouble figuring out fetcher... cant gt it to function... wats missing....

import pandas as pd
import numpy as np

def initialize(context):

database = 'ZES'
dataset = 'AAPL'
url = '{0}/{1}.csv?auth_code={2}'
url = url.format(database, dataset, auth_code)

# ffill: propagate last valid observation forward to next valid def post_func(df): return df.fillna(method='ffill')

def handle_data(context, data):