Best way to represent data as features vectors in Python I have a list of events topic retrieved from a tweets collection.
A set of features have been extracted and their values normalized between 0 and 1. An example of an event:

"paris_attack_news-20150107_100842-20150107_112852": {
    "ages": 0.5557594006583049,
    "density": 0.0012022814250710345,
    "followers": 0.1144661871115895,
    "friends": 0.13507755010659472,
    "hashtagCount": 0.033270950301517985,
    "lifespan": 0.29613227044582224,
    "mediaCount": 0.1095890410958904,
    "mentionCount": 0.020275919732441472,
    "objectivity": 0.2850584551023736,
    "polarity": 0.2963684492294102,
    "retweetCount": 0.21431767337807606,
    "status_count": 0.09222093073720204,
    "truth": 1.0,
    "tweetCount": 0.01300578034682081,
    "urlCount": 0.29494007989347537,
    "verified": 0.3392857142857143
}


Now I need to represent each event as an array of its features:

paris_attack_news-20150107_100842-20150107_112852 = [0.5557594006583049, 0.5557594006583049, 0.1144661871115895, 0.13507755010659472, ...]


After that, I need to manipulate/aggregate the array values in some ways to get a specific events sorting based on the results.

The data are already in a Python Pandas DataFrame (event name as index, features as columns). 

Which is the best way (data structure for further storing or libraries such as NumPy, sklearn or similar) to build the arrays starting from this? 

P.S.: Then I'll need to apply some machine learning alghoritms to detect if an event is TRUE or FALSE, using the feature named "truth": 1 or 0 as label classification.
Numpy is best.  You can just take 

np.array(dict.values()). 

Eg,  dict = Paris_attck

Most ML libraries operate on numpy arrays.