why-not

"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

"""quick way to create an interesting data frame to try things out""" 
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])

mfcabrera

def delimited(filename, delimiter=' ', bufsize=4096):
    '''
    Creates a generator of word from a file based on a delimiter (by default white space).
    '''
    
    buf = ''
    with open(filename) as file:
        while True:
            newbuf = file.read(bufsize)
            if not newbuf:

fonnesbeck

Horizons in Probabilistic Programming and Bayesian Analysis

Representations:

• Hierarchical models
• Hidden Markov models
• Graphical models
• Non-parametric Bayes (distributions over functions)

Inference Approaches:

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

heaven00