I have a pandas dataframe of the following structure: d = {'I': ['A', 'B', 'C', 'D'], 'X': [ 1, 0, 3, 1], 'Y': [0, 1, 2, 1], 'Z': [1, 0, 0, 0], 'W': [3, 2, 0, 0]} df = pd.DataFrame(data=d, columns=['I','X', 'Y', 'Z', 'W']) df.set_index('I', inplace=True, drop=True) I need to create a dict of dict to get data of all existing edges (indicated by nonzero values) between nodes: {'A': {'X': {1}, 'Z': {1}, 'W': {3}}, 'B': {'Y': {1}, 'W': {2}}, 'C': {'X': {3}, 'Y': {2}}, 'D': {'Y': {1}, 'X': {1}}} I need it to create a network graph using Networkx library and perform some calculations on it. Obviously it would be possible to loop over every cell in the data frame to do this but my data is quite large and it would be inefficient. I'm looking for some better way possibly using vectorization and/or list comprehension. I've tried list comprehension but I'm stuck and cannot make it work. Can anyone suggest a more efficient way to do this please? Continue reading...
