Melsauce Melsauce - 9 months ago 104
Python Question

How to create an edge list from pandas dataframe?

I have a pandas dataframe (df) of the form-

A [Green,Red,Purple]
B [Red, Yellow, Blue]
C [Brown, Green, Yellow, Blue]

I need to convert this to an edge list i.e. a dataframe of the form:

Source Target Weight
A B 1
A C 1
B C 2

Note that the new dataframe has rows equal to the total number of possible pairwise combinations. Also, to compute the 'Weight' column, we simply find the intersection between the two lists. For instance, for B&C, the elements share two colors: Blue and Yellow. Therefore, the 'Weight' for the corresponding row is 2.

What is the fastest way to do this? The original dataframe contains about 28,000 elements.

Answer Source

First, starting off with the dataframe:

In [823]: from itertools import combinations

In [824]: df = pd.DataFrame({'Col1': [['Green','Red','Purple'], ['Red', 'Yellow', 'Blue'], ['Brown', 'Green', 'Yellow', 'Blue']]}, index=['A',
     ...:  'B', 'C'])

In [827]: df['Col1'] = df.Col1.apply(lambda x: set(x))

In [828]: df
A          {Purple, Red, Green}
B           {Red, Blue, Yellow}
C  {Green, Yellow, Blue, Brown}

Each list in Col1 has been converted into a set to find the union efficiently. Next, we'll use itertools.combinations to create pairwise combinations of all rows in df:

In [845]: df1 = pd.DataFrame(data=list(combinations(df.index.tolist(), 2)), columns=['Src', 'Dst'])

In [849]: df1
  Src Dst
0   A   B
1   A   C
2   B   C

Now, apply a function to take the union of the sets and find its length. The Src and Dst columns act as a lookup into df.

In [859]: df1['Weights'] = df1.apply(lambda x: len(df.loc[x['Src']]['Col1'].intersection(df.loc[x['Dst']]['Col1'])), axis=1)

In [860]: df1
  Src Dst  Weights
0   A   B        1
1   A   C        1
2   B   C        2

I advice set conversion at the very beginning. Converting your lists to a set each time on the fly is expensive and wasteful.

For more speedup, you'd probably want to also copy the sets into two columns in the new dataframe, as @Wen has done, because calling df.loc constantly will slow it down a notch.

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