Thanks Tamas!
For anyone working on this again in the future, after writing my own
iterator I suddenly discovered one is already in pandas. If your DataFrame
has a structure like the one described above, then you can just use the
itertupes method:
g = ig.Graph.TupleList(df.itertuples(index=False), weights=True)
Nick
On Sat, Apr 15, 2017 at 2:35 PM Tamas Nepusz <[email protected]> wrote:
> Hi,
>
> Graph.DictList() and Graph.TupleList() are meant to be used in cases when
> the graph is already in memory in some format and you can cast the input
> data into either a list-of-dictionaries representation or a list-of-tuples
> representation. I believe in your case the list-of-tuples representation is
> more natural. So, basically, you need to figure out a way to cast a pandas
> DataFrame into a list (or iterable) where each element is a triplet
> containing the source ID, the target ID and the weight, and then call
> Graph.TupleList(your_list, weights=True).
>
> All the best,
>
> T.
>
> On Sat, Apr 15, 2017 at 11:30 PM, Nick Eubank <[email protected]>
> wrote:
>
>> Hell All,
>>
>> I'm trying to figure out the most efficient way of creating an igraph
>> object form an edgelist stored in a numpy array (or rather, technically, a
>> pandas DataFrame).
>>
>> The DataFrame looks something like:
>>
>> a b weight
>> 100 101 1
>> 100 102 5
>> 101 103 1
>> 101 104 4
>>
>> The numbers in a and b are NOT sequential integers, though I can write
>> code to make those conversion if necessary.
>>
>> One option is obviously to write out the data frame as a text file, then
>> re-import as an ncol or such. But my dataset is very large (the edgelist
>> has about 400,000,000 entries), which means that's a massively slow
>> process, and I think loading the text file when importing into igraph may
>> be requiring more ram than I have.
>>
>> I've found resources on importing adjacency matrices
>> <http://stackoverflow.com/questions/29655111/igraph-graph-from-numpy-or-pandas-adjacency-matrix>,
>> but nothing on edgelists.
>>
>> Anyone run into this before / have any suggestions?
>>
>> Thanks!
>>
>> Nick
>>
>>
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>>
>>
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