# Convert VertexCluster to 2d Numpy array **URL:** \ **Category:** Usage\ **Tags:** Python\ **Created:** [13 September 2021 13:32 UTC](https://igraph.discourse.group/t/convert-vertexcluster-to-2d-numpy-array/861 "2021-09-13T13:32:46Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![balandongiv](https://yyz2.discourse-cdn.com/free1/user_avatar/igraph.discourse.group/balandongiv/32/553_2.png) [@balandongiv](https://igraph.discourse.group/u/balandongiv)\ **Post date:** [13 September 2021 13:32 UTC](https://igraph.discourse.group/t/convert-vertexcluster-to-2d-numpy-array/861/1 "2021-09-13T13:32:46Z")
Given a `communities` extracted using VertexClustering as below `partition_all = ig.VertexClustering(G, partitions[0].membership)` I using the following line to convert it into numpy ```auto membership_arr=np.array(partition_all.membership).reshape(3,-1).transpose() label_arr=np.array([f'{v["name"]}-{v["slice"]}' for v in G.vs]).reshape(3,-1).transpose() ``` However, the above solution only work if all node are present across slices and will throw an error if some nodes are missing, `ValueError: cannot reshape array of size 20 into shape (3,newaxis)` Hence, I wonder whether there is build-in function to cater transformation to numpy array? The full code to reproduce the above error ```auto import leidenalg as la import igraph as ig import numpy as np # Create first slice graph G1 = ig.Graph.Formula('A-B-D-C-A, D-E-G-F-D') # Make a copy, remove edge between C and F G2 = G1.copy(); G2.add_edge('C', 'F') # Make a copy, remove vertex A G3 = G1.copy();G3.delete_vertices('A') # Convert from slices to layers G_layers, G_interslice, G = la.time_slices_to_layers([G1, G2, G3], interslice_weight=1e-1, slice_attr='slice', vertex_id_attr='name', edge_type_attr='type', weight_attr='weight' ) # Create partitions gamma = 0.3 partitions = [la.CPMVertexPartition(H, node_sizes='node_size',weights='weight', resolution_parameter=gamma) for H in G_layers] interslice_partition = la.CPMVertexPartition(G_interslice, resolution_parameter=0, node_sizes='node_size', weights='weight') # Detect communities optimiser = la.Optimiser() optimiser.set_rng_seed(11) diff = optimiser.optimise_partition_multiplex(partitions + [interslice_partition]) partition_all = ig.VertexClustering(G, partitions[0].membership) membership_arr=np.array(partition_all.membership).reshape(3,-1).transpose() label_arr=np.array([f'{v["name"]}-{v["slice"]}' for v in G.vs]).reshape(3,-1).transpose() # G3.delete_vertices('A') ``` ---
**Author:** ![vtraag](https://yyz2.discourse-cdn.com/free1/user_avatar/igraph.discourse.group/vtraag/32/38_2.png) [@vtraag](https://igraph.discourse.group/u/vtraag)\ **Post date:** [16 September 2021 07:21 UTC](https://igraph.discourse.group/t/convert-vertexcluster-to-2d-numpy-array/861/2 "2021-09-16T07:21:09Z")
It might be most useful to convert the results first in an easy to use representation. The following code transforms the membership into a dict-of-dicts, representing the membership of each node in each slice: ```python # Get results for each slice separately membership_slice = {} for v, m in zip(G.vs, partitions[0].membership): if v['slice'] not in membership_slice: membership_slice[v['slice']] = {} membership_slice[v['slice']][v['name']] = m ``` This produces something like ```python {0: {'A': 1, 'B': 1, 'D': 0, 'C': 1, 'E': 0, 'G': 0, 'F': 0}, 1: {'A': 1, 'B': 1, 'D': 0, 'C': 0, 'E': 0, 'G': 0, 'F': 0}, 2: {'B': 0, 'D': 0, 'C': 0, 'E': 2, 'G': 2, 'F': 2}} ``` Note that indeed the second slice does not have a node `A`. This is something that is expected, graphs across different slices do not all necessarily need to have all nodes present. It is then most convenient to convert it to a `pandas` dataframe, using ```python membership_df = pd.DataFrame(membership_slice) ``` We now have a `pandas` `DataFrame` that looks like ```python 0 1 2 A 1 1 NaN B 1 1 0.0 D 0 0 0.0 C 1 0 0.0 E 0 0 2.0 G 0 0 2.0 F 0 0 2.0 ``` Here, node `A` for slice `2` is missing, indicated by `NaN` (not a number). Notice that the membership for slice 2 is now a `float` instead of an `int`, because an `int` cannot represent `NaN`. If you really need a `numpy` representation of this, you can simply get that by using `membership_df.values`. ---
**Author:** ![balandongiv](https://yyz2.discourse-cdn.com/free1/user_avatar/igraph.discourse.group/balandongiv/32/553_2.png) [@balandongiv](https://igraph.discourse.group/u/balandongiv)\ **Post date:** [16 September 2021 12:51 UTC](https://igraph.discourse.group/t/convert-vertexcluster-to-2d-numpy-array/861/3 "2021-09-16T12:51:45Z")
> [@vtraag](#): > > It might be most useful to convert the results first in an easy to use representation. The following code transforms the membership into a dict-of-dicts, representing the membership of each node in each slice: Thanks for the suggestion, presenting the membership as proposed above remove ambiguity about the node membership. > [@vtraag](#): > > Here, node `A` for slice `2` is missing, indicated by `NaN` (not a number). Notice that the membership for slice 2 is now a `float` instead of an `int` , because an `int` cannot represent `NaN` . The graph (with missing node A) used in this discussion is not ideal for the [flexibility calculation](https://igraph.discourse.group/t/how-to-quantify-time-dependent-network-flexibility-in-igraph/843/7). This metric is usually used in neuro-image research, and rarely there are missing node (brain region/node).