Assigning multiple attributes to nodes

I would like to assign to my nodes an attribute. Currently I am creating a network using the following sample of data:

Attribute   Source       Target Weight  Label
    87.5    Heisenberg   Pauli  66.3    1
    12.5    Beckham      Messi  38.1    0
    12.5    Beckham      Maradona 12    0
    43.5    water        melon  33.6    1

Label should give the colour of nodes (1=yellow, 0=blue).

Code for network:

 G = nx.from_pandas_edgelist(df, source='Source', target='Target', edge_attr='Weight') 

    collist = df.drop('Weight', axis=1).melt('Label').dropna() # I need this for the below lines of code because I want to draw nodes - their size - based on their degree

    degrees=[]
    for x in collist['value']:
        deg=G.degree[x]  
        degrees.append(100*deg)

    
    pos=nx.spring_layout(G)

    nx.draw_networkx_labels(G, pos, font_size=10)
    nx.draw_networkx_nodes(G, pos, nodelist=collist['value'], node_size = degrees, node_color=collist['Label'])
    nx.draw_networkx_edges(G, pos)

What this code is supposed to do is the following: the nodes should have size equal their degree (this explains degrees and collist in my code). Edges should have thickness equal to Weight. Attribute should be assigned (and updated) as in this link: (Changing attributes of nodes). Currently, my code does not include the assignment as in the link mentioned, where it was added and updated as follows:

G = nx.Graph()
G.add_node(0, weight=8)
G.add_node(1, weight=5)
G.add_node(2, weight=3)
G.add_node(3, weight=2)

nx.add_path(G, [2,5])
nx.add_path(G, [2,3])


labels = {
    n: str(n) + '\nweight=' + str(G.nodes[n]['weight']) if 'weight' in G.nodes[n] else str(n)
    for n in G.nodes
}

newWeights = \
    [
        sum( # summ for averaging
            [G.nodes[neighbor]['weight'] for neighbor in G.neighbors(node)] # weight of every neighbor
            + [G.nodes[i]['weight']] # adds the node itsself to the average
        ) / (len(list(G.neighbors(node)))+1) # average over number of neighbours+1
        if len(list(G.neighbors(node))) > 0 # if there are no neighbours
        else G.nodes[i]['weight'] # weight stays the same if no neighbours
    for i,node in enumerate(G.nodes) # do the above for every node
    ]
print(newWeights) 
for i, node in enumerate(G.nodes):
    G.nodes[i]['weight'] = newWeights[i] # writes new weights after it calculated them all.

Please note that I have more than 100 nodes so I cannot do it manually. I tried to include the Attribute in my code as follows:

G = nx.from_pandas_edgelist(df_net, source='Source', target='Target', edge_attr=['Weight'])
    nx.set_node_attributes(G, pd.Series(nodes.Attribute, index=nodes.node).to_dict(), 'Attribute')

However, I have got the error:

----> 1 network(df)

<ipython-input-72-f68985d20046> in network(dataset)
     24     degrees=[]
     25     for x in collist['value']:
---> 26         deg=G.degree[x]
     27         degrees.append(100*deg)
     28 

~/opt/anaconda3/lib/python3.8/site-packages/networkx/classes/reportviews.py in __getitem__(self, n)
    445     def __getitem__(self, n):
    446         weight = self._weight
--> 447         nbrs = self._succ[n]
    448         if weight is None:
    449             return len(nbrs) + (n in nbrs)

KeyError: 87.5

What I would like to have as expected output is a network where nodes are in the Source column and their neighbors are within the Target column. Edges have thickness based on Weight. Label gives the colour of the source, while Attribute value should be added as label and updated as in the question/answer on this link: Changing attributes of nodes .

Please see below a visual example of the type of net that I am trying to build. The attribute value in the figure is meant before the update (newWeights), and this explains why some nodes have missing value. Attribute is related to Source only, which is colored based on Label. The thickness of the edge is given by Weight.

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