Python NetworkX -- 根据属性选项的数量自动设置节点颜色

Python NetworkX -- set node color automatically based on number of attribute options

提问by CurtLH

I am using NetworkX to analyze and visualize social networks. Often, the nodes within the network have attribute information associated with them, such as division. However, I do not always know how many options there might be for the attribute division. For example, sometimes there might be only 3 divisions represented as node attributes within the network, other times there might be 30 divisions.

我正在使用 NetworkX 来分析和可视化社交网络。通常，网络中的节点具有与其相关联的属性信息，例如划分。但是，我并不总是知道属性划分可能有多少选项。例如，有时可能只有 3 个分区表示网络中的节点属性，有时可能有 30 个分区。

I've figured out how to set node colors based on node attributes (see code below). However, in this example, I knew how different options there were for the node attribute group (5 options), and set each color automatically.

我已经想出了如何根据节点属性设置节点颜色（见下面的代码）。但是，在此示例中，我知道节点属性组（5 个选项）有哪些不同的选项，并自动设置每种颜色。

When there are only 3 or 5 options for node attributes, it's not difficult to pick the node attribute colors, but this becomes unrealistic when there are many more options.

当节点属性只有3或5个选项时，选择节点属性颜色并不难，但是当有更多选项时，这变得不现实。

What I would like to figure out is how can I automatically pick the optimal node attribute colors based on the number of node attribute options provided.

我想弄清楚的是如何根据提供的节点属性选项的数量自动选择最佳节点属性颜色。

Sometime I have have 5 options for the attribute to color by, other times I might have 30 options for the node attribute to color by, and I don't want to set each node color individually.

有时我有 5 个用于着色的属性选项，有时我可能有 30 个用于着色的节点属性选项，而且我不想单独设置每个节点的颜色。

I'm not sure if this is something that I should be able to do with the colormapfunction, or if that is only for color nodes by numeric measures such as degree centrality.

我不确定这是否是我应该能够使用该colormap函数做的事情，或者这是否仅适用于通过数字度量（例如度中心性）的颜色节点。

NETWORKX CODE

网络代码

import networkx as nx

pylab inline

# create an empty graph
g = nx.Graph()

# open csv edgelist and read edges into graph
for line in open('phils_network_edgelist.csv', 'rb'):
    edge = line.rstrip().split(',')
    g.add_edge(edge[0], edge[1])

# draw network without node color
nx.draw(g, with_labels=False, node_size=25)

# read in node attributes as list of tuples
group_attr = []
for line in open('phils_network_attribute_group.csv', 'rb'):
    group_attr.append(tuple(line.rstrip().split(',')))

# convert list of tuples into a dict
group_attr_dict = dict(set(sorted(group_attr)))

# set nodes attributes
nx.set_node_attributes(g, "group", group_attr_dict)

# create empty list for node colors
node_color = []

# for each node in the graph
for node in g.nodes(data=True):

    # if the node has the attribute group1
    if 'group1' in node[1]['group']:
        node_color.append('blue')

    # if the node has the attribute group1
    elif 'group2' in node[1]['group']:
        node_color.append('red')

    # if the node has the attribute group1
    elif 'group3' in node[1]['group']:
        node_color.append('green')

    # if the node has the attribute group1
    elif 'group4' in node[1]['group']:
        node_color.append('yellow')

    # if the node has the attribute group1
    elif 'group5' in node[1]['group']:
        node_color.append('orange')  

# draw graph with node attribute color
nx.draw(g, with_labels=False, node_size=25, node_color=node_color)

NETWORK DATA

网络数据

In[58]: 

g.nodes(data=True)

Out[58]:

[('BD', {'group': 'group5'}),
 ('WC', {'group': 'group3'}),
 ('BA', {'group': 'group4'}),
 ('WM', {'group': 'group3'}),
 ('JR', {'group': 'group1'}),
 ('JS', {'group': 'group3'}),
 ('JL', {'group': 'group4'}),
 ('JM', {'group': 'group2'}),
 ('JK', {'group': 'group2'}),
 ('JF', {'group': 'group2'}),
 ('JG', {'group': 'group2'}),
 ('JA', {'group': 'group2'}),
 ('JB', {'group': 'group4'}),
 ('JC', {'group': 'group4'}),
 ('RR', {'group': 'group3'}),
 ('RS', {'group': 'group3'}),
 ('TTI', {'group': 'group3'}),
 ('RB', {'group': 'group1'}),
 ('RL', {'group': 'group3'}),
 ('RO', {'group': 'group4'}),
 ('LHA', {'group': 'group2'}),
 ('LHI', {'group': 'group1'}),
 ('GF', {'group': 'group2'}),
 ('GB', {'group': 'group4'}),
 ('EM', {'group': 'group2'}),
 ('HR', {'group': 'group5'}),
 ('BS', {'group': 'group3'}),
 ('HH', {'group': 'group4'}),
 ('HA', {'group': 'group1'}),
 ('PS', {'group': 'group1'}),
 ('PW', {'group': 'group1'}),
 ('PB', {'group': 'group1'}),
 ('PC', {'group': 'group5'}),
 ('MFR', {'group': 'group4'}),
 ('JMA', {'group': 'group5'}),
 ('PN', {'group': 'group4'}),
 ('PL', {'group': 'group3'}),
 ('ZL', {'group': 'group4'}),
 ('EB', {'group': 'group2'}),
 ('ET', {'group': 'group3'}),
 ('EW', {'group': 'group1'}),
 ('ER', {'group': 'group3'}),
 ('MF', {'group': 'group3'}),
 ('MA', {'group': 'group4'}),
 ('MM', {'group': 'group2'}),
 ('MN', {'group': 'group4'}),
 ('MH', {'group': 'group3'}),
 ('MK', {'group': 'group2'}),
 ('JLA', {'group': 'group2'}),
 ('MP', {'group': 'group1'}),
 ('MS', {'group': 'group4'}),
 ('MR', {'group': 'group4'}),
 ('FI', {'group': 'group5'}),
 ('CJ', {'group': 'group4'}),
 ('CO', {'group': 'group5'}),
 ('CM', {'group': 'group4'}),
 ('CB', {'group': 'group2'}),
 ('CG', {'group': 'group2'}),
 ('CF', {'group': 'group5'}),
 ('CD', {'group': 'group3'}),
 ('CS', {'group': 'group2'}),
 ('CP', {'group': 'group2'}),
 ('CV', {'group': 'group2'}),
 ('KC', {'group': 'group1'}),
 ('KB', {'group': 'group3'}),
 ('SY', {'group': 'group2'}),
 ('KF', {'group': 'group2'}),
 ('KD', {'group': 'group3'}),
 ('KH', {'group': 'group1'}),
 ('SW', {'group': 'group1'}),
 ('KL', {'group': 'group2'}),
 ('KP', {'group': 'group3'}),
 ('KW', {'group': 'group1'}),
 ('SM', {'group': 'group2'}),
 ('SB', {'group': 'group4'}),
 ('DJ', {'group': 'group2'}),
 ('DD', {'group': 'group2'}),
 ('DV', {'group': 'group5'}),
 ('BJ', {'group': 'group3'}),
 ('DR', {'group': 'group2'}),
 ('KWI', {'group': 'group4'}),
 ('TW', {'group': 'group2'}),
 ('TT', {'group': 'group2'}),
 ('LH', {'group': 'group3'}),
 ('LW', {'group': 'group3'}),
 ('TM', {'group': 'group3'}),
 ('LS', {'group': 'group3'}),
 ('LP', {'group': 'group2'}),
 ('TG', {'group': 'group3'}),
 ('JCU', {'group': 'group2'}),
 ('AL', {'group': 'group1'}),
 ('AP', {'group': 'group3'}),
 ('AS', {'group': 'group3'}),
 ('IM', {'group': 'group4'}),
 ('AW', {'group': 'group3'}),
 ('HHI', {'group': 'group1'})]

In [59]:

g.edges(data=True)

Out[59]:

[('BD', 'ZL', {}),
 ('BD', 'JCU', {}),
 ('BD', 'DJ', {}),
 ('BD', 'BA', {}),
 ('BD', 'CB', {}),
 ('BD', 'CG', {}),
 ('BD', 'AS', {}),
 ('BD', 'MH', {}),
 ('BD', 'AP', {}),
 ('BD', 'HH', {}),
 ('BD', 'TM', {}),
 ('BD', 'CF', {}),
 ('BD', 'CP', {}),
 ('BD', 'DR', {}),
 ('BD', 'CV', {}),
 ('BD', 'EB', {}),
 ('WC', 'JCU', {}),
 ('WC', 'JS', {}),
 ('BA', 'JR', {}),
 ('BA', 'JB', {}),
 ('BA', 'RR', {}),
 ('BA', 'RS', {}),
 ('BA', 'LH', {}),
 ('BA', 'PC', {}),
 ('BA', 'TTI', {}),
 ('BA', 'PL', {}),
 ('BA', 'JCU', {}),
 ('BA', 'CF', {}),
 ('BA', 'EB', {}),
 ('BA', 'GF', {}),
 ('BA', 'AS', {}),
 ('BA', 'IM', {}),
 ('BA', 'BJ', {}),
 ('BA', 'CS', {}),
 ('BA', 'KH', {}),
 ('BA', 'SW', {}),
 ('BA', 'MH', {}),
 ('BA', 'MR', {}),
 ('BA', 'HHI', {}),
 ('WM', 'EM', {}),
 ('WM', 'JCU', {}),
 ('WM', 'CO', {}),
 ('WM', 'LP', {}),
 ('WM', 'AW', {}),
 ('WM', 'KD', {}),
 ('WM', 'TT', {}),
 ('WM', 'JS', {}),
 ('WM', 'PB', {}),
 ('WM', 'JM', {}),
 ('WM', 'MFR', {}),
 ('WM', 'RB', {}),
 ('WM', 'MR', {}),
 ('WM', 'DV', {}),
 ('WM', 'TG', {}),
 ('WM', 'JF', {}),
 ('WM', 'JMA', {}),
 ('WM', 'FI', {}),
 ('WM', 'JB', {}),
 ('JR', 'GF', {}),
 ('JR', 'MFR', {}),
 ('JR', 'KH', {}),
 ('JR', 'JB', {}),
 ('JS', 'EM', {}),
 ('JS', 'PS', {}),
 ('JS', 'MF', {}),
 ('JS', 'JCU', {}),
 ('JS', 'KD', {}),
 ('JS', 'MH', {}),
 ('JS', 'TTI', {}),
 ('JS', 'RB', {}),
 ('JS', 'TG', {}),
 ('JL', 'KB', {}),
 ('JL', 'MN', {}),
 ('JL', 'LW', {}),
 ('JL', 'CS', {}),
 ('JL', 'ET', {}),
 ('JL', 'ER', {}),
 ('JM', 'EM', {}),
 ('JM', 'PS', {}),
 ('JM', 'KD', {}),
 ('JM', 'CD', {}),
 ('JM', 'JK', {}),
 ('JM', 'TG', {}),
 ('JM', 'RO', {}),
 ('JM', 'CV', {}),
 ('JK', 'HR', {}),
 ('JK', 'PS', {}),
 ('JF', 'EM', {}),
 ('JF', 'PS', {}),
 ('JF', 'LP', {}),
 ('JF', 'LHA', {}),
 ('JF', 'CD', {}),
 ('JF', 'RB', {}),
 ('JF', 'JG', {}),
 ('JF', 'KF', {}),
 ('JG', 'CJ', {}),
 ('JG', 'SY', {}),
 ('JG', 'KF', {}),
 ('JG', 'LHA', {}),
 ('JG', 'CD', {}),
 ('JG', 'RB', {}),
 ('JG', 'BS', {}),
 ('JA', 'CS', {}),
 ('JB', 'KC', {}),
 ('JB', 'JCU', {}),
 ('JB', 'MA', {}),
 ('JB', 'AW', {}),
 ('JB', 'KWI', {}),
 ('JB', 'KH', {}),
 ('JB', 'CF', {}),
 ('JB', 'EB', {}),
 ('JB', 'PB', {}),
 ('JB', 'MFR', {}),
 ('JB', 'KW', {}),
 ('JB', 'RB', {}),
 ('JB', 'MR', {}),
 ('JB', 'RL', {}),
 ('JB', 'FI', {}),
 ('JB', 'JMA', {}),
 ('JC', 'SM', {}),
 ('RR', 'MS', {}),
 ('RR', 'SW', {}),
 ('RR', 'LH', {}),
 ('RS', 'LH', {}),
 ('TTI', 'JCU', {}),
 ('TTI', 'SW', {}),
 ('TTI', 'CF', {}),
 ('RB', 'EM', {}),
 ('RB', 'PS', {}),
 ('RB', 'SY', {}),
 ('RB', 'JCU', {}),
 ('RB', 'KD', {}),
 ('RB', 'CF', {}),
 ('RB', 'LHI', {}),
 ('RB', 'CD', {}),
 ('RB', 'MH', {}),
 ('RB', 'CJ', {}),
 ('RB', 'TG', {}),
 ('RB', 'EB', {}),
 ('RO', 'PS', {}),
 ('LHA', 'CJ', {}),
 ('LHA', 'SY', {}),
 ('LHA', 'KF', {}),
 ('LHA', 'CD', {}),
 ('LHI', 'PS', {}),
 ('LHI', 'CJ', {}),
 ('GF', 'KC', {}),
 ('GF', 'MA', {}),
 ('GB', 'HR', {}),
 ('GB', 'MM', {}),
 ('GB', 'LS', {}),
 ('EM', 'LP', {}),
 ('EM', 'DV', {}),
 ('EM', 'TG', {}),
 ('HR', 'MM', {}),
 ('HR', 'MH', {}),
 ('HR', 'EB', {}),
 ('HR', 'LS', {}),
 ('BS', 'CD', {}),
 ('HH', 'ZL', {}),
 ('HH', 'CB', {}),
 ('HH', 'CP', {}),
 ('HH', 'DR', {}),
 ('HH', 'CV', {}),
 ('HA', 'SM', {}),
 ('PS', 'KD', {}),
 ('PS', 'CF', {}),
 ('PS', 'TG', {}),
 ('PW', 'CM', {}),
 ('PW', 'TW', {}),
 ('PW', 'TT', {}),
 ('PW', 'MH', {}),
 ('PW', 'AL', {}),
 ('PW', 'MP', {}),
 ('PW', 'CS', {}),
 ('PW', 'HHI', {}),
 ('PW', 'EW', {}),
 ('PB', 'CO', {}),
 ('PB', 'KH', {}),
 ('PB', 'CF', {}),
 ('PB', 'MFR', {}),
 ('PB', 'AW', {}),
 ('PB', 'MA', {}),
 ('PC', 'CS', {}),
 ('PC', 'JCU', {}),
 ('PC', 'SW', {}),
 ('MFR', 'KC', {}),
 ('MFR', 'JCU', {}),
 ('MFR', 'KH', {}),
 ('MFR', 'MH', {}),
 ('MFR', 'MR', {}),
 ('JMA', 'KWI', {}),
 ('JMA', 'AW', {}),
 ('PN', 'SB', {}),
 ('PL', 'HHI', {}),
 ('PL', 'MK', {}),
 ('PL', 'LH', {}),
 ('ZL', 'CB', {}),
 ('ZL', 'AP', {}),
 ('ZL', 'CP', {}),
 ('ZL', 'DR', {}),
 ('ZL', 'CV', {}),
 ('EB', 'JCU', {}),
 ('EB', 'DJ', {}),
 ('EB', 'CM', {}),
 ('EB', 'SW', {}),
 ('EB', 'MM', {}),
 ('EB', 'LS', {}),
 ('EB', 'CS', {}),
 ('EB', 'CP', {}),
 ('EB', 'CV', {}),
 ('ET', 'LW', {}),
 ('ET', 'ER', {}),
 ('ET', 'KB', {}),
 ('EW', 'TW', {}),
 ('EW', 'TT', {}),
 ('EW', 'HHI', {}),
 ('EW', 'AL', {}),
 ('ER', 'LW', {}),
 ('ER', 'KB', {}),
 ('MA', 'KW', {}),
 ('MA', 'AW', {}),
 ('MA', 'MR', {}),
 ('MM', 'LS', {}),
 ('MH', 'JCU', {}),
 ('MH', 'SY', {}),
 ('MH', 'DJ', {}),
 ('MH', 'CM', {}),
 ('MH', 'AL', {}),
 ('MH', 'SW', {}),
 ('MH', 'CF', {}),
 ('MH', 'LS', {}),
 ('MH', 'CS', {}),
 ('MH', 'TG', {}),
 ('MH', 'CP', {}),
 ('MH', 'CV', {}),
 ('MK', 'LH', {}),
 ('MK', 'KL', {}),
 ('MK', 'JLA', {}),
 ('MK', 'MS', {}),
 ('MK', 'CS', {}),
 ('JLA', 'CM', {}),
 ('JLA', 'KL', {}),
 ('JLA', 'MS', {}),
 ('JLA', 'CS', {}),
 ('JLA', 'SB', {}),
 ('JLA', 'HHI', {}),
 ('MP', 'TW', {}),
 ('MP', 'TT', {}),
 ('MP', 'HHI', {}),
 ('MS', 'CS', {}),
 ('MS', 'HHI', {}),
 ('FI', 'KW', {}),
 ('FI', 'AW', {}),
 ('FI', 'CF', {}),
 ('CJ', 'SY', {}),
 ('CJ', 'DD', {}),
 ('CJ', 'CD', {}),
 ('CO', 'AW', {}),
 ('CM', 'TW', {}),
 ('CM', 'TT', {}),
 ('CM', 'AL', {}),
 ('CM', 'CS', {}),
 ('CB', 'DJ', {}),
 ('CB', 'CP', {}),
 ('CB', 'CV', {}),
 ('CG', 'CF', {}),
 ('CF', 'JCU', {}),
 ('CF', 'AW', {}),
 ('CF', 'KH', {}),
 ('CF', 'LH', {}),
 ('CF', 'AP', {}),
 ('CF', 'AS', {}),
 ('CF', 'KW', {}),
 ('CF', 'CS', {}),
 ('CF', 'CV', {}),
 ('CD', 'SY', {}),
 ('CD', 'LP', {}),
 ('CD', 'KF', {}),
 ('CS', 'JCU', {}),
 ('CS', 'TW', {}),
 ('CS', 'TT', {}),
 ('CS', 'AS', {}),
 ('CS', 'LH', {}),
 ('CS', 'SB', {}),
 ('CS', 'HHI', {}),
 ('CP', 'DJ', {}),
 ('CP', 'AP', {}),
 ('CP', 'DR', {}),
 ('CP', 'CV', {}),
 ('CV', 'DJ', {}),
 ('CV', 'AP', {}),
 ('CV', 'DR', {}),
 ('KB', 'LW', {}),
 ('SY', 'KF', {}),
 ('KF', 'AP', {}),
 ('KD', 'TG', {}),
 ('SW', 'BJ', {}),
 ('SW', 'IM', {}),
 ('SW', 'LH', {}),
 ('KL', 'TT', {}),
 ('KP', 'TM', {}),
 ('KW', 'JCU', {}),
 ('SB', 'AL', {}),
 ('DJ', 'TG', {}),
 ('BJ', 'IM', {}),
 ('KWI', 'AW', {}),
 ('TW', 'TT', {}),
 ('TW', 'AL', {}),
 ('TW', 'HHI', {}),
 ('TT', 'AL', {}),
 ('TT', 'HHI', {}),
 ('LH', 'JCU', {}),
 ('JCU', 'AP', {}),
 ('JCU', 'AS', {}),
 ('AL', 'HHI', {})]

采纳答案by Aric

Here is an example of how to use a colormap. It's a little tricky. If you want a customized discrete colormap you can try this SO answer Matplotlib discrete colorbar

以下是如何使用颜色图的示例。这有点棘手。如果你想要一个定制的离散颜色图，你可以试试这个 SO answer Matplotlib 离散颜色条

import matplotlib.pyplot as plt
# create number for each group to allow use of colormap
from itertools import count
# get unique groups
groups = set(nx.get_node_attributes(g,'group').values())
mapping = dict(zip(sorted(groups),count()))
nodes = g.nodes()
colors = [mapping[g.node[n]['group']] for n in nodes]

# drawing nodes and edges separately so we can capture collection for colobar
pos = nx.spring_layout(g)
ec = nx.draw_networkx_edges(g, pos, alpha=0.2)
nc = nx.draw_networkx_nodes(g, pos, nodelist=nodes, node_color=colors, 
                            with_labels=False, node_size=100, cmap=plt.cm.jet)
plt.colorbar(nc)
plt.axis('off')
plt.show()