ValueError: Invalid RGBA argument: nan
ValueError: Invalid RGBA argument: nan
当我尝试绘制具有 1000 个节点的图形时出现错误。原因似乎是由于
事实上我可以在映射器中看到一些 nan 值：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py in _parse_scatter_color_args(c, edgecolors, kwargs, xsize, get_next_color_func)
4290 try: # Is 'c' acceptable as PathCollection facecolors?
-> 4291 colors = mcolors.to_rgba_array(c)
4292 except (TypeError, ValueError) as err:
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in to_rgba_array(c, alpha)
340 else:
--> 341 return np.array([to_rgba(cc, alpha) for cc in c])
342
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in <listcomp>(.0)
340 else:
--> 341 return np.array([to_rgba(cc, alpha) for cc in c])
342
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in to_rgba(c, alpha)
188 if rgba is None: # Suppress exception chaining of cache lookup failure.
--> 189 rgba = _to_rgba_no_colorcycle(c, alpha)
190 try:
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in _to_rgba_no_colorcycle(c, alpha)
262 if not np.iterable(c):
--> 263 raise ValueError(f"Invalid RGBA argument: {orig_c!r}")
264 if len(c) not in [3, 4]:
ValueError: Invalid RGBA argument: nan
然后
ValueError: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not dict_values(['#0010ff', '#40ffb7', '#00a4ff', '#40ffb7', '#00a4ff', '#40ffb7', '#ffb900', '#0010ff', nan, '#000080', '#000080', '#000080', '#000080', nan, '#0010ff', '#0010ff', '#800000', '#0010ff', '#0010ff', '#ff3000', '#0010ff', nan, '#00a4ff', '#0010ff', '#0010ff', '#ff3000', nan, nan, '#000080', '#0010ff', '#0010ff', '#0010ff', nan, nan, '#0010ff', nan, nan, '#0010ff', '#0010ff', nan, '#40ffb7', '#00a4ff', '#00a4ff', '#00a4ff', '#0010ff', '#0010ff', '#0010ff', nan, '#800000', nan])
代码（来自）。请注意下面的代码是有效的，但是当我扩展节点数>500时，会出现上面的错误：
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, colors as mcolor
# Sample DataFrames
df1 = pd.DataFrame({
'Node': ['A', 'A', 'B', 'B', 'B', 'Z'],
'Edge': ['B', 'D', 'N', 'A', 'X', 'C']
})
df2 = pd.DataFrame({
'Nodes': ['A', 'B', 'C', 'D', 'N', 'S', 'X'],
'Attribute': [-1, 0, -1.5, 1, 1, 9, 0]
})
# Simplified construction of `colour_map`
uni_val = df2['Attribute'].unique()
colors = plt.cm.jet(np.linspace(0, 1, len(uni_val)))
# Map colours to_hex then zip with
mapper = dict(zip(uni_val, map(mcolor.to_hex, colors)))
G = nx.from_pandas_edgelist(df1, source='Node', target='Edge')
# Create Colour map. Ensure all nodes have a value via reindex
color_map = (
df2.set_index('Nodes')['Attribute'].map(mapper)
.reindex(G.nodes(), fill_value='black')
)
# Add Attribute to each node
nx.set_node_attributes(G, color_map, name="colour")
# Then draw with colours based on attribute values:
nx.draw(G,
node_color=nx.get_node_attributes(G, 'colour').values(),
with_labels=True)
plt.show()
我不知道如何在 nan 值的情况下使用 if 条件来避免 ValueError 消息并正确地将节点与其颜色相关联。希望您能提供一些帮助。
即使有 1000 个节点，您的代码也适用于我，试试 运行 这个：
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, colors as mcolor
G = nx.fast_gnp_random_graph(1000,0.05)
df2 = pd.DataFrame({
'Nodes': [i for i in G.nodes()],
'Attribute': [np.random.rand()*9 for i in G.nodes()]
})
# Simplified construction of `colour_map`
uni_val = df2['Attribute'].unique()
colors = plt.cm.jet(np.linspace(0, 1, len(uni_val)))
# Map colours to_hex then zip with
mapper = dict(zip(uni_val, map(mcolor.to_hex, colors)))
# Create Colour map. Ensure all nodes have a value via reindex
color_map = (
df2.set_index('Nodes')['Attribute'].map(mapper)
.reindex(G.nodes(), fill_value='black')
)
# Add Attribute to each node
print(color_map)
nx.set_node_attributes(G, color_map, name="colour")
# Then draw with colours based on attribute values:
nx.draw(G,
node_color=nx.get_node_attributes(G, 'colour').values(),
with_labels=True)
plt.show()
当我尝试绘制具有 1000 个节点的图形时出现错误。原因似乎是由于
事实上我可以在映射器中看到一些 nan 值：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py in _parse_scatter_color_args(c, edgecolors, kwargs, xsize, get_next_color_func)
4290 try: # Is 'c' acceptable as PathCollection facecolors?
-> 4291 colors = mcolors.to_rgba_array(c)
4292 except (TypeError, ValueError) as err:
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in to_rgba_array(c, alpha)
340 else:
--> 341 return np.array([to_rgba(cc, alpha) for cc in c])
342
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in <listcomp>(.0)
340 else:
--> 341 return np.array([to_rgba(cc, alpha) for cc in c])
342
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in to_rgba(c, alpha)
188 if rgba is None: # Suppress exception chaining of cache lookup failure.
--> 189 rgba = _to_rgba_no_colorcycle(c, alpha)
190 try:
~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/colors.py in _to_rgba_no_colorcycle(c, alpha)
262 if not np.iterable(c):
--> 263 raise ValueError(f"Invalid RGBA argument: {orig_c!r}")
264 if len(c) not in [3, 4]:
ValueError: Invalid RGBA argument: nan
然后
ValueError: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not dict_values(['#0010ff', '#40ffb7', '#00a4ff', '#40ffb7', '#00a4ff', '#40ffb7', '#ffb900', '#0010ff', nan, '#000080', '#000080', '#000080', '#000080', nan, '#0010ff', '#0010ff', '#800000', '#0010ff', '#0010ff', '#ff3000', '#0010ff', nan, '#00a4ff', '#0010ff', '#0010ff', '#ff3000', nan, nan, '#000080', '#0010ff', '#0010ff', '#0010ff', nan, nan, '#0010ff', nan, nan, '#0010ff', '#0010ff', nan, '#40ffb7', '#00a4ff', '#00a4ff', '#00a4ff', '#0010ff', '#0010ff', '#0010ff', nan, '#800000', nan])
代码（来自
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, colors as mcolor
# Sample DataFrames
df1 = pd.DataFrame({
'Node': ['A', 'A', 'B', 'B', 'B', 'Z'],
'Edge': ['B', 'D', 'N', 'A', 'X', 'C']
})
df2 = pd.DataFrame({
'Nodes': ['A', 'B', 'C', 'D', 'N', 'S', 'X'],
'Attribute': [-1, 0, -1.5, 1, 1, 9, 0]
})
# Simplified construction of `colour_map`
uni_val = df2['Attribute'].unique()
colors = plt.cm.jet(np.linspace(0, 1, len(uni_val)))
# Map colours to_hex then zip with
mapper = dict(zip(uni_val, map(mcolor.to_hex, colors)))
G = nx.from_pandas_edgelist(df1, source='Node', target='Edge')
# Create Colour map. Ensure all nodes have a value via reindex
color_map = (
df2.set_index('Nodes')['Attribute'].map(mapper)
.reindex(G.nodes(), fill_value='black')
)
# Add Attribute to each node
nx.set_node_attributes(G, color_map, name="colour")
# Then draw with colours based on attribute values:
nx.draw(G,
node_color=nx.get_node_attributes(G, 'colour').values(),
with_labels=True)
plt.show()
我不知道如何在 nan 值的情况下使用 if 条件来避免 ValueError 消息并正确地将节点与其颜色相关联。希望您能提供一些帮助。
即使有 1000 个节点，您的代码也适用于我，试试 运行 这个：
import networkx as nx
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt, colors as mcolor
G = nx.fast_gnp_random_graph(1000,0.05)
df2 = pd.DataFrame({
'Nodes': [i for i in G.nodes()],
'Attribute': [np.random.rand()*9 for i in G.nodes()]
})
# Simplified construction of `colour_map`
uni_val = df2['Attribute'].unique()
colors = plt.cm.jet(np.linspace(0, 1, len(uni_val)))
# Map colours to_hex then zip with
mapper = dict(zip(uni_val, map(mcolor.to_hex, colors)))
# Create Colour map. Ensure all nodes have a value via reindex
color_map = (
df2.set_index('Nodes')['Attribute'].map(mapper)
.reindex(G.nodes(), fill_value='black')
)
# Add Attribute to each node
print(color_map)
nx.set_node_attributes(G, color_map, name="colour")
# Then draw with colours based on attribute values:
nx.draw(G,
node_color=nx.get_node_attributes(G, 'colour').values(),
with_labels=True)
plt.show()