我正在尝试使用 seaborn 创建一个猫图。我准备了这样的数据
PDB_throughput=pd.DataFrame()
PDCPlayer_throughputD['Type'] = 'Drone-BS'
perfect_throughput['Type'] = 'Perfect'
PDB_throughput=PDB_throughput.append(PDCPlayer_throughputD)
PDB_throughput=PDB_throughput.append(perfect_throughput)
print(PDB_throughput)
这是最终的数据框：
Throughput [Mbit/s] Type numUe
0 195.498916 Drone-BS 11
1 192.380980 Drone-BS 14
2 192.037444 Drone-BS 17
3 160.040452 Drone-BS 15
4 201.267220 Drone-BS 25
5 197.800036 Drone-BS 28
6 199.214980 Drone-BS 29
7 198.483028 Drone-BS 10
8 197.064004 Drone-BS 28
9 197.154580 Drone-BS 27
10 192.037444 Drone-BS 17
11 222.887140 Drone-BS 23
12 155.018788 Drone-BS 22
13 192.380980 Drone-BS 14
14 193.452388 Drone-BS 27
15 191.702068 Drone-BS 30
16 201.326788 Drone-BS 13
17 194.959540 Drone-BS 34
18 197.411620 Drone-BS 26
0 431.933760 Perfect 12
1 299.260800 Perfect 8
2 2214.725760 Perfect 26
3 957.402000 Perfect 15
4 1773.351360 Perfect 22
5 526.555920 Perfect 11
6 956.444400 Perfect 15
7 581.627520 Perfect 12
8 2189.540160 Perfect 27
9 682.550880 Perfect 11
10 1555.985520 Perfect 17
11 1651.017840 Perfect 31
12 1998.955200 Perfect 17
13 2028.858000 Perfect 25
14 472.330560 Perfect 13
15 2032.578000 Perfect 25
16 2155.689120 Perfect 26
17 1091.718240 Perfect 17
18 586.302720 Perfect 16
我想用 2 条不同颜色的曲线创建一个图，以查看差异。一条曲线Type=Perfect，另一条Type=Drone-BS
我尝试了 2 种 seaborn 地块
sns.catplot(data=PDB_throughput,
x='numUe',
y='Throughput [Mbit/s]',
hue='Type',
kind='point',
height=5, aspect=2)
和
g = sns.factorplot('numUe', 'Throughput [Mbit/s]', 'Type', data=PDB_throughput, kind="point")
我总是得到相同的ValueError: Invalid RGBA argument: masked并且我不理解代码或逻辑中的错误。
这里是整个错误的详细信息
ValueError Traceback (most recent call last)
<ipython-input-57-7f71b1dbaaa5> in <module>
22
23 #e = sns.FacetGrid(data= PDB_throughput)
---> 24 g = sns.factorplot('numUe', 'Throughput [Mbit/s]', 'Type', data=PDB_throughput, kind="point")
25 #e.map_dataframe(sns.pointplot, 'numUe', 'Throughput [Mbit/s]', hue='Type', order=["Drone-BS", "Perfect"], hue_order=[0, 1])
26 #e.add_legend()
~/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py in factorplot(*args, **kwargs)
3674 kwargs.setdefault("kind", "point")
3675
-> 3676 return catplot(*args, **kwargs)
3677
3678
~/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py in catplot(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, height, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)
3753
3754 # Draw the plot onto the facets
-> 3755 g.map_dataframe(plot_func, x, y, hue, **plot_kws)
3756
3757 # Special case axis labels for a count type plot
~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py in map_dataframe(self, func, *args, **kwargs)
818
819 # Draw the plot
--> 820 self._facet_plot(func, ax, args, kwargs)
821
822 # Finalize the annotations and layout
~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
836
837 # Draw the plot
--> 838 func(*plot_args, **plot_kwargs)
839
840 # Sort out the supporting information
~/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py in pointplot(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, markers, linestyles, dodge, join, scale, orient, color, palette, errwidth, capsize, ax, **kwargs)
3338 ax = plt.gca()
3339
-> 3340 plotter.plot(ax)
3341 return ax
3342
~/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py in plot(self, ax)
1809 def plot(self, ax):
1810 """Make the plot."""
-> 1811 self.draw_points(ax)
1812 self.annotate_axes(ax)
1813 if self.orient == "h":
~/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py in draw_points(self, ax)
1805 c=point_colors, edgecolor=point_colors,
1806 linewidth=mew, marker=marker, s=markersize,
-> 1807 zorder=z)
1808
1809 def plot(self, ax):
~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1587 def inner(ax, *args, data=None, **kwargs):
1588 if data is None:
-> 1589 return func(ax, *map(sanitize_sequence, args), **kwargs)
1590
1591 bound = new_sig.bind(ax, *args, **kwargs)
~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
4488 offsets=offsets,
4489 transOffset=kwargs.pop('transform', self.transData),
-> 4490 alpha=alpha
4491 )
4492 collection.set_transform(mtransforms.IdentityTransform())
~/anaconda3/lib/python3.7/site-packages/matplotlib/collections.py in __init__(self, paths, sizes, **kwargs)
881 """
882
--> 883 Collection.__init__(self, **kwargs)
884 self.set_paths(paths)
885 self.set_sizes(sizes)
~/anaconda3/lib/python3.7/site-packages/matplotlib/collections.py in __init__(self, edgecolors, facecolors, linewidths, linestyles, capstyle, joinstyle, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)
126 self._hatch_color = mcolors.to_rgba(mpl.rcParams['hatch.color'])
127 self.set_facecolor(facecolors)
--> 128 self.set_edgecolor(edgecolors)
129 self.set_linewidth(linewidths)
130 self.set_linestyle(linestyles)
~/anaconda3/lib/python3.7/site-packages/matplotlib/collections.py in set_edgecolor(self, c)
726 """
727 self._original_edgecolor = c
--> 728 self._set_edgecolor(c)
729
730 def set_alpha(self, alpha):
~/anaconda3/lib/python3.7/site-packages/matplotlib/collections.py in _set_edgecolor(self, c)
710 except AttributeError:
711 pass
--> 712 self._edgecolors = mcolors.to_rgba_array(c, self._alpha)
713 if set_hatch_color and len(self._edgecolors):
714 self._hatch_color = tuple(self._edgecolors[0])
~/anaconda3/lib/python3.7/site-packages/matplotlib/colors.py in to_rgba_array(c, alpha)
284 result = np.empty((len(c), 4), float)
285 for i, cc in enumerate(c):
--> 286 result[i] = to_rgba(cc, alpha)
287 return result
288
~/anaconda3/lib/python3.7/site-packages/matplotlib/colors.py in to_rgba(c, alpha)
175 rgba = None
176 if rgba is None: # Suppress exception chaining of cache lookup failure.
--> 177 rgba = _to_rgba_no_colorcycle(c, alpha)
178 try:
179 _colors_full_map.cache[c, alpha] = rgba
~/anaconda3/lib/python3.7/site-packages/matplotlib/colors.py in _to_rgba_no_colorcycle(c, alpha)
236 # float)` and `np.array(...).astype(float)` all convert "0.5" to 0.5.
237 # Test dimensionality to reject single floats.
--> 238 raise ValueError("Invalid RGBA argument: {!r}".format(orig_c))
239 # Return a tuple to prevent the cached value from being modified.
240 c = tuple(c.astype(float))
ValueError: Invalid RGBA argument: masked
