我今天遇到了完全相同的问题。我的解决方案是从 Pandas 复制并行坐标并根据我的特殊需要进行调整。我认为这对其他人有用,这是我的实现:
def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
use_columns=False, xticks=None, colormap=None,
**kwds):
import matplotlib.pyplot as plt
import matplotlib as mpl
n = len(frame)
class_col = frame[class_column]
class_min = np.amin(class_col)
class_max = np.amax(class_col)
if cols is None:
df = frame.drop(class_column, axis=1)
else:
df = frame[cols]
used_legends = set([])
ncols = len(df.columns)
# determine values to use for xticks
if use_columns is True:
if not np.all(np.isreal(list(df.columns))):
raise ValueError('Columns must be numeric to be used as xticks')
x = df.columns
elif xticks is not None:
if not np.all(np.isreal(xticks)):
raise ValueError('xticks specified must be numeric')
elif len(xticks) != ncols:
raise ValueError('Length of xticks must match number of columns')
x = xticks
else:
x = range(ncols)
fig = plt.figure()
ax = plt.gca()
Colorm = plt.get_cmap(colormap)
for i in range(n):
y = df.iloc[i].values
kls = class_col.iat[i]
ax.plot(x, y, color=Colorm((kls - class_min)/(class_max-class_min)), **kwds)
for i in x:
ax.axvline(i, linewidth=1, color='black')
ax.set_xticks(x)
ax.set_xticklabels(df.columns)
ax.set_xlim(x[0], x[-1])
ax.legend(loc='upper right')
ax.grid()
bounds = np.linspace(class_min,class_max,10)
cax,_ = mpl.colorbar.make_axes(ax)
cb = mpl.colorbar.ColorbarBase(cax, cmap=Colorm, spacing='proportional', ticks=bounds, boundaries=bounds, format='%.2f')
return fig
我不知道它是否适用于 pandas 原始函数提供的每个选项。但对于你的例子,它给出了这样的东西:
parallel_coordinates(mydata, 'res', colormap="binary")
您可以通过更改上一个函数中的这一行来添加 alpha 值:
ax.plot(x, y, color=Colorm((kls - class_min)/(class_max-class_min)), alpha=(kls - class_min)/(class_max-class_min), **kwds)
对于 pandas 原始示例,删除名称并使用最后一列作为值:
sampdata = read_csv('iris_modified.csv')
parallel_coordinates(sampdata, 'Value')
我希望这能帮到您!
克里斯托夫