Pandas 中连续数据的平行坐标图
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parallel coordinates plot for continous data in pandas
提问by qed
The parallel_coordinates function from pandas is very useful:
pandas 的 parallel_coordinates 函数非常有用：
import pandas
import matplotlib.pyplot as plt
from pandas.tools.plotting import parallel_coordinates
sampdata = read_csv('/usr/local/lib/python3.3/dist-packages/pandas/tests/data/iris.csv')
parallel_coordinates(sampdata, 'Name')
But when you have continous data, its behavior is not what you would expect:
但是当你有连续的数据时，它的行为并不是你所期望的：
mypos = np.random.randint(10, size=(100, 2))
mydata = DataFrame(mypos, columns=['x', 'y'])
myres = np.random.rand(100, 1)
mydata['res'] = myres
parallel_coordinates(mydata, 'res')
I would like to have the color of the lines to reflect the magnitude of the continuous variable, e.g. in a gradient from white to black, preferably also with the possibility of some transparency (alpha value), and with a color bar beside.
我想要线条的颜色来反映连续变量的大小，例如在从白色到黑色的渐变中，最好还有一些透明度（alpha 值）的可能性，并且旁边有一个颜色条。
回答by Christophe
I had the exact same problem today. My solution was to copy the parallel_coordinates from pandas and to adapt it for my special needs. As I think it can be useful for others, here is my implementation:
我今天遇到了完全相同的问题。我的解决方案是从 Pandas 复制 parallel_coordinates 并根据我的特殊需要进行调整。因为我认为它对其他人有用，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
I don't know if it will works with every option that pandas original function provides. But for your example, it gives something like this:
我不知道它是否适用于 pandas 原始函数提供的每个选项。但是对于您的示例，它给出了如下内容：
parallel_coordinates(mydata, 'res', colormap="binary")
You can add alpha value by changing this line in the previous function:
您可以通过更改前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)
And for pandas original example, removing names and using the last column as values:
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')
I hope this will help you!
我希望这能帮到您！
Christophe
克里斯托夫
