更高效的matplotlib堆积条形图 - 如何计算底值

Question

我需要一些帮助,使用matlibplot在python中制作一组堆积的条形图.我的基本代码如下,但我的问题是如何产生的价值底部超越二号一个任何元素有效.我可以让示例图正确堆叠(总是a,b,c,d从下到上)

import numpy as np
import matplotlib.pyplot as plt

ind = np.arange(3)

a = [3,6,9]
b = [2,7,1]
c = [0,3,1]
d = [4,0,3]

p1 = plt.bar(ind, a, 1, color='#ff3333')
p2 = plt.bar(ind, b, 1, color='#33ff33', bottom=a)
p3 = plt.bar(ind, c, 1, color='#3333ff', bottom=[a[j] +b[j] for j in range(len(a))])
p4 = plt.bar(ind, d, 1, color='#33ffff', bottom=[a[j] +b[j] +c[j] for j in range(len(a))])

plt.show()

我的最终代码可能有非常多的条形和不断扩展的函数bottom = [...]不能是最好的解决方案.如果您还可以解释我需要如何获得该值,那将会很棒.有一个numpy功能.

非常感谢你!!!PS我找了答案,但我不明白我能找到什么.

Answer 1

我刚刚遇到了同样的问题.之后我决定把它全部包装在一个很好的课堂上.对于任何感兴趣的人,您可以在此处获得堆积条形图类的实现:

https://github.com/minillinim/stackedBarGraph

它允许缩放堆叠图形以及设置条形宽度和设置高度(使用缩放内部).

给定这样的数据集:

    d = np.array([[101.,0.,0.,0.,0.,0.,0.],
                  [92.,3.,0.,4.,5.,6.,0.],
                  [56.,7.,8.,9.,23.,4.,5.],
                  [81.,2.,4.,5.,32.,33.,4.],
                  [0.,45.,2.,3.,45.,67.,8.],
                  [99.,5.,0.,0.,0.,43.,56.]])

    d_heights = [1.,2.,3.,4.,5.,6.]
    d_widths = [.5,1.,3.,2.,1.,2.]
    d_labels = ["fred","julie","sam","peter","rob","baz"]
    d_colors = ['#2166ac',
                '#fee090',
                '#fdbb84',
                '#fc8d59',
                '#e34a33',
                '#b30000',
                '#777777']

它可以像这样制作图像:

GPLv3充满爱意.

Answer 2

将您的值转换为numpy数组将使您的生活更轻松:

data = np.array([a, b, c, d])
bottom = np.cumsum(data, axis=0)
colors = ('#ff3333', '#33ff33', '#3333ff', '#33ffff')

plt.bar(ind, data[0], color=colors[0])
for j in xrange(1, data.shape[0]):
    plt.bar(ind, data[1], color=colors[j], bottom=bottom[i-1])

或者,为了摆脱第一个酒吧令人讨厌的特殊情况:

data = np.array([a, b, c, d])
bottom = np.vstack((np.zeros((data.shape[1],), dtype=data.dtype),
                    np.cumsum(data, axis=0)[:-1]))
colors = ('#ff3333', '#33ff33', '#3333ff', '#33ffff')
for dat, col, bot in zip(data, colors, bottom):
    plt.bar(ind, dat, color=col, bottom=bot)

Answer 3

[sum(values) for values in zip(a, b, c)]

在Python 2中,您也可以这样做

map(sum, zip(a, b, c))

但Python 3需要

list(map(sum, zip(a, b, c)))

哪个不太好看.

你可以封装这个:

def sumzip(*items):
    return [sum(values) for values in zip(*items)]

然后呢

p1 = plt.bar(ind, a, 1, color='#ff3333')
p2 = plt.bar(ind, b, 1, color='#33ff33', bottom=sumzip(a))
p3 = plt.bar(ind, c, 1, color='#3333ff', bottom=sumzip(a, b))
p4 = plt.bar(ind, d, 1, color='#33ffff', bottom=sumzip(a, b, c))

太.

如果a,b,c并且d是numpy的阵列,你也可以做sum([a, b, c]):

a = np.array([3,6,9])
b = np.array([2,7,1])
c = np.array([0,3,1])
d = np.array([4,0,3])

p1 = plt.bar(ind, a, 1, color='#ff3333')
p2 = plt.bar(ind, b, 1, color='#33ff33', bottom=sum([a]))
p3 = plt.bar(ind, c, 1, color='#3333ff', bottom=sum([a, b]))
p4 = plt.bar(ind, d, 1, color='#33ffff', bottom=sum([a, b, c]))