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使用matplotlib创建百分比堆积柱状图的思路与堆积柱状图类似，只不过bottom参数累计的不是数值而是百分比，因此，需要事先计算每组柱子的数值总和，进而求百分比。
适用于少量数据，数据结构需要手动构造。
import matplotlib.pyplot as plt
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
first = [20, 34, 30, 35, 27]
second = [25, 32, 34, 20, 25]
third = [21, 31, 37, 21, 28]
fourth = [26, 31, 35, 27, 21]
data = [first, second, third, fourth]
x = range(len(labels))
width = 0.35
# 将bottom_y元素都初始化为0
bottom_y = [0] * len(labels)
# 计算每组柱子的总和，为计算百分比做准备
sums = [sum(i) for i in zip(first, second, third, fourth)]
for i in data:
# 计算每个柱子的高度，即百分比
y = [a/b for a, b in zip(i, sums)]
plt.bar(x, y, width, bottom=bottom_y)
# 计算bottom参数的位置
bottom_y = [(a+b) for a, b in zip(y, bottom_y)]
plt.xticks(x, labels)
plt.title('Percent stacked bar ')
plt.show()
第一个版本的缺陷在于数据需要手动构造，而且计算稍微繁琐一些。
使用numpy便于处理规模比较大且已存储在文件中数据的数据，计算更简便。
import numpy as np
import matplotlib.pyplot as plt
labels = ['G1', 'G2', 'G3', 'G4', 'G5']
first = [20, 34, 30, 35, 27]
second = [25, 32, 34, 20, 25]
third = [21, 31, 37, 21, 28]
fourth = [26, 31, 35, 27, 21]
data = [first, second, third, fourth]
x = range(len(labels))
width = 0.35
# 将bottom_y元素都初始化为0
bottom_y = np.zeros(len(labels))
data = np.array(data)
# 按列计算计算每组柱子的总和，为计算百分比做准备
sums = np.sum(data, axis=0)
for i in data:
# 计算每个柱子的高度，即百分比
y = i / sums
plt.bar(x, y, width, bottom=bottom_y)
# 计算bottom参数的位置
bottom_y = y + bottom_y
plt.xticks(x, labels)
plt.title('Percent stacked bar ')
plt.show()
