Stacked bar plot using matplotlib

Question

I'm creating bar plots utilizing matplotlib and it would seem that there is a bug with the stacked bar plot. The total for every vertical stack ought to be 100. Notwithstanding, for X-AXIS ticks 65, 70, 75, and 80 we get totally self-assertive outcomes that don't bode well. I don't comprehend what the issue is. Kindly discover the MWE beneath.

import numpy as np

import matplotlib.pyplot as plt

import matplotlib

header = ['a','b','c','d']

dataset= [('60.0', '65.0', '70.0', '75.0', '80.0', '85.0', '90.0', '95.0', '100.0', '105.0', '110.0', '115.0', '120.0', '125.0', '130.0', '135.0', '140.0', '145.0', '150.0', '155.0', '160.0', '165.0', '170.0', '175.0', '180.0', '185.0', '190.0', '195.0', '200.0'), (0.0, 25.0, 48.93617021276596, 83.01886792452831, 66.66666666666666, 66.66666666666666, 70.96774193548387, 84.61538461538461, 93.33333333333333, 85.0, 92.85714285714286, 93.75, 95.0, 100.0, 100.0, 100.0, 100.0, 80.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0), (0.0, 50.0, 36.17021276595745, 11.320754716981133, 26.666666666666668, 33.33333333333333, 29.03225806451613, 15.384615384615385, 6.666666666666667, 15.0, 7.142857142857142, 6.25, 5.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), (0.0, 12.5, 10.638297872340425, 3.7735849056603774, 4.444444444444445, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), (100.0, 12.5, 4.25531914893617, 1.8867924528301887, 2.2222222222222223, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)]

X_AXIS = dataset[0]

matplotlib.rc('font', serif='Helvetica Neue')

matplotlib.rc('text', usetex='false')

matplotlib.rcParams.update({'font.size': 40})

fig = matplotlib.pyplot.gcf()

fig.set_size_inches(18.5, 10.5)

configs = dataset[0]

N = len(configs)

ind = np.arange(N)

width = 0.4

p1 = plt.bar(ind, dataset[1], width, color='r')

p2 = plt.bar(ind, dataset[2], width, bottom=dataset[1], color='b')

p3 = plt.bar(ind, dataset[3], width, bottom=dataset[2], color='g')

p4 = plt.bar(ind, dataset[4], width, bottom=dataset[3], color='c')

plt.ylim([0,120])

plt.yticks(fontsize=12)

plt.ylabel(output, fontsize=12)

plt.xticks(ind, X_AXIS, fontsize=12, rotation=90)

plt.xlabel('test', fontsize=12)

plt.legend((p1[0], p2[0], p3[0], p4[0]), (header[0], header[1], header[2], header[3]), fontsize=12, ncol=4, framealpha=0, fancybox=True)

plt.show()

Answer 1

You need the lower part of each dataset to be the amount of all the datasets that preceded. you may likewise have to change over the datasets to NumPy arrays to add them together.

p1 = plt.bar(ind, dataset[1], width, color='r')

p2 = plt.bar(ind, dataset[2], width, bottom=dataset[1], color='b')

p3 = plt.bar(ind, dataset[3], width, 

             bottom=np.array(dataset[1])+np.array(dataset[2]), color='g')

p4 = plt.bar(ind, dataset[4], width,

             bottom=np.array(dataset[1])+np.array(dataset[2])+np.array(dataset[3]),

             color='c')

Then again, you could change them over to NumPy arrays before you begin plotting. 

dataset1 = np.array(dataset[1])

dataset2 = np.array(dataset[2])

dataset3 = np.array(dataset[3])

dataset4 = np.array(dataset[4])

p1 = plt.bar(ind, dataset1, width, color='r')

p2 = plt.bar(ind, dataset2, width, bottom=dataset1, color='b')

p3 = plt.bar(ind, dataset3, width, bottom=dataset1+dataset2, color='g')

p4 = plt.bar(ind, dataset4, width, bottom=dataset1+dataset2+dataset3,

             color='c')

Or then again at long last on the off chance that you need to try not to change over to NumPy arrays, you could utilize a list comprehensions:

p1 = plt.bar(ind, dataset[1], width, color='r')

p2 = plt.bar(ind, dataset[2], width, bottom=dataset[1], color='b')

p3 = plt.bar(ind, dataset[3], width,

             bottom=[sum(x) for x in zip(dataset[1],dataset[2])], color='g')

p4 = plt.bar(ind, dataset[4], width,

             bottom=[sum(x) for x in zip(dataset[1],dataset[2],dataset[3])],

             color='c')

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