Python 使用 matplotlib 的堆积条形图
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/44309507/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
提示：将鼠标放在中文语句上可以显示对应的英文。显示中英文
时间：2020-08-19 23:54:31 来源:igfitidea点击:
stacked bar plot using matplotlib
提问by tandem
I am generating bar plots using matplotlib and it looks like there is a bug with the stacked bar plot. The sum for each vertical stack should be 100. However, for X-AXIS ticks 65, 70, 75 and 80 we get completely arbitrary results which do not make any sense. I do not understand what the problem is. Please find the MWE below.
我正在使用 matplotlib 生成条形图，看起来堆积条形图存在错误。每个垂直堆栈的总和应该是 100。然而，对于 X-AXIS 刻度 65、70、75 和 80，我们得到完全任意的结果，没有任何意义。我不明白问题是什么。请在下面找到 MWE。
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()
回答by tmdavison
You need the bottomof each dataset to be the sum of all the datasets that came before. you may also need to convert the datasets to numpy arrays to add them together.
您需要bottom每个数据集的 是之前所有数据集的总和。您可能还需要将数据集转换为 numpy 数组以将它们加在一起。
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')
Alternatively, you could convert them to numpy arrays before you start plotting.
或者，您可以在开始绘图之前将它们转换为 numpy 数组。
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 finally if you want to avoid converting to numpy arrays, you could use a list comprehension:
或者最后，如果你想避免转换为 numpy 数组，你可以使用列表理解：
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')
回答by Bill
I found this such a pain that I wrote a function to do it. I'm sharing it in the hope that others find it useful:
我发现这很痛苦，所以我写了一个函数来做到这一点。我正在分享它，希望其他人觉得它有用：
import numpy as np
import matplotlib.pyplot as plt
def plot_stacked_bar(data, series_labels, category_labels=None,
show_values=False, value_format="{}", y_label=None,
colors=None, grid=True, reverse=False):
"""Plots a stacked bar chart with the data and labels provided.
Keyword arguments:
data -- 2-dimensional numpy array or nested list
containing data for each series in rows
series_labels -- list of series labels (these appear in
the legend)
category_labels -- list of category labels (these appear
on the x-axis)
show_values -- If True then numeric value labels will
be shown on each bar
value_format -- Format string for numeric value labels
(default is "{}")
y_label -- Label for y-axis (str)
colors -- List of color labels
grid -- If True display grid
reverse -- If True reverse the order that the
series are displayed (left-to-right
or right-to-left)
"""
ny = len(data[0])
ind = list(range(ny))
axes = []
cum_size = np.zeros(ny)
data = np.array(data)
if reverse:
data = np.flip(data, axis=1)
category_labels = reversed(category_labels)
for i, row_data in enumerate(data):
color = colors[i] if colors is not None else None
axes.append(plt.bar(ind, row_data, bottom=cum_size,
label=series_labels[i], color=color))
cum_size += row_data
if category_labels:
plt.xticks(ind, category_labels)
if y_label:
plt.ylabel(y_label)
plt.legend()
if grid:
plt.grid()
if show_values:
for axis in axes:
for bar in axis:
w, h = bar.get_width(), bar.get_height()
plt.text(bar.get_x() + w/2, bar.get_y() + h/2,
value_format.format(h), ha="center",
va="center")
Example:
例子：
plt.figure(figsize=(6, 4))
series_labels = ['Series 1', 'Series 2']
data = [
[0.2, 0.3, 0.35, 0.3],
[0.8, 0.7, 0.6, 0.5]
]
category_labels = ['Cat A', 'Cat B', 'Cat C', 'Cat D']
plot_stacked_bar(
data,
series_labels,
category_labels=category_labels,
show_values=True,
value_format="{:.1f}",
colors=['tab:orange', 'tab:green'],
y_label="Quantity (units)"
)
plt.savefig('bar.png')
plt.show()
回答by Bill
This is probably your most convenient solution if you are willing to use Pandas:
如果您愿意使用 Pandas，这可能是您最方便的解决方案：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
X_AXIS = (
'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'
)
index = pd.Index(X_AXIS, name='test')
data = {
'a': (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),
'b': (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),
'c': (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),
'd': (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)
}
df = pd.DataFrame(data, index=index)
ax = df.plot(kind='bar', stacked=True, figsize=(18.5, 10.5))
ax.set_ylabel('foo')
plt.savefig('stacked.png')
plt.show()
Output:
输出：
回答by cosmic_inquiry
If you're interested in ordered stacking (longest bars at bottom), here is how you can do it:
如果您对有序堆叠（底部最长的条形）感兴趣，您可以这样做：
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
a = pd.DataFrame({'a':[0.25, 0.5, 0.15, 0], 'b':[0.15, 0.25, 0.35, 0.15],
'c':[0.50, 0.15, 0.5, 0.35], 'd':[0.35, 0.35, 0.25, 0.5],})
# a b c d
# 0 0.25 0.15 0.50 0.35
# 1 0.50 0.25 0.15 0.35
# 2 0.15 0.35 0.50 0.25
# 3 0.00 0.15 0.35 0.50
fig, ax = plt.subplots()
x = a.index
indexes = np.argsort(a.values).T
heights = np.sort(a.values).T
order = -1
bottoms = heights[::order].cumsum(axis=0)
bottoms = np.insert(bottoms, 0, np.zeros(len(bottoms[0])), axis=0)
mpp_colors = dict(zip(a.columns, plt.rcParams['axes.prop_cycle'].by_key()['color']))
for btms, (idxs, vals) in enumerate(list(zip(indexes, heights))[::order]):
mps = np.take(np.array(a.columns), idxs)
ax.bar(x, height=vals, bottom=bottoms[btms], color=[mpp_colors[m] for m in mps])
ax.set_ylim(bottom=0, top=2)
plt.legend((np.take(np.array(a.columns), np.argsort(a.values)[0]))[::order], loc='upper right')
