Matplotlib -为每个bin添加标签
Matplotlib -为每个bin添加标签
提问于 2011-06-15 11:34:06
我目前正在使用Matplotlib创建一个直方图：
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as pyplot
...
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1,)
n, bins, patches = ax.hist(measurements, bins=50, range=(graph_minimum, graph_maximum), histtype='bar')
#ax.set_xticklabels([n], rotation='vertical')
for patch in patches:
patch.set_facecolor('r')
pyplot.title('Spam and Ham')
pyplot.xlabel('Time (in seconds)')
pyplot.ylabel('Bits of Ham')
pyplot.savefig(output_filename)我想让x轴标签更有意义一些。
首先，这里的x轴刻度似乎被限制为五个刻度。无论我做什么，我似乎不能改变这一点-即使我添加更多的xticklabels，它也只使用前五个。我不确定Matplotlib是如何计算的，但我假设它是从范围/数据自动计算出来的？
有没有什么方法可以提高x-tick标签的分辨率--甚至可以提高到每个条形/条形的分辨率？
(理想情况下，我还希望将秒重新格式化为微秒/毫秒，但这是另一个问题)。
其次，我想要每个标记为的条形图--带有该条形图中的实际数量，以及占所有条形图总数的百分比。
最终输出可能如下所示：
这样的事情在Matplotlib中是可能的吗？
干杯，维克多
回答 3
Stack Overflow用户
回答已采纳
发布于 2011-06-15 12:35:49
好的!去设定时间，只是，嗯.设置刻度(请参见matplotlib.pyplot.xticks或ax.set_xticks)。(此外，您也不需要手动设置补丁的面部颜色。您可以只传递一个关键字参数。)
对于其余部分，您需要对标签做一些稍微花哨的事情，但是matplotlib使它变得相当简单。
举个例子：
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FormatStrFormatter
data = np.random.randn(82)
fig, ax = plt.subplots()
counts, bins, patches = ax.hist(data, facecolor='yellow', edgecolor='gray')
# Set the ticks to be at the edges of the bins.
ax.set_xticks(bins)
# Set the xaxis's tick labels to be formatted with 1 decimal place...
ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
# Change the colors of bars at the edges...
twentyfifth, seventyfifth = np.percentile(data, [25, 75])
for patch, rightside, leftside in zip(patches, bins[1:], bins[:-1]):
if rightside < twentyfifth:
patch.set_facecolor('green')
elif leftside > seventyfifth:
patch.set_facecolor('red')
# Label the raw counts and the percentages below the x-axis...
bin_centers = 0.5 * np.diff(bins) + bins[:-1]
for count, x in zip(counts, bin_centers):
# Label the raw counts
ax.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'),
xytext=(0, -18), textcoords='offset points', va='top', ha='center')
# Label the percentages
percent = '%0.0f%%' % (100 * float(count) / counts.sum())
ax.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),
xytext=(0, -32), textcoords='offset points', va='top', ha='center')
# Give ourselves some more room at the bottom of the plot
plt.subplots_adjust(bottom=0.15)
plt.show()
Stack Overflow用户
发布于 2017-07-16 08:46:29
若要将SI前缀添加到轴标签，请使用QuantiPhy。事实上，在它的文档中有一个例子展示了如何做这件事：MatPlotLib Example。
我认为你会在你的代码中添加类似这样的东西：
from matplotlib.ticker import FuncFormatter
from quantiphy import Quantity
time_fmtr = FuncFormatter(lambda v, p: Quantity(v, 's').render(prec=2))
ax.xaxis.set_major_formatter(time_fmtr)Stack Overflow用户
发布于 2020-07-27 10:02:23
我想要添加到直方图中"density = True“的曲线图中的一件事是每个bin搜索的相对频率值，但我找不到一个可以做到这一点的函数。我制作的解决方案如下图所示：
函数：
def label_densityHist(ax, n, bins, x=4, y=0.01, r=2, **kwargs):
"""
Add labels,relative value of bin, to each bin in a density histogram .
:param ax: Object axe of matplotlib
The axis to plot.
:param n: list, array of int, float
The values of the histogram bins.
:param bins: list, array of int, float
The edges of the bins.
:param x: int, float
Related the x position of the bin labels. The higher, the lower the value on the x-axis.
Default: 4
:param y: int, float
Related the y position of the bin labels. The higher, the greater the value on the y-axis.
Default: 0.01
:param r: int
Number of decimal places.
Default: 2
:param **kwargs: Text properties in matplotlib
:return: None
Example
import matplotlib.pyplot as plt
import numpy as np
dados = np.random.randn(100)
axe = plt.gca()
n, bins, _ = axe.hist(x=dados, edgecolor='black')
label_densityHist(axe,n, bins)
plt.show()
Example:
import matplotlib.pyplot as plt
import numpy as np
dados = np.random.randn(100)
axe = plt.gca()
n, bins, _ = axe.hist(x=dados, edgecolor='black')
label_densityHist(axe,n, bins, x=6, fontsize='large')
plt.show()
Reference:
[1]https://matplotlib.org/3.1.1/api/text_api.html#matplotlib.text.Text
"""
k = []
# calculate the relative frequency of each bin
for i in range(0,len(n)):
k.append((bins[i+1]-bins[i])*n[i])
# rounded
k = around(k,r); #print(k)
# plot the label/text to each bin
for i in range(0, len(n)):
x_pos = (bins[i + 1] - bins[i]) / x + bins[i]
y_pos = n[i] + (n[i] * y)
label = str(k[i]) # relative frequency of each bin
ax.text(x_pos, y_pos, label, kwargs)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/6352740
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