Matplotlib - 标记每个 bin [英] Matplotlib - label each bin

问题描述

我目前正在使用 Matplotlib 创建直方图:

I'm currently using Matplotlib to create a histogram:

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.

I'd like to make the x-axis labels a bit more meaningful.

首先，这里的 x 轴刻度似乎仅限于五个刻度.无论我做什么，我似乎都无法改变这一点——即使我添加了更多的 xticklabels，它也只使用前五个.我不确定 Matplotlib 是如何计算的，但我认为它是根据范围/数据自动计算的?

Firstly, the x-axis ticks here seem to be limited to five ticks. No matter what I do, I can't seem to change this - even if I add more xticklabels, it only uses the first five. I'm not sure how Matplotlib calculates this, but I assume it's auto-calculated from the range/data?

有什么方法可以提高 x-tick 标签的分辨率 - 甚至每个条形/箱体都增加一个分辨率?

Is there some way I can increase the resolution of x-tick labels - even to the point of one for each bar/bin?

(理想情况下，我还希望以微秒/毫秒为单位重新格式化秒数，但这是另一天的问题).

(Ideally, I'd also like the seconds to be reformatted in micro-seconds/milli-seconds, but that's a question for another day).

其次，我希望标记每个单独的条形 - 带有该 bin 中的实际数量，以及所有 bin 总数的百分比.

Secondly, I'd like each individual bar labeled - with the actual number in that bin, as well as the percentage of the total of all bins.

最终输出可能如下所示:

The final output might look something like this:

Matplotlib 可以实现类似的功能吗?

Is something like that possible with Matplotlib?

干杯，维克多

推荐答案

好的！要设置刻度，只需……设置刻度(参见 matplotlib.pyplot.xticks 或 ax.set_xticks).(另外，您不需要手动设置补丁的 facecolor.您只需传入关键字参数即可.)

Sure! To set the ticks, just, well... Set the ticks (see matplotlib.pyplot.xticks or ax.set_xticks). (Also, you don't need to manually set the facecolor of the patches. You can just pass in a keyword argument.)

其余的，您需要对标签做一些稍微花哨的事情，但 matplotlib 使它变得相当容易.

For the rest, you'll need to do some slightly more fancy things with the labeling, but matplotlib makes it fairly easy.

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()

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