如何在Python中绘制条形高度是bin宽度函​​数的直方图？

I have this data:

我有这个数据：

[-152, -132, -132, -128, -122, -121, -120, -113, -112, -108, 
-107, -107, -106, -106, -106, -105, -101, -101, -99, -89, -87, 
-86, -83, -83, -80, -80, -79, -74, -74, -74, -71, -71, -69, 
-67, -67, -65, -62, -61, -60, -60, -59, -55, -54, -54, -52, 
-50, -49, -48, -48, -47, -44, -43, -38, -37, -35, -34, -34, 
-29, -27, -27, -26, -24, -24, -19, -19, -19, -19, -18, -16, 
-16, -16, -15, -14, -14, -12, -12, -12, -4, -1, 0, 0, 1, 2, 7, 
14, 14, 14, 14, 18, 18, 19, 24, 29, 29, 41, 45, 51, 72, 150, 155]

I wanna plot it by using a histogram with these bins:

我想通过使用这些箱子的直方图来绘制它：

[-160,-110,-90,-70,-40,-10,20,50,80,160]

I've used this code for that:

我已经使用了这段代码：

import matplotlib.pyplot as plt
...
plt.hist(data, bins)
plt.show()

But the problem with this plot is that bars height is not according to bins width, because frequency should symbolize the area of a bar (see this page). So how could I plot this type of histogram? Thanks in advance.

但是这个图的问题是条形高度不是根据区间宽度，因为频率应该象征条形区域（参见本页）。那么我怎么能绘制这种类型的直方图？提前致谢。

2 个解决方案

#1

From the docstring:

从文档字符串：

normed : boolean, optional

normed：布尔值，可选

If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., n/(len(x)`dbin), i.e., the integral of the histogram will sum to 1. If stacked is also True, the sum of the histograms is normalized to 1.

如果为True，则返回元组的第一个元素将是规范化以形成概率密度的计数，即n /（len（x）`dbin），即直方图的积分将总和为1.如果堆叠也是确实，直方图的总和标准化为1。

Default is False

默认值为False

plt.hist(data, bins=bins, normed=True)

#2

Thanks Nikos Tavoularis for this post.

感谢Nikos Tavoularis的这篇文章。

My solution code:

我的解决方案码：

import requests
from bs4 import BeautifulSoup
import re
import matplotlib.pyplot as plt
import numpy as np

regex = r"((-?\d+(\s?,\s?)?)+)\n"
page = requests.get('http://www.stat.berkeley.edu/~stark/SticiGui/Text/histograms.htm')
soup = BeautifulSoup(page.text, 'lxml')
# La data se halla dentro de los scripts y no dentro de la etiqueta html TABLE
scripts = soup.find_all('script')
target = scripts[23].string
hits = re.findall(regex, target, flags=re.MULTILINE)
data = []
if hits:
    for val, _, _ in hits:
        data.extend([int(x) for x in re.findall(r"-?\d+", val)])
print(sorted(data))
print('Length of data:', len(data), "\n")

# Intervals
bins = np.array([-160, -110, -90, -70, -40, -10, 20, 50, 80, 160])

# calculating histogram
widths = bins[1:] - bins[:-1]
freqs = np.histogram(data, bins)[0]
heights = freqs / widths
mainlabel = 'The deviations of the 100 measurements from a ' \
                'base value of {}, times {}'.format(r'$9.792838\ ^m/s^2$', r'$10^8$')
hlabel = 'Data gravity'

# plot with various axes scales
plt.close('all')
fig = plt.figure()
plt.suptitle(mainlabel, fontsize=16)
# My screen resolution is: 1920x1080
plt.get_current_fig_manager().window.wm_geometry("900x1100+1050+0")

# Bar chart
ax1 = plt.subplot(211)  # 2-rows, 1-column, position-1
barlist = plt.bar(bins[:-1], heights, width=widths, facecolor='yellow', alpha=0.7, edgecolor='gray')
plt.title('Bar chart')
plt.xlabel(hlabel, labelpad=30)
plt.ylabel('Heights')
plt.xticks(bins, fontsize=10)
# Change the colors of bars at the edges...
twentyfifth, seventyfifth = np.percentile(data, [25, 75])
for patch, rightside, leftside in zip(barlist, bins[1:], bins[:-1]):
    if rightside < twentyfifth:
        patch.set_facecolor('green')
    elif leftside > seventyfifth:
        patch.set_facecolor('red')
# code from: https://*.com/questions/6352740/matplotlib-label-each-bin
# 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(freqs, bin_centers):
    # Label the raw counts
    ax1.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'),
                    xytext=(0, -18), textcoords='offset points', va='top', ha='center', fontsize=9)

    # Label the percentages
    percent = '%0.0f%%' % (100 * float(count) / freqs.sum())
    ax1.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),
                    xytext=(0, -28), textcoords='offset points', va='top', ha='center', fontsize=9)
plt.grid(True)

# Histogram Plot
ax2 = plt.subplot(223)  # 2-rows, 2-column, position-3
plt.hist(data, bins, alpha=0.5)
plt.title('Histogram')
plt.xlabel(hlabel)
plt.ylabel('Frequency')
plt.grid(True)

# Histogram Plot
ax3 = plt.subplot(224)  # 2-rows, 2-column, position-4
plt.hist(data, bins, alpha=0.5, normed=True, facecolor='g')
plt.title('Histogram (normed)')
plt.xlabel(hlabel)
plt.ylabel('???')
plt.grid(True)

plt.tight_layout(pad=1.5, w_pad=0, h_pad=0)
plt.show()