{"id":1097975,"date":"2025-01-08T15:16:55","date_gmt":"2025-01-08T07:16:55","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1097975.html"},"modified":"2025-01-08T15:16:57","modified_gmt":"2025-01-08T07:16:57","slug":"python%e7%9b%b4%e6%96%b9%e5%9b%be%e5%a6%82%e4%bd%95%e8%ae%be%e7%bd%ae%e5%9d%90%e6%a0%87%e7%9a%84%e5%88%86%e5%ba%a6%e5%80%bc-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1097975.html","title":{"rendered":"python\u76f4\u65b9\u56fe\u5982\u4f55\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c"},"content":{"rendered":"

\"python\u76f4\u65b9\u56fe\u5982\u4f55\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c\"<\/p>\n

\u5728Python\u4e2d\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u65b9\u56fe\u65f6\uff0c\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c\u4e3b\u8981\u901a\u8fc7xticks\u548cyticks\u51fd\u6570\u6765\u5b9e\u73b0\uff0c\u53ef\u4ee5\u8bbe\u5b9a\u5750\u6807\u8f74\u7684\u523b\u5ea6\u4f4d\u7f6e\u548c\u6807\u7b7e\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709\u8bbe\u7f6e\u523b\u5ea6\u95f4\u9694\u3001\u8bbe\u5b9a\u81ea\u5b9a\u4e49\u523b\u5ea6\u7b49\u3002<\/strong> \u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236\u76f4\u65b9\u56fe\u5e76\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u548c\u6570\u636e\u51c6\u5907<\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u76f4\u65b9\u56fe\u4e4b\u524d\uff0c\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u5e76\u51c6\u5907\u597d\u6570\u636e\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecmatplotlib\u548cnumpy\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u4e00\u4e9b\u6570\u636e<\/strong><\/h2>\n
data = np.random.randn(1000)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u7ed8\u5236\u76f4\u65b9\u56fe<\/p>\n<\/p>\n
\u4f7f\u7528matplotlib\u5e93\u7684hist\u51fd\u6570\u6765\u7ed8\u5236\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, edgecolor='black')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Histogram of Data')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8bbe\u7f6eX\u8f74\u7684\u5206\u5ea6\u503c<\/p>\n<\/p>\n
\u4f7f\u7528xticks\u51fd\u6570\u6765\u8bbe\u7f6eX\u8f74\u7684\u523b\u5ea6\u4f4d\u7f6e\u548c\u6807\u7b7e\u3002\u53ef\u4ee5\u901a\u8fc7linspace\u51fd\u6570\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u523b\u5ea6\u95f4\u9694\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6eX\u8f74\u7684\u523b\u5ea6\u4f4d\u7f6e\u548c\u6807\u7b7e<\/p>\n
x_ticks = np.linspace(min(data), max(data), num=10)  # \u751f\u621010\u4e2a\u5747\u5300\u5206\u5e03\u7684\u523b\u5ea6<\/p>\n
plt.xticks(x_ticks)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8bbe\u7f6eY\u8f74\u7684\u5206\u5ea6\u503c<\/p>\n<\/p>\n
\u4f7f\u7528yticks\u51fd\u6570\u6765\u8bbe\u7f6eY\u8f74\u7684\u523b\u5ea6\u4f4d\u7f6e\u548c\u6807\u7b7e\uff0c\u540c\u6837\u53ef\u4ee5\u4f7f\u7528linspace\u51fd\u6570\u751f\u6210\u5747\u5300\u5206\u5e03\u7684\u523b\u5ea6\u95f4\u9694\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6eY\u8f74\u7684\u523b\u5ea6\u4f4d\u7f6e\u548c\u6807\u7b7e<\/p>\n
y_ticks = np.linspace(0, 100, num=11)  # \u751f\u621011\u4e2a\u5747\u5300\u5206\u5e03\u7684\u523b\u5ea6<\/p>\n
plt.yticks(y_ticks)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u663e\u793a\u56fe\u50cf<\/p>\n<\/p>\n
\u4f7f\u7528show\u51fd\u6570\u6765\u663e\u793a\u7ed8\u5236\u7684\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5230\u6b64\u4e3a\u6b62\uff0c\u4f60\u5e94\u8be5\u80fd\u591f\u5728Python\u4e2d\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u65b9\u56fe\u5e76\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c\u3002\u63a5\u4e0b\u6765\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u8fdb\u4e00\u6b65\u5b9a\u5236\u76f4\u65b9\u56fe\u7684\u5916\u89c2\u548c\u529f\u80fd\u3002<\/p>\n<\/p>\n
\n
\u4e00\u3001\u7ed8\u5236\u76f4\u65b9\u56fe\u7684\u57fa\u672c\u8bbe\u7f6e<\/h3>\n<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe\u65f6\uff0c\u9664\u4e86\u57fa\u672c\u7684hist\u51fd\u6570\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u53c2\u6570\u6765\u5b9a\u5236\u76f4\u65b9\u56fe\u7684\u5916\u89c2\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u8bbe\u7f6e\u76f4\u65b9\u56fe\u7684\u989c\u8272\u3001\u8fb9\u6846\u989c\u8272\u3001\u900f\u660e\u5ea6\u7b49\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, color='blue', edgecolor='black', alpha=0.7)<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Customized Histogram of Data')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u81ea\u5b9a\u4e49X\u8f74\u548cY\u8f74\u7684\u523b\u5ea6\u6807\u7b7e<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u9ed8\u8ba4\u7684\u523b\u5ea6\u6807\u7b7e\u53ef\u80fd\u4e0d\u7b26\u5408\u9700\u6c42\uff0c\u53ef\u4ee5\u901a\u8fc7xticks\u548cyticks\u51fd\u6570\u81ea\u5b9a\u4e49\u523b\u5ea6\u6807\u7b7e\u3002\u4f8b\u5982\uff0c\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u523b\u5ea6\u6807\u7b7e\u4e3a\u7279\u5b9a\u7684\u6570\u503c\u3002<\/p>\n<\/p>\n
# \u81ea\u5b9a\u4e49X\u8f74\u7684\u523b\u5ea6\u6807\u7b7e<\/p>\n
x_ticks_custom = np.arange(-3, 4, 1)  # \u8bbe\u7f6e\u4ece-3\u52303\uff0c\u6bcf\u96941\u4e2a\u5355\u4f4d<\/p>\n
plt.xticks(x_ticks_custom)<\/p>\n
\u81ea\u5b9a\u4e49Y\u8f74\u7684\u523b\u5ea6\u6807\u7b7e<\/strong><\/h2>\n
y_ticks_custom = [0, 20, 40, 60, 80, 100]<\/p>\n
plt.yticks(y_ticks_custom)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Logarithmic Scale<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5177\u6709\u8f83\u5927\u8303\u56f4\u7684\u6570\u636e\u65f6\uff0c\u53ef\u80fd\u9700\u8981\u4f7f\u7528\u5bf9\u6570\u523b\u5ea6\u3002\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6exscale\u548cyscale\u53c2\u6570\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, edgecolor='black')<\/p>\n
plt.yscale('log')  # \u8bbe\u7f6eY\u8f74\u4e3a\u5bf9\u6570\u523b\u5ea6<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency (log scale)')<\/p>\n
plt.title('Histogram with Logarithmic Y-Axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u53ccY\u8f74\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u6709\u65f6\u9700\u8981\u5728\u540c\u4e00\u56fe\u8868\u4e2d\u663e\u793a\u4e24\u4e2aY\u8f74\uff0c\u53ef\u4ee5\u4f7f\u7528twinx\u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u5171\u4eabX\u8f74\u4f46\u5177\u6709\u4e0d\u540cY\u8f74\u7684\u56fe\u8868\u3002\u4f8b\u5982\uff0c\u7ed8\u5236\u4e00\u4e2a\u663e\u793a\u9891\u7387\u548c\u7d2f\u79ef\u9891\u7387\u7684\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
fig, ax1 = plt.subplots()<\/p>\n
\u7ed8\u5236\u7b2c\u4e00\u4e2aY\u8f74\u7684\u76f4\u65b9\u56fe<\/strong><\/h2>\n
ax1.hist(data, bins=30, edgecolor='black', alpha=0.7)<\/p>\n
ax1.set_xlabel('Value')<\/p>\n
ax1.set_ylabel('Frequency', color='blue')<\/p>\n
\u521b\u5efa\u5171\u4eabX\u8f74\u7684\u7b2c\u4e8c\u4e2aY\u8f74<\/strong><\/h2>\n
ax2 = ax1.twinx()<\/p>\n
ax2.hist(data, bins=30, edgecolor='black', cumulative=True, histtype='step', color='red')<\/p>\n
ax2.set_ylabel('Cumulative Frequency', color='red')<\/p>\n
plt.title('Histogram with Dual Y-Axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6dfb\u52a0\u7f51\u683c\u548c\u6ce8\u91ca<\/h3>\n<\/p>\n
\u4e3a\u4e86\u63d0\u9ad8\u56fe\u8868\u7684\u53ef\u8bfb\u6027\uff0c\u53ef\u4ee5\u6dfb\u52a0\u7f51\u683c\u548c\u6ce8\u91ca\u3002\u4f7f\u7528grid\u51fd\u6570\u53ef\u4ee5\u6dfb\u52a0\u7f51\u683c\uff0c\u4f7f\u7528annotate\u51fd\u6570\u53ef\u4ee5\u6dfb\u52a0\u6ce8\u91ca\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, edgecolor='black', alpha=0.7)<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Histogram with Grid and Annotations')<\/p>\n
\u6dfb\u52a0\u7f51\u683c<\/strong><\/h2>\n
plt.grid(True)<\/p>\n
\u6dfb\u52a0\u6ce8\u91ca<\/strong><\/h2>\n
plt.annotate('Peak', xy=(0, 50), xytext=(1, 80),<\/p>\n
             arrowprops=dict(facecolor='black', shrink=0.05))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4fdd\u5b58\u56fe\u50cf<\/h3>\n<\/p>\n
\u7ed8\u5236\u5b8c\u56fe\u50cf\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528savefig\u51fd\u6570\u5c06\u56fe\u50cf\u4fdd\u5b58\u4e3a\u6587\u4ef6\u3002\u53ef\u4ee5\u9009\u62e9\u4fdd\u5b58\u4e3a\u4e0d\u540c\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u5982PNG\u3001PDF\u3001SVG\u7b49\u3002<\/p>\n<\/p>\n
plt.hist(data, bins=30, edgecolor='black', alpha=0.7)<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Histogram of Data')<\/p>\n
\u4fdd\u5b58\u56fe\u50cf<\/strong><\/h2>\n
plt.savefig('histogram.png', dpi=300, bbox_inches='tight')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u7ed8\u5236\u5e26\u6709\u5bc6\u5ea6\u66f2\u7ebf\u7684\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\uff0c\u53ef\u4ee5\u5728\u76f4\u65b9\u56fe\u4e0a\u53e0\u52a0\u5bc6\u5ea6\u66f2\u7ebf\u3002\u4f7f\u7528seaborn\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
sns.histplot(data, bins=30, kde=True, color='blue')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Histogram with Density Curve')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u4ea4\u4e92\u5f0f\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u4ea4\u4e92\u5f0f\u56fe\u8868\u53ef\u4ee5\u63d0\u4f9b\u66f4\u597d\u7684\u7528\u6237\u4f53\u9a8c\u3002\u4f7f\u7528plotly\u5e93\u53ef\u4ee5\u521b\u5efa\u4ea4\u4e92\u5f0f\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
fig = px.histogram(data, nbins=30, title='Interactive Histogram of Data')<\/p>\n
fig.update_xaxes(title_text='Value')<\/p>\n
fig.update_yaxes(title_text='Frequency')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u7ed8\u5236\u5206\u7ec4\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u5f53\u9700\u8981\u6bd4\u8f83\u591a\u4e2a\u6570\u636e\u96c6\u7684\u5206\u5e03\u65f6\uff0c\u53ef\u4ee5\u7ed8\u5236\u5206\u7ec4\u76f4\u65b9\u56fe\u3002\u4f7f\u7528matplotlib\u5e93\u7684hist\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u591a\u4e2a\u6570\u636e\u96c6\u7684\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
data1 = np.random.randn(1000)<\/p>\n
data2 = np.random.randn(1000) + 1<\/p>\n
plt.hist([data1, data2], bins=30, edgecolor='black', alpha=0.7, label=['Data1', 'Data2'])<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Grouped Histogram of Data')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u7ed8\u5236\u5806\u53e0\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u5806\u53e0\u76f4\u65b9\u56fe\u53ef\u4ee5\u5c55\u793a\u591a\u4e2a\u6570\u636e\u96c6\u7684\u7d2f\u79ef\u9891\u7387\u3002\u4f7f\u7528matplotlib\u5e93\u7684hist\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u5806\u53e0\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
plt.hist([data1, data2], bins=30, edgecolor='black', alpha=0.7, stacked=True, label=['Data1', 'Data2'])<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Stacked Histogram of Data')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e00\u3001\u7ed8\u5236\u591a\u7ef4\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u5f53\u9700\u8981\u5c55\u793a\u591a\u7ef4\u6570\u636e\u7684\u5206\u5e03\u65f6\uff0c\u53ef\u4ee5\u7ed8\u5236\u591a\u7ef4\u76f4\u65b9\u56fe\u3002\u4f7f\u7528matplotlib\u5e93\u7684hist2d\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u4e8c\u7ef4\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
x = np.random.randn(1000)<\/p>\n
y = np.random.randn(1000)<\/p>\n
plt.hist2d(x, y, bins=30, cmap='Blues')<\/p>\n
plt.xlabel('X Value')<\/p>\n
plt.ylabel('Y Value')<\/p>\n
plt.title('2D Histogram of Data')<\/p>\n
plt.colorbar(label='Frequency')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e8c\u3001\u7ed8\u5236\u6781\u5750\u6807\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u6781\u5750\u6807\u76f4\u65b9\u56fe\u53ef\u4ee5\u5c55\u793a\u89d2\u5ea6\u6570\u636e\u7684\u5206\u5e03\u3002\u4f7f\u7528matplotlib\u5e93\u7684polar\u53c2\u6570\u53ef\u4ee5\u7ed8\u5236\u6781\u5750\u6807\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
theta = 2 * np.pi * np.random.rand(1000)<\/p>\n
plt.subplot(projection='polar')<\/p>\n
plt.hist(theta, bins=30, edgecolor='black', alpha=0.7)<\/p>\n
plt.title('Polar Histogram of Data')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e09\u3001\u5728\u76f4\u65b9\u56fe\u4e0a\u7ed8\u5236\u62df\u5408\u66f2\u7ebf<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5c55\u793a\u6570\u636e\u7684\u62df\u5408\u6548\u679c\uff0c\u53ef\u4ee5\u5728\u76f4\u65b9\u56fe\u4e0a\u53e0\u52a0\u62df\u5408\u66f2\u7ebf\u3002\u4f7f\u7528scipy\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u62df\u5408\u6570\u636e\u5e76\u7ed8\u5236\u62df\u5408\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
from scipy.stats import norm<\/p>\n
\u62df\u5408\u6570\u636e<\/strong><\/h2>\n
mu, std = norm.fit(data)<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist(data, bins=30, density=True, edgecolor='black', alpha=0.7)<\/p>\n
\u7ed8\u5236\u62df\u5408\u66f2\u7ebf<\/strong><\/h2>\n
xmin, xmax = plt.xlim()<\/p>\n
x = np.linspace(xmin, xmax, 100)<\/p>\n
p = norm.pdf(x, mu, std)<\/p>\n
plt.plot(x, p, 'k', linewidth=2)<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Density')<\/p>\n
plt.title('Histogram with Fitted Curve')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u56db\u3001\u7ed8\u5236\u5e26\u6709\u8bef\u5dee\u6761\u7684\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5c55\u793a\u6570\u636e\u7684\u4e0d\u786e\u5b9a\u6027\uff0c\u53ef\u4ee5\u5728\u76f4\u65b9\u56fe\u4e0a\u6dfb\u52a0\u8bef\u5dee\u6761\u3002\u4f7f\u7528matplotlib\u5e93\u7684errorbar\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u8bef\u5dee\u6761\u3002<\/p>\n<\/p>\n
counts, bins, patches = plt.hist(data, bins=30, edgecolor='black', alpha=0.7)<\/p>\n
bin_centers = 0.5 * (bins[:-1] + bins[1:])<\/p>\n
errors = np.sqrt(counts)<\/p>\n
\u6dfb\u52a0\u8bef\u5dee\u6761<\/strong><\/h2>\n
plt.errorbar(bin_centers, counts, yerr=errors, fmt='o', color='black', capsize=5)<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Histogram with Error Bars')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5728Python\u4e2d\u4f7f\u7528matplotlib\u5e93\u548c\u5176\u4ed6\u8f85\u52a9\u5e93\uff08\u5982seaborn\u3001plotly\u3001scipy\uff09\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u76f4\u65b9\u56fe\uff0c\u5e76\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u7ed8\u56fe\u65b9\u6cd5\u548c\u53c2\u6570\uff0c\u5b9a\u5236\u51fa\u7b26\u5408\u9700\u6c42\u7684\u76f4\u65b9\u56fe\u3002\u5e0c\u671b\u8fd9\u4e9b\u793a\u4f8b\u548c\u89e3\u91ca\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528Python\u7ed8\u5236\u76f4\u65b9\u56fe\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u76f4\u65b9\u56fe\u5e76\u81ea\u5b9a\u4e49\u5750\u6807\u7684\u5206\u5ea6\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u4f7f\u7528Matplotlib\u5e93\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u76f4\u65b9\u56fe\u3002\u4e3a\u4e86\u81ea\u5b9a\u4e49\u5750\u6807\u7684\u5206\u5ea6\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528plt.xticks()<\/code>\u548cplt.yticks()<\/code>\u51fd\u6570\u6765\u8bbe\u7f6eX\u8f74\u548cY\u8f74\u7684\u523b\u5ea6\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u5728\u751f\u6210\u76f4\u65b9\u56fe\u540e\uff0c\u8c03\u7528\u8fd9\u4e24\u4e2a\u51fd\u6570\u5e76\u4f20\u5165\u6240\u9700\u7684\u523b\u5ea6\u503c\u5217\u8868\u3002<\/p>\n
\u4f7f\u7528Python\u7ed8\u5236\u76f4\u65b9\u56fe\u65f6\uff0c\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u5206\u7bb1\uff08bins\uff09\u6570\u91cf\uff1f<\/strong>
\u9009\u62e9\u5408\u9002\u7684\u5206\u7bb1\u6570\u91cf\u5bf9\u4e8e\u76f4\u65b9\u56fe\u7684\u53ef\u8bfb\u6027\u548c\u6570\u636e\u5206\u6790\u81f3\u5173\u91cd\u8981\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u9009\u62e9\u5206\u7bb1\u6570\u91cf\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Sturges\u516c\u5f0f\u3001\u5e73\u65b9\u6839\u6cd5\u6216Freedman-Diaconis\u89c4\u5219\u3002\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u60a8\u8ba1\u7b97\u51fa\u4e00\u4e2a\u5408\u7406\u7684\u5206\u7bb1\u6570\u91cf\uff0c\u4ece\u800c\u4f7f\u76f4\u65b9\u56fe\u66f4\u5177\u4ee3\u8868\u6027\u3002<\/p>\n
\u5982\u4f55\u5728\u76f4\u65b9\u56fe\u4e2d\u6dfb\u52a0\u56fe\u4f8b\u6216\u6807\u7b7e\u4ee5\u589e\u5f3a\u53ef\u8bfb\u6027\uff1f<\/strong>
\u5728\u7ed8\u5236\u76f4\u65b9\u56fe\u65f6\uff0c\u6dfb\u52a0\u56fe\u4f8b\u6216\u6807\u7b7e\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u53ef\u8bfb\u6027\u3002\u53ef\u4ee5\u4f7f\u7528plt.legend()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u56fe\u4f8b\uff0c\u4e3a\u4e0d\u540c\u7684\u6570\u636e\u96c6\u6216\u7c7b\u522b\u63d0\u4f9b\u8bf4\u660e\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u4e3aX\u8f74\u548cY\u8f74\u6dfb\u52a0\u6807\u7b7e\uff0c\u8fd9\u6837\u89c2\u4f17\u5c31\u80fd\u66f4\u6e05\u695a\u5730\u7406\u89e3\u56fe\u8868\u6240\u4f20\u8fbe\u7684\u4fe1\u606f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u65b9\u56fe\u65f6\uff0c\u8bbe\u7f6e\u5750\u6807\u7684\u5206\u5ea6\u503c\u4e3b\u8981\u901a\u8fc7xticks\u548cyticks\u51fd\u6570 […]","protected":false},"author":3,"featured_media":1097979,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1097975"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1097975"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1097975\/revisions"}],"predecessor-version":[{"id":1097980,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1097975\/revisions\/1097980"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1097979"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1097975"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1097975"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1097975"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}