{"id":1166166,"date":"2025-01-15T15:29:02","date_gmt":"2025-01-15T07:29:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1166166.html"},"modified":"2025-01-15T15:29:04","modified_gmt":"2025-01-15T07:29:04","slug":"python%e4%b8%adfig%e5%9b%be%e7%89%87%e5%a6%82%e4%bd%95%e4%bf%9d%e5%ad%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1166166.html","title":{"rendered":"python\u4e2dfig\u56fe\u7247\u5982\u4f55\u4fdd\u5b58"},"content":{"rendered":"

\"python\u4e2dfig\u56fe\u7247\u5982\u4f55\u4fdd\u5b58\"<\/p>\n

\u5728Python\u4e2d\uff0c\u6709\u591a\u79cd\u65b9\u5f0f\u53ef\u4ee5\u4fdd\u5b58matplotlib\u751f\u6210\u7684fig\u56fe\u7247\u3002\u4f7f\u7528savefig<\/code>\u65b9\u6cd5\u3001\u6307\u5b9a\u6587\u4ef6\u683c\u5f0f\u3001\u8c03\u6574\u5206\u8fa8\u7387\u3001\u4fdd\u5b58\u77e2\u91cf\u56fe<\/strong>\u7b49\u65b9\u5f0f\u53ef\u4ee5\u8ba9\u4f60\u7075\u6d3b\u5730\u4fdd\u5b58\u56fe\u7247\u3002\u5176\u4e2d\uff0c\u4f7f\u7528savefig<\/code>\u65b9\u6cd5\u662f\u6700\u5e38\u89c1\u548c\u4fbf\u6377\u7684\u65b9\u5f0f\u3002\u4e0b\u9762\u8be6\u7ec6\u4ecb\u7ecd\u4e00\u4e0b\u5982\u4f55\u4f7f\u7528savefig<\/code>\u65b9\u6cd5\u6765\u4fdd\u5b58fig\u56fe\u7247\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528savefig<\/code>\u65b9\u6cd5<\/h3>\n<\/p>\n

savefig<\/code>\u662fmatplotlib\u4e2d\u7684\u4e00\u4e2a\u65b9\u6cd5\uff0c\u7528\u4e8e\u5c06\u751f\u6210\u7684fig\u5bf9\u8c61\u4fdd\u5b58\u4e3a\u56fe\u7247\u6587\u4ef6\u3002\u4f60\u53ef\u4ee5\u6307\u5b9a\u4fdd\u5b58\u7684\u6587\u4ef6\u540d\u53ca\u683c\u5f0f\uff0c\u5982PNG\u3001JPG\u3001SVG\u7b49\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u8868<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
\u4f7f\u7528savefig\u65b9\u6cd5\u4fdd\u5b58\u56fe\u7247<\/strong><\/h2>\n
fig.savefig('my_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6307\u5b9a\u6587\u4ef6\u683c\u5f0f<\/h3>\n<\/p>\n
\u5f53\u4f60\u4fdd\u5b58\u56fe\u7247\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u6587\u4ef6\u540d\u7684\u6269\u5c55\u540d\u6765\u6307\u5b9a\u56fe\u7247\u683c\u5f0f\u3002\u5e38\u89c1\u7684\u683c\u5f0f\u6709PNG\u3001JPG\u3001SVG\u548cPDF\u7b49\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u4e3aJPG\u683c\u5f0f<\/p>\n
fig.savefig('my_figure.jpg')<\/p>\n
\u4fdd\u5b58\u4e3aSVG\u683c\u5f0f<\/strong><\/h2>\n
fig.savefig('my_figure.svg')<\/p>\n
\u4fdd\u5b58\u4e3aPDF\u683c\u5f0f<\/strong><\/h2>\n
fig.savefig('my_figure.pdf')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8c03\u6574\u5206\u8fa8\u7387<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u4fdd\u5b58\u9ad8\u5206\u8fa8\u7387\u7684\u56fe\u7247\u3002\u53ef\u4ee5\u901a\u8fc7dpi<\/code>\u53c2\u6570\u6765\u6307\u5b9a\u56fe\u7247\u7684\u5206\u8fa8\u7387\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u9ad8\u5206\u8fa8\u7387\u56fe\u7247<\/p>\n
fig.savefig('high_res_figure.png', dpi=300)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4fdd\u5b58\u77e2\u91cf\u56fe<\/h3>\n<\/p>\n
\u77e2\u91cf\u56fe\u683c\u5f0f\u5982SVG\u548cPDF\uff0c\u53ef\u4ee5\u5728\u7f29\u653e\u65f6\u4fdd\u6301\u56fe\u50cf\u7684\u6e05\u6670\u5ea6\uff0c\u8fd9\u5bf9\u4e8e\u6253\u5370\u6216\u9700\u8981\u9ad8\u8d28\u91cf\u56fe\u50cf\u7684\u5e94\u7528\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u4e3a\u77e2\u91cf\u56fe\u683c\u5f0f<\/p>\n
fig.savefig('vector_figure.svg')<\/p>\n
fig.savefig('vector_figure.pdf')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4fdd\u5b58\u900f\u660e\u80cc\u666f\u7684\u56fe\u7247<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u9700\u8981\u4fdd\u5b58\u900f\u660e\u80cc\u666f\u7684\u56fe\u7247\uff0c\u53ef\u4ee5\u4f7f\u7528transparent<\/code>\u53c2\u6570\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u900f\u660e\u80cc\u666f\u7684\u56fe\u7247<\/p>\n
fig.savefig('transparent_figure.png', transparent=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4fdd\u5b58\u90e8\u5206\u56fe\u7247<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u4f60\u53ef\u80fd\u53ea\u60f3\u4fdd\u5b58\u56fe\u7247\u7684\u4e00\u90e8\u5206\u3002\u53ef\u4ee5\u4f7f\u7528bbox_inches<\/code>\u53c2\u6570\u6765\u88c1\u526a\u56fe\u7247\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u88c1\u526a\u540e\u7684\u56fe\u7247<\/p>\n
fig.savefig('cropped_figure.png', bbox_inches='tight')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u4fdd\u5b58\u4e0d\u540c\u5c3a\u5bf8\u7684\u56fe\u7247<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u56fe\u8868\u5927\u5c0f\u6765\u4fdd\u5b58\u4e0d\u540c\u5c3a\u5bf8\u7684\u56fe\u7247\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6e\u56fe\u8868\u5927\u5c0f<\/p>\n
fig.set_size_inches(8, 6)  # \u5bbd8\u82f1\u5bf8\uff0c\u9ad86\u82f1\u5bf8<\/p>\n
fig.savefig('resized_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u4fdd\u5b58\u5e26\u6709\u6ce8\u91ca\u548c\u56fe\u4f8b\u7684\u56fe\u7247<\/h3>\n<\/p>\n
\u5728\u4fdd\u5b58\u56fe\u7247\u4e4b\u524d\uff0c\u4f60\u53ef\u4ee5\u6dfb\u52a0\u6ce8\u91ca\u548c\u56fe\u4f8b\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u89e3\u91ca\u56fe\u8868\u5185\u5bb9\u3002<\/p>\n<\/p>\n
# \u6dfb\u52a0\u6ce8\u91ca\u548c\u56fe\u4f8b<\/p>\n
ax.annotate('Important Point', xy=(2, 4), xytext=(3, 4.5),<\/p>\n
            arrowprops=dict(facecolor='black', shrink=0.05))<\/p>\n
ax.legend(['Sample Data'])<\/p>\n
\u4fdd\u5b58\u56fe\u7247<\/strong><\/h2>\n
fig.savefig('annotated_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u4f7f\u7528\u4e0d\u540c\u540e\u7aef\u4fdd\u5b58\u56fe\u7247<\/h3>\n<\/p>\n
matplotlib\u652f\u6301\u591a\u79cd\u540e\u7aef\uff0c\u4f60\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u540e\u7aef\u6765\u4fdd\u5b58\u56fe\u7247\u3002<\/p>\n<\/p>\n
import matplotlib<\/p>\n
matplotlib.use('Agg')  # \u4f7f\u7528Agg\u540e\u7aef<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u56fe\u8868\u5e76\u4fdd\u5b58<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
fig.savefig('agg_backend_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u4fdd\u5b58\u52a8\u753b<\/h3>\n<\/p>\n
\u5982\u679c\u4f60\u751f\u6210\u4e86\u52a8\u753b\uff0c\u53ef\u4ee5\u4f7f\u7528FuncAnimation<\/code>\u548csave<\/code>\u65b9\u6cd5\u6765\u4fdd\u5b58\u52a8\u753b\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import matplotlib.animation as animation<\/p>\n
fig, ax = plt.subplots()<\/p>\n
x = [1, 2, 3, 4]<\/p>\n
y = [1, 4, 2, 3]<\/p>\n
line, = ax.plot(x, y)<\/p>\n
def update(num, x, y, line):<\/p>\n
    line.set_data(x[:num], y[:num])<\/p>\n
    return line,<\/p>\n
ani = animation.FuncAnimation(fig, update, frames=len(x), fargs=[x, y, line])<\/p>\n
\u4fdd\u5b58\u52a8\u753b<\/strong><\/h2>\n
ani.save('animation.mp4', writer='ffmpeg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e00\u3001\u4fdd\u5b58\u56fe\u8868\u5230\u5185\u5b58<\/h3>\n<\/p>\n
\u6709\u65f6\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u5c06\u56fe\u8868\u4fdd\u5b58\u5230\u5185\u5b58\u800c\u4e0d\u662f\u6587\u4ef6\u4e2d\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528BytesIO<\/code>\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
import io<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
\u4fdd\u5b58\u5230\u5185\u5b58<\/strong><\/h2>\n
buf = io.BytesIO()<\/p>\n
fig.savefig(buf, format='png')<\/p>\n
buf.seek(0)<\/p>\n
\u4ece\u5185\u5b58\u4e2d\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
import PIL.Image<\/p>\n
image = PIL.Image.open(buf)<\/p>\n
image.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e8c\u3001\u4fdd\u5b58\u56fe\u8868\u5230PDF\u62a5\u544a<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528matplotlib.backends.backend_pdf.PdfPages<\/code>\u6765\u5c06\u591a\u4e2a\u56fe\u8868\u4fdd\u5b58\u5230\u4e00\u4e2aPDF\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
from matplotlib.backends.backend_pdf import PdfPages<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig1, ax1 = plt.subplots()<\/p>\n
ax1.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
fig2, ax2 = plt.subplots()<\/p>\n
ax2.plot([1, 2, 3, 4], [4, 3, 2, 1])<\/p>\n
\u4fdd\u5b58\u5230PDF\u6587\u4ef6<\/strong><\/h2>\n
with PdfPages('multipage_pdf.pdf') as pdf:<\/p>\n
    pdf.savefig(fig1)<\/p>\n
    pdf.savefig(fig2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e09\u3001\u4fdd\u5b58\u56fe\u8868\u5230SVG\u6587\u4ef6<\/h3>\n<\/p>\n
SVG\u662f\u4e00\u79cd\u77e2\u91cf\u56fe\u683c\u5f0f\uff0c\u9002\u5408\u9700\u8981\u9ad8\u8d28\u91cf\u56fe\u50cf\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u4e3aSVG\u6587\u4ef6<\/p>\n
fig.savefig('figure.svg')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u56db\u3001\u4fdd\u5b58\u56fe\u8868\u5230EPS\u6587\u4ef6<\/h3>\n<\/p>\n
EPS\u662f\u4e00\u79cd\u7528\u4e8e\u6253\u5370\u7684\u77e2\u91cf\u56fe\u683c\u5f0f\u3002<\/p>\n<\/p>\n
# \u4fdd\u5b58\u4e3aEPS\u6587\u4ef6<\/p>\n
fig.savefig('figure.eps')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e94\u3001\u4fdd\u5b58\u56fe\u8868\u5230JSON\u6587\u4ef6<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u5c06\u56fe\u8868\u6570\u636e\u4fdd\u5b58\u5230JSON\u6587\u4ef6\uff0c\u4ee5\u4fbf\u65e5\u540e\u52a0\u8f7d\u548c\u518d\u73b0\u56fe\u8868\u3002<\/p>\n<\/p>\n
import json<\/p>\n
\u521b\u5efa\u56fe\u8868\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'x': [1, 2, 3, 4],<\/p>\n
    'y': [1, 4, 2, 3]<\/p>\n
}<\/p>\n
\u4fdd\u5b58\u5230JSON\u6587\u4ef6<\/strong><\/h2>\n
with open('figure.json', 'w') as f:<\/p>\n
    json.dump(data, f)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u516d\u3001\u4eceJSON\u6587\u4ef6\u52a0\u8f7d\u56fe\u8868<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4eceJSON\u6587\u4ef6\u52a0\u8f7d\u56fe\u8868\u6570\u636e\uff0c\u5e76\u91cd\u65b0\u751f\u6210\u56fe\u8868\u3002<\/p>\n<\/p>\n
import json<\/p>\n
\u4eceJSON\u6587\u4ef6\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
with open('figure.json', 'r') as f:<\/p>\n
    data = json.load(f)<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot(data['x'], data['y'])<\/p>\n
\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('loaded_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e03\u3001\u4fdd\u5b58\u56fe\u8868\u5230HTML\u6587\u4ef6<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u5c06\u56fe\u8868\u5d4c\u5165\u5230HTML\u6587\u4ef6\u4e2d\uff0c\u4ee5\u4fbf\u5728\u7f51\u9875\u4e0a\u663e\u793a\u3002<\/p>\n<\/p>\n
import mpld3<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
\u4fdd\u5b58\u5230HTML\u6587\u4ef6<\/strong><\/h2>\n
html_str = mpld3.fig_to_html(fig)<\/p>\n
with open('figure.html', 'w') as f:<\/p>\n
    f.write(html_str)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u516b\u3001\u4fdd\u5b58\u56fe\u8868\u5230LaTeX\u6587\u4ef6<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u5c06\u56fe\u8868\u4fdd\u5b58\u4e3aPGF\u6587\u4ef6\uff0c\u7136\u540e\u5728LaTeX\u6587\u6863\u4e2d\u5f15\u7528\u3002<\/p>\n<\/p>\n
# \u8bbe\u7f6e\u540e\u7aef\u4e3aPGF<\/p>\n
matplotlib.use('pgf')<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
\u4fdd\u5b58\u4e3aPGF\u6587\u4ef6<\/strong><\/h2>\n
fig.savefig('figure.pgf')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e5d\u3001\u4f7f\u7528Seaborn\u4fdd\u5b58\u56fe\u8868<\/h3>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8ematplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u7c7b\u4f3c\u7684\u65b9\u6cd5\u4fdd\u5b58Seaborn\u751f\u6210\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
tips = sns.load_dataset('tips')<\/p>\n
fig, ax = plt.subplots()<\/p>\n
sns.scatterplot(x='total_bill', y='tip', data=tips, ax=ax)<\/p>\n
\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('seaborn_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u3001\u4f7f\u7528Plotly\u4fdd\u5b58\u56fe\u8868<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528plotly.io<\/code>\u6a21\u5757\u4fdd\u5b58\u56fe\u8868\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
import plotly.io as pio<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
df = px.data.iris()<\/p>\n
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')<\/p>\n
\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n
pio.write_image(fig, 'plotly_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u4e00\u3001\u4fdd\u5b583D\u56fe\u8868<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528mpl_toolkits.mplot3d<\/code>\u6a21\u5757\u521b\u5efa\u548c\u4fdd\u5b583D\u56fe\u8868\u3002<\/p>\n<\/p>\n
from mpl_toolkits.mplot3d import Axes3D<\/p>\n
\u521b\u5efa3D\u56fe\u8868<\/strong><\/h2>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='3d')<\/p>\n
ax.scatter([1, 2, 3, 4], [1, 4, 2, 3], [10, 20, 15, 10])<\/p>\n
\u4fdd\u5b583D\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('3d_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u4e8c\u3001\u4fdd\u5b58\u6781\u5750\u6807\u56fe\u8868<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528projection='polar'<\/code>\u521b\u5efa\u548c\u4fdd\u5b58\u6781\u5750\u6807\u56fe\u8868\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6781\u5750\u6807\u56fe\u8868<\/p>\n
fig = plt.figure()<\/p>\n
ax = fig.add_subplot(111, projection='polar')<\/p>\n
ax.plot([0, 1, 2, 3], [1, 4, 2, 3])<\/p>\n
\u4fdd\u5b58\u6781\u5750\u6807\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('polar_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u4e09\u3001\u4fdd\u5b58\u70ed\u56fe<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u4f7f\u7528imshow<\/code>\u521b\u5efa\u548c\u4fdd\u5b58\u70ed\u56fe\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u70ed\u56fe<\/strong><\/h2>\n
data = np.random.random((10, 10))<\/p>\n
fig, ax = plt.subplots()<\/p>\n
cax = ax.imshow(data, cmap='hot')<\/p>\n
\u4fdd\u5b58\u70ed\u56fe<\/strong><\/h2>\n
fig.savefig('heatmap.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u56db\u3001\u4fdd\u5b58\u5e26\u6709\u989c\u8272\u6761\u7684\u56fe\u8868<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u6dfb\u52a0\u989c\u8272\u6761\u5e76\u4fdd\u5b58\u56fe\u8868\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u70ed\u56fe<\/p>\n
data = np.random.random((10, 10))<\/p>\n
fig, ax = plt.subplots()<\/p>\n
cax = ax.imshow(data, cmap='hot')<\/p>\n
\u6dfb\u52a0\u989c\u8272\u6761<\/strong><\/h2>\n
fig.colorbar(cax)<\/p>\n
\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('heatmap_with_colorbar.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u4e94\u3001\u4fdd\u5b58\u5b50\u56fe<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u521b\u5efa\u591a\u4e2a\u5b50\u56fe\u5e76\u4fdd\u5b58\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u5b50\u56fe<\/p>\n
fig, axs = plt.subplots(2, 2)<\/p>\n
axs[0, 0].plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
axs[0, 1].plot([1, 2, 3, 4], [4, 3, 2, 1])<\/p>\n
axs[1, 0].plot([1, 2, 3, 4], [2, 3, 2, 3])<\/p>\n
axs[1, 1].plot([1, 2, 3, 4], [3, 2, 3, 2])<\/p>\n
\u4fdd\u5b58\u5b50\u56fe<\/strong><\/h2>\n
fig.savefig('subplots.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u516d\u3001\u4fdd\u5b58\u5e26\u6709\u6807\u9898\u548c\u6807\u7b7e\u7684\u56fe\u8868<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u5e76\u4fdd\u5b58\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u56fe\u8868<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
ax.set_title('Sample Plot')<\/p>\n
ax.set_xlabel('X Axis')<\/p>\n
ax.set_ylabel('Y Axis')<\/p>\n
\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('labeled_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u4e03\u3001\u4fdd\u5b58\u5e26\u6709\u7f51\u683c\u7684\u56fe\u8868<\/h3>\n<\/p>\n
\u4f60\u53ef\u4ee5\u6dfb\u52a0\u7f51\u683c\u5e76\u4fdd\u5b58\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u56fe\u8868<\/p>\n
fig, ax = plt.subplots()<\/p>\n
ax.plot([1, 2, 3, 4], [1, 4, 2, 3])<\/p>\n
\u6dfb\u52a0\u7f51\u683c<\/strong><\/h2>\n
ax.grid(True)<\/p>\n
\u4fdd\u5b58\u56fe\u8868<\/strong><\/h2>\n
fig.savefig('grid_figure.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u591a\u79cd\u65b9\u5f0f\uff0c\u4f60\u53ef\u4ee5\u7075\u6d3b\u5730\u4fdd\u5b58matplotlib\u751f\u6210\u7684fig\u56fe\u7247\uff0c\u6ee1\u8db3\u4e0d\u540c\u7684\u9700\u6c42\u548c\u5e94\u7528\u573a\u666f\u3002\u65e0\u8bba\u662f\u4fdd\u5b58\u9759\u6001\u56fe\u8868\u3001\u52a8\u6001\u52a8\u753b\uff0c\u8fd8\u662f\u9ad8\u5206\u8fa8\u7387\u56fe\u7247\u3001\u77e2\u91cf\u56fe\uff0c\u90fd\u53ef\u4ee5\u901a\u8fc7\u9002\u5f53\u7684\u53c2\u6570\u8bbe\u7f6e\u548c\u65b9\u6cd5\u8c03\u7528\u6765\u5b9e\u73b0\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u4fdd\u5b58\u548c\u5206\u4eab\u4f60\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4fdd\u5b58\u56fe\u50cf\u6587\u4ef6\uff1f<\/strong>
\u5728Python\u4e2d\u4fdd\u5b58\u56fe\u50cf\u6587\u4ef6\u901a\u5e38\u4f7f\u7528Matplotlib\u5e93\uff0c\u901a\u8fc7savefig()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u3002\u60a8\u53ea\u9700\u63d0\u4f9b\u6587\u4ef6\u540d\u548c\u6240\u9700\u683c\u5f0f\uff08\u5982PNG\u3001JPEG\u7b49\uff09\uff0c\u4f8b\u5982\uff1aplt.savefig('my_plot.png')<\/code>\uff0c\u8fd9\u5c06\u628a\u5f53\u524d\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u683c\u5f0f\u7684\u6587\u4ef6\u3002\u786e\u4fdd\u5728\u8c03\u7528\u6b64\u51fd\u6570\u4e4b\u524d\u5df2\u7ecf\u521b\u5efa\u4e86\u56fe\u5f62\u3002<\/p>\n
\u53ef\u4ee5\u4fdd\u5b58\u54ea\u4e9b\u683c\u5f0f\u7684\u56fe\u50cf\u6587\u4ef6\uff1f<\/strong>
Matplotlib\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u56fe\u50cf\u6587\u4ef6\uff0c\u5305\u62ecPNG\u3001JPEG\u3001PDF\u3001SVG\u7b49\u3002\u7528\u6237\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u683c\u5f0f\uff0c\u4f8b\u5982\u9700\u8981\u9ad8\u8d28\u91cf\u8f93\u51fa\u65f6\u53ef\u4ee5\u9009\u62e9PDF\u683c\u5f0f\uff0c\u800c\u9700\u8981\u5728\u7f51\u9875\u4e0a\u4f7f\u7528\u65f6\u53ef\u4ee5\u9009\u62e9PNG\u6216SVG\u683c\u5f0f\u3002<\/p>\n
\u5982\u4f55\u8bbe\u7f6e\u4fdd\u5b58\u56fe\u50cf\u7684\u5206\u8fa8\u7387\uff1f<\/strong>
\u5728\u4f7f\u7528savefig()<\/code>\u51fd\u6570\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7dpi<\/code>\u53c2\u6570\u8bbe\u7f6e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u3002\u4f8b\u5982\uff0cplt.savefig('my_plot.png', dpi=300)<\/code>\u4f1a\u5c06\u56fe\u50cf\u4fdd\u5b58\u4e3a300 DPI\u7684\u9ad8\u5206\u8fa8\u7387\u6587\u4ef6\u3002\u8c03\u6574DPI\u503c\u53ef\u4ee5\u6539\u5584\u56fe\u50cf\u7684\u6e05\u6670\u5ea6\uff0c\u7279\u522b\u662f\u5728\u6253\u5370\u65f6\u975e\u5e38\u91cd\u8981\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u6709\u591a\u79cd\u65b9\u5f0f\u53ef\u4ee5\u4fdd\u5b58matplotlib\u751f\u6210\u7684fig\u56fe\u7247\u3002\u4f7f\u7528savefig\u65b9\u6cd5\u3001\u6307\u5b9a\u6587\u4ef6\u683c\u5f0f […]","protected":false},"author":3,"featured_media":1166173,"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\/1166166"}],"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=1166166"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1166166\/revisions"}],"predecessor-version":[{"id":1166175,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1166166\/revisions\/1166175"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1166173"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1166166"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1166166"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1166166"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}