{"id":1122250,"date":"2025-01-08T19:18:31","date_gmt":"2025-01-08T11:18:31","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1122250.html"},"modified":"2025-01-08T19:18:35","modified_gmt":"2025-01-08T11:18:35","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e6%95%b0%e7%bb%84%e7%94%bb%e5%87%ba%e6%9d%a5","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1122250.html","title":{"rendered":"python\u5982\u4f55\u5c06\u6570\u7ec4\u753b\u51fa\u6765"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u6570\u7ec4\u753b\u51fa\u6765\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5c06\u6570\u7ec4\u753b\u51fa\u6765\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528Matplotlib\u7ed8\u5236\u6298\u7ebf\u56fe\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u70ed\u529b\u56fe\u3001\u5229\u7528Pandas\u7ed8\u5236\u6570\u636e\u6846\u56fe\u3002<\/strong> \u5176\u4e2d\uff0cMatplotlib<\/strong>\u662f\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u548c\u4e30\u5bcc\u7684\u529f\u80fd\u3002\u4f7f\u7528Matplotlib\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u7ed8\u5236\u51fa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u4ee5\u53ca\u5176\u4ed6\u4e00\u4e9b\u5e38\u89c1\u7684\u65b9\u6cd5\u6765\u5c06\u6570\u7ec4\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u6298\u7ebf\u56fe<\/h3>\n<\/p>\n

Matplotlib<\/strong>\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u6570\u636e\u53ef\u89c6\u5316\u9700\u6c42\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u4f60\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5bfc\u5165\u5e93\u5e76\u7ed8\u5236\u57fa\u672c\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528Matplotlib\u7ed8\u5236\u4e00\u4e2a\u57fa\u672c\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
plt.title('\u57fa\u672c\u6298\u7ebf\u56fe')<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u5e93\u751f\u6210\u4e86\u4e00\u4e2a\u5305\u542b100\u4e2a\u70b9\u7684\u6570\u7ec4\uff0c\u7136\u540e\u4f7f\u7528Matplotlib\u7684plot<\/code>\u51fd\u6570\u7ed8\u5236\u51fa\u8fd9\u4e9b\u70b9\u7684\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
3. \u6dfb\u52a0\u66f4\u591a\u7ec6\u8282<\/h4>\n<\/p>\n
\u4e3a\u4e86\u8ba9\u56fe\u8868\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u8bfb\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u66f4\u591a\u7684\u7ec6\u8282\uff0c\u4f8b\u5982\u7f51\u683c\u7ebf\u3001\u56fe\u4f8b\u3001\u4e0d\u540c\u989c\u8272\u548c\u6837\u5f0f\u7684\u7ebf\u6761\u7b49\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
x = np.linspace(0, 10, 100)<\/p>\n
y1 = np.sin(x)<\/p>\n
y2 = np.cos(x)<\/p>\n
plt.plot(x, y1, label='sin(x)', color='blue', linestyle='-')<\/p>\n
plt.plot(x, y2, label='cos(x)', color='red', linestyle='--')<\/p>\n
plt.title('Sin\u548cCos\u51fd\u6570\u7684\u6298\u7ebf\u56fe')<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.legend()<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u7ed8\u5236\u4e86\u4e24\u4e2a\u51fd\u6570\u7684\u6298\u7ebf\u56fe\uff0c\u5206\u522b\u4f7f\u7528\u4e0d\u540c\u7684\u989c\u8272\u548c\u7ebf\u6761\u6837\u5f0f\uff0c\u5e76\u4e14\u6dfb\u52a0\u4e86\u56fe\u4f8b\u548c\u7f51\u683c\u7ebf\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u70ed\u529b\u56fe<\/h3>\n<\/p>\n
Seaborn<\/strong>\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u80fd\u591f\u7b80\u5316\u8bb8\u591a\u590d\u6742\u7684\u7ed8\u56fe\u4efb\u52a1\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u70ed\u529b\u56fe\u7684\u8be6\u7ec6\u6b65\u9aa4\u548c\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Seaborn<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5bfc\u5165\u5e93\u5e76\u7ed8\u5236\u70ed\u529b\u56fe<\/h4>\n<\/p>\n
\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528Seaborn\u7ed8\u5236\u4e00\u4e2a\u57fa\u672c\u7684\u70ed\u529b\u56fe\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u968f\u673a\u6570\u7ec4<\/strong><\/h2>\n
data = np.random.rand(10, 12)<\/p>\n
\u7ed8\u5236\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(data, annot=True, fmt=".1f", cmap="YlGnBu")<\/p>\n
plt.title('\u57fa\u672c\u70ed\u529b\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u4e00\u4e2a10×12\u7684\u968f\u673a\u6570\u7ec4\uff0c\u5e76\u4e14\u4f7f\u7528Seaborn\u7684heatmap<\/code>\u51fd\u6570\u5c06\u5176\u7ed8\u5236\u4e3a\u70ed\u529b\u56fe\u3002<\/p>\n<\/p>\n
3. \u6dfb\u52a0\u66f4\u591a\u7ec6\u8282<\/h4>\n<\/p>\n
\u4e3a\u4e86\u8ba9\u70ed\u529b\u56fe\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u8bfb\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u66f4\u591a\u7684\u7ec6\u8282\uff0c\u4f8b\u5982\u8c03\u6574\u8272\u5f69\u6620\u5c04\u3001\u6dfb\u52a0\u6ce8\u91ca\u7b49\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
data = np.random.rand(10, 12)<\/p>\n
plt.figure(figsize=(10, 8))<\/p>\n
sns.heatmap(data, annot=True, fmt=".2f", cmap="coolwarm", linewidths=.5)<\/p>\n
plt.title('\u589e\u5f3a\u7248\u70ed\u529b\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u8c03\u6574\u4e86\u56fe\u8868\u7684\u5c3a\u5bf8\uff0c\u4f7f\u7528\u4e86\u4e0d\u540c\u7684\u8272\u5f69\u6620\u5c04\uff0c\u5e76\u4e14\u6dfb\u52a0\u4e86\u7ebf\u6761\u5206\u9694\u7b26\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5229\u7528Pandas\u7ed8\u5236\u6570\u636e\u6846\u56fe<\/h3>\n<\/p>\n
Pandas<\/strong>\u4e0d\u4ec5\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u8fd8\u5185\u7f6e\u4e86\u4e00\u4e9b\u7b80\u5355\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u5feb\u901f\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Pandas<\/h4>\n<\/p>\n
\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5bfc\u5165\u5e93\u5e76\u7ed8\u5236\u57fa\u672c\u6570\u636e\u6846\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528Pandas\u7ed8\u5236\u4e00\u4e2a\u57fa\u672c\u7684\u6570\u636e\u6846\u56fe\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846<\/strong><\/h2>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [5, 4, 3, 2, 1],<\/p>\n
    'C': [2, 3, 4, 5, 6]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6570\u636e\u6846\u56fe<\/strong><\/h2>\n
df.plot(kind='line')<\/p>\n
plt.title('\u57fa\u672c\u6570\u636e\u6846\u56fe')<\/p>\n
plt.xlabel('\u7d22\u5f15')<\/p>\n
plt.ylabel('\u503c')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u6846\uff0c\u5e76\u4f7f\u7528Pandas\u7684plot<\/code>\u51fd\u6570\u5c06\u5176\u7ed8\u5236\u4e3a\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
3. \u6dfb\u52a0\u66f4\u591a\u7ec6\u8282<\/h4>\n<\/p>\n
\u4e3a\u4e86\u8ba9\u6570\u636e\u6846\u56fe\u66f4\u52a0\u7f8e\u89c2\u548c\u6613\u8bfb\uff0c\u6211\u4eec\u53ef\u4ee5\u6dfb\u52a0\u66f4\u591a\u7684\u7ec6\u8282\uff0c\u4f8b\u5982\u8c03\u6574\u56fe\u8868\u7c7b\u578b\u3001\u989c\u8272\u3001\u6807\u9898\u7b49\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [5, 4, 3, 2, 1],<\/p>\n
    'C': [2, 3, 4, 5, 6]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6570\u636e\u6846\u56fe<\/strong><\/h2>\n
df.plot(kind='bar', color=['blue', 'green', 'red'])<\/p>\n
plt.title('\u589e\u5f3a\u7248\u6570\u636e\u6846\u56fe')<\/p>\n
plt.xlabel('\u7d22\u5f15')<\/p>\n
plt.ylabel('\u503c')<\/p>\n
plt.legend(title='\u7c7b\u522b')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u56fe\u8868\u7c7b\u578b\u8c03\u6574\u4e3a\u67f1\u72b6\u56fe\uff0c\u5e76\u4f7f\u7528\u4e86\u4e0d\u540c\u7684\u989c\u8272\u548c\u56fe\u4f8b\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5176\u4ed6\u5e38\u89c1\u65b9\u6cd5<\/h3>\n<\/p>\n
\u9664\u4e86\u4ee5\u4e0a\u63d0\u5230\u7684\u51e0\u79cd\u65b9\u6cd5\u5916\uff0cPython\u4e2d\u8fd8\u6709\u8bb8\u591a\u5176\u4ed6\u5e93\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u4f8b\u5982Plotly\u3001Bokeh\u7b49\u3002\u6bcf\u79cd\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\uff0c\u4e0b\u9762\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0b\u8fd9\u4e9b\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Plotly\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
Plotly<\/strong>\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u9002\u7528\u4e8e\u521b\u5efa\u9ad8\u5ea6\u4e92\u52a8\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
\u7ed8\u5236\u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe<\/strong><\/h2>\n
fig = px.line(x=x, y=y, title='\u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Bokeh\u7ed8\u5236\u9ad8\u6027\u80fd\u56fe\u8868<\/h4>\n<\/p>\n
Bokeh<\/strong>\u662f\u4e00\u79cd\u9ad8\u6027\u80fd\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
from bokeh.plotting import figure, show<\/p>\n
from bokeh.io import output_notebook<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4<\/strong><\/h2>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
\u8f93\u51fa\u5230notebook<\/strong><\/h2>\n
output_notebook()<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
p = figure(title='\u9ad8\u6027\u80fd\u6298\u7ebf\u56fe', x_axis_label='X\u8f74', y_axis_label='Y\u8f74')<\/p>\n
p.line(x, y, legend_label='sin(x)', line_width=2)<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
show(p)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7efc\u4e0a\u6240\u8ff0\uff0cPython\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5c06\u6570\u7ec4\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Pandas\u4ee5\u53ca\u5176\u4ed6\u9ad8\u7ea7\u5e93\u5982Plotly\u548cBokeh\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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