{"id":1148001,"date":"2025-01-13T16:31:51","date_gmt":"2025-01-13T08:31:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1148001.html"},"modified":"2025-01-13T16:31:54","modified_gmt":"2025-01-13T08:31:54","slug":"python%e5%a6%82%e4%bd%95%e6%94%b9%e7%94%bb%e5%9b%be%e9%80%9f%e5%ba%a6","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1148001.html","title":{"rendered":"python\u5982\u4f55\u6539\u753b\u56fe\u901f\u5ea6"},"content":{"rendered":"

\"python\u5982\u4f55\u6539\u753b\u56fe\u901f\u5ea6\"<\/p>\n

Python\u63d0\u9ad8\u753b\u56fe\u901f\u5ea6\u7684\u4e3b\u8981\u65b9\u6cd5\u662f\uff1a\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u7ed8\u56fe\u5e93\u3001\u51cf\u5c11\u7ed8\u56fe\u5143\u7d20\u3001\u4f18\u5316\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u3001\u7f13\u5b58\u6570\u636e\u3002<\/strong>\u5176\u4e2d\uff0c\u9009\u62e9\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u7ed8\u56fe\u5e93\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n

\u4ee5Matplotlib\u4e3a\u4f8b\uff0c\u5b83\u662f\u4e00\u6b3e\u529f\u80fd\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u4f46\u5728\u5904\u7406\u5927\u6570\u636e\u91cf\u65f6\u901f\u5ea6\u53ef\u80fd\u8f83\u6162\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0cVisPy\u3001Plotly\u7b49\u7ed8\u56fe\u5e93\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u5904\u7406\u5927\u91cf\u6570\u636e\uff0c\u63d0\u4f9b\u66f4\u5feb\u7684\u7ed8\u56fe\u901f\u5ea6\u3002\u4f8b\u5982\uff0cPlotly\u91c7\u7528WebGL\u6280\u672f\uff0c\u53ef\u4ee5\u5728\u6d4f\u89c8\u5668\u4e2d\u9ad8\u6548\u6e32\u67d3\u56fe\u5f62\uff0c\u7279\u522b\u9002\u5408\u9700\u8981\u5b9e\u65f6\u66f4\u65b0\u7684\u52a8\u6001\u56fe\u8868\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Plotly\u6765\u63d0\u9ad8\u753b\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u7ed8\u56fe\u5e93<\/strong><\/h2>\n

1\u3001VisPy<\/h2>\n<\/p>\n

VisPy\u662f\u4e00\u4e2a\u57fa\u4e8eOpenGL\u7684\u9ad8\u6027\u80fd\u7ed8\u56fe\u5e93\uff0c\u5b83\u80fd\u9ad8\u6548\u5730\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002VisPy\u5229\u7528GPU\u52a0\u901f\u6765\u7ed8\u5236\u56fe\u5f62\uff0c\u56e0\u6b64\u5728\u7ed8\u5236\u5927\u91cf\u70b9\u3001\u7ebf\u6216\u590d\u6742\u56fe\u5f62\u65f6\uff0c\u6027\u80fd\u4f1a\u660e\u663e\u4f18\u4e8e\u4f20\u7edf\u7684\u7ed8\u56fe\u5e93\u3002<\/p>\n<\/p>\n

\u5b89\u88c5VisPy<\/h3>\n<\/p>\n

pip install vispy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7b80\u5355\u793a\u4f8b<\/h3>\n<\/p>\n
import vispy.plot as vp<\/p>\n
fig = vp.Fig()<\/p>\n
scatter = fig[0, 0].plot((0, 0, 0), marker_size=10, face_color='red')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001Plotly<\/h2>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7684\u7ed8\u56fe\u5e93\uff0c\u57fa\u4e8eD3.js\u548cWebGL\u6280\u672f\uff0c\u53ef\u4ee5\u5728\u6d4f\u89c8\u5668\u4e2d\u9ad8\u6548\u6e32\u67d3\u56fe\u5f62\uff0c\u7279\u522b\u9002\u5408\u9700\u8981\u5b9e\u65f6\u66f4\u65b0\u7684\u52a8\u6001\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u5b89\u88c5Plotly<\/h3>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7b80\u5355\u793a\u4f8b<\/h3>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
fig = go.Figure(data=go.Scatter(x=[1, 2, 3], y=[4, 5, 6]))<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u51cf\u5c11\u7ed8\u56fe\u5143\u7d20<\/strong><\/h2>\n
1\u3001\u51cf\u5c11\u7ed8\u5236\u7684\u6570\u636e\u91cf<\/h2>\n<\/p>\n
\u5728\u7ed8\u5236\u5927\u91cf\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u62bd\u6837\u3001\u6570\u636e\u805a\u5408\u7b49\u65b9\u6cd5\u51cf\u5c11\u7ed8\u5236\u7684\u6570\u636e\u91cf\uff0c\u4ece\u800c\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u6570\u636e\u62bd\u6837\u793a\u4f8b<\/h3>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 10, 100000)<\/p>\n
y = np.sin(x)<\/p>\n
\u62bd\u6837\u6570\u636e<\/strong><\/h2>\n
sample_rate = 100<\/p>\n
x_sampled = x[::sample_rate]<\/p>\n
y_sampled = y[::sample_rate]<\/p>\n
plt.plot(x_sampled, y_sampled)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f18\u5316\u7ed8\u56fe\u8bbe\u7f6e<\/h2>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u7ed8\u56fe\u53c2\u6570\uff0c\u5982\u51cf\u5c11\u70b9\u7684\u5927\u5c0f\u3001\u51cf\u5c11\u7ed8\u56fe\u7684\u590d\u6742\u5ea6\uff0c\u53ef\u4ee5\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u8c03\u6574\u7ed8\u56fe\u53c2\u6570\u793a\u4f8b<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
plt.plot(x, y, marker='o', markersize=2)  # \u51cf\u5c0f\u70b9\u7684\u5927\u5c0f<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f18\u5316\u6570\u636e\u5904\u7406<\/strong><\/h2>\n
1\u3001\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h2>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u5904\u7406\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u5904\u7406\u793a\u4f8b<\/h3>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 10, 100000)<\/p>\n
y = np.sin(x)<\/p>\n
\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u5904\u7406<\/strong><\/h2>\n
y_processed = np.log(y + 1)<\/p>\n
plt.plot(x, y_processed)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406<\/h2>\n<\/p>\n
Pandas\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u793a\u4f8b<\/h3>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'x': np.linspace(0, 10, 100000),<\/p>\n
    'y': np.sin(np.linspace(0, 10, 100000))<\/p>\n
})<\/p>\n
\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406<\/strong><\/h2>\n
data['y_processed'] = data['y'].apply(lambda y: np.log(y + 1))<\/p>\n
plt.plot(data['x'], data['y_processed'])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b<\/strong><\/h2>\n
1\u3001\u4f7f\u7528\u591a\u7ebf\u7a0b<\/h2>\n<\/p>\n
\u5728\u7ed8\u56fe\u8fc7\u7a0b\u4e2d\uff0c\u4f7f\u7528\u591a\u7ebf\u7a0b\u53ef\u4ee5\u5c06\u7ed8\u56fe\u4efb\u52a1\u5206\u89e3\u6210\u591a\u4e2a\u7ebf\u7a0b\u5e76\u884c\u6267\u884c\uff0c\u4ece\u800c\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u591a\u7ebf\u7a0b\u793a\u4f8b<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import threading<\/p>\n
def plot_data(data):<\/p>\n
    plt.plot(data['x'], data['y'])<\/p>\n
    plt.show()<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
data1 = {'x': np.linspace(0, 10, 100000), 'y': np.sin(np.linspace(0, 10, 100000))}<\/p>\n
data2 = {'x': np.linspace(0, 10, 100000), 'y': np.cos(np.linspace(0, 10, 100000))}<\/p>\n
\u4f7f\u7528\u591a\u7ebf\u7a0b\u8fdb\u884c\u7ed8\u56fe<\/strong><\/h2>\n
thread1 = threading.Thread(target=plot_data, args=(data1,))<\/p>\n
thread2 = threading.Thread(target=plot_data, args=(data2,))<\/p>\n
thread1.start()<\/p>\n
thread2.start()<\/p>\n
thread1.join()<\/p>\n
thread2.join()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528\u591a\u8fdb\u7a0b<\/h2>\n<\/p>\n
\u5728\u7ed8\u56fe\u8fc7\u7a0b\u4e2d\uff0c\u4f7f\u7528\u591a\u8fdb\u7a0b\u53ef\u4ee5\u5c06\u7ed8\u56fe\u4efb\u52a1\u5206\u89e3\u6210\u591a\u4e2a\u8fdb\u7a0b\u5e76\u884c\u6267\u884c\uff0c\u4ece\u800c\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u591a\u8fdb\u7a0b\u793a\u4f8b<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from multiprocessing import Process<\/p>\n
def plot_data(data):<\/p>\n
    plt.plot(data['x'], data['y'])<\/p>\n
    plt.show()<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
data1 = {'x': np.linspace(0, 10, 100000), 'y': np.sin(np.linspace(0, 10, 100000))}<\/p>\n
data2 = {'x': np.linspace(0, 10, 100000), 'y': np.cos(np.linspace(0, 10, 100000))}<\/p>\n
\u4f7f\u7528\u591a\u8fdb\u7a0b\u8fdb\u884c\u7ed8\u56fe<\/strong><\/h2>\n
process1 = Process(target=plot_data, args=(data1,))<\/p>\n
process2 = Process(target=plot_data, args=(data2,))<\/p>\n
process1.start()<\/p>\n
process2.start()<\/p>\n
process1.join()<\/p>\n
process2.join()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u7f13\u5b58\u6570\u636e<\/strong><\/h2>\n
1\u3001\u7f13\u5b58\u8ba1\u7b97\u7ed3\u679c<\/h2>\n<\/p>\n
\u5728\u7ed8\u56fe\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u5c06\u8ba1\u7b97\u7ed3\u679c\u7f13\u5b58\u8d77\u6765\uff0c\u907f\u514d\u91cd\u590d\u8ba1\u7b97\uff0c\u4ece\u800c\u63d0\u9ad8\u7ed8\u56fe\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u7f13\u5b58\u8ba1\u7b97\u7ed3\u679c\u793a\u4f8b<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 10, 100000)<\/p>\n
y = np.sin(x)<\/p>\n
\u7f13\u5b58\u8ba1\u7b97\u7ed3\u679c<\/strong><\/h2>\n
cache = {}<\/p>\n
def compute_and_cache(x):<\/p>\n
    if x not in cache:<\/p>\n
        cache[x] = np.sin(x)<\/p>\n
    return cache[x]<\/p>\n
y_cached = np.array([compute_and_cache(xi) for xi in x])<\/p>\n
plt.plot(x, y_cached)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528\u5185\u5b58\u6620\u5c04<\/h2>\n<\/p>\n
\u5185\u5b58\u6620\u5c04\u662f\u4e00\u79cd\u5c06\u6587\u4ef6\u6620\u5c04\u5230\u5185\u5b58\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u8bbf\u95ee\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u5728\u7ed8\u56fe\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u5c06\u6570\u636e\u6587\u4ef6\u6620\u5c04\u5230\u5185\u5b58\uff0c\u4ece\u800c\u63d0\u9ad8\u6570\u636e\u8bbf\u95ee\u901f\u5ea6\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u793a\u4f8b<\/h3>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u5927\u91cf\u6570\u636e\u5e76\u4fdd\u5b58\u5230\u6587\u4ef6<\/strong><\/h2>\n
data = np.sin(np.linspace(0, 10, 100000))<\/p>\n
np.save('data.npy', data)<\/p>\n
\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
data_mmap = np.load('data.npy', mmap_mode='r')<\/p>\n
plt.plot(data_mmap)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/strong><\/h2>\n
\u63d0\u9ad8Python\u7ed8\u56fe\u901f\u5ea6\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u7ed8\u56fe\u5e93\u3001\u51cf\u5c11\u7ed8\u56fe\u5143\u7d20\u3001\u4f18\u5316\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u3001\u7f13\u5b58\u6570\u636e\u3002\u901a\u8fc7\u9009\u62e9\u9002\u5408\u7684\u7ed8\u56fe\u5e93\uff08\u5982VisPy\u3001Plotly\uff09\uff0c\u4f18\u5316\u7ed8\u56fe\u8bbe\u7f6e\uff08\u5982\u51cf\u5c11\u7ed8\u5236\u7684\u6570\u636e\u91cf\u3001\u8c03\u6574\u7ed8\u56fe\u53c2\u6570\uff09\uff0c\u4f7f\u7528\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u5e93\uff08\u5982NumPy\u3001Pandas\uff09\uff0c\u5229\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u5e76\u884c\u6267\u884c\u7ed8\u56fe\u4efb\u52a1\uff0c\u4ee5\u53ca\u7f13\u5b58\u8ba1\u7b97\u7ed3\u679c\u6216\u4f7f\u7528\u5185\u5b58\u6620\u5c04\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8Python\u7684\u7ed8\u56fe\u901f\u5ea6\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u7279\u70b9\uff0c\u7075\u6d3b\u9009\u62e9\u548c\u7ec4\u5408\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u4ee5\u8fbe\u5230\u6700\u4f73\u7684\u7ed8\u56fe\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8c03\u6574\u7ed8\u56fe\u901f\u5ea6\u4ee5\u63d0\u9ad8\u6548\u7387\uff1f<\/strong>
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