![\"python\u5982\u4f55\u5b9e\u73b0\u6570\u636e\u5b9e\u65f6\u56fe\u8868\u663e\u793a\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25080822\/e355d499-040e-4182-9f47-315f18e87c7e.webp\")
<\/p>\n<\/p>\n
Python\u5b9e\u73b0\u6570\u636e\u5b9e\u65f6\u56fe\u8868\u663e\u793a\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Plotly\u3001Bokeh\u7b49\u5e93\u8fdb\u884c\u7ed8\u5236\uff0c\u901a\u8fc7\u66f4\u65b0\u56fe\u8868\u6570\u636e\u6765\u5b9e\u73b0\u5b9e\u65f6\u663e\u793a\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Matplotlib\u7684\u52a8\u753b\u529f\u80fd\u3001\u4f7f\u7528Plotly\u7684\u52a8\u6001\u66f4\u65b0\u529f\u80fd\u3001\u4f7f\u7528Bokeh\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\u529f\u80fd\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528Matplotlib\u7684animation\u6a21\u5757\u662f\u6bd4\u8f83\u5e38\u89c1\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u5b9e\u65f6\u56fe\u8868\u663e\u793a\u3002<\/p>\n<\/p>\n \u4e00\u3001Matplotlib\u7684\u52a8\u753b\u529f\u80fd<\/p>\n<\/p>\n Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u5e76\u4e14\u901a\u8fc7animation\u6a21\u5757\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u5b9e\u73b0\u52a8\u6001\u66f4\u65b0\u56fe\u8868\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n \u5b89\u88c5Matplotlib\u5e93<\/strong><\/p>\n<\/p>\n \u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757<\/strong><\/p>\n<\/p>\n \u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Matplotlib\u548canimation\u6a21\u5757\uff1a<\/p>\n<\/p>\n import matplotlib.animation as animation<\/p>\n import numpy as np<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efa\u6570\u636e\u751f\u6210\u51fd\u6570<\/strong><\/p>\n<\/p>\n \u521b\u5efa\u4e00\u4e2a\u751f\u6210\u6570\u636e\u7684\u51fd\u6570\uff0c\u7528\u4e8e\u6a21\u62df\u5b9e\u65f6\u6570\u636e\u66f4\u65b0\u3002\u8fd9\u91cc\u4ee5\u6b63\u5f26\u6ce2\u4e3a\u4f8b\uff1a<\/p>\n<\/p>\n t = 0<\/p>\n while True:<\/p>\n t += 0.1<\/p>\n yield t, np.sin(t)<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521d\u59cb\u5316\u56fe\u8868<\/strong><\/p>\n<\/p>\n \u521d\u59cb\u5316\u56fe\u8868\u5bf9\u8c61\u548c\u6570\u636e\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n line, = ax.plot([], [], lw=2)<\/p>\n ax.set_ylim(-1.1, 1.1)<\/p>\n ax.set_xlim(0, 10)<\/p>\n ax.grid()<\/p>\n xdata, ydata = [], []<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u66f4\u65b0\u56fe\u8868<\/strong><\/p>\n<\/p>\n \u5b9a\u4e49\u66f4\u65b0\u56fe\u8868\u7684\u51fd\u6570\uff0c\u7528\u4e8e\u6bcf\u6b21\u66f4\u65b0\u6570\u636e\uff1a<\/p>\n<\/p>\n t, y = data<\/p>\n xdata.append(t)<\/p>\n ydata.append(y)<\/p>\n xmin, xmax = ax.get_xlim()<\/p>\n if t >= xmax:<\/p>\n ax.set_xlim(xmin, 2*xmax)<\/p>\n ax.figure.canvas.draw()<\/p>\n line.set_data(xdata, ydata)<\/p>\n return line,<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efa\u52a8\u753b<\/strong><\/p>\n<\/p>\n \u521b\u5efa\u52a8\u753b\u5bf9\u8c61\uff0c\u5e76\u8c03\u7528\u6570\u636e\u751f\u6210\u51fd\u6570\u548c\u66f4\u65b0\u51fd\u6570\uff1a<\/p>\n<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n \u4e8c\u3001\u4f7f\u7528Plotly\u5b9e\u73b0\u52a8\u6001\u66f4\u65b0<\/p>\n<\/p>\n Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u652f\u6301\u52a8\u6001\u66f4\u65b0\u56fe\u8868\u3002\u4f7f\u7528Plotly\u53ef\u4ee5\u521b\u5efa\u975e\u5e38\u6f02\u4eae\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u5e76\u4e14\u53ef\u4ee5\u5b9e\u65f6\u66f4\u65b0\u6570\u636e\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n \u5b89\u88c5Plotly\u5e93<\/strong><\/p>\n<\/p>\n \u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Plotly\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757<\/strong><\/p>\n<\/p>\n \u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Plotly\u6a21\u5757\uff1a<\/p>\n<\/p>\n from plotly.subplots import make_subplots<\/p>\n import numpy as np<\/p>\n import time<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efa\u521d\u59cb\u56fe\u8868<\/strong><\/p>\n<\/p>\n \u521b\u5efa\u4e00\u4e2a\u521d\u59cb\u56fe\u8868\u5bf9\u8c61\uff0c\u5e76\u8bbe\u7f6e\u5e03\u5c40\uff1a<\/p>\n<\/p>\n fig.add_trace(go.Scatter(x=[], y=[]), row=1, col=1)<\/p>\n fig.update_layout(xaxis=dict(range=[0, 10]), yaxis=dict(range=[-1.1, 1.1]))<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u66f4\u65b0\u56fe\u8868\u6570\u636e<\/strong><\/p>\n<\/p>\n \u5b9a\u4e49\u66f4\u65b0\u56fe\u8868\u6570\u636e\u7684\u51fd\u6570\uff1a<\/p>\n<\/p>\n fig.data[0].x += (x,)<\/p>\n fig.data[0].y += (y,)<\/p>\n fig.update_layout(xaxis=dict(range=[max(0, x-10), x+1]))<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u751f\u6210\u5b9e\u65f6\u6570\u636e\u5e76\u66f4\u65b0\u56fe\u8868<\/strong><\/p>\n<\/p>\n \u751f\u6210\u5b9e\u65f6\u6570\u636e\u5e76\u8c03\u7528\u66f4\u65b0\u51fd\u6570\uff1a<\/p>\n<\/p>\n while t < 100:<\/p>\n t += 0.1<\/p>\n y = np.sin(t)<\/p>\n update_trace(fig, t, y)<\/p>\n fig.show()<\/p>\n time.sleep(0.1)<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n \u4e09\u3001\u4f7f\u7528Bokeh\u5b9e\u73b0\u4ea4\u4e92\u5f0f\u56fe\u8868<\/p>\n<\/p>\n Bokeh\u662f\u4e00\u4e2a\u652f\u6301\u4ea4\u4e92\u5f0f\u548c\u52a8\u6001\u66f4\u65b0\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u7528\u4e8eWeb\u5e94\u7528\u7a0b\u5e8f\u3002\u4f7f\u7528Bokeh\u53ef\u4ee5\u521b\u5efa\u9ad8\u6548\u7684\u5b9e\u65f6\u56fe\u8868\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n \u5b89\u88c5Bokeh\u5e93<\/strong><\/p>\n<\/p>\n \u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Bokeh\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757<\/strong><\/p>\n<\/p>\n \u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Bokeh\u6a21\u5757\uff1a<\/p>\n<\/p>\n from bokeh.models import ColumnDataSource<\/p>\n from bokeh.layouts import column<\/p>\n import numpy as np<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efa\u6570\u636e\u6e90<\/strong><\/p>\n<\/p>\n \u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6e90\u5bf9\u8c61\uff0c\u7528\u4e8e\u5b58\u50a8\u5b9e\u65f6\u6570\u636e\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u521b\u5efa\u56fe\u8868\u5bf9\u8c61<\/strong><\/p>\n<\/p>\n \u521b\u5efa\u4e00\u4e2a\u56fe\u8868\u5bf9\u8c61\uff0c\u5e76\u8bbe\u7f6e\u5e03\u5c40\uff1a<\/p>\n<\/p>\n p.line('x', 'y', source=source)<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u66f4\u65b0\u6570\u636e<\/strong><\/p>\n<\/p>\n \u5b9a\u4e49\u66f4\u65b0\u6570\u636e\u7684\u51fd\u6570\uff1a<\/p>\n<\/p>\n new_data = dict(x=[source.data['x'][-1] + 0.1 if source.data['x'] else 0],<\/p>\n y=[np.sin(source.data['x'][-1] + 0.1) if source.data['x'] else 0])<\/p>\n source.stream(new_data, rollover=100)<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n \u6dfb\u52a0\u66f4\u65b0\u51fd\u6570\u5230\u6587\u6863<\/strong><\/p>\n<\/p>\n \u5c06\u66f4\u65b0\u51fd\u6570\u6dfb\u52a0\u5230Bokeh\u6587\u6863\u4e2d\uff0c\u4ee5\u5b9e\u73b0\u5b9e\u65f6\u66f4\u65b0\uff1a<\/p>\n<\/p>\n curdoc().add_root(column(p))<\/p>\n <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n \u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u3001Plotly\u548cBokeh\u5b9e\u73b0Python\u4e2d\u7684\u6570\u636e\u5b9e\u65f6\u56fe\u8868\u663e\u793a\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u3002Matplotlib\u9002\u5408\u7b80\u5355\u7684\u9759\u6001\u548c\u52a8\u6001\u56fe\u8868\u3001Plotly\u9002\u5408\u6f02\u4eae\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\u3001Bokeh\u9002\u5408Web\u5e94\u7528\u4e2d\u7684\u9ad8\u6548\u56fe\u8868\u3002<\/strong><\/p>\n<\/p>\n \u5982\u4f55\u5728Python\u4e2d\u9009\u62e9\u9002\u5408\u7684\u56fe\u8868\u5e93\u6765\u5b9e\u73b0\u5b9e\u65f6\u6570\u636e\u5c55\u793a\uff1f<\/strong> \u5b9e\u73b0\u5b9e\u65f6\u6570\u636e\u56fe\u8868\u9700\u8981\u51c6\u5907\u54ea\u4e9b\u6570\u636e\u6e90\uff1f<\/strong> \u5982\u4f55\u4f18\u5316\u5b9e\u65f6\u6570\u636e\u56fe\u8868\u7684\u6027\u80fd\u4ee5\u4fdd\u8bc1\u6d41\u7545\u6027\uff1f<\/strong>\n
pip install matplotlib<\/p>\nimport matplotlib.pyplot as plt<\/p>\ndef data_gen():<\/p>\nfig, ax = plt.subplots()<\/p>\ndef update(data):<\/p>\nani = animation.FuncAnimation(fig, update, data_gen, blit=True, interval=100, repeat=False)<\/p>\n\n
pip install plotly<\/p>\nimport plotly.graph_objs as go<\/p>\nfig = make_subplots(rows=1, cols=1)<\/p>\ndef update_trace(fig, x, y):<\/p>\nt = 0<\/p>\n\n
pip install bokeh<\/p>\nfrom bokeh.plotting import figure, curdoc<\/p>\nsource = ColumnDataSource(data=dict(x=[], y=[]))<\/p>\np = figure(title="Real-time Data", x_axis_label='Time', y_axis_label='Value', x_range=(0, 10), y_range=(-1.1, 1.1))<\/p>\ndef update():<\/p>\ncurdoc().add_periodic_callback(update, 100)<\/p>\n\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u56fe\u8868\u5e93\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\u5b9e\u65f6\u6570\u636e\u5c55\u793a\uff0c\u6bd4\u5982Matplotlib\u3001Plotly\u548cBokeh\u7b49\u3002Matplotlib\u9002\u5408\u9759\u6001\u56fe\u8868\uff0c\u4f46\u901a\u8fc7\u7ed3\u5408FuncAnimation\u53ef\u4ee5\u5b9e\u73b0\u52a8\u6001\u66f4\u65b0\u3002Plotly\u548cBokeh\u5219\u63d0\u4f9b\u4e86\u66f4\u5f3a\u5927\u7684\u4ea4\u4e92\u6027\u548c\u5b9e\u65f6\u66f4\u65b0\u7684\u80fd\u529b\uff0c\u7279\u522b\u9002\u5408\u4e8eWeb\u5e94\u7528\u3002\u5982\u679c\u9700\u8981\u9ad8\u9891\u7387\u66f4\u65b0\u7684\u56fe\u8868\uff0cBokeh\u53ef\u80fd\u662f\u4e00\u4e2a\u66f4\u597d\u7684\u9009\u62e9\uff0c\u56e0\u4e3a\u5b83\u652f\u6301WebSocket\uff0c\u53ef\u4ee5\u5904\u7406\u5b9e\u65f6\u6570\u636e\u6d41\u3002<\/p>\n
\u5b9e\u73b0\u5b9e\u65f6\u6570\u636e\u56fe\u8868\u65f6\uff0c\u9996\u5148\u9700\u8981\u786e\u5b9a\u6570\u636e\u6e90\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u662f\u4f20\u611f\u5668\u6570\u636e\u3001\u6570\u636e\u5e93\u4e2d\u7684\u5b9e\u65f6\u6570\u636e\uff0c\u6216\u8005\u662fAPI\u63d0\u4f9b\u7684\u6570\u636e\u3002\u786e\u4fdd\u6570\u636e\u6e90\u80fd\u591f\u4ee5\u4e00\u5b9a\u7684\u9891\u7387\u63d0\u4f9b\u6570\u636e\u66f4\u65b0\uff0c\u5e38\u89c1\u7684\u683c\u5f0f\u5305\u62ecCSV\u6587\u4ef6\u3001JSON\u683c\u5f0f\u7684Web API\u54cd\u5e94\u3001\u6216\u8005\u76f4\u63a5\u8fde\u63a5\u5230\u6570\u636e\u5e93\u3002\u4f7f\u7528Python\u7684requests\u5e93\u3001pandas\u5e93\u7b49\u5de5\u5177\u53ef\u4ee5\u65b9\u4fbf\u5730\u83b7\u53d6\u548c\u5904\u7406\u8fd9\u4e9b\u6570\u636e\u3002<\/p>\n
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