{"id":1147876,"date":"2025-01-13T16:30:19","date_gmt":"2025-01-13T08:30:19","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1147876.html"},"modified":"2025-01-13T16:30:21","modified_gmt":"2025-01-13T08:30:21","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e5%81%9a%e5%9b%be%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1147876.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u505a\u56fe\u8868"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u505a\u56fe\u8868\"<\/p>\n

\u5229\u7528Python\u505a\u56fe\u8868\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5176\u4e2d\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\uff0c\u5b83\u4eec\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u6761\u5f62\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/strong> \u5728\u8fd9\u4e9b\u5e93\u4e2d\uff0cMatplotlib<\/strong> \u662f\u6700\u57fa\u7840\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u56fe\u8868\u7ed8\u5236\u5e93\uff0cSeaborn<\/strong> \u5219\u5728\u5176\u57fa\u7840\u4e0a\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u7edf\u8ba1\u56fe\u8868\u7ed8\u5236\u529f\u80fd\uff0c\u800cPlotly<\/strong> \u5219\u4fa7\u91cd\u4e8e\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u521b\u5efa\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u521b\u5efa\u56fe\u8868\uff0c\u5e76\u91cd\u70b9\u4ecb\u7ecdMatplotlib\u7684\u4f7f\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001Matplotlib<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u80fd\u591f\u751f\u6210\u591a\u79cd\u9ad8\u8d28\u91cf\u7684\u56fe\u8868\u3002\u5b83\u975e\u5e38\u7075\u6d3b\uff0c\u53ef\u4ee5\u901a\u8fc7\u7f16\u7a0b\u6765\u63a7\u5236\u6bcf\u4e00\u4e2a\u7ec6\u8282\u3002<\/p>\n<\/p>\n

1.1\u3001\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2\u3001\u521b\u5efa\u57fa\u672c\u56fe\u8868<\/h4>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("Simple Line Plot")<\/p>\n
plt.xlabel("X Axis")<\/p>\n
plt.ylabel("Y Axis")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3\u3001\u5b9a\u5236\u56fe\u8868<\/h4>\n<\/p>\n
\u4f60\u53ef\u4ee5\u901a\u8fc7\u5404\u79cd\u65b9\u6cd5\u6765\u5b9a\u5236\u56fe\u8868\uff0c\u4f8b\u5982\u6539\u53d8\u7ebf\u6761\u7684\u6837\u5f0f\u3001\u989c\u8272\u548c\u6807\u8bb0\uff1a<\/p>\n<\/p>\n
plt.plot(x, y, linestyle='--', color='r', marker='o')<\/p>\n
plt.title("Customized Line Plot")<\/p>\n
plt.xlabel("X Axis")<\/p>\n
plt.ylabel("Y Axis")<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.4\u3001\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868<\/h4>\n<\/p>\n
\u9664\u4e86\u6298\u7ebf\u56fe\uff0cMatplotlib\u8fd8\u652f\u6301\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6761\u5f62\u56fe\u3001\u6563\u70b9\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\u3002<\/p>\n<\/p>\n
# \u6761\u5f62\u56fe<\/p>\n
plt.bar(x, y)<\/p>\n
plt.title("Bar Plot")<\/p>\n
plt.xlabel("X Axis")<\/p>\n
plt.ylabel("Y Axis")<\/p>\n
plt.show()<\/p>\n
\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x, y)<\/p>\n
plt.title("Scatter Plot")<\/p>\n
plt.xlabel("X Axis")<\/p>\n
plt.ylabel("Y Axis")<\/p>\n
plt.show()<\/p>\n
\u76f4\u65b9\u56fe<\/strong><\/h2>\n
data = [1, 2, 2, 2, 3, 4, 4, 5, 6, 6, 6, 7, 8, 8, 9, 10]<\/p>\n
plt.hist(data, bins=5)<\/p>\n
plt.title("Histogram")<\/p>\n
plt.xlabel("Value")<\/p>\n
plt.ylabel("Frequency")<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Seaborn<\/h3>\n<\/p>\n
Seaborn\u662f\u5efa\u7acb\u5728Matplotlib\u4e4b\u4e0a\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u4f7f\u5f97\u521b\u5efa\u7edf\u8ba1\u56fe\u8868\u53d8\u5f97\u66f4\u52a0\u7b80\u5355\u3002<\/p>\n<\/p>\n
2.1\u3001\u5b89\u88c5Seaborn<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Seaborn\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2\u3001\u521b\u5efa\u57fa\u672c\u56fe\u8868<\/h4>\n<\/p>\n
\u4f7f\u7528Seaborn\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
sns.lineplot(x=x, y=y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title("Simple Line Plot with Seaborn")<\/p>\n
plt.xlabel("X Axis")<\/p>\n
plt.ylabel("Y Axis")<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3\u3001\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868<\/h4>\n<\/p>\n
Seaborn\u652f\u6301\u591a\u79cd\u7c7b\u578b\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u5982\u7bb1\u7ebf\u56fe\u3001\u70ed\u529b\u56fe\u3001\u6210\u5bf9\u5173\u7cfb\u56fe\u7b49\u3002<\/p>\n<\/p>\n
# \u7bb1\u7ebf\u56fe<\/p>\n
sns.boxplot(x=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])<\/p>\n
plt.title("Box Plot")<\/p>\n
plt.show()<\/p>\n
\u70ed\u529b\u56fe<\/strong><\/h2>\n
import numpy as np<\/p>\n
data = np.random.rand(10, 12)<\/p>\n
sns.heatmap(data, annot=True, fmt=".2f")<\/p>\n
plt.title("Heatmap")<\/p>\n
plt.show()<\/p>\n
\u6210\u5bf9\u5173\u7cfb\u56fe<\/strong><\/h2>\n
import seaborn as sns<\/p>\n
import pandas as pd<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = sns.load_dataset("iris")<\/p>\n
sns.pAI<\/a>rplot(data, hue="species")<\/p>\n
plt.title("Pair Plot")<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Plotly<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u5e93\uff0c\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
3.1\u3001\u5b89\u88c5Plotly<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Plotly\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install plotly<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2\u3001\u521b\u5efa\u57fa\u672c\u56fe\u8868<\/h4>\n<\/p>\n
\u4f7f\u7528Plotly\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
from plotly.offline import plot<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
trace = go.Scatter(x=x, y=y, mode='lines+markers')<\/p>\n
layout = go.Layout(title="Simple Line Plot with Plotly", xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=[trace], layout=layout)<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plot(fig)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.3\u3001\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868<\/h4>\n<\/p>\n
Plotly\u652f\u6301\u591a\u79cd\u7c7b\u578b\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u5982\u6761\u5f62\u56fe\u3001\u6563\u70b9\u56fe\u3001\u997c\u56fe\u7b49\u3002<\/p>\n<\/p>\n
# \u6761\u5f62\u56fe<\/p>\n
trace = go.Bar(x=x, y=y)<\/p>\n
layout = go.Layout(title="Bar Plot with Plotly", xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=[trace], layout=layout)<\/p>\n
plot(fig)<\/p>\n
\u6563\u70b9\u56fe<\/strong><\/h2>\n
trace = go.Scatter(x=x, y=y, mode='markers')<\/p>\n
layout = go.Layout(title="Scatter Plot with Plotly", xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=[trace], layout=layout)<\/p>\n
plot(fig)<\/p>\n
\u997c\u56fe<\/strong><\/h2>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
values = [10, 20, 30, 40]<\/p>\n
trace = go.Pie(labels=labels, values=values)<\/p>\n
layout = go.Layout(title="Pie Chart with Plotly")<\/p>\n
fig = go.Figure(data=[trace], layout=layout)<\/p>\n
plot(fig)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5b9e\u9645\u5e94\u7528\u4e0e\u6280\u5de7<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u66f4\u597d\u5730\u5e94\u7528\u8fd9\u4e9b\u5e93\uff0c\u4e0b\u9762\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u7528\u7684\u6280\u5de7\u548c\u6848\u4f8b\u3002<\/p>\n<\/p>\n
4.1\u3001\u591a\u56fe\u8868\u5e03\u5c40<\/h4>\n<\/p>\n
\u5728\u4e00\u4e2a\u56fe\u8868\u4e2d\u5c55\u793a\u591a\u4e2a\u5b50\u56fe\uff08subplot\uff09\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u5b50\u56fe<\/strong><\/h2>\n
fig, axs = plt.subplots(2, 2)<\/p>\n
\u7ed8\u5236\u7b2c\u4e00\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[0, 0].plot(x, y)<\/p>\n
axs[0, 0].set_title('Line Plot')<\/p>\n
\u7ed8\u5236\u7b2c\u4e8c\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[0, 1].bar(x, y)<\/p>\n
axs[0, 1].set_title('Bar Plot')<\/p>\n
\u7ed8\u5236\u7b2c\u4e09\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[1, 0].scatter(x, y)<\/p>\n
axs[1, 0].set_title('Scatter Plot')<\/p>\n
\u7ed8\u5236\u7b2c\u56db\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[1, 1].hist(data, bins=5)<\/p>\n
axs[1, 1].set_title('Histogram')<\/p>\n
\u8c03\u6574\u5e03\u5c40<\/strong><\/h2>\n
plt.tight_layout()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2\u3001\u52a8\u753b\u56fe\u8868<\/h4>\n<\/p>\n
\u5229\u7528Matplotlib\u521b\u5efa\u52a8\u753b\u6548\u679c\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import matplotlib.animation as animation<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 2*np.pi, 100)<\/p>\n
y = np.sin(x)<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
fig, ax = plt.subplots()<\/p>\n
line, = ax.plot(x, y)<\/p>\n
\u52a8\u753b\u51fd\u6570<\/strong><\/h2>\n
def animate(i):<\/p>\n
    line.set_ydata(np.sin(x + i\/10.0))<\/p>\n
    return line,<\/p>\n
\u521b\u5efa\u52a8\u753b<\/strong><\/h2>\n
ani = animation.FuncAnimation(fig, animate, frames=100, interval=20, blit=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3\u3001\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
\u5229\u7528Plotly\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import plotly.graph_objs as go<\/p>\n
from plotly.offline import plot<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u8868<\/strong><\/h2>\n
trace = go.Scatter(x=x, y=y, mode='lines+markers')<\/p>\n
layout = go.Layout(title="Interactive Line Plot with Plotly", xaxis=dict(title='X Axis'), yaxis=dict(title='Y Axis'))<\/p>\n
fig = go.Figure(data=[trace], layout=layout)<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plot(fig)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u5185\u5bb9\uff0c\u76f8\u4fe1\u4f60\u5df2\u7ecf\u638c\u63e1\u4e86\u5229\u7528Python\u521b\u5efa\u56fe\u8868\u7684\u57fa\u672c\u65b9\u6cd5\u548c\u6280\u5de7\u3002Matplotlib\u3001Seaborn\u3001Plotly<\/strong> \u5404\u6709\u7279\u8272\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u573a\u666f\u3002Matplotlib<\/strong> \u9002\u5408\u57fa\u7840\u56fe\u8868\u7684\u7ed8\u5236\u548c\u7ec6\u8282\u8c03\u6574\uff0cSeaborn<\/strong> \u9002\u5408\u5feb\u901f\u521b\u5efa\u9ad8\u7ea7\u7edf\u8ba1\u56fe\u8868\uff0cPlotly<\/strong> \u5219\u9002\u5408\u9700\u8981\u4ea4\u4e92\u529f\u80fd\u7684\u56fe\u8868\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u5e76\u7ed3\u5408\u591a\u79cd\u6280\u5de7\uff0c\u521b\u5efa\u51fa\u66f4\u4e3a\u4e30\u5bcc\u548c\u4e13\u4e1a\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u4f7f\u7528Python\u5236\u4f5c\u56fe\u8868\u9700\u8981\u54ea\u4e9b\u57fa\u672c\u5e93\uff1f<\/strong>
\u5236\u4f5c\u56fe\u8868\u901a\u5e38\u4f1a\u4f7f\u7528\u4e00\u4e9b\u5f3a\u5927\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u548cPandas\u3002Matplotlib\u662f\u4e00\u4e2a\u57fa\u7840\u5e93\uff0c\u9002\u5408\u521b\u5efa\u7b80\u5355\u7684\u56fe\u5f62\u548c\u56fe\u8868\uff1bSeaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u548c\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\uff1bPandas\u5219\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u6570\u636e\u5904\u7406\u548c\u7ed8\u56fe\u529f\u80fd\u3002\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u63d0\u9ad8\u56fe\u8868\u5236\u4f5c\u7684\u6548\u7387\u548c\u7f8e\u89c2\u5ea6\u3002<\/p>\n
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