{"id":1081954,"date":"2025-01-08T12:44:19","date_gmt":"2025-01-08T04:44:19","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1081954.html"},"modified":"2025-01-08T12:44:22","modified_gmt":"2025-01-08T04:44:22","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e5%8f%af%e8%a7%86%e5%8c%96%e5%88%97%e8%a1%a8-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1081954.html","title":{"rendered":"python\u5982\u4f55\u505a\u53ef\u89c6\u5316\u5217\u8868"},"content":{"rendered":"

\"python\u5982\u4f55\u505a\u53ef\u89c6\u5316\u5217\u8868\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u5b9e\u73b0\u5217\u8868\u7684\u53ef\u89c6\u5316\uff0c\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u548cPandas\u7b49\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u7684\u4e00\u4e9b\u65b9\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

1. Matplotlib<\/strong>
Matplotlib\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u9759\u6001\u3001\u52a8\u753b\u548c\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u7684Python\u5e93\u3002\u5b83\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u529f\u80fd\u5f3a\u5927\u4e14\u7075\u6d3b\u3002<\/p>\n<\/p>\n

2. Seaborn<\/strong>
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u4f7f\u5f97\u7ed8\u56fe\u66f4\u52a0\u7b80\u6d01\u548c\u7f8e\u89c2\u3002\u5b83\u8fd8\u96c6\u6210\u4e86Pandas\u6570\u636e\u7ed3\u6784\uff0c\u80fd\u591f\u66f4\u65b9\u4fbf\u5730\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n

3. Plotly<\/strong>
Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684Python\u5e93\u3002\u5b83\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5b9a\u5236\u5316\u9009\u9879\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n

4. Pandas<\/strong>
Pandas\u867d\u7136\u4e3b\u8981\u662f\u4e00\u4e2a\u6570\u636e\u5904\u7406\u5e93\uff0c\u4f46\u5b83\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u6bcf\u4e2a\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001Matplotlib<\/h2>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Matplotlib\u53ef\u89c6\u5316\u5217\u8868\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(data)<\/p>\n
plt.title('Simple Line Plot')<\/p>\n
plt.xlabel('Index')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Matplotlib\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\u3002\u6211\u4eec\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u4f7f\u7528plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u3002\u6700\u540e\uff0c\u4f7f\u7528plt.show()<\/code>\u51fd\u6570\u6765\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0Matplotlib\u7684\u529f\u80fd<\/h3>\n<\/p>\n
Matplotlib\u9664\u4e86\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u4e4b\u5916\uff0c\u8fd8\u53ef\u4ee5\u7ed8\u5236\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u997c\u56fe\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u56fe\u8868\u7c7b\u578b\u53ca\u5176\u7ed8\u5236\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1. \u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u662f\u6700\u5e38\u7528\u7684\u56fe\u8868\u7c7b\u578b\u4e4b\u4e00\uff0c\u9002\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
plt.plot(data)<\/p>\n
plt.title('Line Plot')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002\u4f7f\u7528plt.bar()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [1, 2, 3, 4, 5]<\/p>\n
plt.bar(categories, values)<\/p>\n
plt.title('Bar Plot')<\/p>\n
plt.xlabel('Category')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f7f\u7528plt.scatter()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 4, 5, 6]<\/p>\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
<\/code><\/pre>\n<\/p>\n
4. \u997c\u56fe<\/h4>\n<\/p>\n
\u997c\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5360\u6bd4\u3002\u4f7f\u7528plt.pie()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u997c\u56fe\u3002<\/p>\n<\/p>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
plt.pie(sizes, labels=labels, autopct='%1.1f%%')<\/p>\n
plt.title('Pie Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u793a\u4f8b\uff0c\u53ef\u4ee5\u770b\u5230Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u80fd\u591f\u6ee1\u8db3\u5404\u79cd\u6570\u636e\u53ef\u89c6\u5316\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u4e8c\u3001Seaborn<\/h2>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u4f7f\u5f97\u7ed8\u56fe\u66f4\u52a0\u7b80\u6d01\u548c\u7f8e\u89c2\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Seaborn\u53ef\u89c6\u5316\u5217\u8868\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
sns.lineplot(data=data)<\/p>\n
plt.title('Simple Line Plot with Seaborn')<\/p>\n
plt.xlabel('Index')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Seaborn\u548cMatplotlib\u5e93\uff0c\u7136\u540e\u4f7f\u7528sns.lineplot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u4f7f\u7528plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u3002\u6700\u540e\uff0c\u4f7f\u7528plt.show()<\/code>\u51fd\u6570\u6765\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0Seaborn\u7684\u529f\u80fd<\/h3>\n<\/p>\n
Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u80fd\u591f\u521b\u5efa\u66f4\u52a0\u7f8e\u89c2\u548c\u590d\u6742\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u56fe\u8868\u7c7b\u578b\u53ca\u5176\u7ed8\u5236\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1. \u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u4f7f\u7528sns.lineplot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
sns.lineplot(data=data)<\/p>\n
plt.title('Line Plot with Seaborn')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6761\u5f62\u56fe<\/h4>\n<\/p>\n
\u6761\u5f62\u56fe\u9002\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002\u4f7f\u7528sns.barplot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6761\u5f62\u56fe\u3002<\/p>\n<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [1, 2, 3, 4, 5]<\/p>\n
sns.barplot(x=categories, y=values)<\/p>\n
plt.title('Bar Plot with Seaborn')<\/p>\n
plt.xlabel('Category')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f7f\u7528sns.scatterplot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 4, 5, 6]<\/p>\n
sns.scatterplot(x=x, y=y)<\/p>\n
plt.title('Scatter Plot with Seaborn')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u4f7f\u7528sns.boxplot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u7bb1\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]<\/p>\n
sns.boxplot(data=data)<\/p>\n
plt.title('Box Plot with Seaborn')<\/p>\n
plt.xlabel('Data')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u793a\u4f8b\uff0c\u53ef\u4ee5\u770b\u5230Seaborn\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u80fd\u591f\u521b\u5efa\u66f4\u52a0\u7f8e\u89c2\u548c\u590d\u6742\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u4e09\u3001Plotly<\/h2>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684Python\u5e93\u3002\u5b83\u652f\u6301\u591a\u79cd\u56fe\u8868\u7c7b\u578b\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5b9a\u5236\u5316\u9009\u9879\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u573a\u666f\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Plotly\u53ef\u89c6\u5316\u5217\u8868\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
fig = px.line(x=range(len(data)), y=data, title='Simple Line Plot with Plotly')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Plotly\u5e93\uff0c\u7136\u540e\u4f7f\u7528px.line()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u5e76\u4f7f\u7528fig.show()<\/code>\u51fd\u6570\u6765\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0Plotly\u7684\u529f\u80fd<\/h3>\n<\/p>\n
Plotly\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u80fd\u591f\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u56fe\u8868\u7c7b\u578b\u53ca\u5176\u7ed8\u5236\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1. \u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u4f7f\u7528px.line()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
fig = px.line(x=range(len(data)), y=data, title='Line Plot with Plotly')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002\u4f7f\u7528px.bar()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [1, 2, 3, 4, 5]<\/p>\n
fig = px.bar(x=categories, y=values, title='Bar Plot with Plotly')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f7f\u7528px.scatter()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 4, 5, 6]<\/p>\n
fig = px.scatter(x=x, y=y, title='Scatter Plot with Plotly')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u997c\u56fe<\/h4>\n<\/p>\n
\u997c\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5360\u6bd4\u3002\u4f7f\u7528px.pie()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u997c\u56fe\u3002<\/p>\n<\/p>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
fig = px.pie(values=sizes, names=labels, title='Pie Chart with Plotly')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u793a\u4f8b\uff0c\u53ef\u4ee5\u770b\u5230Plotly\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u80fd\u591f\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u56db\u3001Pandas<\/h2>\n<\/p>\n
Pandas\u867d\u7136\u4e3b\u8981\u662f\u4e00\u4e2a\u6570\u636e\u5904\u7406\u5e93\uff0c\u4f46\u5b83\u4e5f\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Pandas\u53ef\u89c6\u5316\u5217\u8868\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df = pd.DataFrame(data, columns=['Value'])<\/p>\n
df.plot()<\/p>\n
plt.title('Simple Line Plot with Pandas')<\/p>\n
plt.xlabel('Index')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5bfc\u5165\u4e86Pandas\u548cMatplotlib\u5e93\uff0c\u7136\u540e\u521b\u5efa\u4e86\u4e00\u4e2aPandas DataFrame\uff0c\u5e76\u4f7f\u7528df.plot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u6298\u7ebf\u56fe\u3002\u6700\u540e\uff0c\u4f7f\u7528plt.title()<\/code>\u3001plt.xlabel()<\/code>\u548cplt.ylabel()<\/code>\u51fd\u6570\u6765\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\uff0c\u5e76\u4f7f\u7528plt.show()<\/code>\u51fd\u6570\u6765\u663e\u793a\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0Pandas\u7684\u529f\u80fd<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u56fe\u8868\u7c7b\u578b\u53ca\u5176\u7ed8\u5236\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1. \u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\u3002\u4f7f\u7528df.plot()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
df.plot()<\/p>\n
plt.title('Line Plot with Pandas')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u67f1\u72b6\u56fe\u9002\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u3002\u4f7f\u7528df.plot.bar()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u67f1\u72b6\u56fe\u3002<\/p>\n<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [1, 2, 3, 4, 5]<\/p>\n
df = pd.DataFrame({'Category': categories, 'Value': values})<\/p>\n
df.plot.bar(x='Category', y='Value')<\/p>\n
plt.title('Bar Plot with Pandas')<\/p>\n
plt.xlabel('Category')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4f7f\u7528df.plot.scatter()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 4, 5, 6]<\/p>\n
df = pd.DataFrame({'X': x, 'Y': y})<\/p>\n
df.plot.scatter(x='X', y='Y')<\/p>\n
plt.title('Scatter Plot with Pandas')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u997c\u56fe<\/h4>\n<\/p>\n
\u997c\u56fe\u9002\u7528\u4e8e\u5c55\u793a\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u5360\u6bd4\u3002\u4f7f\u7528df.plot.pie()<\/code>\u51fd\u6570\u53ef\u4ee5\u7ed8\u5236\u997c\u56fe\u3002<\/p>\n<\/p>\n
sizes = [15, 30, 45, 10]<\/p>\n
labels = ['A', 'B', 'C', 'D']<\/p>\n
df = pd.DataFrame({'Size': sizes}, index=labels)<\/p>\n
df.plot.pie(y='Size', autopct='%1.1f%%')<\/p>\n
plt.title('Pie Chart with Pandas')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u793a\u4f8b\uff0c\u53ef\u4ee5\u770b\u5230Pandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
\u7ed3\u8bba<\/h2>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u548cPandas\u7b49\u5e93\uff0cPython\u53ef\u4ee5\u5b9e\u73b0\u5404\u79cd\u7c7b\u578b\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002Matplotlib\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u9759\u6001\u56fe\u8868\u7684\u7ed8\u5236\uff1bSeaborn\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u4f7f\u5f97\u7ed8\u56fe\u66f4\u52a0\u7b80\u6d01\u548c\u7f8e\u89c2\uff1bPlotly\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u7ed8\u5236\u529f\u80fd\uff0c\u9002\u5408\u9700\u8981\u4ea4\u4e92\u5f0f\u56fe\u8868\u7684\u573a\u666f\uff1bPandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u53ef\u89c6\u5316\u3002\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\uff0c\u5e2e\u52a9\u6211\u4eec\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u51b3\u7b56\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u53ef\u89c6\u5316\u5217\u8868\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u521b\u5efa\u53ef\u89c6\u5316\u5217\u8868\u3002\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u60a8\u53ef\u4ee5\u5c06\u6570\u636e\u4ee5\u56fe\u5f62\u65b9\u5f0f\u5448\u73b0\uff0c\u4f8b\u5982\u6761\u5f62\u56fe\u3001\u6298\u7ebf\u56fe\u7b49\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5b89\u88c5\u76f8\u5e94\u7684\u5e93\uff0c\u5e76\u4f7f\u7528\u9002\u5f53\u7684\u51fd\u6570\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u53ef\u89c6\u5316\u6548\u679c\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u51c6\u5907\u3001\u56fe\u5f62\u7ed8\u5236\u4ee5\u53ca\u56fe\u5f62\u7f8e\u5316\u3002<\/p>\n
Python\u4e2d\u7684\u54ea\u4e9b\u5e93\u6700\u9002\u5408\u53ef\u89c6\u5316\u5217\u8868\uff1f<\/strong>
Python\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5176\u4e2dMatplotlib\u662f\u57fa\u7840\u4e14\u529f\u80fd\u5f3a\u5927\u7684\u5e93\uff0c\u9002\u5408\u5236\u4f5c\u5404\u79cd\u9759\u6001\u56fe\u8868\u3002Seaborn\u5efa\u7acb\u5728Matplotlib\u4e4b\u4e0a\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u548c\u66f4\u9ad8\u5c42\u6b21\u7684\u63a5\u53e3\u3002Plotly\u5219\u9002\u5408\u5236\u4f5c\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u9002\u5408Web\u5e94\u7528\u3002\u6839\u636e\u60a8\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\uff0c\u53ef\u4ee5\u63d0\u5347\u6570\u636e\u5c55\u793a\u7684\u6548\u679c\u548c\u7528\u6237\u4f53\u9a8c\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u7f8e\u5316\u6211\u7684\u53ef\u89c6\u5316\u5217\u8868\uff1f<\/strong>
\u7f8e\u5316\u53ef\u89c6\u5316\u5217\u8868\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u4f8b\u5982\u8c03\u6574\u56fe\u8868\u989c\u8272\u3001\u5b57\u4f53\u3001\u56fe\u4f8b\u4f4d\u7f6e\u3001\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e\u7b49\u3002\u4f7f\u7528Matplotlib\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7plt.style.use()<\/code>\u9009\u62e9\u4e0d\u540c\u7684\u6837\u5f0f\uff0cSeaborn\u63d0\u4f9b\u4e86\u4e3b\u9898\u8bbe\u7f6e\u529f\u80fd\uff0c\u800cPlotly\u5219\u5141\u8bb8\u60a8\u901a\u8fc7\u56fe\u5f62\u5c5e\u6027\u8f7b\u677e\u8c03\u6574\u5916\u89c2\u3002\u786e\u4fdd\u56fe\u8868\u6e05\u6670\u6613\u8bfb\uff0c\u80fd\u591f\u6709\u6548\u4f20\u8fbe\u4fe1\u606f\u662f\u7f8e\u5316\u8fc7\u7a0b\u4e2d\u7684\u5173\u952e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u5b9e\u73b0\u5217\u8868\u7684\u53ef\u89c6\u5316\uff0c\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u548cPa […]","protected":false},"author":3,"featured_media":1081963,"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\/1081954"}],"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=1081954"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1081954\/revisions"}],"predecessor-version":[{"id":1081964,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1081954\/revisions\/1081964"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1081963"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1081954"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1081954"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1081954"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}