![\"\u5982\u4f55\u5728python\u4e2d\u6253\u5f00\u7ed8\u56fe\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25203611\/61b57e5a-5d90-48a7-bfcf-dfce8477f930.webp\")
<\/p>\n<\/p>\n
\u5728Python\u4e2d\u6253\u5f00\u7ed8\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\u3002\u63a8\u8350\u7684\u65b9\u6cd5\u662f\uff1a\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u3002\u8fd9\u91cc\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Matplotlib\u8fdb\u884c\u7ed8\u56fe\u3002<\/strong><\/p>\n<\/p>\n Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u4e14\u6613\u4e8e\u4f7f\u7528\u3002\u6211\u4eec\u5c06\u901a\u8fc7\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\u6765\u6f14\u793a\u5982\u4f55\u4f7f\u7528Matplotlib\u8fdb\u884c\u7ed8\u56fe\uff1a<\/strong><\/p>\n<\/p>\n \u5728\u5f00\u59cb\u7ed8\u56fe\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\uff08\u5982Jupyter Notebook\uff09\u4e2d\u5bfc\u5165Matplotlib\u5e76\u521b\u5efa\u57fa\u672c\u7ed8\u56fe\u3002<\/p>\n<\/p>\n x = [1, 2, 3, 4, 5]<\/p>\n y = [2, 3, 5, 7, 11]<\/p>\n plt.plot(x, y)<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4e0a\u8ff0\u4ee3\u7801\u4f1a\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff0c\u5176\u4e2d Matplotlib\u5141\u8bb8\u60a8\u5bf9\u7ed8\u56fe\u8fdb\u884c\u591a\u79cd\u81ea\u5b9a\u4e49\u8bbe\u7f6e\uff0c\u4f8b\u5982\u6dfb\u52a0\u6807\u9898\u3001\u6807\u7b7e\u3001\u7f51\u683c\u7ebf\u3001\u56fe\u4f8b\u7b49\u3002<\/p>\n<\/p>\n plt.title('Sample Plot')<\/p>\n plt.xlabel('X-axis Label')<\/p>\n plt.ylabel('Y-axis Label')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n plt.title('Customized Plot')<\/p>\n plt.xlabel('X-axis')<\/p>\n plt.ylabel('Y-axis')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n plt.title('Plot with Grid')<\/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 plt.title('Plot with Legend')<\/p>\n plt.xlabel('X-axis')<\/p>\n plt.ylabel('Y-axis')<\/p>\n plt.legend()<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728\u7ed8\u56fe\u5b8c\u6210\u540e\uff0c\u60a8\u53ef\u4ee5\u5c06\u5176\u4fdd\u5b58\u4e3a\u56fe\u50cf\u6587\u4ef6\uff0c\u4f8b\u5982PNG\u3001JPEG\u3001SVG\u7b49\u683c\u5f0f\u3002<\/p>\n<\/p>\n plt.title('Saved Plot')<\/p>\n plt.xlabel('X-axis')<\/p>\n plt.ylabel('Y-axis')<\/p>\n plt.savefig('plot.png')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u6709\u65f6\u60a8\u53ef\u80fd\u9700\u8981\u5728\u540c\u4e00\u4e2a\u7a97\u53e3\u4e2d\u5c55\u793a\u591a\u4e2a\u5b50\u56fe\uff0c\u53ef\u4ee5\u4f7f\u7528 x = [1, 2, 3, 4, 5]<\/p>\n y1 = [2, 3, 5, 7, 11]<\/p>\n y2 = [1, 4, 9, 16, 25]<\/p>\n plt.subplot(1, 2, 1) # 1\u884c2\u5217\uff0c\u7b2c1\u4e2a\u5b50\u56fe<\/p>\n plt.plot(x, y1)<\/p>\n plt.title('Subplot 1')<\/p>\n plt.subplot(1, 2, 2) # 1\u884c2\u5217\uff0c\u7b2c2\u4e2a\u5b50\u56fe<\/p>\n plt.plot(x, y2)<\/p>\n plt.title('Subplot 2')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n categories = ['A', 'B', 'C', 'D']<\/p>\n values = [4, 7, 1, 8]<\/p>\n plt.bar(categories, values)<\/p>\n plt.title('Bar Chart')<\/p>\n plt.xlabel('Categories')<\/p>\n plt.ylabel('Values')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n x = [1, 2, 3, 4, 5]<\/p>\n y = [2, 3, 5, 7, 11]<\/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 import numpy as np<\/p>\n data = np.random.randn(1000)<\/p>\n plt.hist(data, bins=30)<\/p>\n plt.title('Histogram')<\/p>\n plt.xlabel('Bins')<\/p>\n plt.ylabel('Frequency')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n labels = ['A', 'B', 'C', 'D']<\/p>\n sizes = [15, 30, 45, 10]<\/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 \u867d\u7136Matplotlib\u975e\u5e38\u5f3a\u5927\uff0c\u4f46\u6709\u65f6\u5019\u6211\u4eec\u9700\u8981\u66f4\u9ad8\u5c42\u6b21\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u8fd9\u65f6\u53ef\u4ee5\u4f7f\u7528Seaborn\u3002Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u548c\u4fe1\u606f\u4e30\u5bcc\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n \u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset('tips')<\/p>\n sns.scatterplot(x='total_bill', y='tip', data=tips)<\/p>\n plt.title('Seaborn Scatter Plot')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset('tips')<\/p>\n sns.lmplot(x='total_bill', y='tip', data=tips)<\/p>\n plt.title('Seaborn Scatter Plot with Regression Line')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n import matplotlib.pyplot as plt<\/p>\n tips = sns.load_dataset('tips')<\/p>\n sns.catplot(x='day', y='total_bill', hue='sex', kind='swarm', data=tips)<\/p>\n plt.title('Seaborn Categorical Scatter Plot')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Plotly\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684Python\u7ed8\u56fe\u5e93\uff0c\u7279\u522b\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n \u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n df = px.data.iris()<\/p>\n fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species', title='Interactive Scatter Plot')<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n df = px.data.iris()<\/p>\n fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_length', color='species', title='3D Scatter Plot')<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n df = px.data.gapminder()<\/p>\n fig = px.line(df, x='year', y='lifeExp', color='continent', title='Time Series Plot')<\/p>\n fig.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u5728Python\u4e2d\uff0c\u7ed8\u56fe\u662f\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\u3002\u901a\u8fc7\u4f7f\u7528Matplotlib\u3001Seaborn\u548cPlotly\u7b49\u7ed8\u56fe\u5e93\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u6765\u5c55\u793a\u548c\u5206\u6790\u6570\u636e\u3002Matplotlib\u9002\u5408\u521b\u5efa\u57fa\u672c\u4e14\u9ad8\u5ea6\u81ea\u5b9a\u4e49\u7684\u56fe\u8868<\/strong>\uff0cSeaborn\u9002\u5408\u521b\u5efa\u7f8e\u89c2\u4e14\u4fe1\u606f\u4e30\u5bcc\u7684\u7edf\u8ba1\u56fe\u8868<\/strong>\uff0c\u800cPlotly\u5219\u9002\u5408\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868<\/strong>\u3002\u6839\u636e\u4e0d\u540c\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u60a8\u7684\u6570\u636e\u53ef\u89c6\u5316\u6548\u679c\u3002<\/p>\n<\/p>\n \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\u8fdb\u884c\u7ed8\u56fe\uff1f<\/strong> \u6267\u884c\u6b64\u4ee3\u7801\u540e\uff0c\u4f1a\u5f39\u51fa\u4e00\u4e2a\u7a97\u53e3\u5c55\u793a\u6298\u7ebf\u56fe\u3002<\/p>\n \u5728Python\u4e2d\u5982\u4f55\u4fdd\u5b58\u7ed8\u5236\u7684\u56fe\u5f62\uff1f<\/strong> \u8fd9\u884c\u4ee3\u7801\u4f1a\u5c06\u5f53\u524d\u7ed8\u5236\u7684\u56fe\u5f62\u4fdd\u5b58\u4e3a\u540d\u4e3a\u201cmy_plot.png\u201d\u7684\u6587\u4ef6\u3002<\/p>\n \u6211\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u5176\u4ed6\u5e93\u6765\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong>\n
\u4e00\u3001\u5b89\u88c5Matplotlib<\/h3>\n<\/p>\n
pip install matplotlib<\/p>\n\u4e8c\u3001\u5bfc\u5165\u5e93\u5e76\u521b\u5efa\u57fa\u672c\u7ed8\u56fe<\/h3>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u7ed8\u56fe<\/strong><\/h2>\n
\u5c55\u793a\u7ed8\u56fe<\/strong><\/h2>\n
x<\/code>\u548cy<\/code>\u5206\u522b\u662f\u6570\u636e\u7684\u6a2a\u5750\u6807\u548c\u7eb5\u5750\u6807\u3002<\/p>\n<\/p>\n\u4e09\u3001\u81ea\u5b9a\u4e49\u7ed8\u56fe<\/h3>\n<\/p>\n
1. \u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/h4>\n<\/p>\n
plt.plot(x, y)<\/p>\n2. \u66f4\u6539\u7ebf\u6761\u6837\u5f0f\u548c\u989c\u8272<\/h4>\n<\/p>\n
plt.plot(x, y, color='green', linestyle='--', marker='o')<\/p>\n3. \u6dfb\u52a0\u7f51\u683c\u7ebf<\/h4>\n<\/p>\n
plt.plot(x, y)<\/p>\n4. \u6dfb\u52a0\u56fe\u4f8b<\/h4>\n<\/p>\n
plt.plot(x, y, label='Line 1')<\/p>\n\u56db\u3001\u4fdd\u5b58\u548c\u5c55\u793a\u7ed8\u56fe<\/h3>\n<\/p>\n
plt.plot(x, y)<\/p>\n\u4fdd\u5b58\u7ed8\u56fe<\/strong><\/h2>\n
\u5c55\u793a\u7ed8\u56fe<\/strong><\/h2>\n
\u4e94\u3001\u4f7f\u7528\u5b50\u56fe<\/h3>\n<\/p>\n
subplot<\/code>\u529f\u80fd\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\nimport matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u7b2c\u4e00\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
\u521b\u5efa\u7b2c\u4e8c\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
\u5c55\u793a\u5b50\u56fe<\/strong><\/h2>\n
\u516d\u3001\u5176\u4ed6\u9ad8\u7ea7\u529f\u80fd<\/h3>\n<\/p>\n
1. \u7ed8\u5236\u6761\u5f62\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u6761\u5f62\u56fe<\/strong><\/h2>\n
2. \u7ed8\u5236\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
3. \u7ed8\u5236\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u76f4\u65b9\u56fe<\/strong><\/h2>\n
4. \u7ed8\u5236\u997c\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u997c\u56fe<\/strong><\/h2>\n
\u4e03\u3001\u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u7ed8\u56fe<\/h3>\n<\/p>\n
\u5b89\u88c5Seaborn<\/h4>\n<\/p>\n
pip install seaborn<\/p>\n\u4f7f\u7528Seaborn\u7ed8\u5236\u56fe\u8868<\/h4>\n<\/p>\n
import seaborn as sns<\/p>\n\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u7ed8\u5236\u5e26\u6709\u56de\u5f52\u7ebf\u7684\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
import seaborn as sns<\/p>\n\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
\u521b\u5efa\u5e26\u6709\u56de\u5f52\u7ebf\u7684\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u7ed8\u5236\u5206\u7c7b\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
import seaborn as sns<\/p>\n\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
\u521b\u5efa\u5206\u7c7b\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u516b\u3001\u4f7f\u7528Plotly\u8fdb\u884c\u4e92\u52a8\u7ed8\u56fe<\/h3>\n<\/p>\n
\u5b89\u88c5Plotly<\/h4>\n<\/p>\n
pip install plotly<\/p>\n\u4f7f\u7528Plotly\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u8868<\/h4>\n<\/p>\n
import plotly.express as px<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
\u7ed8\u52363D\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
import plotly.express as px<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa3D\u6563\u70b9\u56fe<\/strong><\/h2>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/h4>\n<\/p>\n
import plotly.express as px<\/p>\n\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u56fe<\/strong><\/h2>\n
\u5c55\u793a\u56fe\u8868<\/strong><\/h2>\n
\u4e5d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
Matplotlib\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u80fd\u591f\u8f7b\u677e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002\u8981\u4f7f\u7528\u5b83\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884cpip install matplotlib<\/code>\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u793a\u4f8b\u6253\u5f00\u7ed8\u56fe\u7a97\u53e3\u5e76\u7ed8\u5236\u7b80\u5355\u7684\u6298\u7ebf\u56fe\uff1a <\/p>\nimport matplotlib.pyplot as plt\n\nx = [1, 2, 3, 4, 5]\ny = [2, 3, 5, 7, 11]\nplt.plot(x, y)\nplt.title("Sample Line Plot")\nplt.xlabel("X-axis")\nplt.ylabel("Y-axis")\nplt.show()\n<\/code><\/pre>\n
\u5728Matplotlib\u4e2d\uff0c\u9664\u4e86\u663e\u793a\u56fe\u5f62\u5916\uff0c\u8fd8\u53ef\u4ee5\u8f7b\u677e\u4fdd\u5b58\u56fe\u5f62\u4e3a\u6587\u4ef6\u3002\u4f7f\u7528savefig()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5c06\u5f53\u524d\u56fe\u5f62\u4fdd\u5b58\u4e3aPNG\u3001PDF\u7b49\u683c\u5f0f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a <\/p>\nplt.plot(x, y)\nplt.savefig("my_plot.png")\n<\/code><\/pre>\n
\u9664\u4e86Matplotlib\uff0cPython\u8fd8\u652f\u6301\u591a\u4e2a\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5982Seaborn\u3001Plotly\u548cBokeh\u7b49\u3002Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u63a5\u53e3\uff0c\u9002\u5408\u7edf\u8ba1\u56fe\u5f62\u7684\u7ed8\u5236\u3002Plotly\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u5f62\u7684\u529f\u80fd\uff0c\u9002\u5408Web\u5e94\u7528\u3002Bokeh\u9002\u5408\u5927\u6570\u636e\u53ef\u89c6\u5316\uff0c\u80fd\u591f\u751f\u6210\u6f02\u4eae\u7684\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u9009\u62e9\u6700\u9002\u5408\u7684\u5de5\u5177\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6253\u5f00\u7ed8\u56fe\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u5305\u62ec\u4f7f\u7528Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u5e93\u3002\u63a8\u8350\u7684\u65b9 […]","protected":false},"author":3,"featured_media":1162657,"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\/1162651"}],"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=1162651"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1162651\/revisions"}],"predecessor-version":[{"id":1162659,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1162651\/revisions\/1162659"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1162657"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1162651"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1162651"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1162651"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}