{"id":1128606,"date":"2025-01-08T20:22:13","date_gmt":"2025-01-08T12:22:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1128606.html"},"modified":"2025-01-08T20:22:15","modified_gmt":"2025-01-08T12:22:15","slug":"%e5%a6%82%e4%bd%95%e8%ae%a9x%e8%bd%b4%e4%bb%8e%e4%b8%80%e5%bc%80%e5%a7%8bpython","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1128606.html","title":{"rendered":"\u5982\u4f55\u8ba9x\u8f74\u4ece\u4e00\u5f00\u59cbpython"},"content":{"rendered":"

\"\u5982\u4f55\u8ba9x\u8f74\u4ece\u4e00\u5f00\u59cbpython\"<\/p>\n

\u8981\u8ba9x\u8f74\u4ece\u4e00\u5f00\u59cb\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\uff0c\u901a\u8fc7\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\u6765\u5b9e\u73b0\u3002<\/strong> \u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u8bbe\u7f6ex\u8f74\u7684\u523b\u5ea6\u8303\u56f4\u3001\u8c03\u6574\u6570\u636e\u8303\u56f4\u4ee5\u53ca\u6dfb\u52a0\u81ea\u5b9a\u4e49\u6807\u7b7e\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u793a\u4f8b\u4ee3\u7801\u6765\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Matplotlib\u5e93\u8bbe\u7f6ex\u8f74\u8303\u56f4<\/p>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u521b\u5efa\u5404\u79cd\u56fe\u8868\u3002\u901a\u8fc7\u8bbe\u7f6eMatplotlib\u7684x\u8f74\u8303\u56f4\uff0c\u60a8\u53ef\u4ee5\u8ba9x\u8f74\u4ece\u4e00\u5f00\u59cb\u3002<\/p>\n<\/p>\n

1\u3001\u5bfc\u5165Matplotlib\u5e93<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4e4b\u524d\uff0c\u60a8\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Matplotlib\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5bfc\u5165Matplotlib\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u57fa\u7840\u56fe\u8868<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u7ed8\u5236\u4e00\u4e2a\u57fa\u7840\u7684\u6298\u7ebf\u56fe\uff0c\u5e76\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [10, 15, 13, 17, 19]<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.title('\u6298\u7ebf\u56fe\u793a\u4f8b')<\/p>\n
\u8bbe\u7f6ex\u8f74\u4ece1\u5f00\u59cb<\/strong><\/h2>\n
plt.xlim(1, max(x))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.xlim(1, max(x))<\/code>\u8bbe\u7f6e\u4e86x\u8f74\u7684\u8303\u56f4\u4ece1\u5230x\u6570\u636e\u7684\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n
3\u3001\u8bbe\u7f6e\u81ea\u5b9a\u4e49x\u8f74\u523b\u5ea6<\/h3>\n<\/p>\n
\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528plt.xticks<\/code>\u51fd\u6570\u6765\u8bbe\u7f6e\u81ea\u5b9a\u4e49\u7684x\u8f74\u523b\u5ea6\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [10, 15, 13, 17, 19]<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.title('\u81ea\u5b9a\u4e49x\u8f74\u523b\u5ea6\u793a\u4f8b')<\/p>\n
\u8bbe\u7f6ex\u8f74\u4ece1\u5f00\u59cb\uff0c\u5e76\u8bbe\u7f6e\u81ea\u5b9a\u4e49\u523b\u5ea6<\/strong><\/h2>\n
plt.xlim(1, max(x))<\/p>\n
plt.xticks([1, 2, 3, 4, 5])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.xticks([1, 2, 3, 4, 5])<\/code>\u8bbe\u7f6e\u4e86\u81ea\u5b9a\u4e49\u7684x\u8f74\u523b\u5ea6\uff0c\u4f7f\u5f97x\u8f74\u4ece1\u5f00\u59cb\u5e76\u663e\u793a\u7279\u5b9a\u7684\u523b\u5ea6\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Pandas\u548cSeaborn\u5e93\u8bbe\u7f6ex\u8f74\u8303\u56f4<\/p>\n<\/p>\n
\u9664\u4e86Matplotlib\u5e93\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Pandas\u548cSeaborn\u5e93\u6765\u8bbe\u7f6ex\u8f74\u8303\u56f4\u3002Pandas\u548cSeaborn\u5728\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\u65b9\u9762\u63d0\u4f9b\u4e86\u66f4\u9ad8\u5c42\u6b21\u7684\u63a5\u53e3\uff0c\u4f7f\u5f97\u64cd\u4f5c\u66f4\u52a0\u7b80\u4fbf\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165Pandas\u548cSeaborn\u5e93<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u548cSeaborn\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5bfc\u5165Pandas\u548cSeaborn\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Pandas\u548cSeaborn\u7ed8\u5236\u56fe\u8868<\/h3>\n<\/p>\n
\u4f7f\u7528Pandas\u548cSeaborn\u7ed8\u5236\u56fe\u8868\uff0c\u5e76\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'x': [1, 2, 3, 4, 5],<\/p>\n
    'y': [10, 15, 13, 17, 19]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
sns.lineplot(x='x', y='y', data=df)<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.title('Pandas\u548cSeaborn\u793a\u4f8b')<\/p>\n
\u8bbe\u7f6ex\u8f74\u4ece1\u5f00\u59cb<\/strong><\/h2>\n
plt.xlim(1, df['x'].max())<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.xlim(1, df['x'].max())<\/code>\u8bbe\u7f6e\u4e86x\u8f74\u7684\u8303\u56f4\u4ece1\u5230x\u6570\u636e\u7684\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n
3\u3001\u4f7f\u7528Pandas\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n
\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Pandas\u6765\u5904\u7406\u6570\u636e\uff0c\u5e76\u8bbe\u7f6e\u81ea\u5b9a\u4e49x\u8f74\u523b\u5ea6\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'x': [1, 2, 3, 4, 5],<\/p>\n
    'y': [10, 15, 13, 17, 19]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
sns.lineplot(x='x', y='y', data=df)<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.title('Pandas\u5904\u7406\u6570\u636e\u793a\u4f8b')<\/p>\n
\u8bbe\u7f6ex\u8f74\u4ece1\u5f00\u59cb\uff0c\u5e76\u8bbe\u7f6e\u81ea\u5b9a\u4e49\u523b\u5ea6<\/strong><\/h2>\n
plt.xlim(1, df['x'].max())<\/p>\n
plt.xticks([1, 2, 3, 4, 5])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.xticks([1, 2, 3, 4, 5])<\/code>\u8bbe\u7f6e\u4e86\u81ea\u5b9a\u4e49\u7684x\u8f74\u523b\u5ea6\uff0c\u4f7f\u5f97x\u8f74\u4ece1\u5f00\u59cb\u5e76\u663e\u793a\u7279\u5b9a\u7684\u523b\u5ea6\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528NumPy\u751f\u6210\u6570\u636e\u5e76\u8bbe\u7f6ex\u8f74\u8303\u56f4<\/p>\n<\/p>\n
NumPy\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u7ec4\u5904\u7406\u529f\u80fd\u3002\u901a\u8fc7\u4f7f\u7528NumPy\u751f\u6210\u6570\u636e\uff0c\u5e76\u7ed3\u5408Matplotlib\u5e93\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u8bbe\u7f6ex\u8f74\u8303\u56f4\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165NumPy\u5e93<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86NumPy\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5bfc\u5165NumPy\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528NumPy\u751f\u6210\u6570\u636e<\/h3>\n<\/p>\n
\u4f7f\u7528NumPy\u751f\u6210\u6570\u636e\uff0c\u5e76\u7ed8\u5236\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = np.arange(1, 6)<\/p>\n
y = np.random.randint(10, 20, size=5)<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.title('NumPy\u751f\u6210\u6570\u636e\u793a\u4f8b')<\/p>\n
\u8bbe\u7f6ex\u8f74\u4ece1\u5f00\u59cb<\/strong><\/h2>\n
plt.xlim(1, np.max(x))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.xlim(1, np.max(x))<\/code>\u8bbe\u7f6e\u4e86x\u8f74\u7684\u8303\u56f4\u4ece1\u5230x\u6570\u636e\u7684\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n
3\u3001\u4f7f\u7528NumPy\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n
\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528NumPy\u6765\u5904\u7406\u6570\u636e\uff0c\u5e76\u8bbe\u7f6e\u81ea\u5b9a\u4e49x\u8f74\u523b\u5ea6\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x = np.arange(1, 6)<\/p>\n
y = np.random.randint(10, 20, size=5)<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X\u8f74')<\/p>\n
plt.ylabel('Y\u8f74')<\/p>\n
plt.title('NumPy\u5904\u7406\u6570\u636e\u793a\u4f8b')<\/p>\n
\u8bbe\u7f6ex\u8f74\u4ece1\u5f00\u59cb\uff0c\u5e76\u8bbe\u7f6e\u81ea\u5b9a\u4e49\u523b\u5ea6<\/strong><\/h2>\n
plt.xlim(1, np.max(x))<\/p>\n
plt.xticks(np.arange(1, 6))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0cplt.xticks(np.arange(1, 6))<\/code>\u8bbe\u7f6e\u4e86\u81ea\u5b9a\u4e49\u7684x\u8f74\u523b\u5ea6\uff0c\u4f7f\u5f97x\u8f74\u4ece1\u5f00\u59cb\u5e76\u663e\u793a\u7279\u5b9a\u7684\u523b\u5ea6\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684Matplotlib\u3001Pandas\u548cSeaborn\u5e93\u8bbe\u7f6ex\u8f74\u8303\u56f4\uff0c\u4f7f\u5f97x\u8f74\u4ece\u4e00\u5f00\u59cb\u3002\u901a\u8fc7\u8bbe\u7f6ex\u8f74\u7684\u523b\u5ea6\u8303\u56f4\u3001\u8c03\u6574\u6570\u636e\u8303\u56f4\u4ee5\u53ca\u6dfb\u52a0\u81ea\u5b9a\u4e49\u6807\u7b7e\u7b49\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Matplotlib\u5e93\u8bbe\u7f6ex\u8f74\u8303\u56f4<\/h3>\n<\/p>\n
\u901a\u8fc7plt.xlim<\/code>\u548cplt.xticks<\/code>\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\u548c\u81ea\u5b9a\u4e49\u523b\u5ea6\u3002<\/p>\n<\/p>\n
2\u3001\u4f7f\u7528Pandas\u548cSeaborn\u5e93\u8bbe\u7f6ex\u8f74\u8303\u56f4<\/h3>\n<\/p>\n
\u901a\u8fc7Pandas\u548cSeaborn\u5e93\uff0c\u60a8\u53ef\u4ee5\u66f4\u52a0\u4fbf\u6377\u5730\u5904\u7406\u6570\u636e\u548c\u7ed8\u5236\u56fe\u8868\uff0c\u5e76\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\u548c\u81ea\u5b9a\u4e49\u523b\u5ea6\u3002<\/p>\n<\/p>\n
3\u3001\u4f7f\u7528NumPy\u751f\u6210\u6570\u636e\u5e76\u8bbe\u7f6ex\u8f74\u8303\u56f4<\/h3>\n<\/p>\n
\u901a\u8fc7NumPy\u5e93\uff0c\u60a8\u53ef\u4ee5\u751f\u6210\u548c\u5904\u7406\u6570\u636e\uff0c\u5e76\u7ed3\u5408Matplotlib\u5e93\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\u548c\u81ea\u5b9a\u4e49\u523b\u5ea6\u3002<\/p>\n<\/p>\n
4\u3001\u5b9e\u9645\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u65b9\u6cd5\uff0c\u901a\u8fc7\u8bbe\u7f6ex\u8f74\u8303\u56f4\u6765\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u3002\u5728\u6570\u636e\u53ef\u89c6\u5316\u8fc7\u7a0b\u4e2d\uff0c\u5408\u7406\u5730\u8bbe\u7f6ex\u8f74\u8303\u56f4\u53ef\u4ee5\u8ba9\u56fe\u8868\u66f4\u52a0\u6e05\u6670\u548c\u6613\u4e8e\u7406\u89e3\uff0c\u4ece\u800c\u66f4\u597d\u5730\u4f20\u8fbe\u4fe1\u606f\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u60a8\u5df2\u7ecf\u638c\u63e1\u4e86\u5982\u4f55\u5728Python\u4e2d\u8bbe\u7f6ex\u8f74\u8303\u56f4\uff0c\u4f7f\u5f97x\u8f74\u4ece\u4e00\u5f00\u59cb\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5e93\uff0c\u7075\u6d3b\u5730\u8c03\u6574x\u8f74\u8303\u56f4\uff0c\u4f7f\u56fe\u8868\u66f4\u52a0\u7b26\u5408\u9884\u671f\u6548\u679c\u3002<\/p>\n<\/p>\n
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\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u6765\u8bbe\u7f6ex\u8f74\u7684\u8d77\u59cb\u503c\u3002\u901a\u8fc7\u8c03\u7528plt.xlim()<\/code>\u51fd\u6570\uff0c\u60a8\u53ef\u4ee5\u6307\u5b9ax\u8f74\u7684\u6700\u5c0f\u548c\u6700\u5927\u503c\u3002\u4f8b\u5982\uff0cplt.xlim(left=0)<\/code>\u53ef\u4ee5\u5c06x\u8f74\u7684\u8d77\u59cb\u503c\u8bbe\u7f6e\u4e3a0\u3002\u786e\u4fdd\u5728\u7ed8\u5236\u56fe\u5f62\u4e4b\u524d\u8c03\u7528\u6b64\u51fd\u6570\uff0c\u4ee5\u4fbf\u5b83\u80fd\u6b63\u786e\u5730\u5e94\u7528\u5230\u56fe\u8868\u4e2d\u3002<\/p>\n
\u5728\u7ed8\u56fe\u65f6\uff0c\u5982\u4f55\u786e\u4fddx\u8f74\u663e\u793a\u7684\u8303\u56f4\u5408\u7406\uff1f<\/strong>
\u8981\u786e\u4fddx\u8f74\u7684\u663e\u793a\u8303\u56f4\u5408\u7406\uff0c\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u52a8\u6001\u8c03\u6574x\u8f74\u7684\u8303\u56f4\u3002\u4f7f\u7528plt.xlim()<\/code>\u51fd\u6570\u914d\u5408min()<\/code>\u548cmax()<\/code>\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7plt.xlim(min(data), max(data))<\/code>\u6765\u81ea\u52a8\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u7684\u6ce2\u52a8\u3002<\/p>\n
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\u5728Python\u7684Matplotlib\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528plt.xticks()<\/code>\u51fd\u6570\u6765\u8c03\u6574x\u8f74\u7684\u523b\u5ea6\u548c\u6807\u7b7e\u3002\u901a\u8fc7\u4f20\u9012\u4e00\u4e2a\u5217\u8868\u4f5c\u4e3a\u7b2c\u4e00\u4e2a\u53c2\u6570\uff0c\u53ef\u4ee5\u6307\u5b9a\u523b\u5ea6\u7684\u4f4d\u7f6e\uff0c\u800c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u5219\u53ef\u4ee5\u7528\u6765\u8bbe\u7f6e\u5bf9\u5e94\u7684\u6807\u7b7e\u3002\u4f8b\u5982\uff0cplt.xticks([0, 1, 2], ['\u96f6', '\u4e00', '\u4e8c'])<\/code>\u5c06\u628ax\u8f74\u7684\u523b\u5ea6\u4f4d\u7f6e\u8bbe\u7f6e\u4e3a0, 1, 2\uff0c\u5e76\u4e3a\u5176\u5206\u914d\u81ea\u5b9a\u4e49\u6807\u7b7e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u8ba9x\u8f74\u4ece\u4e00\u5f00\u59cb\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u4f7f\u7528Matplotlib\u5e93\uff0c\u901a\u8fc7\u8bbe\u7f6ex\u8f74\u7684\u8303\u56f4\u6765\u5b9e\u73b0\u3002 \u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec […]","protected":false},"author":3,"featured_media":1128621,"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\/1128606"}],"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=1128606"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1128606\/revisions"}],"predecessor-version":[{"id":1128623,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1128606\/revisions\/1128623"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1128621"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1128606"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1128606"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1128606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}