{"id":1146121,"date":"2025-01-08T23:14:17","date_gmt":"2025-01-08T15:14:17","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1146121.html"},"modified":"2025-01-08T23:14:19","modified_gmt":"2025-01-08T15:14:19","slug":"%e5%9c%a8python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%b0%86%e4%ba%8c%e7%bb%b4%e6%95%b0%e6%8d%ae%e9%9b%86%e5%8f%af%e8%a7%86%e5%8c%96","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1146121.html","title":{"rendered":"\u5728python\u4e2d\u5982\u4f55\u5c06\u4e8c\u7ef4\u6570\u636e\u96c6\u53ef\u89c6\u5316"},"content":{"rendered":"

\"\u5728python\u4e2d\u5982\u4f55\u5c06\u4e8c\u7ef4\u6570\u636e\u96c6\u53ef\u89c6\u5316\"<\/p>\n

\u5728Python\u4e2d\u5c06\u4e8c\u7ef4\u6570\u636e\u96c6\u53ef\u89c6\u5316\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u6709\uff1aMatplotlib\u3001Seaborn\u3001Pandas\u4e2d\u7684plot\u529f\u80fd\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u5de5\u5177\uff0c\u5e76\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u548c\u5177\u4f53\u7684\u4f7f\u7528\u573a\u666f\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528Matplotlib\u8fdb\u884c\u4e8c\u7ef4\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB<\/h3>\n<\/p>\n

Matplotlib<\/strong>\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002\u5b83\u7684\u7075\u6d3b\u6027\u548c\u5f3a\u5927\u7684\u81ea\u5b9a\u4e49\u529f\u80fd\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u53ef\u89c6\u5316\u7684\u9996\u9009\u5de5\u5177\u4e4b\u4e00\u3002<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\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
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5f00\u59cb\u7ed8\u5236\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u7528Matplotlib\u7ed8\u5236\u4e00\u4e2a\u57fa\u672c\u7684\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e\u96c6<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x, y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Simple Scatter 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
2\u3001\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
\u6563\u70b9\u56fe\u662f\u7528\u4e8e\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5173\u7cfb\u7684\u56fe\u8868\u3002\u5728\u6570\u636e\u79d1\u5b66\u4e2d\uff0c\u6563\u70b9\u56fe\u5e38\u7528\u4e8e\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
np.random.seed(0)<\/p>\n
x = np.random.rand(50)<\/p>\n
y = np.random.rand(50)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter(x, y, c='blue', alpha=0.5, label='Data points')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Scatter Plot Example')<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.legend()<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6298\u7ebf\u56fe<\/h4>\n<\/p>\n
\u6298\u7ebf\u56fe\u662f\u53e6\u4e00\u79cd\u5e38\u7528\u7684\u4e8c\u7ef4\u56fe\u8868\uff0c\u7528\u4e8e\u663e\u793a\u6570\u636e\u70b9\u968f\u65f6\u95f4\u7684\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y, marker='o')<\/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
4\u3001\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
\u67f1\u72b6\u56fe\u7528\u4e8e\u6bd4\u8f83\u4e0d\u540c\u7c7b\u522b\u7684\u6570\u636e\u70b9\u7684\u503c\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
categories = ['A', 'B', 'C', 'D', 'E']<\/p>\n
values = [5, 7, 3, 8, 4]<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
plt.bar(categories, values, color='green')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Bar Chart Example')<\/p>\n
plt.xlabel('Categories')<\/p>\n
plt.ylabel('Values')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001SEABORN<\/h3>\n<\/p>\n
Seaborn<\/strong>\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7f8e\u89c2\u548c\u590d\u6742\u7684\u56fe\u8868\u3002\u5b83\u7b80\u5316\u4e86\u8bb8\u591aMatplotlib\u7684\u8bbe\u7f6e\uff0c\u5e76\u6dfb\u52a0\u4e86\u8bb8\u591a\u65b0\u7684\u56fe\u8868\u7c7b\u578b\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u5b89\u88c5Seaborn\u5e93\uff1a<\/p>\n<\/p>\n
pip install seaborn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684Seaborn\u6563\u70b9\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u751f\u6210\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
np.random.seed(0)<\/p>\n
x = np.random.rand(50)<\/p>\n
y = np.random.rand(50)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.scatterplot(x=x, y=y)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Seaborn Scatter Plot Example')<\/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\u3001\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
Seaborn\u7684\u6563\u70b9\u56fe\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u6dfb\u52a0\u56de\u5f52\u7ebf\u548c\u4e0d\u540c\u7684\u8c03\u8272\u677f\u3002<\/p>\n<\/p>\n
# \u751f\u6210\u968f\u673a\u6570\u636e<\/p>\n
np.random.seed(0)<\/p>\n
x = np.random.rand(50)<\/p>\n
y = np.random.rand(50)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe\u5e76\u6dfb\u52a0\u56de\u5f52\u7ebf<\/strong><\/h2>\n
sns.regplot(x=x, y=y, scatter_kws={'s':50, 'alpha':0.5}, line_kws={'color':'red'})<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Seaborn Scatter Plot with Regression Line')<\/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
3\u3001\u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5305\u62ec\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u548c\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
data = np.random.normal(size=(100, 4))<\/p>\n
\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(data=data)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Box Plot Example')<\/p>\n
plt.xlabel('Categories')<\/p>\n
plt.ylabel('Values')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u70ed\u56fe<\/h4>\n<\/p>\n
\u70ed\u56fe\u7528\u4e8e\u663e\u793a\u77e9\u9635\u6570\u636e\u7684\u70ed\u5ea6\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
data = np.random.rand(10, 12)<\/p>\n
\u7ed8\u5236\u70ed\u56fe<\/strong><\/h2>\n
sns.heatmap(data, annot=True, cmap='coolwarm')<\/p>\n
\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
plt.title('Heatmap Example')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001PANDAS\u4e2d\u7684PLOT\u529f\u80fd<\/h3>\n<\/p>\n
Pandas<\/strong>\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5185\u7f6e\u4e86\u7b80\u5355\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u6570\u636e\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u57fa\u672c\u4f7f\u7528<\/h4>\n<\/p>\n
Pandas\u901a\u5e38\u4e0eMatplotlib\u4e00\u8d77\u4f7f\u7528\uff0c\u786e\u4fdd\u5df2\u5b89\u88c5\u8fd9\u4e24\u4e2a\u5e93\uff1a<\/p>\n<\/p>\n
pip install pandas matplotlib<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0b\u662fPandas\u7684\u57fa\u672c\u7ed8\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u6570\u636e\u96c6<\/strong><\/h2>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [2, 3, 5, 7, 11]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
df.plot(x='A', y='B', kind='line', marker='o')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Pandas Line Plot Example')<\/p>\n
plt.xlabel('A')<\/p>\n
plt.ylabel('B')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6563\u70b9\u56fe<\/h4>\n<\/p>\n
Pandas\u53ef\u4ee5\u76f4\u63a5\u751f\u6210\u6563\u70b9\u56fe\uff0c\u9002\u7528\u4e8e\u5feb\u901f\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
data = {<\/p>\n
    'A': np.random.rand(50),<\/p>\n
    'B': np.random.rand(50)<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
df.plot(kind='scatter', x='A', y='B', color='blue', alpha=0.5)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Pandas Scatter Plot Example')<\/p>\n
plt.xlabel('A')<\/p>\n
plt.ylabel('B')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u67f1\u72b6\u56fe<\/h4>\n<\/p>\n
Pandas\u7684\u67f1\u72b6\u56fe\u529f\u80fd\u975e\u5e38\u7b80\u4fbf\uff0c\u9002\u7528\u4e8e\u6570\u636e\u7684\u5feb\u901f\u6bd4\u8f83\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
data = {<\/p>\n
    'Categories': ['A', 'B', 'C', 'D', 'E'],<\/p>\n
    'Values': [5, 7, 3, 8, 4]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
df.plot(kind='bar', x='Categories', y='Values', color='green')<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Pandas Bar Chart Example')<\/p>\n
plt.xlabel('Categories')<\/p>\n
plt.ylabel('Values')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
\u76f4\u65b9\u56fe\u7528\u4e8e\u663e\u793a\u6570\u636e\u7684\u9891\u7387\u5206\u5e03\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
data = {<\/p>\n
    'Values': np.random.randn(1000)<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
df['Values'].plot(kind='hist', bins=30, alpha=0.5)<\/p>\n
\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h2>\n
plt.title('Pandas Histogram Example')<\/p>\n
plt.xlabel('Values')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
\u663e\u793a\u56fe\u8868<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u4e2d\u6709\u591a\u79cd\u5de5\u5177\u53ef\u4ee5\u7528\u4e8e\u4e8c\u7ef4\u6570\u636e\u96c6\u7684\u53ef\u89c6\u5316\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u662fMatplotlib\u3001Seaborn\u548cPandas\u3002\u8fd9\u4e9b\u5de5\u5177\u5404\u6709\u4f18\u52a3\uff0c\u9009\u62e9\u54ea\u4e00\u79cd\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u4f7f\u7528\u573a\u666f\u548c\u9700\u6c42\u3002<\/p>\n<\/p>\n
    \n
  • Matplotlib<\/strong>\uff1a\u63d0\u4f9b\u4e86\u6700\u4e3a\u7075\u6d3b\u548c\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u9002\u5408\u9700\u8981\u9ad8\u5ea6\u81ea\u5b9a\u4e49\u7684\u56fe\u8868\u3002<\/li>\n
  • Seaborn<\/strong>\uff1a\u57fa\u4e8eMatplotlib\uff0c\u7b80\u5316\u4e86\u8bb8\u591a\u64cd\u4f5c\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\uff0c\u9002\u5408\u5feb\u901f\u751f\u6210\u590d\u6742\u56fe\u8868\u3002<\/li>\n
  • Pandas<\/strong>\uff1a\u5185\u7f6e\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u9002\u5408\u5feb\u901f\u6570\u636e\u53ef\u89c6\u5316\uff0c\u7279\u522b\u662f\u4e0e\u6570\u636e\u5904\u7406\u76f8\u7ed3\u5408\u65f6\u3002<\/li>\n<\/ul>\n
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