{"id":1048764,"date":"2024-12-31T13:53:53","date_gmt":"2024-12-31T05:53:53","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1048764.html"},"modified":"2024-12-31T13:53:58","modified_gmt":"2024-12-31T05:53:58","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e5%81%9a%e5%8f%af%e8%a7%86%e5%8c%96%e6%8a%a5%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1048764.html","title":{"rendered":"\u5982\u4f55\u4f7f\u7528python\u505a\u53ef\u89c6\u5316\u62a5\u8868"},"content":{"rendered":"

\"\u5982\u4f55\u4f7f\u7528python\u505a\u53ef\u89c6\u5316\u62a5\u8868\"<\/p>\n

\u5982\u4f55\u4f7f\u7528Python\u505a\u53ef\u89c6\u5316\u62a5\u8868<\/strong><\/p>\n<\/p>\n

\u4f7f\u7528Python\u505a\u53ef\u89c6\u5316\u62a5\u8868\u7684\u5173\u952e\u5728\u4e8e\u9009\u62e9\u5408\u9002\u7684\u53ef\u89c6\u5316\u5e93\u3001\u7406\u89e3\u6570\u636e\u3001\u521b\u5efa\u56fe\u8868\u3001\u5e76\u8fdb\u884c\u6570\u636e\u7684\u7f8e\u5316\u548c\u89e3\u91ca<\/strong>\u3002\u5176\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u53ef\u89c6\u5316\u5e93\u662f\u6700\u4e3a\u91cd\u8981\u7684\u4e00\u6b65\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5f3a\u5927\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u6bd4\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u521b\u5efa\u4e13\u4e1a\u7684\u53ef\u89c6\u5316\u62a5\u8868\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u9009\u62e9\u5408\u9002\u7684\u53ef\u89c6\u5316\u5e93<\/h3>\n<\/p>\n

Python\u7684\u53ef\u89c6\u5316\u5e93\u4f17\u591a\uff0c\u6bcf\u4e2a\u5e93\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\uff1a<\/p>\n<\/p>\n

1. Matplotlib<\/h4>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u548c\u5e7f\u6cdb\u4f7f\u7528\u7684\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u521b\u5efa\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002\u9002\u7528\u4e8e\u7b80\u5355\u7684\u7ed8\u56fe\u9700\u6c42\u548c\u7ec6\u81f4\u7684\u81ea\u5b9a\u4e49\u8c03\u6574\u3002<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u7b80\u5355\u7684\u6298\u7ebf\u56fe<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [10, 20, 25, 30, 35]<\/p>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X\u8f74\u6807\u7b7e')<\/p>\n
plt.ylabel('Y\u8f74\u6807\u7b7e')<\/p>\n
plt.title('\u7b80\u5355\u7684\u6298\u7ebf\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. Seaborn<\/h4>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u4e4b\u4e0a\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u548c\u9ad8\u7ea7\u7684\u7edf\u8ba1\u56fe\u8868\u3002\u5b83\u975e\u5e38\u9002\u5408\u4e8e\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5efa\u6a21\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u521b\u5efa\u590d\u6742\u7684\u53ef\u89c6\u5316\u56fe\u8868\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u4f7f\u7528Seaborn\u521b\u5efa\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
tips = sns.load_dataset('tips')<\/p>\n
sns.boxplot(x='day', y='total_bill', data=tips)<\/p>\n
plt.title('\u6bcf\u65e5\u8d26\u5355\u603b\u989d\u7684\u7bb1\u7ebf\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. Plotly<\/h4>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u5e93\uff0c\u652f\u6301\u4e30\u5bcc\u7684\u56fe\u8868\u7c7b\u578b\u548c\u5f3a\u5927\u7684\u4ea4\u4e92\u529f\u80fd\uff0c\u9002\u7528\u4e8e\u9700\u8981\u4ea4\u4e92\u548c\u52a8\u6001\u5c55\u793a\u7684\u6570\u636e\u53ef\u89c6\u5316\u9700\u6c42\u3002<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u4f7f\u7528Plotly\u521b\u5efa\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe<\/strong><\/h2>\n
df = px.data.iris()<\/p>\n
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u7406\u89e3\u6570\u636e<\/h3>\n<\/p>\n
\u5728\u521b\u5efa\u53ef\u89c6\u5316\u62a5\u8868\u4e4b\u524d\uff0c\u7406\u89e3\u6570\u636e\u662f\u81f3\u5173\u91cd\u8981\u7684\u3002\u9700\u8981\u901a\u8fc7\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u7b49\u6b65\u9aa4\u6765\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('data.csv')<\/p>\n
\u6570\u636e\u9884\u89c8<\/strong><\/h2>\n
print(data.head())<\/p>\n
\u68c0\u67e5\u7f3a\u5931\u503c<\/strong><\/h2>\n
print(data.isnull().sum())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u521b\u5efa\u56fe\u8868<\/h3>\n<\/p>\n
\u6839\u636e\u6570\u636e\u7279\u70b9\u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u7c7b\u578b\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002\u4e0d\u540c\u7684\u56fe\u8868\u7c7b\u578b\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u6570\u636e\u5c55\u793a\u9700\u6c42\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<\/p>\n<\/p>\n
plt.plot(data['date'], data['value'])<\/p>\n
plt.xlabel('\u65e5\u671f')<\/p>\n
plt.ylabel('\u6570\u503c')<\/p>\n
plt.title('\u6570\u636e\u53d8\u5316\u8d8b\u52bf\u56fe')<\/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<\/p>\n<\/p>\n
plt.bar(data['category'], data['value'])<\/p>\n
plt.xlabel('\u7c7b\u522b')<\/p>\n
plt.ylabel('\u6570\u503c')<\/p>\n
plt.title('\u7c7b\u522b\u6bd4\u8f83\u67f1\u72b6\u56fe')<\/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\u663e\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
plt.scatter(data['variable1'], data['variable2'])<\/p>\n
plt.xlabel('\u53d8\u91cf1')<\/p>\n
plt.ylabel('\u53d8\u91cf2')<\/p>\n
plt.title('\u53d8\u91cf\u5173\u7cfb\u6563\u70b9\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u7f8e\u5316\u548c\u89e3\u91ca<\/h3>\n<\/p>\n
\u521b\u5efa\u56fe\u8868\u540e\uff0c\u9700\u8981\u5bf9\u56fe\u8868\u8fdb\u884c\u7f8e\u5316\u548c\u89e3\u91ca\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u4f20\u8fbe\u6570\u636e\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
1. \u56fe\u8868\u7f8e\u5316<\/h4>\n<\/p>\n
\u901a\u8fc7\u8c03\u6574\u989c\u8272\u3001\u5b57\u4f53\u3001\u6807\u7b7e\u7b49\u6765\u7f8e\u5316\u56fe\u8868\uff0c\u4f7f\u5176\u66f4\u5177\u5438\u5f15\u529b\u548c\u6613\u8bfb\u6027\u3002<\/p>\n<\/p>\n
plt.plot(data['date'], data['value'], color='skyblue', linewidth=2)<\/p>\n
plt.xlabel('\u65e5\u671f', fontsize=14)<\/p>\n
plt.ylabel('\u6570\u503c', fontsize=14)<\/p>\n
plt.title('\u6570\u636e\u53d8\u5316\u8d8b\u52bf\u56fe', fontsize=16)<\/p>\n
plt.grid(True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6570\u636e\u89e3\u91ca<\/h4>\n<\/p>\n
\u5728\u53ef\u89c6\u5316\u62a5\u8868\u4e2d\uff0c\u6dfb\u52a0\u6570\u636e\u7684\u89e3\u91ca\u548c\u6ce8\u91ca\uff0c\u6709\u52a9\u4e8e\u8bfb\u8005\u7406\u89e3\u56fe\u8868\u4e2d\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
plt.plot(data['date'], data['value'], color='skyblue', linewidth=2)<\/p>\n
plt.xlabel('\u65e5\u671f', fontsize=14)<\/p>\n
plt.ylabel('\u6570\u503c', fontsize=14)<\/p>\n
plt.title('\u6570\u636e\u53d8\u5316\u8d8b\u52bf\u56fe', fontsize=16)<\/p>\n
plt.grid(True)<\/p>\n
plt.annotate('\u91cd\u8981\u4e8b\u4ef6', xy=('2023-01-01', 50), xytext=('2023-01-10', 60),<\/p>\n
             arrowprops=dict(facecolor='black', shrink=0.05))<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u7efc\u5408\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5f80\u5f80\u9700\u8981\u7efc\u5408\u4f7f\u7528\u591a\u79cd\u56fe\u8868\u548c\u6280\u672f\uff0c\u521b\u5efa\u590d\u6742\u7684\u53ef\u89c6\u5316\u62a5\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7efc\u5408\u5e94\u7528\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
import plotly.express as px<\/p>\n
\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('data.csv')<\/p>\n
\u6570\u636e\u6e05\u6d17<\/strong><\/h2>\n
data.dropna(inplace=True)<\/p>\n
\u521b\u5efa\u5b50\u56fe<\/strong><\/h2>\n
fig, axes = plt.subplots(2, 2, figsize=(15, 10))<\/p>\n
\u6298\u7ebf\u56fe<\/strong><\/h2>\n
axes[0, 0].plot(data['date'], data['value'], color='skyblue')<\/p>\n
axes[0, 0].set_title('\u6570\u636e\u53d8\u5316\u8d8b\u52bf\u56fe')<\/p>\n
axes[0, 0].set_xlabel('\u65e5\u671f')<\/p>\n
axes[0, 0].set_ylabel('\u6570\u503c')<\/p>\n
\u67f1\u72b6\u56fe<\/strong><\/h2>\n
sns.barplot(x='category', y='value', data=data, ax=axes[0, 1])<\/p>\n
axes[0, 1].set_title('\u7c7b\u522b\u6bd4\u8f83\u67f1\u72b6\u56fe')<\/p>\n
\u6563\u70b9\u56fe<\/strong><\/h2>\n
axes[1, 0].scatter(data['variable1'], data['variable2'], color='red')<\/p>\n
axes[1, 0].set_title('\u53d8\u91cf\u5173\u7cfb\u6563\u70b9\u56fe')<\/p>\n
axes[1, 0].set_xlabel('\u53d8\u91cf1')<\/p>\n
axes[1, 0].set_ylabel('\u53d8\u91cf2')<\/p>\n
\u70ed\u529b\u56fe<\/strong><\/h2>\n
corr = data.corr()<\/p>\n
sns.heatmap(corr, annot=True, cmap='coolwarm', ax=axes[1, 1])<\/p>\n
axes[1, 1].set_title('\u76f8\u5173\u6027\u70ed\u529b\u56fe')<\/p>\n
\u8c03\u6574\u5e03\u5c40<\/strong><\/h2>\n
plt.tight_layout()<\/p>\n
plt.show()<\/p>\n
\u4f7f\u7528Plotly\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868<\/strong><\/h2>\n
fig = px.scatter(data, x='variable1', y='variable2', color='category', title='\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
\u4f7f\u7528Python\u505a\u53ef\u89c6\u5316\u62a5\u8868\u7684\u8fc7\u7a0b\u6d89\u53ca\u591a\u4e2a\u6b65\u9aa4\uff0c\u4ece\u9009\u62e9\u5408\u9002\u7684\u53ef\u89c6\u5316\u5e93\u3001\u7406\u89e3\u6570\u636e\u3001\u521b\u5efa\u56fe\u8868\uff0c\u5230\u6700\u540e\u7684\u6570\u636e\u7f8e\u5316\u548c\u89e3\u91ca\u3002\u6bcf\u4e00\u6b65\u90fd\u9700\u8981\u4ed4\u7ec6\u601d\u8003\u548c\u64cd\u4f5c\uff0c\u4ee5\u786e\u4fdd\u6700\u7ec8\u7684\u53ef\u89c6\u5316\u62a5\u8868\u80fd\u591f\u51c6\u786e\u3001\u6e05\u6670\u5730\u4f20\u8fbe\u6570\u636e\u7684\u4fe1\u606f\u3002\u901a\u8fc7\u4e0d\u65ad\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u53ef\u4ee5\u638c\u63e1\u66f4\u591a\u7684\u6280\u5de7\u548c\u65b9\u6cd5\uff0c\u521b\u5efa\u51fa\u4e13\u4e1a\u7684\u53ef\u89c6\u5316\u62a5\u8868\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5176\u4e2d\u6700\u53d7\u6b22\u8fce\u7684\u5305\u62ecMatplotlib\u3001Seaborn\u548cPlotly\u3002Matplotlib\u662f\u4e00\u4e2a\u57fa\u7840\u5e93\uff0c\u9002\u5408\u521b\u5efa\u7b80\u5355\u7684\u56fe\u8868\uff1bSeaborn\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u66f4\u7f8e\u89c2\u7684\u56fe\u8868\u6837\u5f0f\uff0c\u9002\u5408\u7edf\u8ba1\u6570\u636e\u7684\u53ef\u89c6\u5316\uff1bPlotly\u5219\u652f\u6301\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u975e\u5e38\u9002\u5408\u5728Web\u5e94\u7528\u4e2d\u4f7f\u7528\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53d6\u51b3\u4e8e\u4f60\u7684\u9700\u6c42\u548c\u6570\u636e\u7c7b\u578b\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u6570\u636e\u4ee5\u4fbf\u53ef\u89c6\u5316\uff1f<\/strong>
\u5728\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u7406\u548c\u5904\u7406\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7Pandas\u5e93\u6765\u5b9e\u73b0\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Pandas\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff08\u5982CSV\u3001Excel\u7b49\uff09\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u7b5b\u9009\u3001\u5206\u7ec4\u3001\u805a\u5408\u7b49\u64cd\u4f5c\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u5b8c\u6574\u6027\u3002\u6570\u636e\u5904\u7406\u7684\u597d\u574f\u76f4\u63a5\u5f71\u54cd\u5230\u53ef\u89c6\u5316\u7ed3\u679c\u7684\u8d28\u91cf\u3002<\/p>\n
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