{"id":1022013,"date":"2024-12-27T13:29:38","date_gmt":"2024-12-27T05:29:38","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1022013.html"},"modified":"2024-12-27T13:29:41","modified_gmt":"2024-12-27T05:29:41","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%94%bb%e5%9b%beseth","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1022013.html","title":{"rendered":"\u5982\u4f55\u7528python\u753b\u56feseth"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u753b\u56feseth\"<\/p>\n

\u5728Python\u4e2d\uff0c\u7ed8\u56fe\u662f\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002\u4f7f\u7528Python\u7ed8\u56fe\u7684\u6700\u4f73\u5de5\u5177\u4e4b\u4e00\u662fMatplotlib\u3001Seaborn\u548cPlotly<\/strong>\u3002\u8fd9\u4e09\u4e2a\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u4e14\u7075\u6d3b\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u7ed8\u5236\u4ece\u7b80\u5355\u7684\u7ebf\u56fe\u5230\u590d\u6742\u76843D\u56fe\u5f62\u7684\u5404\u79cd\u56fe\u5f62\u3002Matplotlib\u662f\u6700\u57fa\u7840\u7684\u5e93\uff0c\u9002\u5408\u521d\u5b66\u8005\uff0cSeaborn\u5728Matplotlib\u7684\u57fa\u7840\u4e0a\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\uff0c\u66f4\u9002\u5408\u7edf\u8ba1\u56fe\u8868\uff0c\u800cPlotly\u5219\u63d0\u4f9b\u4e86\u4ea4\u4e92\u5f0f\u56fe\u5f62\u7684\u80fd\u529b<\/strong>\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5229\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u7ed8\u5236\u4e0d\u540c\u7c7b\u578b\u7684\u56fe\u5f62\u3002<\/p>\n<\/p>\n

\u4e00\u3001MATPLOTLIB\u7684\u4f7f\u7528<\/h3>\n<\/p>\n

Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u521b\u5efa\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002<\/p>\n<\/p>\n

1\u3001\u57fa\u7840\u7ed8\u56fe<\/h4>\n<\/p>\n

Matplotlib\u7684\u6838\u5fc3\u662fpyplot<\/code>\u6a21\u5757\uff0c\u5b83\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eMATLAB\u7684\u7ed8\u56fe\u63a5\u53e3\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff0c\u5c55\u793a\u5982\u4f55\u7ed8\u5236\u57fa\u672c\u7684\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n

import matplotlib.pyplot as plt<\/p>\n
\u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
plt.plot(x, y, label='\u7ebf\u56fe', color='b', marker='o')<\/p>\n
plt.xlabel('X \u8f74')<\/p>\n
plt.ylabel('Y \u8f74')<\/p>\n
plt.title('\u7b80\u5355\u7ebf\u56fe')<\/p>\n
plt.legend()<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u7ebf\u56fe\uff0c\u5e76\u901a\u8fc7xlabel<\/code>\u548cylabel<\/code>\u6765\u8bbe\u7f6e\u8f74\u6807\u7b7e\uff0ctitle<\/code>\u6765\u8bbe\u7f6e\u56fe\u5f62\u6807\u9898\u3002<\/p>\n<\/p>\n
2\u3001\u5b50\u56fe\u548c\u56fe\u5f62\u5e03\u5c40<\/h4>\n<\/p>\n
Matplotlib\u5141\u8bb8\u901a\u8fc7subplot<\/code>\u51fd\u6570\u521b\u5efa\u591a\u4e2a\u5b50\u56fe\uff0c\u4ee5\u4fbf\u5728\u4e00\u4e2a\u56fe\u5f62\u7a97\u53e3\u4e2d\u663e\u793a\u591a\u4e2a\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u56fe\u5f62<\/strong><\/h2>\n
plt.figure(figsize=(10, 5))<\/p>\n
\u7b2c\u4e00\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
plt.subplot(1, 2, 1)<\/p>\n
plt.plot([1, 2, 3], [4, 5, 6], 'r')<\/p>\n
plt.title('\u5b50\u56fe 1')<\/p>\n
\u7b2c\u4e8c\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
plt.subplot(1, 2, 2)<\/p>\n
plt.plot([1, 2, 3], [1, 2, 3], 'b')<\/p>\n
plt.title('\u5b50\u56fe 2')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.tight_layout()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
subplot()<\/code>\u51fd\u6570\u7684\u53c2\u6570\u6307\u5b9a\u4e86\u5b50\u56fe\u7684\u5e03\u5c40\uff1a(\u884c\u6570, \u5217\u6570, \u5f53\u524d\u5b50\u56fe\u7684\u7d22\u5f15)<\/code>\u3002<\/p>\n<\/p>\n
\u4e8c\u3001SEABORN\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u4e13\u4e3a\u7edf\u8ba1\u56fe\u8868\u8bbe\u8ba1\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u56fe\u8868<\/h4>\n<\/p>\n
Seaborn\u7b80\u5316\u4e86\u521b\u5efa\u7f8e\u89c2\u4e14\u4fe1\u606f\u4e30\u5bcc\u7684\u7edf\u8ba1\u56fe\u8868\u7684\u8fc7\u7a0b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Seaborn\u7ed8\u5236\u6563\u70b9\u56fe\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = pd.DataFrame({<\/p>\n
    'x': [1, 2, 3, 4, 5],<\/p>\n
    'y': [2, 3, 5, 7, 11],<\/p>\n
    'z': [5, 4, 3, 2, 1]<\/p>\n
})<\/p>\n
\u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
sns.scatterplot(x='x', y='y', size='z', data=data, hue='z', palette='viridis')<\/p>\n
plt.title('Seaborn \u6563\u70b9\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
Seaborn\u901a\u8fc7\u66f4\u9ad8\u5c42\u7684\u63a5\u53e3\u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u76f4\u89c2\u7684\u65b9\u5f0f\u6765\u521b\u5efa\u56fe\u8868\u3002scatterplot<\/code>\u51fd\u6570\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9ahue<\/code>\u548csize<\/code>\u53c2\u6570\u6765\u521b\u5efa\u591a\u7ef4\u6570\u636e\u7684\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
2\u3001\u70ed\u56fe\u548c\u5206\u7c7b\u56fe<\/h4>\n<\/p>\n
Seaborn\u7279\u522b\u64c5\u957f\u4e8e\u7ed8\u5236\u70ed\u56fe\u548c\u5206\u7c7b\u56fe\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u968f\u673a\u6570\u636e<\/strong><\/h2>\n
data = np.random.rand(10, 12)<\/p>\n
\u7ed8\u5236\u70ed\u56fe<\/strong><\/h2>\n
sns.heatmap(data, annot=True, fmt=".1f")<\/p>\n
plt.title('Seaborn \u70ed\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u70ed\u56fe\u80fd\u591f\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u77e9\u9635\u4e2d\u7684\u6570\u503c\u6a21\u5f0f\uff0cannot=True<\/code>\u53c2\u6570\u53ef\u4ee5\u5728\u6bcf\u4e2a\u5355\u5143\u683c\u4e0a\u663e\u793a\u6570\u503c\u3002<\/p>\n<\/p>\n
\u4e09\u3001PLOTLY\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
Plotly\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u521b\u5efa\u7f51\u7edc\u5e94\u7528\u548c\u52a8\u6001\u56fe\u8868\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
Plotly\u63d0\u4f9b\u4e86\u975e\u5e38\u76f4\u89c2\u7684\u63a5\u53e3\u6765\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import plotly.express as px<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
df = px.data.iris()<\/p>\n
\u7ed8\u5236\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe<\/strong><\/h2>\n
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species',<\/p>\n
                 title='Plotly \u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
Plotly\u7684express<\/code>\u6a21\u5757\u4f7f\u5f97\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u53d8\u5f97\u975e\u5e38\u7b80\u5355\uff0c\u901a\u8fc7scatter<\/code>\u51fd\u6570\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe\u3002<\/p>\n<\/p>\n
2\u30013D\u7ed8\u56fe<\/h4>\n<\/p>\n
Plotly\u8fd8\u652f\u6301\u7ed8\u52363D\u56fe\u8868\uff0c\u8fd9\u5728\u5904\u7406\u591a\u7ef4\u6570\u636e\u65f6\u975e\u5e38\u6709\u7528\uff1a<\/p>\n<\/p>\n
import plotly.graph_objects as go<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
x, y, z = [1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [5, 6, 7, 8, 9]<\/p>\n
\u521b\u5efa3D\u6563\u70b9\u56fe<\/strong><\/h2>\n
fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z, mode='markers')])<\/p>\n
fig.update_layout(title='3D \u6563\u70b9\u56fe')<\/p>\n
fig.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
plotly.graph_objects<\/code>\u6a21\u5757\u5141\u8bb8\u66f4\u9ad8\u7ea7\u7684\u56fe\u5f62\u5b9a\u5236\u548c3D\u7ed8\u56fe\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u7ed8\u56fe\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u5b83\u4e0d\u4ec5\u4ec5\u662f\u5c55\u793a\u6570\u636e\uff0c\u66f4\u662f\u5e2e\u52a9\u5206\u6790\u8005\u4ece\u6570\u636e\u4e2d\u53d1\u73b0\u6a21\u5f0f\u3001\u8d8b\u52bf\u548c\u5f02\u5e38\u70b9\u3002<\/p>\n<\/p>\n
1\u3001\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u7684\u65e9\u671f\u9636\u6bb5\uff0c\u7ed8\u56fe\u7528\u4e8e\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09\uff0c\u5e2e\u52a9\u8bc6\u522b\u6570\u636e\u7684\u57fa\u672c\u7279\u5f81\u548c\u6a21\u5f0f\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u52a0\u8f7d\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
df = sns.load_dataset('tips')<\/p>\n
\u4f7f\u7528Seaborn\u8fdb\u884cEDA<\/strong><\/h2>\n
sns.pAI<\/a>rplot(df, hue='sex')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cpairplot<\/code>\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5feb\u901f\u67e5\u770b\u6570\u636e\u96c6\u4e2d\u4e0d\u540c\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406<\/h4>\n<\/p>\n
\u7ed8\u56fe\u8fd8\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u6216\u7f3a\u5931\u503c\uff0c\u4ee5\u4fbf\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5, 100]<\/p>\n
\u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
plt.boxplot(data)<\/p>\n
plt.title('\u7bb1\u7ebf\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u662f\u4e00\u79cd\u5e38\u7528\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u68c0\u6d4b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u7efc\u5408\u5b9e\u4f8b\uff1a\u4ece\u6570\u636e\u5230\u56fe\u8868<\/h3>\n<\/p>\n
\u4e3a\u4e86\u5c55\u793a\u5982\u4f55\u5c06\u524d\u8ff0\u6280\u672f\u5e94\u7528\u4e8e\u5b9e\u9645\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4ece\u6570\u636e\u52a0\u8f7d\u5230\u53ef\u89c6\u5316\u7684\u5b8c\u6574\u5b9e\u4f8b\uff1a<\/p>\n<\/p>\n
1\u3001\u52a0\u8f7d\u6570\u636e<\/h4>\n<\/p>\n
\u6211\u4eec\u5c06\u4f7f\u7528Pandas\u52a0\u8f7d\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u52a0\u8f7d\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
df = pd.read_csv('your_dataset.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u52a0\u8f7d\u6570\u636e\u540e\uff0c\u6211\u4eec\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u8d28\u91cf\uff1a<\/p>\n<\/p>\n
# \u68c0\u67e5\u7f3a\u5931\u503c<\/p>\n
missing_values = df.isnull().sum()<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(df.mean(), inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8fdb\u884c\u57fa\u672c\u7684\u6570\u636e\u5206\u6790\uff0c\u67e5\u770b\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f\uff1a<\/p>\n<\/p>\n
# \u67e5\u770b\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1<\/p>\n
df.describe()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u548cSeaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u76f8\u5173\u77e9\u9635\u7684\u70ed\u56fe<\/strong><\/h2>\n
corr = df.corr()<\/p>\n
sns.heatmap(corr, annot=True)<\/p>\n
plt.title('\u76f8\u5173\u77e9\u9635\u70ed\u56fe')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u7ed8\u5236\u76f8\u5173\u77e9\u9635\u7684\u70ed\u56fe\uff0c\u6211\u4eec\u53ef\u4ee5\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n<\/p>\n
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2. \u4f7f\u7528Seth\u7ed8\u56fe\u65f6\uff0c\u5982\u4f55\u81ea\u5b9a\u4e49\u56fe\u5f62\u7684\u5916\u89c2\u548c\u98ce\u683c\uff1f<\/strong>
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