{"id":1103384,"date":"2025-01-08T16:09:47","date_gmt":"2025-01-08T08:09:47","guid":{"rendered":""},"modified":"2025-01-08T16:09:53","modified_gmt":"2025-01-08T08:09:53","slug":"python%e5%a6%82%e4%bd%95%e8%bf%90%e7%94%a8%e5%88%b0%e6%97%a5%e5%b8%b8%e5%b7%a5%e4%bd%9c%e4%b8%ad","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1103384.html","title":{"rendered":"python\u5982\u4f55\u8fd0\u7528\u5230\u65e5\u5e38\u5de5\u4f5c\u4e2d"},"content":{"rendered":"

\"python\u5982\u4f55\u8fd0\u7528\u5230\u65e5\u5e38\u5de5\u4f5c\u4e2d\"<\/p>\n

Python\u5728\u65e5\u5e38\u5de5\u4f5c\u4e2d\u7684\u5e94\u7528<\/strong>\u5305\u62ec\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406\u3001\u81ea\u52a8\u5316\u4efb\u52a1\u3001\u7f51\u7edc\u722c\u866b\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u673a\u5668\u5b66\u4e60<\/a>\u3001\u7f51\u9875\u5f00\u53d1\u7b49\u3002\u5176\u4e2d\uff0c\u81ea\u52a8\u5316\u4efb\u52a1\u662f\u6700\u5e38\u89c1\u4e14\u5b9e\u7528\u7684\u4e00\u9879\u5e94\u7528<\/strong>\u3002\u901a\u8fc7\u7f16\u5199Python\u811a\u672c\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u81ea\u52a8\u5316\u65e5\u5e38\u7e41\u7410\u7684\u4efb\u52a1\uff0c\u5982\u6587\u4ef6\u7ba1\u7406\u3001\u6570\u636e\u6e05\u6d17\u3001\u5b9a\u671f\u62a5\u544a\u751f\u6210\u7b49\u3002Python\u7684\u5f3a\u5927\u5e93\u5982Pandas\u3001Openpyxl\u7b49\u6781\u5927\u5730\u7b80\u5316\u4e86\u8fd9\u4e9b\u4efb\u52a1\u7684\u5b9e\u73b0\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406<\/h3>\n<\/p>\n

\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406<\/strong>\u662fPython\u5728\u65e5\u5e38\u5de5\u4f5c\u4e2d\u6700\u91cd\u8981\u7684\u5e94\u7528\u4e4b\u4e00\u3002Python\u62e5\u6709\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u5982Pandas\u3001NumPy\u7b49\uff0c\u80fd\u591f\u8f7b\u677e\u5904\u7406\u5927\u91cf\u6570\u636e\u3002<\/p>\n<\/p>\n

1\u3001Pandas\u5e93\u7684\u5e94\u7528<\/h4>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u80fd\u591f\u5904\u7406\u8868\u683c\u6570\u636e\u3001\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7b49\u3002\u901a\u8fc7Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u6570\u636e\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u5bfc\u51fa\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u8bfb\u53d6Excel\u6587\u4ef6\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_excel('data.xlsx')<\/p>\n
\u67e5\u770b\u524d\u51e0\u884c\u6570\u636e<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001NumPy\u5e93\u7684\u5e94\u7528<\/h4>\n<\/p>\n
NumPy\u662fPython\u7684\u4e00\u4e2a\u57fa\u672c\u5e93\uff0c\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\uff0c\u5e76\u4e14\u5177\u6709\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\u5e93\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u521b\u5efa\u4e00\u4e2aNumPy\u6570\u7ec4\u5e76\u8fdb\u884c\u7b80\u5355\u7684\u6570\u5b66\u8fd0\u7b97\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2aNumPy\u6570\u7ec4<\/strong><\/h2>\n
array = np.array([1, 2, 3, 4, 5])<\/p>\n
\u6570\u7ec4\u52a0\u6cd5\u8fd0\u7b97<\/strong><\/h2>\n
result = array + 10<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u81ea\u52a8\u5316\u4efb\u52a1<\/h3>\n<\/p>\n
\u81ea\u52a8\u5316\u4efb\u52a1<\/strong>\u662fPython\u5728\u65e5\u5e38\u5de5\u4f5c\u4e2d\u6700\u5e38\u89c1\u4e14\u5b9e\u7528\u7684\u5e94\u7528\u4e4b\u4e00\u3002\u901a\u8fc7\u7f16\u5199Python\u811a\u672c\uff0c\u53ef\u4ee5\u81ea\u52a8\u5316\u8bb8\u591a\u7e41\u7410\u7684\u4efb\u52a1\uff0c\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u3002<\/p>\n<\/p>\n
1\u3001\u6587\u4ef6\u7ba1\u7406\u81ea\u52a8\u5316<\/h4>\n<\/p>\n
\u5728\u65e5\u5e38\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u5e38\u5e38\u9700\u8981\u5bf9\u5927\u91cf\u6587\u4ef6\u8fdb\u884c\u64cd\u4f5c\u3002\u4f7f\u7528Python\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6587\u4ef6\u7684\u6279\u91cf\u91cd\u547d\u540d\u3001\u79fb\u52a8\u3001\u5220\u9664\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528os\u6a21\u5757\u6279\u91cf\u91cd\u547d\u540d\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import os<\/p>\n
\u83b7\u53d6\u6307\u5b9a\u76ee\u5f55\u4e0b\u7684\u6240\u6709\u6587\u4ef6<\/strong><\/h2>\n
files = os.listdir('path\/to\/directory')<\/p>\n
\u6279\u91cf\u91cd\u547d\u540d\u6587\u4ef6<\/strong><\/h2>\n
for file in files:<\/p>\n
    os.rename(f'path\/to\/directory\/{file}', f'path\/to\/directory\/new_{file}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5b9a\u671f\u62a5\u544a\u751f\u6210\u81ea\u52a8\u5316<\/h4>\n<\/p>\n
\u5728\u8bb8\u591a\u5de5\u4f5c\u573a\u666f\u4e2d\uff0c\u5b9a\u671f\u751f\u6210\u62a5\u544a\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u4efb\u52a1\u3002\u901a\u8fc7Python\u811a\u672c\uff0c\u53ef\u4ee5\u81ea\u52a8\u5316\u6570\u636e\u6536\u96c6\u3001\u5904\u7406\u548c\u62a5\u544a\u751f\u6210\u7684\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u548cMatplotlib\u751f\u6210\u5b9a\u671f\u62a5\u544a\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_excel('data.xlsx')<\/p>\n
\u6570\u636e\u5904\u7406<\/strong><\/h2>\n
summary = data.groupby('category').sum()<\/p>\n
\u751f\u6210\u56fe\u8868<\/strong><\/h2>\n
summary.plot(kind='bar')<\/p>\n
plt.savefig('report.png')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u7f51\u7edc\u722c\u866b<\/h3>\n<\/p>\n
\u7f51\u7edc\u722c\u866b<\/strong>\u662fPython\u5728\u6570\u636e\u91c7\u96c6\u65b9\u9762\u7684\u91cd\u8981\u5e94\u7528\u3002\u901a\u8fc7\u7f16\u5199\u722c\u866b\u811a\u672c\uff0c\u80fd\u591f\u81ea\u52a8\u5316\u5730\u4ece\u4e92\u8054\u7f51\u4e0a\u83b7\u53d6\u6240\u9700\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Requests\u5e93\u8fdb\u884c\u6570\u636e\u91c7\u96c6<\/h4>\n<\/p>\n
Requests\u662f\u4e00\u4e2a\u7b80\u5355\u6613\u7528\u7684HTTP\u5e93\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u8fdb\u884c\u7f51\u9875\u8bf7\u6c42\uff0c\u83b7\u53d6\u7f51\u9875\u5185\u5bb9\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Requests\u83b7\u53d6\u7f51\u9875\u5185\u5bb9\uff1a<\/p>\n<\/p>\n
import requests<\/p>\n
\u53d1\u9001GET\u8bf7\u6c42<\/strong><\/h2>\n
response = requests.get('https:\/\/example.com')<\/p>\n
\u83b7\u53d6\u7f51\u9875\u5185\u5bb9<\/strong><\/h2>\n
content = response.text<\/p>\n
print(content)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528BeautifulSoup\u8fdb\u884c\u7f51\u9875\u89e3\u6790<\/h4>\n<\/p>\n
BeautifulSoup\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u7f51\u9875\u89e3\u6790\u5e93\uff0c\u80fd\u591f\u65b9\u4fbf\u5730\u89e3\u6790HTML\u548cXML\u6587\u6863\uff0c\u63d0\u53d6\u6240\u9700\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528BeautifulSoup\u89e3\u6790\u7f51\u9875\u5185\u5bb9\uff1a<\/p>\n<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
\u89e3\u6790\u7f51\u9875\u5185\u5bb9<\/strong><\/h2>\n
soup = BeautifulSoup(content, 'html.parser')<\/p>\n
\u63d0\u53d6\u7279\u5b9a\u6570\u636e<\/strong><\/h2>\n
data = soup.find_all('div', class_='data')<\/p>\n
for item in data:<\/p>\n
    print(item.text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316<\/strong>\u662fPython\u5728\u6570\u636e\u5c55\u793a\u65b9\u9762\u7684\u91cd\u8981\u5e94\u7528\u3002\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u8d8b\u52bf\u548c\u89c4\u5f8b\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u80fd\u591f\u751f\u6210\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u997c\u56fe\u7b49\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Matplotlib\u751f\u6210\u6298\u7ebf\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
x = [1, 2, 3, 4, 5]<\/p>\n
y = [2, 3, 5, 7, 11]<\/p>\n
\u751f\u6210\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('X-axis')<\/p>\n
plt.ylabel('Y-axis')<\/p>\n
plt.title('Line Chart')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u80fd\u591f\u751f\u6210\u66f4\u7f8e\u89c2\u3001\u66f4\u590d\u6742\u7684\u56fe\u8868\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Seaborn\u751f\u6210\u70ed\u529b\u56fe\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
data = np.random.rand(10, 12)<\/p>\n
\u751f\u6210\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(data, annot=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u673a\u5668\u5b66\u4e60<\/h3>\n<\/p>\n
\u673a\u5668\u5b66\u4e60<\/strong>\u662fPython\u5728\u667a\u80fd\u5316\u5206\u6790\u548c\u9884\u6d4b\u65b9\u9762\u7684\u91cd\u8981\u5e94\u7528\u3002\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u53ef\u4ee5\u4ece\u6570\u636e\u4e2d\u6316\u6398\u89c4\u5f8b\uff0c\u5e76\u8fdb\u884c\u9884\u6d4b\u548c\u51b3\u7b56\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/h4>\n<\/p>\n
Scikit-learn\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5305\u542b\u4e86\u5404\u79cd\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5982\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u652f\u6301\u5411\u91cf\u673a\u7b49\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Scikit-learn\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\uff1a<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
y = np.array([2, 3, 5, 7, 11])<\/p>\n
\u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
predictions = model.predict(X)<\/p>\n
print(predictions)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528TensorFlow\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60<\/h4>\n<\/p>\n
TensorFlow\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u80fd\u591f\u6784\u5efa\u548c\u8bad\u7ec3\u590d\u6742\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528TensorFlow\u6784\u5efa\u548c\u8bad\u7ec3\u7b80\u5355\u7684\u795e\u7ecf\u7f51\u7edc\uff1a<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\n
import numpy as np<\/p>\n
\u6570\u636e<\/strong><\/h2>\n
X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
y = np.array([[2], [3], [5], [7], [11]])<\/p>\n
\u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u6a21\u578b<\/strong><\/h2>\n
model = tf.keras.Sequential([<\/p>\n
    tf.keras.layers.Dense(10, input_dim=1, activation='relu'),<\/p>\n
    tf.keras.layers.Dense(1)<\/p>\n
])<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='adam', loss='mean_squared_error')<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X, y, epochs=100)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
predictions = model.predict(X)<\/p>\n
print(predictions)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u7f51\u9875\u5f00\u53d1<\/h3>\n<\/p>\n
\u7f51\u9875\u5f00\u53d1<\/strong>\u662fPython\u5728\u6784\u5efa\u548c\u7ba1\u7406\u7f51\u7ad9\u65b9\u9762\u7684\u91cd\u8981\u5e94\u7528\u3002\u901a\u8fc7Python\u7684Web\u6846\u67b6\uff0c\u53ef\u4ee5\u5feb\u901f\u6784\u5efa\u529f\u80fd\u5f3a\u5927\u7684\u7f51\u7ad9\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Flask\u8fdb\u884c\u8f7b\u91cf\u7ea7\u7f51\u9875\u5f00\u53d1<\/h4>\n<\/p>\n
Flask\u662f\u4e00\u4e2a\u8f7b\u91cf\u7ea7\u7684Web\u6846\u67b6\uff0c\u9002\u7528\u4e8e\u6784\u5efa\u5c0f\u578b\u7f51\u7ad9\u548cAPI\u670d\u52a1\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Flask\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684Web\u5e94\u7528\uff1a<\/p>\n<\/p>\n
from flask import Flask<\/p>\n
app = Flask(__name__)<\/p>\n
@app.route('\/')<\/p>\n
def home():<\/p>\n
    return 'Hello, World!'<\/p>\n
if __name__ == '__mAI<\/a>n__':<\/p>\n
    app.run(debug=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Django\u8fdb\u884c\u5927\u578b\u7f51\u7ad9\u5f00\u53d1<\/h4>\n<\/p>\n
Django\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684Web\u6846\u67b6\uff0c\u9002\u7528\u4e8e\u6784\u5efa\u5927\u578b\u7f51\u7ad9\u548c\u590d\u6742\u7684Web\u5e94\u7528\u3002<\/p>\n<\/p>\n
\u4f8b\u5982\uff0c\u4f7f\u7528Django\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684Web\u5e94\u7528\uff1a<\/p>\n<\/p>\n
# \u5728\u547d\u4ee4\u884c\u4e2d\u521b\u5efaDjango\u9879\u76ee<\/p>\n
django-admin startproject myproject<\/strong><\/h2>\n
\u521b\u5efaDjango\u5e94\u7528<\/strong><\/h2>\n
cd myproject<\/strong><\/h2>\n
python manage.py startapp myapp<\/strong><\/h2>\n
\u7f16\u8f91myapp\/views.py<\/strong><\/h2>\n
from django.http import HttpResponse<\/p>\n
def home(request):<\/p>\n
    return HttpResponse('Hello, World!')<\/p>\n
\u7f16\u8f91myproject\/urls.py<\/strong><\/h2>\n
from django.contrib import admin<\/p>\n
from django.urls import path<\/p>\n
from myapp import views<\/p>\n
urlpatterns = [<\/p>\n
    path('admin\/', admin.site.urls),<\/p>\n
    path('', views.home),<\/p>\n
]<\/p>\n
\u8fd0\u884cDjango\u9879\u76ee<\/strong><\/h2>\n
python manage.py runserver<\/strong><\/h2>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u51ed\u501f\u5176\u6613\u7528\u6027\u548c\u5f3a\u5927\u7684\u5e93\uff0c\u6210\u4e3a\u65e5\u5e38\u5de5\u4f5c\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002\u65e0\u8bba\u662f\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406\u3001\u81ea\u52a8\u5316\u4efb\u52a1\u3001\u7f51\u7edc\u722c\u866b\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u673a\u5668\u5b66\u4e60\u8fd8\u662f\u7f51\u9875\u5f00\u53d1\uff0cPython\u90fd\u80fd\u63d0\u4f9b\u9ad8\u6548\u7684\u89e3\u51b3\u65b9\u6848\u3002\u901a\u8fc7\u4e0d\u65ad\u5b66\u4e60\u548c\u5b9e\u8df5\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528Python\u663e\u8457\u63d0\u9ad8\u5de5\u4f5c\u6548\u7387\u548c\u8d28\u91cf\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5229\u7528Python\u81ea\u52a8\u5316\u65e5\u5e38\u4efb\u52a1\uff1f<\/strong>
Python\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u80fd\u591f\u901a\u8fc7\u7f16\u5199\u811a\u672c\u6765\u81ea\u52a8\u5316\u91cd\u590d\u6027\u7684\u5de5\u4f5c\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u7684os<\/code>\u548cshutil<\/code>\u6a21\u5757\u6765\u7ba1\u7406\u6587\u4ef6\u548c\u76ee\u5f55\uff0c\u6216\u4f7f\u7528pandas<\/code>\u5e93\u6765\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002\u901a\u8fc7\u521b\u5efa\u5b9a\u65f6\u4efb\u52a1\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u8c03\u5ea6\u8fd9\u4e9b\u811a\u672c\uff0c\u4f7f\u5f97\u65e5\u5e38\u5de5\u4f5c\u53d8\u5f97\u9ad8\u6548\u4e14\u7701\u65f6\u3002<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0cPython\u6709\u54ea\u4e9b\u5e38\u7528\u5de5\u5177\u548c\u5e93\uff1f<\/strong>
Python\u5728\u6570\u636e\u5206\u6790\u9886\u57df\u975e\u5e38\u53d7\u6b22\u8fce\uff0c\u4e3b\u8981\u5f97\u76ca\u4e8e\u5176\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\u3002\u5176\u4e2d\uff0cpandas<\/code>\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0cNumPy<\/code>\u63d0\u4f9b\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0cMatplotlib<\/code>\u548cSeaborn<\/code>\u5219\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002\u8fd9\u4e9b\u5e93\u7684\u7ed3\u5408\u80fd\u591f\u5e2e\u52a9\u60a8\u5feb\u901f\u83b7\u5f97\u6d1e\u5bdf\uff0c\u63d0\u5347\u5de5\u4f5c\u6548\u7387\u3002<\/p>\n
\u5982\u4f55\u7528Python\u8fdb\u884c\u7f51\u7edc\u722c\u866b\u4ee5\u83b7\u53d6\u4fe1\u606f\uff1f<\/strong>
\u7f51\u7edc\u722c\u866b\u662f\u4e00\u79cd\u83b7\u53d6\u4e92\u8054\u7f51\u4e0a\u4fe1\u606f\u7684\u6709\u6548\u65b9\u5f0f\u3002\u4f7f\u7528Python\uff0c\u60a8\u53ef\u4ee5\u5229\u7528requests<\/code>\u5e93\u53d1\u9001HTTP\u8bf7\u6c42\uff0c\u4f7f\u7528BeautifulSoup<\/code>\u6216Scrapy<\/code>\u5e93\u89e3\u6790\u7f51\u9875\u5185\u5bb9\u3002\u8fd9\u79cd\u65b9\u5f0f\u53ef\u4ee5\u5e2e\u52a9\u60a8\u81ea\u52a8\u6536\u96c6\u9700\u8981\u7684\u6570\u636e\uff0c\u6bd4\u5982\u5e02\u573a\u8c03\u7814\u3001\u7ade\u4e89\u5bf9\u624b\u5206\u6790\u7b49\uff0c\u8282\u7701\u4e86\u5927\u91cf\u7684\u624b\u52a8\u67e5\u627e\u65f6\u95f4\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5728\u65e5\u5e38\u5de5\u4f5c\u4e2d\u7684\u5e94\u7528\u5305\u62ec\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406\u3001\u81ea\u52a8\u5316\u4efb\u52a1\u3001\u7f51\u7edc\u722c\u866b\u3001\u6570\u636e\u53ef\u89c6\u5316\u3001\u673a\u5668\u5b66\u4e60\u3001\u7f51\u9875\u5f00\u53d1\u7b49\u3002\u5176\u4e2d […]","protected":false},"author":3,"featured_media":1103399,"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\/1103384"}],"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=1103384"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1103384\/revisions"}],"predecessor-version":[{"id":1103400,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1103384\/revisions\/1103400"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1103399"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1103384"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1103384"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1103384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}