{"id":1170737,"date":"2025-01-15T16:26:13","date_gmt":"2025-01-15T08:26:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1170737.html"},"modified":"2025-01-15T16:26:16","modified_gmt":"2025-01-15T08:26:16","slug":"python%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1170737.html","title":{"rendered":"python\u6570\u636e\u5206\u6790\u5982\u4f55\u4f7f\u7528"},"content":{"rendered":"

\"python\u6570\u636e\u5206\u6790\u5982\u4f55\u4f7f\u7528\"<\/p>\n

Python\u6570\u636e\u5206\u6790\u662f\u4e00\u9879\u5f3a\u5927\u7684\u6280\u80fd\uff0c\u5e38\u7528\u4e8e\u5904\u7406\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u6570\u636e\u3002Python\u6570\u636e\u5206\u6790\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001NumPy\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3001Matplotlib\u548cSeaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3001Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/a>\u5efa\u6a21\u3001Jupyter Notebook\u8fdb\u884c\u4ea4\u4e92\u5f0f\u7f16\u7a0b<\/strong>\u3002\u5176\u4e2d\uff0cPandas\u662f\u6570\u636e\u5206\u6790\u7684\u6838\u5fc3\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u5de5\u5177\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u5408\u5e76\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86DataFrame\u548cSeries\u4e24\u79cd\u6570\u636e\u7ed3\u6784\u3002DataFrame\u7c7b\u4f3c\u4e8eExcel\u8868\u683c\uff0c\u53ef\u4ee5\u5b58\u50a8\u591a\u5217\u6570\u636e\uff0c\u800cSeries\u5219\u662f\u4e00\u7ef4\u6570\u7ec4\uff0c\u7c7b\u4f3c\u4e8ePython\u4e2d\u7684\u5217\u8868\u3002<\/p>\n<\/p>\n

1\u3001\u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
Pandas\u53ef\u4ee5\u8bfb\u53d6\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\uff0c\u5305\u62ecCSV\u3001Excel\u3001SQL\u7b49\u3002\u4ee5\u4e0b\u662f\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u63a2\u7d22<\/h4>\n<\/p>\n
\u4e00\u65e6\u6570\u636e\u88ab\u8bfb\u53d6\u5230DataFrame\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u63a2\u7d22\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
# \u67e5\u770b\u6570\u636e\u7684\u524d\u4e94\u884c<\/p>\n
print(df.head())<\/p>\n
\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u4fe1\u606f<\/strong><\/h2>\n
print(df.info())<\/p>\n
\u67e5\u770b\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
print(df.describe())<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u6570\u636e\u6e05\u6d17\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
df.dropna(inplace=True)<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(0, inplace=True)<\/p>\n
\u5220\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
df.drop_duplicates(inplace=True)<\/p>\n
\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong><\/h2>\n
df['column_name'] = df['column_name'].astype('int')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001NumPy\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\u548c\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\u3002\u5b83\u662f\u8bb8\u591a\u79d1\u5b66\u8ba1\u7b97\u5e93\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165NumPy\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165NumPy\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u7ec4<\/h4>\n<\/p>\n
NumPy\u53ef\u4ee5\u521b\u5efa\u591a\u79cd\u7c7b\u578b\u7684\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u7ef4\u6570\u7ec4<\/p>\n
arr = np.array([1, 2, 3, 4, 5])<\/p>\n
\u521b\u5efa\u4e8c\u7ef4\u6570\u7ec4<\/strong><\/h2>\n
arr = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
\u521b\u5efa\u5168\u96f6\u6570\u7ec4<\/strong><\/h2>\n
arr = np.zeros((3, 3))<\/p>\n
\u521b\u5efa\u5168\u4e00\u6570\u7ec4<\/strong><\/h2>\n
arr = np.ones((3, 3))<\/p>\n
\u521b\u5efa\u968f\u673a\u6570\u7ec4<\/strong><\/h2>\n
arr = np.random.rand(3, 3)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u7ec4\u8fd0\u7b97<\/h4>\n<\/p>\n
NumPy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u8fd0\u7b97\u529f\u80fd\uff1a<\/p>\n<\/p>\n
# \u6570\u7ec4\u52a0\u6cd5<\/p>\n
result = arr1 + arr2<\/p>\n
\u6570\u7ec4\u51cf\u6cd5<\/strong><\/h2>\n
result = arr1 - arr2<\/p>\n
\u6570\u7ec4\u4e58\u6cd5<\/strong><\/h2>\n
result = arr1 * arr2<\/p>\n
\u6570\u7ec4\u9664\u6cd5<\/strong><\/h2>\n
result = arr1 \/ arr2<\/p>\n
\u6570\u7ec4\u6c42\u548c<\/strong><\/h2>\n
result = np.sum(arr)<\/p>\n
\u6570\u7ec4\u5747\u503c<\/strong><\/h2>\n
result = np.mean(arr)<\/p>\n
\u6570\u7ec4\u6807\u51c6\u5dee<\/strong><\/h2>\n
result = np.std(arr)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001Matplotlib\u548cSeaborn\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
Matplotlib\u548cSeaborn\u662f\u4e24\u4e2a\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u524d\u8005\u662f\u4e00\u4e2a\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u540e\u8005\u662f\u5728Matplotlib\u57fa\u7840\u4e0a\u8fdb\u884c\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u66f4\u7f8e\u89c2\u7684\u6837\u5f0f\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165Matplotlib\u548cSeaborn\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u8fd9\u4e24\u4e2a\u5e93\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u6298\u7ebf\u56fe<\/p>\n
plt.plot([1, 2, 3, 4, 5])<\/p>\n
plt.show()<\/p>\n
\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter([1, 2, 3, 4, 5], [5, 4, 3, 2, 1])<\/p>\n
plt.show()<\/p>\n
\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u9ad8\u7ea7\u7ed8\u56fe<\/h4>\n<\/p>\n
Seaborn\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u7ed8\u56fe\u529f\u80fd\uff1a<\/p>\n<\/p>\n
# \u6761\u5f62\u56fe<\/p>\n
sns.barplot(x='column_name', y='column_name', data=df)<\/p>\n
plt.show()<\/p>\n
\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(x='column_name', y='column_name', data=df)<\/p>\n
plt.show()<\/p>\n
\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(df.corr(), annot=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001Scikit-learn\u5e93\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
Scikit-learn\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u5de5\u5177\u3002<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165Scikit-learn\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165Scikit-learn\u5e93\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u51c6\u5907<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u51c6\u5907\u6570\u636e\uff1a<\/p>\n<\/p>\n
# \u5206\u5272\u6570\u636e\u96c6<\/p>\n
X = df.drop('target', axis=1)<\/p>\n
y = df['target']<\/p>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bad\u7ec3\u6a21\u578b<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u6a21\u578b<\/p>\n
model = LinearRegression()<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u8bc4\u4f30\u6a21\u578b<\/h4>\n<\/p>\n
\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u662f\u673a\u5668\u5b66\u4e60\u7684\u91cd\u8981\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5747\u65b9\u8bef\u5dee<\/p>\n
mse = mean_squared_error(y_test, y_pred)<\/p>\n
print(f'Mean Squared Error: {mse}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001Jupyter Notebook\u7684\u4f7f\u7528<\/h3>\n<\/p>\n
Jupyter Notebook\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7f16\u7a0b\u73af\u5883\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Jupyter Notebook<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528pip\u5b89\u88c5Jupyter Notebook\uff1a<\/p>\n<\/p>\n
pip install jupyter<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u542f\u52a8Jupyter Notebook<\/h4>\n<\/p>\n
\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\u542f\u52a8Jupyter Notebook\uff1a<\/p>\n<\/p>\n
jupyter notebook<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528Jupyter Notebook<\/h4>\n<\/p>\n
\u5728\u6d4f\u89c8\u5668\u4e2d\u6253\u5f00Jupyter Notebook\u540e\uff0c\u53ef\u4ee5\u521b\u5efa\u65b0\u7684Notebook\uff0c\u5e76\u5728\u5176\u4e2d\u7f16\u5199Python\u4ee3\u7801\u3002Jupyter Notebook\u652f\u6301Markdown\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u6dfb\u52a0\u6587\u672c\u8bf4\u660e\u548c\u516c\u5f0f\u3002<\/p>\n<\/p>\n
# \u793a\u4f8b\u4ee3\u7801<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
df = pd.DataFrame({<\/p>\n
    'A': [1, 2, 3],<\/p>\n
    'B': [4, 5, 6]<\/p>\n
})<\/p>\n
\u663e\u793aDataFrame<\/strong><\/h2>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6570\u636e\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u6570\u636e\u9884\u5904\u7406\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53d8\u6362\u3001\u6570\u636e\u7f29\u653e\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u662f\u6307\u5904\u7406\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u7b49\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
df.dropna(inplace=True)<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
df.fillna(0, inplace=True)<\/p>\n
\u5220\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
df.drop_duplicates(inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u53d8\u6362<\/h4>\n<\/p>\n
\u6570\u636e\u53d8\u6362\u662f\u6307\u5bf9\u6570\u636e\u8fdb\u884c\u8f6c\u6362\uff0c\u4f7f\u5176\u66f4\u9002\u5408\u5206\u6790\u548c\u5efa\u6a21\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
# \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/p>\n
df['column_name'] = df['column_name'].astype('int')<\/p>\n
\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f<\/strong><\/h2>\n
df['date_column'] = pd.to_datetime(df['date_column'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u7f29\u653e<\/h4>\n<\/p>\n
\u6570\u636e\u7f29\u653e\u662f\u6307\u5c06\u6570\u636e\u7f29\u653e\u5230\u7279\u5b9a\u8303\u56f4\u5185\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler, MinMaxScaler<\/p>\n
\u6807\u51c6\u5316<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
df_scaled = scaler.fit_transform(df)<\/p>\n
\u6700\u5c0f-\u6700\u5927\u7f29\u653e<\/strong><\/h2>\n
scaler = MinMaxScaler()<\/p>\n
df_scaled = scaler.fit_transform(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u7279\u5f81\u5de5\u7a0b<\/h3>\n<\/p>\n
\u7279\u5f81\u5de5\u7a0b\u662f\u6307\u4ece\u539f\u59cb\u6570\u636e\u4e2d\u63d0\u53d6\u7279\u5f81\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u7279\u5f81\u9009\u62e9<\/h4>\n<\/p>\n
\u7279\u5f81\u9009\u62e9\u662f\u6307\u9009\u62e9\u5bf9\u6a21\u578b\u6709\u7528\u7684\u7279\u5f81\uff0c\u53bb\u9664\u65e0\u5173\u6216\u5197\u4f59\u7684\u7279\u5f81\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
from sklearn.feature_selection import SelectKBest, f_classif<\/p>\n
\u9009\u62e9K\u4e2a\u6700\u4f73\u7279\u5f81<\/strong><\/h2>\n
selector = SelectKBest(score_func=f_classif, k=10)<\/p>\n
X_new = selector.fit_transform(X, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7279\u5f81\u63d0\u53d6<\/h4>\n<\/p>\n
\u7279\u5f81\u63d0\u53d6\u662f\u6307\u4ece\u539f\u59cb\u6570\u636e\u4e2d\u63d0\u53d6\u65b0\u7684\u7279\u5f81\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
from sklearn.decomposition import PCA<\/p>\n
\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09<\/strong><\/h2>\n
pca = PCA(n_components=2)<\/p>\n
X_new = pca.fit_transform(X)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u8bc4\u4f30<\/h3>\n<\/p>\n
\u6a21\u578b\u9009\u62e9\u4e0e\u8bc4\u4f30\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5305\u62ec\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u3001\u8c03\u6574\u8d85\u53c2\u6570\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7b49\u3002<\/p>\n<\/p>\n
1\u3001\u6a21\u578b\u9009\u62e9<\/h4>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u5173\u952e\u6b65\u9aa4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u6a21\u578b\uff1a<\/p>\n<\/p>\n
from sklearn.linear_model import LogisticRegression<\/p>\n
from sklearn.ensemble import RandomForestClassifier<\/p>\n
\u903b\u8f91\u56de\u5f52<\/strong><\/h2>\n
model = LogisticRegression()<\/p>\n
\u968f\u673a\u68ee\u6797<\/strong><\/h2>\n
model = RandomForestClassifier()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8d85\u53c2\u6570\u8c03\u6574<\/h4>\n<\/p>\n
\u8d85\u53c2\u6570\u8c03\u6574\u662f\u6307\u8c03\u6574\u6a21\u578b\u7684\u8d85\u53c2\u6570\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import GridSearchCV<\/p>\n
\u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\n
param_grid = {'n_estimators': [50, 100, 200]}<\/p>\n
\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\n
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)<\/p>\n
grid_search.fit(X_train, y_train)<\/p>\n
\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\n
print(grid_search.best_params_)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6a21\u578b\u8bc4\u4f30<\/h4>\n<\/p>\n
\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report<\/p>\n
\u51c6\u786e\u7387<\/strong><\/h2>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy}')<\/p>\n
\u6df7\u6dc6\u77e9\u9635<\/strong><\/h2>\n
cm = confusion_matrix(y_test, y_pred)<\/p>\n
print(cm)<\/p>\n
\u5206\u7c7b\u62a5\u544a<\/strong><\/h2>\n
report = classification_report(y_test, y_pred)<\/p>\n
print(report)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u548c\u6a21\u578b\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e9b\u57fa\u672c\u7684\u7ed8\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u6298\u7ebf\u56fe<\/strong><\/h2>\n
plt.plot([1, 2, 3, 4, 5])<\/p>\n
plt.show()<\/p>\n
\u6563\u70b9\u56fe<\/strong><\/h2>\n
plt.scatter([1, 2, 3, 4, 5], [5, 4, 3, 2, 1])<\/p>\n
plt.show()<\/p>\n
\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9ad8\u7ea7\u7ed8\u56fe<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e9b\u9ad8\u7ea7\u7684\u7ed8\u56fe\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u6761\u5f62\u56fe<\/strong><\/h2>\n
sns.barplot(x='column_name', y='column_name', data=df)<\/p>\n
plt.show()<\/p>\n
\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
sns.boxplot(x='column_name', y='column_name', data=df)<\/p>\n
plt.show()<\/p>\n
\u70ed\u529b\u56fe<\/strong><\/h2>\n
sns.heatmap(df.corr(), annot=True)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790<\/h3>\n<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u662f\u6307\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u5206\u6790\u548c\u5efa\u6a21\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('time_series_data.csv', parse_dates=['date'], index_col='date')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u89e3<\/h4>\n<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u5206\u89e3\u662f\u6307\u5c06\u65f6\u95f4\u5e8f\u5217\u5206\u89e3\u4e3a\u8d8b\u52bf\u3001\u5b63\u8282\u6027\u548c\u6b8b\u5dee\u4e09\u4e2a\u90e8\u5206\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.seasonal import seasonal_decompose<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u5206\u89e3<\/strong><\/h2>\n
result = seasonal_decompose(df['value'], model='additive')<\/p>\n
result.plot()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/h4>\n<\/p>\n
\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u662f\u6307\u5bf9\u672a\u6765\u7684\u65f6\u95f4\u5e8f\u5217\u8fdb\u884c\u9884\u6d4b\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528ARIMA\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.arima.model import ARIMA<\/p>\n
\u62df\u5408ARIMA\u6a21\u578b<\/strong><\/h2>\n
model = ARIMA(df['value'], order=(1, 1, 1))<\/p>\n
model_fit = model.fit()<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
forecast = model_fit.forecast(steps=10)<\/p>\n
print(forecast)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e00\u3001\u6587\u672c\u5206\u6790<\/h3>\n<\/p>\n
\u6587\u672c\u5206\u6790\u662f\u6307\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u5904\u7406\u548c\u5206\u6790\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u6587\u672c\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u6587\u672c\u9884\u5904\u7406\u662f\u6307\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u8f6c\u6362\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import re<\/p>\n
\u8bfb\u53d6\u6587\u672c\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('text_data.csv')<\/p>\n
\u6587\u672c\u6e05\u6d17<\/strong><\/h2>\n
df['text'] = df['text'].apply(lambda x: re.sub(r'\\W', ' ', x.lower()))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6587\u672c\u5411\u91cf\u5316<\/h4>\n<\/p>\n
\u6587\u672c\u5411\u91cf\u5316\u662f\u6307\u5c06\u6587\u672c\u6570\u636e\u8f6c\u6362\u4e3a\u6570\u503c\u8868\u793a\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528TF-IDF\u8fdb\u884c\u5411\u91cf\u5316\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
TF-IDF\u5411\u91cf\u5316<\/strong><\/h2>\n
vectorizer = TfidfVectorizer()<\/p>\n
X = vectorizer.fit_transform(df['text'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6587\u672c\u5206\u7c7b<\/h4>\n<\/p>\n
\u6587\u672c\u5206\u7c7b\u662f\u6307\u5bf9\u6587\u672c\u6570\u636e\u8fdb\u884c\u5206\u7c7b\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u8fdb\u884c\u6587\u672c\u5206\u7c7b\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
from sklearn.naive_bayes import MultinomialNB<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
\u5206\u5272\u6570\u636e\u96c6<\/strong><\/h2>\n
X_train, X_test, y_train, y_test = train_test_split(X, df['label'], test_size=0.2, random_state=42)<\/p>\n
\u521b\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = MultinomialNB()<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X_test)<\/p>\n
\u51c6\u786e\u7387<\/strong><\/h2>\n
accuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f'Accuracy: {accuracy}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e8c\u3001\u6df1\u5ea6\u5b66\u4e60<\/h3>\n<\/p>\n
\u6df1\u5ea6\u5b66\u4e60\u662f\u673a\u5668\u5b66\u4e60\u7684\u4e00\u4e2a\u5206\u652f\uff0c\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u590d\u6742\u6570\u636e\uff0c\u5982\u56fe\u50cf\u3001\u8bed\u97f3\u548c\u6587\u672c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
1\u3001\u5bfc\u5165\u6df1\u5ea6\u5b66\u4e60\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u6df1\u5ea6\u5b66\u4e60\u5e93\uff0c\u5982TensorFlow\u548cKeras\uff1a<\/p>\n<\/p>\n
import tensorflow as tf<\/p>\n
from tensorflow import keras<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u795e\u7ecf\u7f51\u7edc\u6a21\u578b<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u6a21\u578b<\/p>\n
model = keras.Sequential([<\/p>\n
    keras.layers.Dense(128, activation='relu', input_shape=(784,)),<\/p>\n
    keras.layers.Dense(10, activation='softmax')<\/p>\n
])<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u8bad\u7ec3\u6a21\u578b<\/p>\n
model.fit(X_train, y_train, epochs=10, batch_size=32)<\/p>\n
\u8bc4\u4f30\u6a21\u578b<\/strong><\/h2>\n
loss, accuracy = model.evaluate(X_test, y_test)<\/p>\n
print(f'Loss: {loss}, Accuracy: {accuracy}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e09\u3001\u9879\u76ee\u5b9e\u8df5<\/h3>\n<\/p>\n
\u9879\u76ee\u5b9e\u8df5\u662f\u5b66\u4e60\u6570\u636e\u5206\u6790\u6700\u6709\u6548\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u6570\u636e\u5206\u6790\u9879\u76ee\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
1\u3001\u5b9a\u4e49\u95ee\u9898<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b9a\u4e49\u95ee\u9898\u3002\u4f8b\u5982\uff0c\u9884\u6d4b\u623f\u4ef7\u3002<\/p>\n<\/p>\n
2\u3001\u6570\u636e\u83b7\u53d6<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u83b7\u53d6\u6570\u636e\u3002\u4f8b\u5982\uff0c\u4eceKaggle\u4e0b\u8f7d\u623f\u4ef7\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
3\u3001\u6570\u636e\u63a2\u7d22<\/h4>\n<\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u63a2\u7d22\u548c\u53ef\u89c6\u5316\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import seaborn as sns<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('house_prices.csv')<\/p>\n
\u6570\u636e\u63a2\u7d22<\/strong><\/h2>\n
print(df.head())<\/p>\n
print(df.info())<\/p>\n
print(df.describe())<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316<\/strong><\/h2>\n
sns.pairplot(df)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
df.dropna(inplace=True)<\/p>\n
\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong><\/h2>\n
df['column_name'] = df['column_name'].astype('int')<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u7279\u5f81\u5de5\u7a0b<\/h4>\n<\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u9700\u8981\u8fdb\u884c\u7279\u5f81\u5de5\u7a0b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
\u7279\u5f81\u7f29\u653e<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
df_scaled = scaler.fit_transform(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
6\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u8bc4\u4f30<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u9700\u8981\u9009\u62e9\u548c\u8bc4\u4f30\u6a21\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
from sklearn.metrics import mean_squared_error<\/p>\n
\u5206\u5272\u6570\u636e\u96c6<\/strong><\/h2>\n
<\/code><\/pre>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\uff1f<\/strong>
\u8981\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u60a8\u9700\u8981\u5b89\u88c5Python\u548c\u76f8\u5173\u7684\u5e93\uff0c\u5982Pandas\u3001NumPy\u548cMatplotlib\u3002\u9996\u5148\uff0c\u786e\u4fdd\u60a8\u7684\u8ba1\u7b97\u673a\u4e0a\u5b89\u88c5\u4e86Python\u73af\u5883\u3002\u63a5\u4e0b\u6765\uff0c\u901a\u8fc7pip\u547d\u4ee4\u5b89\u88c5\u6240\u9700\u7684\u5e93\uff0c\u4f8b\u5982\uff1apip install pandas numpy matplotlib<\/code>\u3002\u5b66\u4e60\u57fa\u672c\u7684Python\u8bed\u6cd5\u548c\u6570\u636e\u64cd\u4f5c\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u5728\u7ebf\u6559\u7a0b\u548c\u4e66\u7c4d\u6765\u589e\u5f3a\u60a8\u7684\u6280\u80fd\u3002<\/p>\n
Python\u6570\u636e\u5206\u6790\u4e2d\u5e38\u7528\u7684\u5e93\u6709\u54ea\u4e9b\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u3001NumPy\u3001Matplotlib\u548cSeaborn\u3002Pandas\u975e\u5e38\u9002\u5408\u6570\u636e\u5904\u7406\u548c\u6e05\u6d17\uff0cNumPy\u5219\u7528\u4e8e\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\u3002Matplotlib\u548cSeaborn\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u60a8\u5c06\u6570\u636e\u4ee5\u56fe\u5f62\u7684\u5f62\u5f0f\u5c55\u793a\uff0c\u66f4\u52a0\u76f4\u89c2\u6613\u61c2\u3002\u6b64\u5916\uff0cSciPy\u548cStatsmodels\u4e5f\u5e38\u7528\u4e8e\u7edf\u8ba1\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u63d0\u9ad8\u5206\u6790\u7684\u51c6\u786e\u6027\uff1f<\/strong>
\u5904\u7406\u7f3a\u5931\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u4e00\u6b65\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684isnull()<\/code>\u548cdropna()<\/code>\u51fd\u6570\u6765\u8bc6\u522b\u548c\u5220\u9664\u7f3a\u5931\u503c\u3002\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528fillna()<\/code>\u51fd\u6570\u586b\u8865\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u9009\u62e9\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u5176\u4ed6\u7edf\u8ba1\u91cf\u6765\u66ff\u4ee3\u7f3a\u5931\u6570\u636e\u3002\u6839\u636e\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u5904\u7406\u65b9\u6cd5\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u6570\u636e\u5206\u6790\u662f\u4e00\u9879\u5f3a\u5927\u7684\u6280\u80fd\uff0c\u5e38\u7528\u4e8e\u5904\u7406\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u6570\u636e\u3002Python\u6570\u636e\u5206\u6790\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Panda […]","protected":false},"author":3,"featured_media":1170742,"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\/1170737"}],"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=1170737"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1170737\/revisions"}],"predecessor-version":[{"id":1170745,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1170737\/revisions\/1170745"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1170742"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1170737"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1170737"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1170737"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}