{"id":1132380,"date":"2025-01-08T20:56:35","date_gmt":"2025-01-08T12:56:35","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1132380.html"},"modified":"2025-01-08T20:56:38","modified_gmt":"2025-01-08T12:56:38","slug":"python%e5%a6%82%e4%bd%95%e5%86%99%e5%85%a5%e9%9d%9e%e6%96%87%e6%9c%ac%e6%95%b0%e6%8d%ae%e7%b1%bb%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1132380.html","title":{"rendered":"python\u5982\u4f55\u5199\u5165\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b"},"content":{"rendered":"

\"python\u5982\u4f55\u5199\u5165\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5199\u5165\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pickle\u6a21\u5757\u3001numpy\u5e93\u3001\u4ee5\u53caPandas\u5e93\u3002\u6700\u5e38\u7528\u7684\u65b9\u5f0f\u662f\u4f7f\u7528pickle\u6a21\u5757\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u5e8f\u5217\u5316\u548c\u53cd\u5e8f\u5217\u5316\u51e0\u4e4e\u6240\u6709\u7684Python\u5bf9\u8c61\u3002pickle\u6a21\u5757\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\u3001numpy\u9002\u7528\u4e8e\u6570\u503c\u6570\u7ec4\u3001Pandas\u9002\u7528\u4e8e\u6570\u636e\u6846\u3002<\/strong> \u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528pickle\u6a21\u5757\u6765\u5199\u5165\u548c\u8bfb\u53d6\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n

\u4e00\u3001PICKLE\u6a21\u5757\u7684\u4f7f\u7528<\/h2>\n<\/p>\n

1\u3001\u4ec0\u4e48\u662fPickle\u6a21\u5757<\/h3>\n<\/p>\n

Pickle\u6a21\u5757\u662fPython\u4e2d\u7528\u4e8e\u5c06\u5bf9\u8c61\u7ed3\u6784\u5316\u6570\u636e\u5e8f\u5217\u5316\u548c\u53cd\u5e8f\u5217\u5316\u7684\u6a21\u5757\u3002\u5e8f\u5217\u5316\u662f\u5c06\u5bf9\u8c61\u8f6c\u6362\u4e3a\u5b57\u8282\u6d41\u7684\u8fc7\u7a0b\uff0c\u53cd\u5e8f\u5217\u5316\u662f\u5c06\u5b57\u8282\u6d41\u8f6c\u6362\u56de\u5bf9\u8c61\u7684\u8fc7\u7a0b\u3002\u8fd9\u79cd\u529f\u80fd\u975e\u5e38\u6709\u7528\uff0c\u4f8b\u5982\u5728\u5c06\u6570\u636e\u4fdd\u5b58\u5230\u6587\u4ef6\u3001\u5728\u7f51\u7edc\u95f4\u4f20\u8f93\u6570\u636e\u65f6\u3002<\/p>\n<\/p>\n

2\u3001\u5982\u4f55\u4f7f\u7528Pickle\u6a21\u5757<\/h3>\n<\/p>\n

(1) \u5e8f\u5217\u5316\u6570\u636e<\/h4>\n<\/p>\n

\u5e8f\u5217\u5316\u6570\u636e\u7684\u8fc7\u7a0b\u79f0\u4e3a\u201cpickling\u201d\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u4e00\u4e2aPython\u5bf9\u8c61\u5e8f\u5217\u5316\u5e76\u5199\u5165\u6587\u4ef6\uff1a<\/p>\n<\/p>\n

import pickle<\/p>\n
data = {'name': 'John', 'age': 30, 'is_member': True}<\/p>\n
with open('data.pkl', 'wb') as file:<\/p>\n
    pickle.dump(data, file)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdata<\/code>\u662f\u4e00\u4e2a\u5b57\u5178\u5bf9\u8c61\uff0c\u901a\u8fc7pickle.dump()<\/code>\u65b9\u6cd5\u5c06\u5176\u5199\u5165\u540d\u4e3adata.pkl<\/code>\u7684\u6587\u4ef6\u4e2d\u3002\u6587\u4ef6\u4ee5\u4e8c\u8fdb\u5236\u5199\u5165\u6a21\u5f0f\u6253\u5f00\uff08'wb'<\/code>\uff09\u3002<\/p>\n<\/p>\n
(2) \u53cd\u5e8f\u5217\u5316\u6570\u636e<\/h4>\n<\/p>\n
\u53cd\u5e8f\u5217\u5316\u6570\u636e\u7684\u8fc7\u7a0b\u79f0\u4e3a\u201cunpickling\u201d\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u4e86\u5982\u4f55\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u5e8f\u5217\u5316\u7684\u6570\u636e\u5e76\u5c06\u5176\u8f6c\u6362\u56de\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
import pickle<\/p>\n
with open('data.pkl', 'rb') as file:<\/p>\n
    data = pickle.load(file)<\/p>\n
print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdata<\/code>\u5c06\u5305\u542b\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u7684\u5b57\u5178\u5bf9\u8c61\u3002\u6587\u4ef6\u4ee5\u4e8c\u8fdb\u5236\u8bfb\u53d6\u6a21\u5f0f\u6253\u5f00\uff08'rb'<\/code>\uff09\u3002<\/p>\n<\/p>\n
3\u3001Pickle\u6a21\u5757\u7684\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
Pickle\u6a21\u5757\u5e7f\u6cdb\u5e94\u7528\u4e8e\u4fdd\u5b58\u6a21\u578b\u3001\u7f13\u5b58\u6570\u636e\u3001\u4f20\u8f93\u5bf9\u8c61\u7b49\u591a\u4e2a\u573a\u666f\u3002\u4f8b\u5982\uff0c\u5728\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pickle\u6a21\u5757\u4fdd\u5b58\u8bad\u7ec3\u597d\u7684\u6a21\u578b\uff0c\u4ee5\u4fbf\u5c06\u6765\u4f7f\u7528\uff1a<\/p>\n<\/p>\n
import pickle<\/p>\n
from sklearn.linear_model import LogisticRegression<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e9b\u8bad\u7ec3\u6570\u636e<\/strong><\/h2>\n
X_trAI<\/a>n = [[1, 2], [3, 4], [5, 6]]<\/p>\n
y_train = [0, 1, 0]<\/p>\n
model = LogisticRegression()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u4fdd\u5b58\u6a21\u578b<\/strong><\/h2>\n
with open('model.pkl', 'wb') as file:<\/p>\n
    pickle.dump(model, file)<\/p>\n
\u52a0\u8f7d\u6a21\u578b<\/strong><\/h2>\n
with open('model.pkl', 'rb') as file:<\/p>\n
    loaded_model = pickle.load(file)<\/p>\n
print(loaded_model.predict([[1, 2]]))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8bad\u7ec3\u4e86\u4e00\u4e2a\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u5c06\u5176\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002\u7136\u540e\uff0c\u6211\u4eec\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u5b83\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
\u4e8c\u3001NUMPY\u5e93\u7684\u4f7f\u7528<\/h2>\n<\/p>\n
1\u3001\u4ec0\u4e48\u662fNumPy\u5e93<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u7684\u57fa\u7840\u5e93\u3002\u5b83\u5141\u8bb8\u6211\u4eec\u9ad8\u6548\u5730\u8fdb\u884c\u5927\u89c4\u6a21\u7684\u6570\u503c\u8ba1\u7b97\u3002NumPy\u63d0\u4f9b\u4e86\u8bb8\u591a\u4fbf\u5229\u7684\u51fd\u6570\u6765\u521b\u5efa\u3001\u64cd\u4f5c\u548c\u4fdd\u5b58\u6570\u7ec4\u3002<\/p>\n<\/p>\n
2\u3001\u5982\u4f55\u4f7f\u7528NumPy\u5e93<\/h3>\n<\/p>\n
(1) \u4fdd\u5b58\u6570\u7ec4\u5230\u6587\u4ef6<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy.save()<\/code>\u51fd\u6570\u5c06\u6570\u7ec4\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
arr = np.array([1, 2, 3, 4, 5])<\/p>\n
np.save('array.npy', arr)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0carr<\/code>\u662f\u4e00\u4e2aNumPy\u6570\u7ec4\uff0c\u901a\u8fc7numpy.save()<\/code>\u51fd\u6570\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3aarray.npy<\/code>\u7684\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
(2) \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6570\u7ec4<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528numpy.load()<\/code>\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
arr = np.load('array.npy')<\/p>\n
print(arr)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0carr<\/code>\u5c06\u5305\u542b\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u7684NumPy\u6570\u7ec4\u3002<\/p>\n<\/p>\n
3\u3001NumPy\u5e93\u7684\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
NumPy\u5e93\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3001\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u7b49\u591a\u4e2a\u9886\u57df\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u751f\u6210\u968f\u673a\u6570\u3001\u8ba1\u7b97\u6570\u7ec4\u7684\u7edf\u8ba1\u4fe1\u606f\u7b49\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u751f\u6210\u968f\u673a\u6570<\/strong><\/h2>\n
random_numbers = np.random.rand(5)<\/p>\n
print(random_numbers)<\/p>\n
\u8ba1\u7b97\u6570\u7ec4\u7684\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
arr = np.array([1, 2, 3, 4, 5])<\/p>\n
mean = np.mean(arr)<\/p>\n
std_dev = np.std(arr)<\/p>\n
print(f"Mean: {mean}, Standard Deviation: {std_dev}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u751f\u6210\u4e86\u4e94\u4e2a\u968f\u673a\u6570\uff0c\u5e76\u8ba1\u7b97\u4e86\u4e00\u4e2a\u6570\u7ec4\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n
\u4e09\u3001PANDAS\u5e93\u7684\u4f7f\u7528<\/h2>\n<\/p>\n
1\u3001\u4ec0\u4e48\u662fPandas\u5e93<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u6846\u7684\u57fa\u7840\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002Pandas\u5141\u8bb8\u6211\u4eec\u65b9\u4fbf\u5730\u64cd\u4f5c\u3001\u5206\u6790\u548c\u4fdd\u5b58\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n
2\u3001\u5982\u4f55\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n
(1) \u4fdd\u5b58\u6570\u636e\u6846\u5230\u6587\u4ef6<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528DataFrame.to_pickle()<\/code>\u65b9\u6cd5\u5c06\u6570\u636e\u6846\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
df = pd.DataFrame({'name': ['John', 'Jane', 'Alice'], 'age': [30, 25, 28]})<\/p>\n
df.to_pickle('dataframe.pkl')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdf<\/code>\u662f\u4e00\u4e2aPandas\u6570\u636e\u6846\uff0c\u901a\u8fc7DataFrame.to_pickle()<\/code>\u65b9\u6cd5\u5c06\u5176\u4fdd\u5b58\u5230\u540d\u4e3adataframe.pkl<\/code>\u7684\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
(2) \u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pd.read_pickle()<\/code>\u51fd\u6570\u4ece\u6587\u4ef6\u4e2d\u52a0\u8f7d\u6570\u636e\u6846\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
df = pd.read_pickle('dataframe.pkl')<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cdf<\/code>\u5c06\u5305\u542b\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u7684Pandas\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n
3\u3001Pandas\u5e93\u7684\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
Pandas\u5e93\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u591a\u4e2a\u9886\u57df\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u8ba1\u7b97\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f\u7b49\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u5904\u7406\u7f3a\u5931\u6570\u636e<\/strong><\/h2>\n
df.fillna(0, inplace=True)<\/p>\n
\u8ba1\u7b97\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
mean_age = df['age'].mean()<\/p>\n
print(f"Mean Age: {mean_age}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u8bfb\u53d6\u4e86\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5904\u7406\u4e86\u7f3a\u5931\u6570\u636e\uff0c\u5e76\u8ba1\u7b97\u4e86\u6570\u636e\u7684\u5747\u503c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u5199\u5165\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pickle\u6a21\u5757\u3001numpy\u5e93\u3001\u4ee5\u53caPandas\u5e93\u3002Pickle\u6a21\u5757\u9002\u7528\u4e8e\u5927\u591a\u6570\u60c5\u51b5\u3001numpy\u9002\u7528\u4e8e\u6570\u503c\u6570\u7ec4\u3001Pandas\u9002\u7528\u4e8e\u6570\u636e\u6846\u3002<\/strong> \u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7684\u4fdd\u5b58\u548c\u8bfb\u53d6\uff0c\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u6570\u636e\u5904\u7406\u9700\u6c42\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5199\u5165\u4e8c\u8fdb\u5236\u6570\u636e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684open()<\/code>\u51fd\u6570\u4ee5\u4e8c\u8fdb\u5236\u6a21\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u65b9\u6cd5\u662f\u5c06\u6a21\u5f0f\u8bbe\u7f6e\u4e3a'wb'<\/code>\uff08\u5199\u5165\u4e8c\u8fdb\u5236\uff09\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u7528\u4ee5\u4e0b\u4ee3\u7801\u5199\u5165\u5b57\u8282\u6570\u636e\uff1a  <\/p>\n
with open('data.bin', 'wb') as file:\n    file.write(b'\\x00\\x01\\x02\\x03')\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u5f0f\u9002\u7528\u4e8e\u5199\u5165\u56fe\u7247\u3001\u97f3\u9891\u6587\u4ef6\u6216\u5176\u4ed6\u975e\u6587\u672c\u6570\u636e\u3002<\/p>\n
Python\u652f\u6301\u54ea\u4e9b\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\u7684\u5199\u5165\uff1f<\/strong>
\u9664\u4e86\u5b57\u8282\u6570\u636e\uff0cPython\u8fd8\u652f\u6301\u5199\u5165\u5176\u4ed6\u591a\u79cd\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\uff0c\u5305\u62ecNumPy\u6570\u7ec4\u3001Pandas\u6570\u636e\u6846\u548c\u81ea\u5b9a\u4e49\u5bf9\u8c61\u3002\u5bf9\u4e8eNumPy\u6570\u7ec4\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528numpy.save()<\/code>\u51fd\u6570\u5c06\u6570\u7ec4\u4fdd\u5b58\u4e3a.npy<\/code>\u6587\u4ef6\uff1b\u5bf9\u4e8ePandas\u6570\u636e\u6846\uff0c\u53ef\u4ee5\u4f7f\u7528to_pickle()<\/code>\u65b9\u6cd5\u5c06\u5176\u5199\u5165Pickle\u683c\u5f0f\u3002<\/p>\n
\u5982\u4f55\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u975e\u6587\u672c\u6570\u636e\uff1f<\/strong>
\u8981\u4ece\u6587\u4ef6\u4e2d\u8bfb\u53d6\u975e\u6587\u672c\u6570\u636e\uff0c\u60a8\u9700\u8981\u4ee5\u76f8\u5e94\u7684\u6a21\u5f0f\u6253\u5f00\u6587\u4ef6\uff0c\u4f8b\u5982\u4ee5\u4e8c\u8fdb\u5236\u6a21\u5f0f'rb'<\/code>\u8bfb\u53d6\u5b57\u8282\u6570\u636e\u3002\u5bf9\u4e8eNumPy\u6570\u7ec4\uff0c\u53ef\u4ee5\u4f7f\u7528numpy.load()<\/code>\u6765\u8bfb\u53d6.npy<\/code>\u6587\u4ef6\uff0cPandas\u6570\u636e\u6846\u5219\u53ef\u4ee5\u901a\u8fc7pd.read_pickle()<\/code>\u65b9\u6cd5\u8bfb\u53d6Pickle\u6587\u4ef6\u3002\u786e\u4fdd\u5728\u8bfb\u53d6\u65f6\u4f7f\u7528\u6b63\u786e\u7684\u683c\u5f0f\u548c\u65b9\u6cd5\uff0c\u4ee5\u907f\u514d\u6570\u636e\u635f\u574f\u6216\u89e3\u6790\u9519\u8bef\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u5199\u5165\u975e\u6587\u672c\u6570\u636e\u7c7b\u578b\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pickle\u6a21\u5757\u3001numpy\u5e93\u3001\u4ee5\u53caPandas\u5e93\u3002\u6700\u5e38 […]","protected":false},"author":3,"featured_media":1132389,"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\/1132380"}],"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=1132380"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1132380\/revisions"}],"predecessor-version":[{"id":1132391,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1132380\/revisions\/1132391"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1132389"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1132380"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1132380"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1132380"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}