{"id":1118655,"date":"2025-01-08T18:37:22","date_gmt":"2025-01-08T10:37:22","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1118655.html"},"modified":"2025-01-08T18:37:26","modified_gmt":"2025-01-08T10:37:26","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8python%e4%b8%ad%e7%94%9f%e6%88%90%e4%b8%80%e4%b8%aanan","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1118655.html","title":{"rendered":"\u5982\u4f55\u5728python\u4e2d\u751f\u6210\u4e00\u4e2anan"},"content":{"rendered":"

\"\u5982\u4f55\u5728python\u4e2d\u751f\u6210\u4e00\u4e2anan\"<\/p>\n

\u5728Python\u4e2d\u751f\u6210\u4e00\u4e2aNaN\u503c\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u4f8b\u5982\u4f7f\u7528NumPy\u5e93\u3001math\u5e93\u6216\u8005\u76f4\u63a5\u901a\u8fc7\u6d6e\u70b9\u6570\u64cd\u4f5c\u6765\u5b9e\u73b0\u3002\u6700\u5e38\u89c1\u7684\u65b9\u6cd5\u662f\u4f7f\u7528NumPy\u5e93\u4e2d\u7684numpy.nan<\/code>\u3001\u4f7f\u7528math\u5e93\u4e2d\u7684math.nan<\/code>\u3001\u6216\u8005\u901a\u8fc7\u6d6e\u70b9\u6570\u64cd\u4f5cfloat('nan')<\/code>\u3002\u5176\u4e2d\uff0c\u6700\u63a8\u8350\u4f7f\u7528NumPy\u5e93\uff0c\u56e0\u4e3aNumPy\u63d0\u4f9b\u4e86\u66f4\u591a\u4e0e\u6570\u503c\u8ba1\u7b97\u76f8\u5173\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5904\u7406NaN\u503c\u3002<\/strong><\/p>\n<\/p>\n

NumPy\u5e93\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002\u901a\u8fc7NumPy\u5e93\u4e2d\u7684numpy.nan<\/code>\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u751f\u6210\u4e00\u4e2aNaN\u503c\u3002\u9664\u4e86\u751f\u6210NaN\u503c\uff0cNumPy\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u5904\u7406NaN\u503c\uff0c\u4f8b\u5982numpy.isnan<\/code>\u7528\u4e8e\u68c0\u67e5\u6570\u7ec4\u4e2d\u662f\u5426\u5305\u542bNaN\u503c\uff0cnumpy.nan_to_num<\/code>\u7528\u4e8e\u5c06NaN\u503c\u66ff\u6362\u4e3a\u6307\u5b9a\u7684\u6570\u503c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NumPy\u5e93\u751f\u6210NaN<\/h3>\n<\/p>\n

NumPy\u5e93\u662fPython\u4e2d\u5904\u7406\u6570\u503c\u8ba1\u7b97\u7684\u4e3b\u8981\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u64cd\u4f5c\u6570\u7ec4\u548c\u77e9\u9635\u3002\u751f\u6210NaN\u503c\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u662f\u4f7f\u7528numpy.nan<\/code>\u3002<\/p>\n<\/p>\n

import numpy as np<\/p>\n
nan_value = np.nan<\/p>\n
print(nan_value)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u7b80\u6d01\uff0c\u800c\u4e14\u5728\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u65f6\u975e\u5e38\u65b9\u4fbf\u3002NumPy\u5e93\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u4e0eNaN\u503c\u76f8\u5173\u7684\u51fd\u6570\uff0c\u4f8b\u5982numpy.isnan<\/code>\u7528\u4e8e\u68c0\u67e5\u6570\u7ec4\u4e2d\u7684NaN\u503c\uff0cnumpy.nan_to_num<\/code>\u7528\u4e8e\u5c06NaN\u503c\u66ff\u6362\u4e3a\u6307\u5b9a\u7684\u6570\u503c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528math\u5e93\u751f\u6210NaN<\/h3>\n<\/p>\n
Python\u7684math\u5e93\u662f\u4e00\u4e2a\u5185\u7f6e\u7684\u6570\u5b66\u51fd\u6570\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u57fa\u7840\u7684\u6570\u5b66\u8fd0\u7b97\u51fd\u6570\u3002\u5728math\u5e93\u4e2d\uff0c\u751f\u6210NaN\u503c\u53ef\u4ee5\u4f7f\u7528math.nan<\/code>\u3002<\/p>\n<\/p>\n
import math<\/p>\n
nan_value = math.nan<\/p>\n
print(nan_value)<\/p>\n
<\/code><\/pre>\n<\/p>\n
math\u5e93\u63d0\u4f9b\u7684NaN\u503c\u751f\u6210\u65b9\u6cd5\u540c\u6837\u7b80\u6d01\u6613\u7528\uff0c\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u57fa\u7840\u6570\u5b66\u8fd0\u7b97\u7684\u573a\u666f\u3002\u7136\u800c\uff0cmath\u5e93\u5728\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u65b9\u9762\u4e0d\u5982NumPy\u5e93\u5f3a\u5927\uff0c\u56e0\u6b64\u5728\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u4e2d\uff0c\u5efa\u8bae\u4f18\u5148\u4f7f\u7528NumPy\u5e93\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u901a\u8fc7\u6d6e\u70b9\u6570\u64cd\u4f5c\u751f\u6210NaN<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u6d6e\u70b9\u6570\u64cd\u4f5c\u6765\u751f\u6210NaN\u503c\uff0c\u4f8b\u5982\u4f7f\u7528float('nan')<\/code>\u3002<\/p>\n<\/p>\n
nan_value = float('nan')<\/p>\n
print(nan_value)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u9700\u8981\u5bfc\u5165\u4efb\u4f55\u5e93\uff0c\u9002\u7528\u4e8e\u4e00\u4e9b\u7b80\u5355\u7684\u573a\u666f\u3002\u7136\u800c\uff0c\u5728\u590d\u6742\u7684\u6570\u503c\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u4e2d\uff0c\u5efa\u8bae\u4f7f\u7528NumPy\u5e93\u6216math\u5e93\u6765\u751f\u6210\u548c\u5904\u7406NaN\u503c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5728\u6570\u636e\u5904\u7406\u4e2d\u5904\u7406NaN\u503c<\/h3>\n<\/p>\n
\u751f\u6210NaN\u503c\u53ea\u662f\u6570\u636e\u5904\u7406\u7684\u7b2c\u4e00\u6b65\uff0c\u5728\u5b9e\u9645\u7684\u6570\u636e\u5904\u7406\u4e2d\uff0c\u8fd8\u9700\u8981\u5bf9NaN\u503c\u8fdb\u884c\u68c0\u67e5\u3001\u66ff\u6362\u6216\u5220\u9664\u7b49\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u5904\u7406\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1\u3001\u68c0\u67e5NaN\u503c<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5904\u7406\u4e2d\uff0c\u9996\u5148\u9700\u8981\u68c0\u67e5\u6570\u636e\u4e2d\u662f\u5426\u5305\u542bNaN\u503c\u3002NumPy\u5e93\u63d0\u4f9b\u4e86numpy.isnan<\/code>\u51fd\u6570\u6765\u68c0\u67e5\u6570\u7ec4\u4e2d\u7684NaN\u503c\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([1, 2, np.nan, 4])<\/p>\n
print(np.isnan(data))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
[False False  True False]<\/p>\n
<\/code><\/pre>\n<\/p>\n
numpy.isnan<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u6570\u7ec4\uff0c\u8868\u793a\u539f\u6570\u7ec4\u4e2d\u6bcf\u4e2a\u5143\u7d20\u662f\u5426\u4e3aNaN\u503c\u3002<\/p>\n<\/p>\n
2\u3001\u66ff\u6362NaN\u503c<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5904\u7406\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u5c06NaN\u503c\u66ff\u6362\u4e3a\u6307\u5b9a\u7684\u6570\u503c\u3002NumPy\u5e93\u63d0\u4f9b\u4e86numpy.nan_to_num<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([1, 2, np.nan, 4])<\/p>\n
clean_data = np.nan_to_num(data, nan=-1)<\/p>\n
print(clean_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
[ 1.  2. -1.  4.]<\/p>\n
<\/code><\/pre>\n<\/p>\n
numpy.nan_to_num<\/code>\u51fd\u6570\u5c06\u6570\u7ec4\u4e2d\u7684NaN\u503c\u66ff\u6362\u4e3a\u6307\u5b9a\u7684\u6570\u503c\uff08\u4f8b\u5982-1\uff09\u3002<\/p>\n<\/p>\n
3\u3001\u5220\u9664NaN\u503c<\/h4>\n<\/p>\n
\u5728\u67d0\u4e9b\u6570\u636e\u5904\u7406\u4e2d\uff0c\u53ef\u80fd\u9700\u8981\u5220\u9664\u5305\u542bNaN\u503c\u7684\u884c\u6216\u5217\u3002NumPy\u5e93\u63d0\u4f9b\u4e86\u76f8\u5173\u7684\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
data = np.array([[1, 2, np.nan], [4, 5, 6], [np.nan, 8, 9]])<\/p>\n
clean_data = data[~np.isnan(data).any(axis=1)]<\/p>\n
print(clean_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
[[4. 5. 6.]]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u5220\u9664\u4e86\u5305\u542bNaN\u503c\u7684\u884c\uff0c\u4fdd\u7559\u4e86\u6240\u6709\u6570\u503c\u6709\u6548\u7684\u884c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u7684\u6570\u636e\u5206\u6790\u4e2d\uff0cNaN\u503c\u7684\u5904\u7406\u662f\u4e00\u4e2a\u5e38\u89c1\u4e14\u91cd\u8981\u7684\u4efb\u52a1\u3002\u4ee5\u4e0b\u662f\u51e0\u4e2a\u5e38\u89c1\u7684\u6570\u636e\u5206\u6790\u573a\u666f\u53ca\u5176\u5904\u7406\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u5904\u7406\u7f3a\u5931\u503c\uff08NaN\u503c\uff09\u3002\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6216pandas\u5e93\uff08\u53e6\u4e00\u4e2a\u5e38\u7528\u7684\u6570\u636e\u5206\u6790\u5e93\uff09\u6765\u6e05\u6d17\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = pd.DataFrame({'A': [1, 2, np.nan, 4], 'B': [5, np.nan, 7, 8]})<\/p>\n
clean_data = data.fillna(0)  # \u5c06NaN\u503c\u66ff\u6362\u4e3a0<\/p>\n
print(clean_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
     A    B<\/p>\n
0  1.0  5.0<\/p>\n
1  2.0  0.0<\/p>\n
2  0.0  7.0<\/p>\n
3  4.0  8.0<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u53ef\u89c6\u5316\u8fc7\u7a0b\u4e2d\uff0cNaN\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u56fe\u8868\u7684\u663e\u793a\u6548\u679c\u3002\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6216pandas\u5e93\u5904\u7406NaN\u503c\uff0c\u7136\u540e\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import pandas as pd<\/p>\n
data = pd.DataFrame({'A': [1, 2, np.nan, 4], 'B': [5, 6, 7, np.nan]})<\/p>\n
clean_data = data.dropna()  # \u5220\u9664\u5305\u542bNaN\u503c\u7684\u884c<\/p>\n
plt.plot(clean_data['A'], clean_data['B'])<\/p>\n
plt.xlabel('A')<\/p>\n
plt.ylabel('B')<\/p>\n
plt.title('Data Visualization')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u5220\u9664\u4e86\u5305\u542bNaN\u503c\u7684\u884c\uff0c\u7136\u540e\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff0c\u786e\u4fdd\u56fe\u8868\u7684\u663e\u793a\u6548\u679c\u4e0d\u53d7\u5f71\u54cd\u3002<\/p>\n<\/p>\n
3\u3001\u673a\u5668\u5b66\u4e60<\/a><\/h4>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0cNaN\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u9884\u6d4b\u3002\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6216pandas\u5e93\u5904\u7406NaN\u503c\uff0c\u7136\u540e\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import pandas as pd<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
data = pd.DataFrame({'X': [1, 2, 3, 4, 5], 'Y': [2, 4, np.nan, 8, 10]})<\/p>\n
clean_data = data.dropna()  # \u5220\u9664\u5305\u542bNaN\u503c\u7684\u884c<\/p>\n
X = clean_data[['X']]<\/p>\n
Y = clean_data['Y']<\/p>\n
model = LinearRegression()<\/p>\n
model.fit(X, Y)<\/p>\n
predicted = model.predict([[6]])<\/p>\n
print(predicted)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u5220\u9664\u4e86\u5305\u542bNaN\u503c\u7684\u884c\uff0c\u7136\u540e\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\u751f\u6210NaN\u503c\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u6700\u5e38\u89c1\u7684\u662f\u4f7f\u7528NumPy\u5e93\u4e2d\u7684numpy.nan<\/code>\u3001math\u5e93\u4e2d\u7684math.nan<\/code>\u548c\u901a\u8fc7\u6d6e\u70b9\u6570\u64cd\u4f5cfloat('nan')<\/code>\u3002\u5728\u5b9e\u9645\u7684\u6570\u636e\u5904\u7406\u4e2d\uff0c\u9664\u4e86\u751f\u6210NaN\u503c\uff0c\u8fd8\u9700\u8981\u5bf9NaN\u503c\u8fdb\u884c\u68c0\u67e5\u3001\u66ff\u6362\u6216\u5220\u9664\u7b49\u64cd\u4f5c\u3002NumPy\u5e93\u548cpandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u5904\u7406NaN\u503c\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53ef\u89c6\u5316\u548c\u673a\u5668\u5b66\u4e60\u7b49\u9886\u57df\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u5408\u7406\u5730\u751f\u6210\u548c\u5904\u7406NaN\u503c\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\uff0c\u4e3a\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002\u5e0c\u671b\u672c\u6587\u80fd\u591f\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528NaN\u503c\u5904\u7406\u65b9\u6cd5\uff0c\u63d0\u9ad8\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u7684\u6c34\u5e73\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u521b\u5efaNaN\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u521b\u5efaNaN\u503c\u3002\u5177\u4f53\u65b9\u6cd5\u662f\u901a\u8fc7numpy.nan<\/code>\u6765\u751f\u6210\u4e00\u4e2aNaN\u3002\u4ee5\u4e0b\u662f\u793a\u4f8b\u4ee3\u7801\uff1a  <\/p>\n
import numpy as np\nnan_value = np.nan\nprint(nan_value)  # \u8f93\u51fa\uff1anan\n<\/code><\/pre>\n
\u6b64\u5916\uff0cPandas\u5e93\u4e5f\u5141\u8bb8\u60a8\u4f7f\u7528pd.NA<\/code>\u6216float('nan')<\/code>\u6765\u521b\u5efaNaN\u503c\u3002<\/p>\n
NaN\u503c\u5728\u6570\u636e\u5904\u7406\u4e2d\u6709\u4ec0\u4e48\u7528\u5904\uff1f<\/strong>
NaN\u503c\u5728\u6570\u636e\u5206\u6790\u548c\u79d1\u5b66\u8ba1\u7b97\u4e2d\u975e\u5e38\u91cd\u8981\u3002\u5b83\u4eec\u901a\u5e38\u7528\u4e8e\u8868\u793a\u7f3a\u5931\u503c\u6216\u65e0\u6548\u6570\u636e\u3002\u4f8b\u5982\uff0c\u5728\u5904\u7406\u6570\u636e\u96c6\u65f6\uff0cNaN\u53ef\u4ee5\u5e2e\u52a9\u60a8\u6807\u8bc6\u672a\u6536\u96c6\u7684\u6570\u636e\uff0c\u4ece\u800c\u907f\u514d\u5bf9\u5206\u6790\u7ed3\u679c\u7684\u9519\u8bef\u5f71\u54cd\u3002<\/p>\n
\u5982\u4f55\u68c0\u6d4bPython\u4e2d\u7684NaN\u503c\uff1f<\/strong>
\u8981\u68c0\u6d4bNaN\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u7684numpy.isnan()<\/code>\u51fd\u6570\u6216Pandas\u7684pd.isna()<\/code>\u51fd\u6570\u3002\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5feb\u901f\u5224\u65ad\u6570\u7ec4\u6216\u6570\u636e\u6846\u4e2d\u7684\u54ea\u4e9b\u5143\u7d20\u662fNaN\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NumPy\u7684\u793a\u4f8b\uff1a  <\/p>\n
import numpy as np\ndata = [1, 2, np.nan, 4]\nnan_check = np.isnan(data)\nprint(nan_check)  # \u8f93\u51fa\uff1a[False False  True False]\n<\/code><\/pre>\n
\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u8bc6\u522b\u5e76\u5904\u7406\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u751f\u6210\u4e00\u4e2aNaN\u503c\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u4f8b\u5982\u4f7f\u7528NumPy\u5e93\u3001math\u5e93\u6216\u8005\u76f4\u63a5\u901a\u8fc7\u6d6e\u70b9\u6570\u64cd\u4f5c\u6765\u5b9e\u73b0\u3002\u6700\u5e38 […]","protected":false},"author":3,"featured_media":1118661,"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\/1118655"}],"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=1118655"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1118655\/revisions"}],"predecessor-version":[{"id":1118662,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1118655\/revisions\/1118662"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1118661"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1118655"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1118655"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1118655"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}