{"id":1045021,"date":"2024-12-31T13:19:36","date_gmt":"2024-12-31T05:19:36","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1045021.html"},"modified":"2024-12-31T13:19:38","modified_gmt":"2024-12-31T05:19:38","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%81%9a%e6%8f%8f%e8%bf%b0%e6%80%a7%e7%bb%9f%e8%ae%a1","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1045021.html","title":{"rendered":"\u5982\u4f55\u7528python\u505a\u63cf\u8ff0\u6027\u7edf\u8ba1"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u505a\u63cf\u8ff0\u6027\u7edf\u8ba1\"<\/p>\n

\u5982\u4f55\u7528Python\u505a\u63cf\u8ff0\u6027\u7edf\u8ba1<\/strong><\/p>\n<\/p>\n

Python\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u9886\u57df\u3002\u4f7f\u7528Python\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5de5\u5177\u548c\u5e93\uff0c\u5982pandas\u3001numpy\u3001scipy\u3001matplotlib<\/strong>\u3002\u5176\u4e2d\uff0cpandas\u662f\u6700\u5e38\u7528\u7684\u5de5\u5177\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u80fd\u65b9\u4fbf\u5730\u5904\u7406\u6570\u636e\u6846\u67b6\uff0c\u5e76\u4e14\u5177\u6709\u4e30\u5bcc\u7684\u7edf\u8ba1\u529f\u80fd\u3002\u901a\u8fc7pandas\u53ef\u4ee5\u8f7b\u677e\u5730\u8ba1\u7b97\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u3001\u5206\u4f4d\u6570\u7b49\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf\uff0c\u5e76\u4e14\u53ef\u4ee5\u901a\u8fc7matplotlib\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u7b49\u56fe\u5f62\u6765\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u57fa\u672c\u64cd\u4f5c<\/h3>\n<\/p>\n

1\u3001\u5bfc\u5165\u6570\u636e<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u5e76\u8bfb\u53d6\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('your_data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528pandas\u5e93\u8bfb\u53d6\u4e00\u4e2aCSV\u6587\u4ef6\u3002pd.read_csv<\/code>\u51fd\u6570\u4f1a\u81ea\u52a8\u5c06\u6570\u636e\u52a0\u8f7d\u5230\u4e00\u4e2aDataFrame\u4e2d\uff0c\u8fd9\u662fpandas\u4e2d\u7684\u4e00\u79cd\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8eExcel\u4e2d\u7684\u8868\u683c\u3002<\/p>\n<\/p>\n
2\u3001\u67e5\u770b\u6570\u636e\u57fa\u672c\u4fe1\u606f<\/h4>\n<\/p>\n
\u5bfc\u5165\u6570\u636e\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u4fe1\u606f\uff1a<\/p>\n<\/p>\n
# \u67e5\u770b\u524d5\u884c\u6570\u636e<\/p>\n
print(data.head())<\/p>\n
\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
print(data.describe())<\/p>\n
\u67e5\u770b\u6570\u636e\u7684\u5217\u540d<\/strong><\/h2>\n
print(data.columns)<\/p>\n
\u67e5\u770b\u6570\u636e\u7684\u7c7b\u578b<\/strong><\/h2>\n
print(data.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
head()<\/code>\u51fd\u6570\u53ef\u4ee5\u67e5\u770b\u6570\u636e\u7684\u524d5\u884c\uff0cdescribe()<\/code>\u51fd\u6570\u53ef\u4ee5\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff0c\u5305\u62ec\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u3001\u6700\u5927\u503c\u7b49\uff0ccolumns<\/code>\u5c5e\u6027\u53ef\u4ee5\u67e5\u770b\u6570\u636e\u7684\u5217\u540d\uff0cdtypes<\/code>\u5c5e\u6027\u53ef\u4ee5\u67e5\u770b\u6bcf\u5217\u6570\u636e\u7684\u7c7b\u578b\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf<\/h3>\n<\/p>\n
1\u3001\u5747\u503c\u548c\u4e2d\u4f4d\u6570<\/h4>\n<\/p>\n
\u5747\u503c\u548c\u4e2d\u4f4d\u6570\u662f\u6700\u5e38\u7528\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf\uff0c\u5206\u522b\u8868\u793a\u6570\u636e\u7684\u5e73\u5747\u503c\u548c\u4e2d\u95f4\u503c\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5747\u503c<\/p>\n
mean_value = data['column_name'].mean()<\/p>\n
print(f"Mean: {mean_value}")<\/p>\n
\u8ba1\u7b97\u4e2d\u4f4d\u6570<\/strong><\/h2>\n
median_value = data['column_name'].median()<\/p>\n
print(f"Median: {median_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6807\u51c6\u5dee\u548c\u65b9\u5dee<\/h4>\n<\/p>\n
\u6807\u51c6\u5dee\u548c\u65b9\u5dee\u662f\u8861\u91cf\u6570\u636e\u79bb\u6563\u7a0b\u5ea6\u7684\u91cd\u8981\u6307\u6807\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6807\u51c6\u5dee<\/p>\n
std_dev = data['column_name'].std()<\/p>\n
print(f"Standard Deviation: {std_dev}")<\/p>\n
\u8ba1\u7b97\u65b9\u5dee<\/strong><\/h2>\n
variance = data['column_name'].var()<\/p>\n
print(f"Variance: {variance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5206\u4f4d\u6570<\/h4>\n<\/p>\n
\u5206\u4f4d\u6570\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b9725%\u300150%\u300175%\u7684\u5206\u4f4d\u6570<\/p>\n
quantiles = data['column_name'].quantile([0.25, 0.5, 0.75])<\/p>\n
print(f"Quantiles:\\n{quantiles}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u6570\u636e\u53ef\u89c6\u5316\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u76f4\u89c2\u5730\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u7bb1\u7ebf\u56fe\u7b49\u56fe\u5f62\u3002<\/p>\n<\/p>\n
1\u3001\u76f4\u65b9\u56fe<\/h4>\n<\/p>\n
\u76f4\u65b9\u56fe\u53ef\u4ee5\u663e\u793a\u6570\u636e\u7684\u9891\u7387\u5206\u5e03\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u7ed8\u5236\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist(data['column_name'], bins=30, edgecolor='k')<\/p>\n
plt.title('Histogram')<\/p>\n
plt.xlabel('Value')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7bb1\u7ebf\u56fe<\/h4>\n<\/p>\n
\u7bb1\u7ebf\u56fe\u53ef\u4ee5\u663e\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u5305\u62ec\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u3001\u5f02\u5e38\u503c\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u7ed8\u5236\uff1a<\/p>\n<\/p>\n
# \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/p>\n
plt.boxplot(data['column_name'])<\/p>\n
plt.title('Boxplot')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u9ad8\u7ea7\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
1\u3001\u76f8\u5173\u7cfb\u6570<\/h4>\n<\/p>\n
\u76f8\u5173\u7cfb\u6570\u53ef\u4ee5\u8861\u91cf\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u7ebf\u6027\u5173\u7cfb\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u76f8\u5173\u7cfb\u6570<\/p>\n
correlation = data['column1_name'].corr(data['column2_name'])<\/p>\n
print(f"Correlation: {correlation}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5206\u7ec4\u7edf\u8ba1<\/h4>\n<\/p>\n
\u5206\u7ec4\u7edf\u8ba1\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5206\u6790\u4e0d\u540c\u7ec4\u522b\u7684\u6570\u636e\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\uff1a<\/p>\n<\/p>\n
# \u6309\u67d0\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u5747\u503c<\/p>\n
grouped_data = data.groupby('group_column_name').mean()<\/p>\n
print(grouped_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528Numpy\u5e93\u8fdb\u884c\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
\u9664\u4e86pandas\u5e93\uff0cnumpy\u5e93\u4e5f\u662f\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u7684\u5e38\u7528\u5de5\u5177\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u7edf\u8ba1\u51fd\u6570\uff1a<\/p>\n<\/p>\n
1\u3001\u8ba1\u7b97\u5747\u503c\u548c\u4e2d\u4f4d\u6570<\/h4>\n<\/p>\n
import numpy as np<\/p>\n
\u8ba1\u7b97\u5747\u503c<\/strong><\/h2>\n
mean_value = np.mean(data['column_name'])<\/p>\n
print(f"Mean: {mean_value}")<\/p>\n
\u8ba1\u7b97\u4e2d\u4f4d\u6570<\/strong><\/h2>\n
median_value = np.median(data['column_name'])<\/p>\n
print(f"Median: {median_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8ba1\u7b97\u6807\u51c6\u5dee\u548c\u65b9\u5dee<\/h4>\n<\/p>\n
# \u8ba1\u7b97\u6807\u51c6\u5dee<\/p>\n
std_dev = np.std(data['column_name'])<\/p>\n
print(f"Standard Deviation: {std_dev}")<\/p>\n
\u8ba1\u7b97\u65b9\u5dee<\/strong><\/h2>\n
variance = np.var(data['column_name'])<\/p>\n
print(f"Variance: {variance}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8ba1\u7b97\u5206\u4f4d\u6570<\/h4>\n<\/p>\n
# \u8ba1\u7b9725%\u300150%\u300175%\u7684\u5206\u4f4d\u6570<\/p>\n
quantiles = np.percentile(data['column_name'], [25, 50, 75])<\/p>\n
print(f"Quantiles: {quantiles}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4f7f\u7528Scipy\u5e93\u8fdb\u884c\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
Scipy\u5e93\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u7edf\u8ba1\u51fd\u6570\uff0c\u53ef\u4ee5\u8fdb\u884c\u66f4\u52a0\u9ad8\u7ea7\u7684\u7edf\u8ba1\u5206\u6790\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u7edf\u8ba1\u51fd\u6570\uff1a<\/p>\n<\/p>\n
1\u3001\u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf<\/h4>\n<\/p>\n
from scipy import stats<\/p>\n
\u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u91cf<\/strong><\/h2>\n
desc_stats = stats.describe(data['column_name'])<\/p>\n
print(desc_stats)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8ba1\u7b97t\u68c0\u9a8c<\/h4>\n<\/p>\n
t\u68c0\u9a8c\u53ef\u4ee5\u7528\u4e8e\u6bd4\u8f83\u4e24\u4e2a\u6837\u672c\u7684\u5747\u503c\u662f\u5426\u6709\u663e\u8457\u5dee\u5f02\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97t\u68c0\u9a8c<\/p>\n
t_stat, p_value = stats.ttest_ind(data['column1_name'], data['column2_name'])<\/p>\n
print(f"T-statistic: {t_stat}, P-value: {p_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0cPython\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u5e93\u6765\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\u3002\u4f7f\u7528pandas\u3001numpy\u3001scipy\u3001matplotlib\u7b49\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8ba1\u7b97\u5404\u79cd\u7edf\u8ba1\u91cf\uff0c\u5e76\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/strong>\u8fd9\u4e9b\u5de5\u5177\u4e0d\u4ec5\u529f\u80fd\u5f3a\u5927\uff0c\u800c\u4e14\u6613\u4e8e\u4f7f\u7528\uff0c\u975e\u5e38\u9002\u5408\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5206\u6790\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u60a8\u80fd\u591f\u66f4\u597d\u5730\u4f7f\u7528Python\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u5206\u6790\uff0c\u63d0\u5347\u6570\u636e\u5206\u6790\u80fd\u529b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u63cf\u8ff0\u6027\u7edf\u8ba1\u662f\u4ec0\u4e48\uff0c\u5b83\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u4f5c\u7528\u662f\u4ec0\u4e48\uff1f<\/strong>
\u63cf\u8ff0\u6027\u7edf\u8ba1\u662f\u5bf9\u6570\u636e\u96c6\u7684\u57fa\u672c\u7279\u5f81\u8fdb\u884c\u603b\u7ed3\u548c\u63cf\u8ff0\u7684\u7edf\u8ba1\u65b9\u6cd5\u3002\u8fd9\u79cd\u6280\u672f\u901a\u8fc7\u8ba1\u7b97\u8bf8\u5982\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u4f17\u6570\u3001\u6807\u51c6\u5dee\u548c\u8303\u56f4\u7b49\u7edf\u8ba1\u91cf\uff0c\u5e2e\u52a9\u5206\u6790\u5e08\u5feb\u901f\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u548c\u8d8b\u52bf\u3002\u5b83\u5728\u6570\u636e\u5206\u6790\u4e2d\u8d77\u7740\u91cd\u8981\u4f5c\u7528\uff0c\u56e0\u4e3a\u5b83\u4e3a\u540e\u7eed\u7684\u63a8\u65ad\u5206\u6790\u63d0\u4f9b\u4e86\u57fa\u7840\uff0c\u5e2e\u52a9\u8bc6\u522b\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u548c\u6a21\u5f0f\u3002<\/p>\n
\u4f7f\u7528Python\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u6709\u54ea\u4e9b\u5e38\u7528\u5e93\u548c\u65b9\u6cd5\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u548cNumPy\u3002Pandas\u63d0\u4f9b\u4e86describe()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u6570\u636e\u6846\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f\uff0c\u5982\u8ba1\u6570\u3001\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u3001\u6700\u5927\u503c\u548c\u56db\u5206\u4f4d\u6570\u7b49\u3002NumPy\u5219\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u66f4\u57fa\u7840\u7684\u7edf\u8ba1\u91cf\uff0c\u6bd4\u5982\u5747\u503c\u548c\u6807\u51c6\u5dee\u3002\u5229\u7528\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5206\u6790\u5e76\u83b7\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u4ee5\u786e\u4fdd\u63cf\u8ff0\u6027\u7edf\u8ba1\u7684\u51c6\u786e\u6027\uff1f<\/strong>
\u5728\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u4e4b\u524d\uff0c\u5904\u7406\u7f3a\u5931\u503c\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u6240\u5728\u7684\u884c\u6216\u5217\u3001\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u586b\u5145\u7f3a\u5931\u503c\u3002Pandas\u4e2d\u7684dropna()<\/code>\u548cfillna()<\/code>\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002\u786e\u4fdd\u5bf9\u7f3a\u5931\u503c\u7684\u5408\u7406\u5904\u7406\uff0c\u53ef\u4ee5\u63d0\u9ad8\u7edf\u8ba1\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\uff0c\u4ece\u800c\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u96c6\u7684\u7279\u5f81\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u7528Python\u505a\u63cf\u8ff0\u6027\u7edf\u8ba1 Python\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u5206\u6790\u9886\u57df\u3002\u4f7f\u7528Python\u8fdb\u884c […]","protected":false},"author":3,"featured_media":1045030,"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\/1045021"}],"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=1045021"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1045021\/revisions"}],"predecessor-version":[{"id":1045033,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1045021\/revisions\/1045033"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1045030"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1045021"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1045021"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1045021"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}