{"id":951019,"date":"2024-12-27T00:50:28","date_gmt":"2024-12-26T16:50:28","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/951019.html"},"modified":"2024-12-27T00:50:30","modified_gmt":"2024-12-26T16:50:30","slug":"python-pandas%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/951019.html","title":{"rendered":"python pandas\u5982\u4f55\u4f7f\u7528"},"content":{"rendered":"

\"python<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
Python Pandas\u7684\u4f7f\u7528\u5305\u62ec\u6570\u636e\u5bfc\u5165\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u3002<\/strong> Pandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\uff0c\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u8868\u683c\u6570\u636e\u3002\u901a\u8fc7Pandas\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6CSV\u3001Excel\u7b49\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u3002\u6570\u636e\u5206\u6790\u65b9\u9762\uff0cPandas\u63d0\u4f9b\u4e86\u8bb8\u591a\u4fbf\u6377\u7684\u51fd\u6570\u6765\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3001\u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408\u3001\u6570\u636e\u900f\u89c6\u7b49\u64cd\u4f5c\u3002\u6b64\u5916\uff0cPandas\u8fd8\u652f\u6301\u4e0eMatplotlib\u548cSeaborn\u7b49\u53ef\u89c6\u5316\u5e93\u7684\u96c6\u6210\uff0c\u53ef\u4ee5\u5c06\u5206\u6790\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u5c55\u793a\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecdPandas\u7684\u5404\u4e2a\u65b9\u9762\u7684\u4f7f\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u6b63\u6587\uff1a<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u5bfc\u5165\u4e0e\u5bfc\u51fa<\/strong><\/h2>\n

Pandas\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u5bfc\u5165\u4e0e\u5bfc\u51fa\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u6570\u636e\u5bfc\u5165\u662f\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65\uff0cPandas\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u51fd\u6570\u6765\u5b8c\u6210\u8fd9\u4e00\u6b65\u3002<\/p>\n<\/p>\n

1.1 CSV\u6587\u4ef6<\/h2>\n<\/p>\n

CSV\u6587\u4ef6\u662f\u6700\u5e38\u89c1\u7684\u6570\u636e\u5b58\u50a8\u683c\u5f0f\u4e4b\u4e00\u3002Pandas\u63d0\u4f9b\u4e86read_csv<\/code>\u51fd\u6570\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u3002\u4f7f\u7528\u65f6\uff0c\u53ea\u9700\u8981\u6307\u5b9a\u6587\u4ef6\u8def\u5f84\u5373\u53ef\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bfb\u53d6\u5b8c\u6210\u540e\uff0c\u6570\u636e\u4f1a\u5b58\u50a8\u5728\u4e00\u4e2aDataFrame\u5bf9\u8c61\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame\u7684\u5404\u79cd\u65b9\u6cd5\u8fdb\u884c\u8fdb\u4e00\u6b65\u5206\u6790\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n
\u5bfc\u51faCSV\u6587\u4ef6\u540c\u6837\u7b80\u5355\uff0c\u4f7f\u7528to_csv<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
df.to_csv('output.csv', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
index=False<\/code>\u7684\u53c2\u6570\u7528\u4e8e\u4e0d\u4fdd\u5b58\u7d22\u5f15\u5217\u3002<\/p>\n<\/p>\n
1.2 Excel\u6587\u4ef6<\/h2>\n<\/p>\n
Pandas\u4e5f\u53ef\u4ee5\u8bfb\u53d6Excel\u6587\u4ef6\uff0c\u901a\u8fc7read_excel<\/code>\u51fd\u6570\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2dsheet_name<\/code>\u53c2\u6570\u6307\u5b9a\u8981\u8bfb\u53d6\u7684\u5de5\u4f5c\u8868\u3002<\/p>\n<\/p>\n
\u540c\u6837\uff0c\u53ef\u4ee5\u4f7f\u7528to_excel<\/code>\u65b9\u6cd5\u5c06DataFrame\u5bfc\u51fa\u4e3aExcel\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
df.to_excel('output.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 SQL\u6570\u636e\u5e93<\/h2>\n<\/p>\n
Pandas\u652f\u6301\u4eceSQL\u6570\u636e\u5e93\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u4f7f\u7528read_sql<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
engine = create_engine('sqlite:\/\/\/database.db')<\/p>\n
df = pd.read_sql('SELECT * FROM table_name', engine)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bfc\u51fa\u6570\u636e\u5230SQL\u6570\u636e\u5e93\u4f7f\u7528to_sql<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
df.to_sql('table_name', engine, index=False, if_exists='replace')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406<\/strong><\/h2>\n
\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u6b65\u9aa4\uff0cPandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u5e2e\u52a9\u6211\u4eec\u6e05\u6d17\u548c\u9884\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
2.1 \u7f3a\u5931\u503c\u5904\u7406<\/h2>\n<\/p>\n
\u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u5e38\u89c1\u7684\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528isnull<\/code>\u548cnotnull<\/code>\u65b9\u6cd5\u6765\u68c0\u6d4b\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
df.isnull().sum()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u7edf\u8ba1\u6bcf\u4e00\u5217\u7684\u7f3a\u5931\u503c\u6570\u91cf\u3002\u53ef\u4ee5\u4f7f\u7528fillna<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
df.fillna(0, inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
inplace=True<\/code>\u8868\u793a\u76f4\u63a5\u5728\u539fDataFrame\u4e0a\u8fdb\u884c\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
2.2 \u6570\u636e\u8fc7\u6ee4\u4e0e\u9009\u62e9<\/h2>\n<\/p>\n
\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u53ef\u4ee5\u9009\u62e9\u6ee1\u8db3\u6761\u4ef6\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
filtered_df = df[df['column_name'] > 10]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u9009\u62e9column_name<\/code>\u5217\u503c\u5927\u4e8e10\u7684\u6240\u6709\u884c\u3002<\/p>\n<\/p>\n
2.3 \u6570\u636e\u8f6c\u6362<\/h2>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86apply<\/code>\u65b9\u6cd5\u7528\u4e8e\u5bf9DataFrame\u7684\u6bcf\u4e00\u5217\u6216\u6bcf\u4e00\u884c\u8fdb\u884c\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
df['new_column'] = df['column_name'].apply(lambda x: x * 2)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u5c06column_name<\/code>\u5217\u7684\u6bcf\u4e2a\u503c\u4e58\u4ee52\uff0c\u5e76\u5c06\u7ed3\u679c\u5b58\u50a8\u5728new_column<\/code>\u5217\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u6570\u636e\u5206\u6790<\/strong><\/h2>\n
Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u5206\u6790\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002<\/p>\n<\/p>\n
3.1 \u63cf\u8ff0\u6027\u7edf\u8ba1<\/h2>\n<\/p>\n
Pandas\u7684describe<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u6570\u636e\u7684\u63cf\u8ff0\u6027\u7edf\u8ba1\u4fe1\u606f\uff1a<\/p>\n<\/p>\n
df.describe()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u8f93\u51fa\u6bcf\u4e00\u5217\u7684\u8ba1\u6570\u3001\u5e73\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u3001\u56db\u5206\u4f4d\u6570\u548c\u6700\u5927\u503c\u3002<\/p>\n<\/p>\n
3.2 \u6570\u636e\u5206\u7ec4\u4e0e\u805a\u5408<\/h2>\n<\/p>\n
\u4f7f\u7528groupby<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u8fdb\u884c\u805a\u5408\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
grouped = df.groupby('column_name').sum()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u6309column_name<\/code>\u5217\u5206\u7ec4\u5e76\u8ba1\u7b97\u6bcf\u7ec4\u7684\u548c\u3002<\/p>\n<\/p>\n
3.3 \u6570\u636e\u900f\u89c6\u8868<\/h2>\n<\/p>\n
Pandas\u7684pivot_table<\/code>\u65b9\u6cd5\u53ef\u4ee5\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\uff1a<\/p>\n<\/p>\n
pivot = df.pivot_table(index='column1', columns='column2', values='value_column', aggfunc='mean')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u6309column1<\/code>\u548ccolumn2<\/code>\u521b\u5efa\u4e00\u4e2a\u900f\u89c6\u8868\uff0c\u5e76\u8ba1\u7b97value_column<\/code>\u5217\u7684\u5e73\u5747\u503c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/strong><\/h2>\n
Pandas\u53ef\u4ee5\u4e0e\u53ef\u89c6\u5316\u5e93\u5982Matplotlib\u548cSeaborn\u7ed3\u5408\u4f7f\u7528\uff0c\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u5206\u6790\u7ed3\u679c\u3002<\/p>\n<\/p>\n
4.1 Matplotlib<\/h2>\n<\/p>\n
Pandas\u7684DataFrame\u5bf9\u8c61\u6709\u4e00\u4e2aplot<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528Matplotlib\u8fdb\u884c\u7ed8\u56fe\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
df['column_name'].plot(kind='line')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u7ed8\u5236column_name<\/code>\u5217\u7684\u6298\u7ebf\u56fe\u3002<\/p>\n<\/p>\n
4.2 Seaborn<\/h2>\n<\/p>\n
Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
sns.boxplot(x='column1', y='column2', data=df)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u7ed8\u5236\u4e00\u4e2a\u7bb1\u7ebf\u56fe\uff0c\u5c55\u793acolumn1<\/code>\u4e0ecolumn2<\/code>\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u6027\u80fd\u4f18\u5316<\/strong><\/h2>\n
\u5f53\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u6027\u80fd\u53ef\u80fd\u4f1a\u6210\u4e3a\u95ee\u9898\uff0cPandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6280\u5de7\u6765\u63d0\u9ad8\u6027\u80fd\u3002<\/p>\n<\/p>\n
5.1 \u4f7f\u7528\u7c7b\u522b\u6570\u636e<\/h2>\n<\/p>\n
\u5982\u679c\u67d0\u5217\u5305\u542b\u91cd\u590d\u7684\u5b57\u7b26\u4e32\u503c\uff0c\u53ef\u4ee5\u5c06\u5176\u8f6c\u6362\u4e3a\u7c7b\u522b\u6570\u636e\uff0c\u8fd9\u6837\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\u5e76\u63d0\u9ad8\u6027\u80fd\uff1a<\/p>\n<\/p>\n
df['column_name'] = df['column_name'].astype('category')<\/p>\n
<\/code><\/pre>\n<\/p>\n
5.2 \u5e76\u884c\u5316\u64cd\u4f5c<\/h2>\n<\/p>\n
\u901a\u8fc7\u5206\u5757\u5904\u7406\u5927\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u63d0\u9ad8\u5904\u7406\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
for chunk in pd.read_csv('large_file.csv', chunksize=1000):<\/p>\n
    process(chunk)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u6bb5\u4ee3\u7801\u4f1a\u9010\u5757\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u6bcf\u5757\u5305\u542b1000\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/strong><\/h2>\n
Pandas\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u5bfc\u5165\u4e0e\u5bfc\u51fa\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u6e05\u6d17\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u5de5\u5177\u3002\u5728\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u5bfc\u5165\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u6b65\u9aa4\uff0c\u5feb\u901f\u9ad8\u6548\u5730\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u4fe1\u606f\u3002\u6b64\u5916\uff0c\u901a\u8fc7\u5408\u7406\u7684\u6027\u80fd\u4f18\u5316\u7b56\u7565\uff0c\u53ef\u4ee5\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u63d0\u9ad8\u6548\u7387\u3002\u719f\u7ec3\u638c\u63e1Pandas\u7684\u4f7f\u7528\u6280\u5de7\uff0c\u5c06\u5927\u5927\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u6548\u7387\u548c\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 Python Pandas\u7684\u4e3b\u8981\u529f\u80fd\u662f\u4ec0\u4e48\uff1f<\/strong>
Pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\u5e93\uff0c\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002\u5b83\u63d0\u4f9b\u4e86\u6570\u636e\u6846\uff08DataFrame\uff09\u548c\u7cfb\u5217\uff08Series\uff09\u8fd9\u4e24\u79cd\u4e3b\u8981\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5141\u8bb8\u7528\u6237\u8f7b\u677e\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u6574\u7406\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002\u901a\u8fc7Pandas\uff0c\u7528\u6237\u53ef\u4ee5\u6267\u884c\u5982\u6570\u636e\u7b5b\u9009\u3001\u7f3a\u5931\u503c\u5904\u7406\u3001\u5206\u7ec4\u805a\u5408\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7b49\u591a\u79cd\u64cd\u4f5c\uff0c\u4f7f\u5f97\u6570\u636e\u5904\u7406\u53d8\u5f97\u9ad8\u6548\u4e14\u76f4\u89c2\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5Pandas\u5e93\uff1f<\/strong>
\u8981\u5728Python\u4e2d\u5b89\u88c5Pandas\uff0c\u53ef\u4ee5\u4f7f\u7528pip\u8fd9\u4e00\u5305\u7ba1\u7406\u5de5\u5177\u3002\u6253\u5f00\u547d\u4ee4\u884c\u6216\u7ec8\u7aef\uff0c\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4\uff1apip install pandas<\/code>\u3002\u5982\u679c\u4f60\u4f7f\u7528\u7684\u662fAnaconda\uff0c\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884cconda install pandas<\/code>\u6765\u5b89\u88c5\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165Pandas\uff1aimport pandas as pd<\/code>\uff0c\u5373\u53ef\u5f00\u59cb\u4f7f\u7528\u3002<\/p>\n
Pandas\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u4f8b\u5982\u4f7f\u7528isnull()<\/code>\u548cdropna()<\/code>\u65b9\u6cd5\u6765\u68c0\u6d4b\u548c\u5220\u9664\u7f3a\u5931\u503c\u3002\u7528\u6237\u4e5f\u53ef\u4ee5\u901a\u8fc7fillna()<\/code>\u65b9\u6cd5\u6765\u586b\u5145\u7f3a\u5931\u503c\uff0c\u53ef\u4ee5\u9009\u62e9\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u8005\u5176\u4ed6\u6307\u5b9a\u7684\u503c\u8fdb\u884c\u586b\u5145\u3002\u6b64\u5916\uff0cPandas\u8fd8\u652f\u6301\u63d2\u503c\u6cd5\u7b49\u9ad8\u7ea7\u65b9\u6cd5\uff0c\u4ee5\u9002\u5e94\u4e0d\u540c\u7684\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1aPython Pandas\u7684\u4f7f\u7528\u5305\u62ec\u6570\u636e\u5bfc\u5165\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u53ef\u89c6\u5316\u7b49\u3002 Pandas\u662fPy […]","protected":false},"author":3,"featured_media":951021,"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\/951019"}],"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=951019"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/951019\/revisions"}],"predecessor-version":[{"id":951023,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/951019\/revisions\/951023"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/951021"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=951019"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=951019"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=951019"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}