{"id":1125752,"date":"2025-01-08T19:54:34","date_gmt":"2025-01-08T11:54:34","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1125752.html"},"modified":"2025-01-08T19:54:36","modified_gmt":"2025-01-08T11:54:36","slug":"%e5%a6%82%e4%bd%95%e5%88%a0%e9%99%a4%e6%95%b0%e6%8d%ae%e5%b8%a7%e4%b8%ad%e7%9a%84%e6%9f%90%e4%b8%aa%e6%95%b0python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1125752.html","title":{"rendered":"\u5982\u4f55\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e2a\u6570python"},"content":{"rendered":"

\"\u5982\u4f55\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e2a\u6570python\"<\/p>\n

\u5982\u4f55\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e2a\u6570python<\/strong><\/p>\n<\/p>\n

\u8981\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e2a\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u3001\u4f7f\u7528 Pandas \u7684 replace()<\/code> \u65b9\u6cd5\u3001\u4f7f\u7528 applymap()<\/code> \u65b9\u6cd5\u3002<\/strong>\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u7684\u4f7f\u7528 replace()<\/code> \u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e9b\u7279\u5b9a\u503c\u3002\u8fd9\u53ef\u80fd\u662f\u4e3a\u4e86\u6e05\u7406\u6570\u636e\u3001\u5904\u7406\u7f3a\u5931\u503c\u6216\u8fdb\u884c\u6570\u636e\u8f6c\u6362\u3002\u5728Python\u4e2d\uff0cPandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u6218\u6848\u4f8b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15<\/h3>\n<\/p>\n

\u5e03\u5c14\u7d22\u5f15\u662f\u4e00\u79cd\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u9009\u62e9\u65b9\u6cd5\u3002\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u627e\u5230\u5e76\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002<\/p>\n<\/p>\n

1.1 \u57fa\u672c\u6982\u5ff5<\/h4>\n<\/p>\n

\u5e03\u5c14\u7d22\u5f15\u662f\u4e00\u79cd\u901a\u8fc7\u5e03\u5c14\u6761\u4ef6\u6765\u9009\u62e9\u6570\u636e\u7684\u65b9\u6cd5\u3002\u5728Pandas\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5e03\u5c14\u6761\u4ef6\u6765\u7b5b\u9009\u6570\u636e\u5e27\u4e2d\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u5e27<\/strong><\/h2>\n
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u5220\u9664\u6570\u503c\u4e3a3\u7684\u884c<\/strong><\/h2>\n
df = df[df['A'] != 3]<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u6570\u636e\u5e27df<\/code>\uff0c\u7136\u540e\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u5220\u9664A<\/code>\u5217\u4e2d\u6570\u503c\u4e3a3\u7684\u884c\u3002<\/p>\n<\/p>\n
1.2 \u591a\u6761\u4ef6\u5e03\u5c14\u7d22\u5f15<\/h4>\n<\/p>\n
\u6709\u65f6\u5019\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u6839\u636e\u591a\u4e2a\u6761\u4ef6\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u6570\u636e\u3002\u8fd9\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e03\u5c14\u6761\u4ef6\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u5220\u9664\u6570\u503c\u4e3a3\u62165\u7684\u884c<\/p>\n
df = df[(df['A'] != 3) & (df['B'] != 5)]<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u4e24\u4e2a\u5e03\u5c14\u6761\u4ef6(df['A'] != 3)<\/code>\u548c(df['B'] != 5)<\/code>\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u884c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528 Pandas \u7684 replace() \u65b9\u6cd5<\/h3>\n<\/p>\n
Pandas \u7684 replace()<\/code> \u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u66ff\u6362\u4e3a\u5176\u4ed6\u503c\u3002\u901a\u8fc7\u5c06\u7279\u5b9a\u503c\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u6211\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u5220\u9664\u8fd9\u4e9b\u503c\u3002<\/p>\n<\/p>\n
2.1 \u57fa\u672c\u7528\u6cd5<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 replace()<\/code> \u65b9\u6cd5\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u7136\u540e\u4f7f\u7528 dropna()<\/code> \u65b9\u6cd5\u5220\u9664\u8fd9\u4e9b\u503c\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u5e27<\/strong><\/h2>\n
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 3, 8]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u5c06\u6570\u503c3\u66ff\u6362\u4e3aNaN<\/strong><\/h2>\n
df.replace(3, np.nan, inplace=True)<\/p>\n
\u5220\u9664\u5305\u542bNaN\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 replace()<\/code> \u65b9\u6cd5\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u6570\u503c3\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u7136\u540e\u4f7f\u7528 dropna()<\/code> \u65b9\u6cd5\u5220\u9664\u5305\u542b NaN<\/code> \u7684\u884c\u3002<\/p>\n<\/p>\n
2.2 \u591a\u4e2a\u503c\u66ff\u6362<\/h4>\n<\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u540c\u65f6\u66ff\u6362\u591a\u4e2a\u503c\u3002\u53ea\u9700\u8981\u5c06\u8981\u66ff\u6362\u7684\u503c\u653e\u5728\u4e00\u4e2a\u5217\u8868\u4e2d\u5373\u53ef\u3002<\/p>\n<\/p>\n
# \u5c06\u6570\u503c3\u548c5\u66ff\u6362\u4e3aNaN<\/p>\n
df.replace([3, 5], np.nan, inplace=True)<\/p>\n
\u5220\u9664\u5305\u542bNaN\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u540c\u65f6\u5c06\u6570\u503c3\u548c5\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u7136\u540e\u5220\u9664\u5305\u542b NaN<\/code> \u7684\u884c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528 applymap() \u65b9\u6cd5<\/h3>\n<\/p>\n
applymap()<\/code> \u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u5bf9\u6570\u636e\u5e27\u4e2d\u7684\u6bcf\u4e00\u4e2a\u5143\u7d20\u5e94\u7528\u4e00\u4e2a\u51fd\u6570\u3002\u901a\u8fc7\u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002<\/p>\n<\/p>\n
3.1 \u57fa\u672c\u7528\u6cd5<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 applymap()<\/code> \u65b9\u6cd5\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u7136\u540e\u4f7f\u7528 dropna()<\/code> \u65b9\u6cd5\u5220\u9664\u8fd9\u4e9b\u503c\u3002<\/p>\n<\/p>\n
# \u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u5c06\u7279\u5b9a\u503c\u66ff\u6362\u4e3aNaN<\/p>\n
def replace_value(x):<\/p>\n
    if x == 3:<\/p>\n
        return np.nan<\/p>\n
    return x<\/p>\n
\u4f7f\u7528applymap\u65b9\u6cd5\u5e94\u7528\u81ea\u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
df = df.applymap(replace_value)<\/p>\n
\u5220\u9664\u5305\u542bNaN\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u81ea\u5b9a\u4e49\u51fd\u6570 replace_value(x)<\/code>\uff0c\u7136\u540e\u4f7f\u7528 applymap()<\/code> \u65b9\u6cd5\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u6570\u503c3\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u6700\u540e\u5220\u9664\u5305\u542b NaN<\/code> \u7684\u884c\u3002<\/p>\n<\/p>\n
3.2 \u66f4\u590d\u6742\u7684\u81ea\u5b9a\u4e49\u51fd\u6570<\/h4>\n<\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u5b9a\u4e49\u66f4\u590d\u6742\u7684\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u591a\u4e2a\u6761\u4ef6\u6765\u5220\u9664\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u81ea\u5b9a\u4e49\u51fd\u6570\uff0c\u6839\u636e\u591a\u4e2a\u6761\u4ef6\u66ff\u6362\u4e3aNaN<\/p>\n
def replace_value(x):<\/p>\n
    if x == 3 or x == 5:<\/p>\n
        return np.nan<\/p>\n
    return x<\/p>\n
\u4f7f\u7528applymap\u65b9\u6cd5\u5e94\u7528\u81ea\u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
df = df.applymap(replace_value)<\/p>\n
\u5220\u9664\u5305\u542bNaN\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u81ea\u5b9a\u4e49\u51fd\u6570 replace_value(x)<\/code>\uff0c\u6839\u636e\u591a\u4e2a\u6761\u4ef6\uff08x == 3<\/code> \u6216 x == 5<\/code>\uff09\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u6700\u540e\u5220\u9664\u5305\u542b NaN<\/code> \u7684\u884c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u6761\u4ef6\u66ff\u6362<\/h3>\n<\/p>\n
\u9664\u4e86\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u6761\u4ef6\u66ff\u6362\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002\u8fd9\u79cd\u65b9\u6cd5\u901a\u5e38\u7528\u4e8e\u66f4\u590d\u6742\u7684\u6570\u636e\u5904\u7406\u573a\u666f\u3002<\/p>\n<\/p>\n
4.1 \u4f7f\u7528 numpy \u7684 where \u51fd\u6570<\/h4>\n<\/p>\n
numpy<\/code> \u7684 where<\/code> \u51fd\u6570\u53ef\u4ee5\u6839\u636e\u6761\u4ef6\u66ff\u6362\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u5e27<\/strong><\/h2>\n
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 3, 8]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u4f7f\u7528numpy\u7684where\u51fd\u6570\u66ff\u6362\u7279\u5b9a\u503c<\/strong><\/h2>\n
df['A'] = np.where(df['A'] == 3, np.nan, df['A'])<\/p>\n
df['B'] = np.where(df['B'] == 3, np.nan, df['B'])<\/p>\n
\u5220\u9664\u5305\u542bNaN\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 numpy<\/code> \u7684 where<\/code> \u51fd\u6570\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u6570\u503c3\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u7136\u540e\u5220\u9664\u5305\u542b NaN<\/code> \u7684\u884c\u3002<\/p>\n<\/p>\n
4.2 \u4f7f\u7528 Pandas \u7684 mask \u65b9\u6cd5<\/h4>\n<\/p>\n
Pandas \u7684 mask<\/code> \u65b9\u6cd5\u4e5f\u53ef\u4ee5\u7528\u4e8e\u6839\u636e\u6761\u4ef6\u66ff\u6362\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528mask\u65b9\u6cd5\u66ff\u6362\u7279\u5b9a\u503c<\/p>\n
df['A'] = df['A'].mask(df['A'] == 3, np.nan)<\/p>\n
df['B'] = df['B'].mask(df['B'] == 3, np.nan)<\/p>\n
\u5220\u9664\u5305\u542bNaN\u7684\u884c<\/strong><\/h2>\n
df.dropna(inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 mask<\/code> \u65b9\u6cd5\u5c06\u6570\u636e\u5e27\u4e2d\u7684\u6570\u503c3\u66ff\u6362\u4e3a NaN<\/code>\uff0c\u7136\u540e\u5220\u9664\u5305\u542b NaN<\/code> \u7684\u884c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5b9e\u6218\u6848\u4f8b<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5982\u4f55\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\uff0c\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u6848\u4f8b\u6765\u6f14\u793a\u8fd9\u4e9b\u65b9\u6cd5\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n
5.1 \u6848\u4f8b\u63cf\u8ff0<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u6570\u636e\u5e27\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u4e9b\u9519\u8bef\u7684\u6210\u7ee9\u503c\uff08\u4f8b\u5982\uff0c\u8d1f\u6570\u548c\u8d85\u8fc7100\u7684\u5206\u6570\uff09\u3002\u6211\u4eec\u7684\u4efb\u52a1\u662f\u5220\u9664\u8fd9\u4e9b\u9519\u8bef\u7684\u6210\u7ee9\u503c\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u6570\u636e\u5e27<\/p>\n
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],<\/p>\n
        'Math': [95, 85, -10, 105],<\/p>\n
        'English': [88, 92, 85, 120]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print("\u539f\u59cb\u6570\u636e\u5e27:")<\/p>\n
print(df)<\/p>\n
\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u5220\u9664\u9519\u8bef\u7684\u6210\u7ee9\u503c<\/strong><\/h2>\n
df = df[(df['Math'] >= 0) & (df['Math'] <= 100) & (df['English'] >= 0) & (df['English'] <= 100)]<\/p>\n
print("\u5220\u9664\u9519\u8bef\u6210\u7ee9\u503c\u540e\u7684\u6570\u636e\u5e27:")<\/p>\n
print(df)<\/p>\n
\u4f7f\u7528replace\u65b9\u6cd5\u5220\u9664\u9519\u8bef\u7684\u6210\u7ee9\u503c<\/strong><\/h2>\n
df.replace([-10, 105, 120], np.nan, inplace=True)<\/p>\n
df.dropna(inplace=True)<\/p>\n
print("\u4f7f\u7528replace\u65b9\u6cd5\u5220\u9664\u9519\u8bef\u6210\u7ee9\u503c\u540e\u7684\u6570\u636e\u5e27:")<\/p>\n
print(df)<\/p>\n
\u4f7f\u7528applymap\u65b9\u6cd5\u5220\u9664\u9519\u8bef\u7684\u6210\u7ee9\u503c<\/strong><\/h2>\n
def replace_value(x):<\/p>\n
    if x < 0 or x > 100:<\/p>\n
        return np.nan<\/p>\n
    return x<\/p>\n
df = df.applymap(replace_value)<\/p>\n
df.dropna(inplace=True)<\/p>\n
print("\u4f7f\u7528applymap\u65b9\u6cd5\u5220\u9664\u9519\u8bef\u6210\u7ee9\u503c\u540e\u7684\u6570\u636e\u5e27:")<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684\u6570\u636e\u5e27\uff0c\u5e76\u4f7f\u7528\u4e09\u79cd\u4e0d\u540c\u7684\u65b9\u6cd5\u5220\u9664\u9519\u8bef\u7684\u6210\u7ee9\u503c\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u6e05\u7406\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u662f\u4e00\u4e2a\u5e38\u89c1\u7684\u9700\u6c42\u3002\u672c\u6587\u4ecb\u7ecd\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\uff0c\u5305\u62ec\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u3001replace()<\/code> \u65b9\u6cd5\u3001applymap()<\/code> \u65b9\u6cd5\u548c\u6761\u4ef6\u66ff\u6362\u65b9\u6cd5\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u6e05\u7406\u548c\u5904\u7406\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\u3002<\/p>\n<\/p>\n
\u65e0\u8bba\u4f60\u662f\u6570\u636e\u79d1\u5b66\u5bb6\u3001\u6570\u636e\u5206\u6790\u5e08\u8fd8\u662f\u521d\u5b66\u8005\uff0c\u5e0c\u671b\u672c\u6587\u80fd\u4e3a\u4f60\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\uff0c\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u638c\u63e1\u6570\u636e\u5904\u7406\u6280\u5de7\u3002\u5982\u679c\u4f60\u6709\u4efb\u4f55\u7591\u95ee\u6216\u5efa\u8bae\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u4ea4\u6d41\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u6709\u6548\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u3002\u53ef\u4ee5\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\u6216\u4f7f\u7528drop<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u6761\u4ef6\u4e0d\u6ee1\u8db3\u7684\u884c\u6765\u8fc7\u6ee4\u6389\u7279\u5b9a\u503c\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
import pandas as pd\n\n# \u521b\u5efa\u793a\u4f8b\u6570\u636e\u5e27\ndf = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})\n\n# \u5220\u9664\u503c\u4e3a3\u7684\u884c\ndf = df[df['A'] != 3]\n<\/code><\/pre>\n
\u8fd9\u6837\u5c31\u53ef\u4ee5\u5220\u9664\u6570\u636e\u5e27\u4e2d\u503c\u4e3a3\u7684\u884c\u3002<\/p>\n
\u5728\u5220\u9664\u6570\u636e\u5e27\u7684\u7279\u5b9a\u503c\u65f6\uff0c\u4f1a\u5f71\u54cd\u5230\u5176\u4ed6\u6570\u636e\u5417\uff1f<\/strong>
\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u7279\u5b9a\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u5230\u5176\u4ed6\u6570\u636e\uff0c\u5c24\u5176\u662f\u5728\u6570\u636e\u5e27\u4e2d\u5b58\u5728\u4f9d\u8d56\u5173\u7cfb\u6216\u6570\u636e\u5173\u8054\u7684\u60c5\u51b5\u4e0b\u3002\u56e0\u6b64\uff0c\u5728\u8fdb\u884c\u5220\u9664\u64cd\u4f5c\u4e4b\u524d\uff0c\u5efa\u8bae\u5907\u4efd\u539f\u59cb\u6570\u636e\u5e27\uff0c\u4ee5\u4fbf\u5728\u9700\u8981\u65f6\u8fdb\u884c\u6062\u590d\u3002<\/p>\n
\u5982\u4f55\u5728\u5220\u9664\u503c\u540e\u91cd\u7f6e\u6570\u636e\u5e27\u7684\u7d22\u5f15\uff1f<\/strong>
\u5728\u5220\u9664\u7279\u5b9a\u503c\u540e\uff0c\u6570\u636e\u5e27\u7684\u7d22\u5f15\u53ef\u80fd\u4f1a\u53d8\u5f97\u4e0d\u8fde\u7eed\u3002\u53ef\u4ee5\u4f7f\u7528reset_index()<\/code>\u65b9\u6cd5\u6765\u91cd\u7f6e\u7d22\u5f15\uff0c\u786e\u4fdd\u7d22\u5f15\u662f\u8fde\u7eed\u7684\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
df = df.reset_index(drop=True)\n<\/code><\/pre>\n
\u6b64\u65b9\u6cd5\u5c06\u5220\u9664\u65e7\u7d22\u5f15\uff0c\u5e76\u91cd\u65b0\u751f\u6210\u65b0\u7684\u8fde\u7eed\u7d22\u5f15\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e2a\u6570python \u8981\u5220\u9664\u6570\u636e\u5e27\u4e2d\u7684\u67d0\u4e2a\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u3001\u4f7f\u7528 Pan […]","protected":false},"author":3,"featured_media":1125759,"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\/1125752"}],"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=1125752"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1125752\/revisions"}],"predecessor-version":[{"id":1125760,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1125752\/revisions\/1125760"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1125759"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1125752"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1125752"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1125752"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}