{"id":1163437,"date":"2025-01-15T14:55:13","date_gmt":"2025-01-15T06:55:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1163437.html"},"modified":"2025-01-15T14:55:17","modified_gmt":"2025-01-15T06:55:17","slug":"python%e5%a6%82%e4%bd%95%e5%af%b9%e5%88%97%e8%bf%9b%e8%a1%8c%e7%ad%9b%e9%80%89","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1163437.html","title":{"rendered":"python\u5982\u4f55\u5bf9\u5217\u8fdb\u884c\u7b5b\u9009"},"content":{"rendered":"

\"python\u5982\u4f55\u5bf9\u5217\u8fdb\u884c\u7b5b\u9009\"<\/p>\n

Python\u5bf9\u5217\u8fdb\u884c\u7b5b\u9009\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u3001\u548c\u5217\u8868\u89e3\u6790\u7b49\u591a\u79cd\u65b9\u6cd5\u3002<\/strong> \u5176\u4e2d\uff0cPandas\u5e93\u662f\u6700\u5e38\u7528\u548c\u6700\u5f3a\u5927\u7684\u5de5\u5177\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u7075\u6d3b\u4e14\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002\u901a\u8fc7Pandas\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame\u7684\u5217\u7d22\u5f15\u3001\u5e03\u5c14\u7d22\u5f15\u3001\u6761\u4ef6\u7b5b\u9009\u7b49\u65b9\u6cd5\u6765\u7b5b\u9009\u7279\u5b9a\u7684\u5217\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecdPandas\u5e93\u4e2d\u7684\u4e00\u4e9b\u5e38\u89c1\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u57fa\u672c\u4ecb\u7ecd<\/h3>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u5904\u7406\u7684\u5f3a\u5927\u5de5\u5177\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784DataFrame\u548cSeries\uff0c\u80fd\u591f\u8f7b\u677e\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002DataFrame\u7c7b\u4f3c\u4e8eExcel\u4e2d\u7684\u8868\u683c\uff0c\u5305\u542b\u884c\u548c\u5217\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/strong><\/h2>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [10, 20, 30, 40, 50],<\/p>\n
    'C': [100, 200, 300, 400, 500]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u901a\u8fc7\u5217\u540d\u7b5b\u9009<\/h3>\n<\/p>\n
\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\u662f\u901a\u8fc7\u5217\u540d\u6765\u9009\u62e9\u6240\u9700\u7684\u5217\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u5df2\u7ecf\u77e5\u9053\u5217\u540d\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
# \u9009\u62e9\u5355\u5217<\/p>\n
selected_column = df['A']<\/p>\n
print(selected_column)<\/p>\n
\u9009\u62e9\u591a\u5217<\/strong><\/h2>\n
selected_columns = df[['A', 'C']]<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u901a\u8fc7\u5217\u7d22\u5f15\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\u5217\u540d\u53ef\u80fd\u4e0d\u56fa\u5b9a\uff0c\u6216\u8005\u6211\u4eec\u5e0c\u671b\u901a\u8fc7\u5217\u7d22\u5f15\u6765\u9009\u62e9\u5217\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u4f7f\u7528iloc\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
# \u9009\u62e9\u7b2c\u4e00\u5217<\/p>\n
selected_column = df.iloc[:, 0]<\/p>\n
print(selected_column)<\/p>\n
\u9009\u62e9\u7b2c\u4e00\u5217\u548c\u7b2c\u4e09\u5217<\/strong><\/h2>\n
selected_columns = df.iloc[:, [0, 2]]<\/p>\n
print(selected_columns)<\/p>\n
\u9009\u62e9\u524d\u4e24\u5217<\/strong><\/h2>\n
selected_columns = df.iloc[:, :2]<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u901a\u8fc7\u5e03\u5c14\u7d22\u5f15\u8fdb\u884c\u6761\u4ef6\u7b5b\u9009<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u5904\u7406\u4e2d\uff0c\u7ecf\u5e38\u9700\u8981\u6839\u636e\u67d0\u4e9b\u6761\u4ef6\u6765\u7b5b\u9009\u5217\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u5e0c\u671b\u9009\u62e9\u90a3\u4e9b\u6ee1\u8db3\u4e00\u5b9a\u6761\u4ef6\u7684\u5217\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/p>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [10, 20, 30, 40, 50],<\/p>\n
    'C': [100, 200, 300, 400, 500]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u5b9a\u4e49\u7b5b\u9009\u6761\u4ef6\uff1a\u9009\u62e9\u503c\u5927\u4e8e2\u7684\u5217<\/strong><\/h2>\n
condition = df > 2<\/p>\n
\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u8fdb\u884c\u7b5b\u9009<\/strong><\/h2>\n
selected_columns = df.loc[:, condition.any()]<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u901a\u8fc7\u6761\u4ef6\u7b5b\u9009\u884c\u518d\u9009\u5217<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5148\u6839\u636e\u6761\u4ef6\u7b5b\u9009\u884c\uff0c\u7136\u540e\u518d\u9009\u62e9\u7279\u5b9a\u7684\u5217\u3002<\/p>\n<\/p>\n
# \u7b5b\u9009\u51faA\u5217\u4e2d\u503c\u5927\u4e8e2\u7684\u884c\uff0c\u518d\u9009\u62e9B\u548cC\u5217<\/p>\n
filtered_df = df[df['A'] > 2][['B', 'C']]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4f7f\u7528Query\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86query\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528\u7c7b\u4f3cSQL\u8bed\u6cd5\u7684\u8868\u8fbe\u5f0f\u8fdb\u884c\u7b5b\u9009\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528query\u65b9\u6cd5\u7b5b\u9009A\u5217\u4e2d\u503c\u5927\u4e8e2\u7684\u884c\uff0c\u518d\u9009\u62e9B\u548cC\u5217<\/p>\n
filtered_df = df.query('A > 2')[['B', 'C']]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528\u7b5b\u9009\u51fd\u6570<\/h3>\n<\/p>\n
Pandas\u5141\u8bb8\u6211\u4eec\u5b9a\u4e49\u81ea\u5b9a\u4e49\u51fd\u6570\u6765\u7b5b\u9009\u5217\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\uff0c\u7b5b\u9009\u90a3\u4e9b\u5747\u503c\u5927\u4e8e\u67d0\u4e2a\u503c\u7684\u5217\u3002<\/p>\n<\/p>\n
# \u5b9a\u4e49\u7b5b\u9009\u51fd\u6570<\/p>\n
def mean_filter(column, threshold=10):<\/p>\n
    return column.mean() > threshold<\/p>\n
\u4f7f\u7528apply\u65b9\u6cd5\u5e94\u7528\u7b5b\u9009\u51fd\u6570<\/strong><\/h2>\n
selected_columns = df.loc[:, df.apply(mean_filter)]<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u4f7f\u7528\u6761\u4ef6\u7ec4\u5408\u8fdb\u884c\u590d\u6742\u7b5b\u9009<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\uff0c\u6211\u4eec\u9700\u8981\u6839\u636e\u591a\u4e2a\u6761\u4ef6\u7ec4\u5408\u6765\u7b5b\u9009\u5217\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408\u591a\u4e2a\u6761\u4ef6\uff0c\u4f7f\u7528&\uff08\u4e0e\uff09\u3001|\uff08\u6216\uff09\u64cd\u4f5c\u7b26\u8fdb\u884c\u590d\u6742\u7684\u7b5b\u9009\u3002<\/p>\n<\/p>\n
# \u7ec4\u5408\u6761\u4ef6\uff1a\u9009\u62e9A\u5217\u503c\u5927\u4e8e2\u4e14B\u5217\u503c\u5c0f\u4e8e50\u7684\u884c\uff0c\u518d\u9009\u62e9B\u548cC\u5217<\/p>\n
filtered_df = df[(df['A'] > 2) & (df['B'] < 50)][['B', 'C']]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u4f7f\u7528NumPy\u5e93\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
\u9664\u4e86Pandas\uff0cNumPy\u5e93\u4e5f\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u7b5b\u9009\u3002NumPy\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u529f\u80fd\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u7ec4<\/strong><\/h2>\n
data = np.array([[1, 10, 100],<\/p>\n
                 [2, 20, 200],<\/p>\n
                 [3, 30, 300],<\/p>\n
                 [4, 40, 400],<\/p>\n
                 [5, 50, 500]])<\/p>\n
\u5b9a\u4e49\u5217\u540d<\/strong><\/h2>\n
columns = ['A', 'B', 'C']<\/p>\n
\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data, columns=columns)<\/p>\n
\u4f7f\u7528NumPy\u5e03\u5c14\u7d22\u5f15\u8fdb\u884c\u6761\u4ef6\u7b5b\u9009<\/strong><\/h2>\n
condition = df.values > 2<\/p>\n
selected_columns = df.loc[:, condition.any(axis=0)]<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u3001\u4f7f\u7528\u5217\u8868\u89e3\u6790\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u7b80\u6d01\u4e14\u9ad8\u6548\u7684Python\u7279\u6027\uff0c\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u7b5b\u9009\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u89e3\u6790\u76f4\u63a5\u9009\u62e9\u6240\u9700\u7684\u5217\u3002<\/p>\n<\/p>\n
# \u5b9a\u4e49\u5217\u540d<\/p>\n
columns = ['A', 'B', 'C']<\/p>\n
\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
df = pd.DataFrame(data, columns=columns)<\/p>\n
\u4f7f\u7528\u5217\u8868\u89e3\u6790\u8fdb\u884c\u7b5b\u9009<\/strong><\/h2>\n
selected_columns = [col for col in df.columns if df[col].mean() > 10]<\/p>\n
filtered_df = df[selected_columns]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e00\u3001\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u7b5b\u9009\u5217<\/h3>\n<\/p>\n
Pandas\u8fd8\u652f\u6301\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u8fdb\u884c\u5217\u7b5b\u9009\uff0c\u8fd9\u5728\u5904\u7406\u5217\u540d\u590d\u6742\u7684\u60c5\u51b5\u4e0b\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u7b5b\u9009\u5217\u540d\u5305\u542b'A'\u7684\u5217<\/p>\n
selected_columns = df.filter(regex='A')<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e8c\u3001\u4f7f\u7528Pandas\u7684\u9ad8\u7ea7\u7b5b\u9009\u65b9\u6cd5<\/h3>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86\u4e00\u4e9b\u9ad8\u7ea7\u7b5b\u9009\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6570\u636e\u7b5b\u9009\u7684\u7075\u6d3b\u6027\u548c\u6548\u7387\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684DataFrame.query\u65b9\u6cd5\u8fdb\u884c\u590d\u6742\u6761\u4ef6\u7b5b\u9009\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u9ad8\u7ea7\u7b5b\u9009\u65b9\u6cd5\u9009\u62e9\u6ee1\u8db3\u6761\u4ef6\u7684\u5217<\/p>\n
filtered_df = df.query('A > 2 and B < 50')[['B', 'C']]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e09\u3001\u4f7f\u7528Pandas\u7684DataFrame.loc\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.loc\u65b9\u6cd5\u662fPandas\u4e2d\u975e\u5e38\u5f3a\u5927\u548c\u7075\u6d3b\u7684\u7b5b\u9009\u5de5\u5177\u3002\u5b83\u53ef\u4ee5\u901a\u8fc7\u6807\u7b7e\uff08\u5217\u540d\uff09\u8fdb\u884c\u7b5b\u9009\uff0c\u4e5f\u53ef\u4ee5\u7ed3\u5408\u6761\u4ef6\u8fdb\u884c\u590d\u6742\u7b5b\u9009\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528loc\u65b9\u6cd5\u9009\u62e9\u6ee1\u8db3\u6761\u4ef6\u7684\u5217<\/p>\n
filtered_df = df.loc[df['A'] > 2, ['B', 'C']]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u56db\u3001\u4f7f\u7528Pandas\u7684DataFrame.iloc\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.iloc\u65b9\u6cd5\u901a\u8fc7\u4f4d\u7f6e\u7d22\u5f15\u8fdb\u884c\u7b5b\u9009\uff0c\u9002\u7528\u4e8e\u5df2\u77e5\u5217\u4f4d\u7f6e\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528iloc\u65b9\u6cd5\u9009\u62e9\u6ee1\u8db3\u6761\u4ef6\u7684\u5217<\/p>\n
filtered_df = df.iloc[df['A'].values > 2, [1, 2]]<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e94\u3001\u4f7f\u7528Pandas\u7684DataFrame.at\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.at\u65b9\u6cd5\u7528\u4e8e\u901a\u8fc7\u6807\u7b7e\u5feb\u901f\u8bbf\u95ee\u5355\u4e2a\u5143\u7d20\uff0c\u53ef\u4ee5\u7ed3\u5408\u6761\u4ef6\u8fdb\u884c\u7b5b\u9009\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528at\u65b9\u6cd5\u9009\u62e9\u6ee1\u8db3\u6761\u4ef6\u7684\u5217<\/p>\n
filtered_df = df[df['A'] > 2]<\/p>\n
print(filtered_df.at[0, 'B'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u516d\u3001\u4f7f\u7528Pandas\u7684DataFrame.iat\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.iat\u65b9\u6cd5\u901a\u8fc7\u4f4d\u7f6e\u7d22\u5f15\u5feb\u901f\u8bbf\u95ee\u5355\u4e2a\u5143\u7d20\uff0c\u9002\u7528\u4e8e\u5df2\u77e5\u4f4d\u7f6e\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528iat\u65b9\u6cd5\u9009\u62e9\u6ee1\u8db3\u6761\u4ef6\u7684\u5217<\/p>\n
filtered_df = df[df['A'] > 2]<\/p>\n
print(filtered_df.iat[0, 1])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e03\u3001\u4f7f\u7528Pandas\u7684DataFrame.xs\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.xs\u65b9\u6cd5\u7528\u4e8e\u8de8\u7ef4\u5ea6\u8fdb\u884c\u7b5b\u9009\uff0c\u9002\u7528\u4e8e\u591a\u5c42\u7d22\u5f15\u7684\u60c5\u51b5\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u591a\u5c42\u7d22\u5f15\u7684\u793a\u4f8bDataFrame<\/p>\n
data = {<\/p>\n
    'A': [1, 2, 3, 4, 5],<\/p>\n
    'B': [10, 20, 30, 40, 50],<\/p>\n
    'C': [100, 200, 300, 400, 500]<\/p>\n
}<\/p>\n
index = pd.MultiIndex.from_tuples([('a', 'one'), ('a', 'two'), ('b', 'one'), ('b', 'two'), ('c', 'one')])<\/p>\n
df = pd.DataFrame(data, index=index)<\/p>\n
\u4f7f\u7528xs\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/strong><\/h2>\n
filtered_df = df.xs('one', level=1)<\/p>\n
print(filtered_df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u516b\u3001\u4f7f\u7528Pandas\u7684DataFrame.filter\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.filter\u65b9\u6cd5\u7528\u4e8e\u57fa\u4e8e\u5217\u540d\u6216\u884c\u540d\u8fdb\u884c\u7b5b\u9009\uff0c\u652f\u6301\u6b63\u5219\u8868\u8fbe\u5f0f\u548c\u5b57\u7b26\u4e32\u5339\u914d\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528filter\u65b9\u6cd5\u9009\u62e9\u5305\u542b'A'\u7684\u5217<\/p>\n
selected_columns = df.filter(like='A')<\/p>\n
print(selected_columns)<\/p>\n
\u4f7f\u7528filter\u65b9\u6cd5\u9009\u62e9\u5217\u540d\u4ee5'C'\u7ed3\u5c3e\u7684\u5217<\/strong><\/h2>\n
selected_columns = df.filter(regex='C$')<\/p>\n
print(selected_columns)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5341\u4e5d\u3001\u4f7f\u7528Pandas\u7684DataFrame.pop\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009<\/h3>\n<\/p>\n
DataFrame.pop\u65b9\u6cd5\u7528\u4e8e\u5220\u9664\u5e76\u8fd4\u56de\u6307\u5b9a\u5217\uff0c\u8fd9\u5728\u9700\u8981\u7b5b\u9009\u5e76\u79fb\u9664\u67d0\u4e9b\u5217\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528pop\u65b9\u6cd5\u9009\u62e9\u5e76\u79fb\u9664'A'\u5217<\/p>\n
selected_column = df.pop('A')<\/p>\n
print(selected_column)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u5341\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u5bf9\u5217\u8fdb\u884c\u7b5b\u9009\u7684\u65b9\u6cd5\u591a\u79cd\u591a\u6837\uff0c\u6700\u5e38\u7528\u4e14\u5f3a\u5927\u7684\u5de5\u5177\u662fPandas\u5e93\u3002\u901a\u8fc7Pandas\uff0c\u53ef\u4ee5\u4f7f\u7528\u5217\u540d\u3001\u5217\u7d22\u5f15\u3001\u5e03\u5c14\u7d22\u5f15\u3001\u6761\u4ef6\u7b5b\u9009\u3001query\u65b9\u6cd5\u3001\u6b63\u5219\u8868\u8fbe\u5f0f\u7b49\u591a\u79cd\u65b9\u6cd5\u8fdb\u884c\u7075\u6d3b\u9ad8\u6548\u7684\u5217\u7b5b\u9009\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u7ed3\u5408NumPy\u5e93\u548c\u5217\u8868\u89e3\u6790\u7b49\u65b9\u6cd5\uff0c\u5b9e\u73b0\u590d\u6742\u7684\u6570\u636e\u7b5b\u9009\u9700\u6c42\u3002\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5c06\u6781\u5927\u5730\u63d0\u9ad8\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u9700\u6c42\u548c\u6570\u636e\u7684\u590d\u6742\u7a0b\u5ea6\u3002\u901a\u8fc7\u4e0d\u65ad\u5b9e\u8df5\u548c\u63a2\u7d22\uff0c\u53ef\u4ee5\u627e\u5230\u6700\u9002\u5408\u81ea\u5df1\u548c\u9879\u76ee\u7684\u6570\u636e\u7b5b\u9009\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5bf9\u6570\u636e\u6846\u7684\u5217\u8fdb\u884c\u7b5b\u9009\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u5982Pandas\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u6846\u7684\u5217\u8fdb\u884c\u7b5b\u9009\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u6761\u4ef6\u8bed\u53e5\u6765\u9009\u62e9\u7b26\u5408\u7279\u5b9a\u6761\u4ef6\u7684\u5217\u3002\u6bd4\u5982\uff0c\u4f7f\u7528df.loc[:, df.columns[df.columns.str.contAI<\/a>ns('\u67d0\u4e2a\u5b57\u7b26\u4e32')]]<\/code>\u53ef\u4ee5\u7b5b\u9009\u51fa\u5217\u540d\u4e2d\u5305\u542b\u7279\u5b9a\u5b57\u7b26\u4e32\u7684\u5217\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u67e5\u627e\u548c\u5206\u6790\u6240\u9700\u7684\u6570\u636e\u3002<\/p>\n
\u6709\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5bf9Pandas\u6570\u636e\u6846\u7684\u5217\u8fdb\u884c\u6761\u4ef6\u7b5b\u9009\uff1f<\/strong>
\u9664\u4e86\u4f7f\u7528str.contains()<\/code>\u65b9\u6cd5\uff0cPandas\u8fd8\u63d0\u4f9b\u4e86\u591a\u79cd\u7b5b\u9009\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528df[['\u5217\u540d1', '\u5217\u540d2']]<\/code>\u76f4\u63a5\u9009\u62e9\u7279\u5b9a\u7684\u5217\uff0c\u6216\u8005\u7ed3\u5408\u5e03\u5c14\u7d22\u5f15\u6765\u7b5b\u9009\u6ee1\u8db3\u6761\u4ef6\u7684\u5217\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u5f0f\uff0c\u7528\u6237\u53ef\u4ee5\u7075\u6d3b\u5730\u5904\u7406\u6570\u636e\uff0c\u6ee1\u8db3\u4e0d\u540c\u7684\u5206\u6790\u9700\u6c42\u3002<\/p>\n
\u5728\u7b5b\u9009\u5217\u65f6\u5982\u4f55\u907f\u514d\u9009\u62e9\u91cd\u590d\u7684\u5217\uff1f<\/strong>
\u4e3a\u4e86\u907f\u514d\u5728\u7b5b\u9009\u5217\u65f6\u9009\u62e9\u91cd\u590d\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528df.loc[:, ~df.columns.duplicated()]<\/code>\u6765\u8fc7\u6ee4\u6389\u91cd\u590d\u7684\u5217\u540d\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u552f\u4e00\u6027\uff0c\u4ece\u800c\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u51c6\u786e\u6027\u3002\u7528\u6237\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u65f6\uff0c\u4fdd\u6301\u5217\u7684\u552f\u4e00\u6027\u5c24\u4e3a\u91cd\u8981\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5bf9\u5217\u8fdb\u884c\u7b5b\u9009\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u3001\u548c\u5217\u8868\u89e3\u6790\u7b49\u591a\u79cd\u65b9\u6cd5\u3002 \u5176\u4e2d\uff0cPandas\u5e93\u662f\u6700 […]","protected":false},"author":3,"featured_media":1163447,"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\/1163437"}],"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=1163437"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1163437\/revisions"}],"predecessor-version":[{"id":1163450,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1163437\/revisions\/1163450"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1163447"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1163437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1163437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1163437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}