导入CSV文件后的数据筛选：指定条件及DataFrame列筛选简便方法问询

1. 导入依赖并读取CSV文件 #

用pandas读取CSV是Python处理表格数据的标准方式：

import pandas as pd

# 读取目标CSV文件
df = pd.read_csv('adult_census_data.csv')

2. 多条件行筛选 #

根据你给出的所有同时满足的条件，使用布尔索引完成筛选。注意每个条件需用括号包裹，用&（按位与）替代and，避免运算符优先级问题：

# 按条件筛选行
filtered_df = df[
    (df['workclass'] == 'State-Gov') &
    (df['education'] == 'Bachelors') &
    (df['marital-status'] == 'Never-Married') &
    (df['occupation'] == 'Adm-Clerical') &
    (df['relationship'] == 'Not-in-family') &
    (df['race'] == 'White') &
    (df['sex'] == 'Male') &
    (df['native-country'] == 'United States') &
    (df['income'] == '<=50K')
]

3. 筛选指定列 #

要仅保留条件对应的列，有两种常用实现方式：

方式一：先筛行再选列 #

先定义需保留的列列表，再从筛选后的DataFrame中提取：

columns_to_keep = [
    'workclass', 'education', 'marital-status', 'occupation',
    'relationship', 'race', 'sex', 'native-country', 'income'
]

# 提取指定列
filtered_df = filtered_df[columns_to_keep]

方式二：一步到位（行+列筛选） #

用loc方法可同时完成行筛选和列选择，效率更高：

columns_to_keep = [
    'workclass', 'education', 'marital-status', 'occupation',
    'relationship', 'race', 'sex', 'native-country', 'income'
]

filtered_df = df.loc[
    (df['workclass'] == 'State-Gov') &
    (df['education'] == 'Bachelors') &
    (df['marital-status'] == 'Never-Married') &
    (df['occupation'] == 'Adm-Clerical') &
    (df['relationship'] == 'Not-in-family') &
    (df['race'] == 'White') &
    (df['sex'] == 'Male') &
    (df['native-country'] == 'United States') &
    (df['income'] == '<=50K'),
    columns_to_keep
]

4. 更简便的实现方法 #

当条件较多时，推荐以下两种更简洁的写法：

方法一：使用query方法 #

query支持类SQL的语法，可读性更强，注意带连字符的列名要用反引号包裹：

columns_to_keep = [
    'workclass', 'education', 'marital-status', 'occupation',
    'relationship', 'race', 'sex', 'native-country', 'income'
]

# 构造查询语句
filter_query = """
workclass == 'State-Gov' and
education == 'Bachelors' and
marital-status == 'Never-Married' and
occupation == 'Adm-Clerical' and
relationship == 'Not-in-family' and
race == 'White' and
sex == 'Male' and
`native-country` == 'United States' and
income == '<=50K'
"""

# 同时完成筛选和列选择
filtered_df = df.query(filter_query)[columns_to_keep]

方法二：用字典批量处理条件 #

把列名和对应值存入字典，通过循环生成筛选掩码，同时直接用字典的键作为要保留的列，避免重复写列名：

# 将条件存入字典
filter_dict = {
    'workclass': 'State-Gov',
    'education': 'Bachelors',
    'marital-status': 'Never-Married',
    'occupation': 'Adm-Clerical',
    'relationship': 'Not-in-family',
    'race': 'White',
    'sex': 'Male',
    'native-country': 'United States',
    'income': '<=50K'
}

# 生成筛选掩码
mask = pd.Series([True] * len(df))
for col, value in filter_dict.items():
    mask &= df[col] == value

# 筛选行并保留指定列
filtered_df = df.loc[mask, filter_dict.keys()]

注意：确保CSV中的列名、字符串值和条件完全匹配（包括大小写、空格），否则会筛选不到数据。

内容的提问来源于stack exchange，提问作者Shane S