《Pandas 进阶：数据清洗与特征工程的 5 个技巧》（714）

# 🐼 Pandas 进阶：数据清洗与特征工程的 5 个技巧 

在数据分析中，数据清洗和特征工程是提升模型性能的关键步骤。掌握 Pandas 的进阶技巧，能让你的数据处理更高效！以下是 5 个实用技巧，助你轻松应对复杂数据 🚀。 

## 1️⃣ **智能处理缺失值：`interpolate()` + `fillna()`** 
缺失值处理不能简单粗暴！除了 `dropna()` 和 `fillna()`，试试 `interpolate()` 进行插值填充，尤其适用于时间序列数据 📈： 
```python
df['price'].interpolate(method='linear', inplace=True)
``` 

## 2️⃣ **高效分箱：`pd.cut()` 与 `pd.qcut()`** 
连续变量离散化能提升模型鲁棒性。`pd.cut()` 按区间分箱，`pd.qcut()` 按分位数分箱，轻松生成新特征 🗂️： 
```python
df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60, 100], labels=['少年', '青年', '中年', '老年'])
``` 

## 3️⃣ **文本特征提取：`str.extract()` + 正则表达式** 
从杂乱文本中提取关键信息？正则表达式 + `str.extract()` 是绝配！比如提取手机号 📱： 
```python
df['phone'] = df['contact_info'].str.extract(r'(\\d{11})')
``` 

## 4️⃣ **时间特征工程：`dt` 访问器** 
时间数据是宝藏！用 `dt` 快速提取年、月、星期等特征 ⏰： 
```python
df['year'] = df['date'].dt.year
df['day_of_week'] = df['date'].dt.day_name()
``` 

## 5️⃣ **内存优化：`astype()` 选择合适数据类型** 
大数据集内存爆炸？用 `astype()` 降低存储压力 💾： 
```python
df['user_id'] = df['user_id'].astype('int32') # 比默认 int64 省一半空间！
``` 

掌握这些技巧，你的 Pandas 数据处理能力将更上一层楼！🎯 快去试试吧！
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