如何在Python Pandas中不使用正则表达式将字符串转为整数？

问题背景 #

我的DataFrame如下：

我尝试了以下两种代码：

car_sales['Price'] = car_sales['Price'].str.replace('[\$\,\.]', '').astype(int)

以及：

car_sales['Price'] = car_sales['Price'].astype(str).str.replace('[\$\,\.]', '').astype(int)

但触发了如下错误：

ValueError: invalid literal for int() with base 10: '$4,000.00'

完整错误栈：

ValueError                                Traceback (most recent call last)
Cell In[87], line 1
----> 1 car_sales['Price'] = car_sales['Price'].astype(str).str.replace('[\$\,\.]', '').astype(int)
      2 car_sales

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\generic.py:6324, in NDFrame.astype(self, dtype, copy, errors)
   6317     results = [
   6318         self.iloc[:, i].astype(dtype, copy=copy)
   6319         for i in range(len(self.columns))
   6320     ]
   6322 else:
   6323     # else, only a single dtype is given
-> 6324     new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
   6325     return self._constructor(new_data).__finalize__(self, method="astype")
   6327 # GH 33113: handle empty frame or series

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\internals\managers.py:451, in BaseBlockManager.astype(self, dtype, copy, errors)
    448 elif using_copy_on_write():
    449     copy = False
-> 451 return self.apply(
    452     "astype",
    453     dtype=dtype,
    454     copy=copy,
    455     errors=errors,
    456     using_cow=using_copy_on_write(),
    457 )

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\internals\managers.py:352, in BaseBlockManager.apply(self, f, align_keys, **kwargs)
    350         applied = b.apply(f, **kwargs)
    351     else:
-> 352         applied = getattr(b, f)(**kwargs)
    353     result_blocks = extend_blocks(applied, result_blocks)
    355 out = type(self).from_blocks(result_blocks, self.axes)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\internals\blocks.py:511, in Block.astype(self, dtype, copy, errors, using_cow)
    491 """
    492 Coerce to the new dtype.
    493 
   (...)
    507 Block
    508 """
    509 values = self.values
-> 511 new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
    513 new_values = maybe_coerce_values(new_values)
    515 refs = None

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\dtypes\astype.py:242, in astype_array_safe(values, dtype, copy, errors)
    239     dtype = dtype.numpy_dtype
    241 try:
-> 242     new_values = astype_array(values, dtype, copy=copy)
    243 except (ValueError, TypeError):
    244     # e.g. _astype_nansafe can fail on object-dtype of strings
    245     #  trying to convert to float
    246     if errors == "ignore":

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\dtypes\astype.py:187, in astype_array(values, dtype, copy)
    184     values = values.astype(dtype, copy=copy)
    186 else:
-> 187     values = _astype_nansafe(values, dtype, copy=copy)
    189 # in pandas we don't store numpy str dtypes, so convert to object
    190 if isinstance(dtype, np.dtype) and issubclass(values.dtype.type, str):

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\dtypes\astype.py:138, in _astype_nansafe(arr, dtype, copy, skipna)
    134     raise ValueError(msg)
    136 if copy or is_object_dtype(arr.dtype) or is_object_dtype(dtype):
    137     # Explicit copy, or required since NumPy can't view from / to object.
-> 138     return arr.astype(dtype, copy=True)
    140 return arr.astype(dtype, copy=copy)

ValueError: invalid literal for int() with base 10: '$4,000.00'

现寻求不使用正则表达式将价格字符串转为整数的有效方法。

解决方法 #

方法1：分步替换符号（最直观） #

直接逐个清理$、逗号和小数点，先转浮点数再取整，避免因小数位导致的数值偏差：

car_sales['Price'] = (
    car_sales['Price']
    .str.strip('$')  # 移除开头的美元符号
    .str.replace(',', '')  # 移除千分位逗号
    .astype(float)  # 转为浮点数
    .astype(int)  # 转为整数
)

如果确认所有价格都是两位小数，也可以直接移除小数点后转整数：

car_sales['Price'] = (
    car_sales['Price']
    .str.strip('$')
    .str.replace(',', '')
    .str.replace('.', '')
    .astype(int)
)

方法2：用locale模块处理本地化格式 #

利用系统本地化解析带格式的价格字符串，适合批量处理美式/欧式价格格式：

import locale

# 设置美式英语本地化（支持$和千分位逗号）
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')

# 解析字符串为浮点数后转整数
car_sales['Price'] = car_sales['Price'].apply(lambda x: int(locale.atof(x.strip('$'))))

注意：若系统未安装en_US.UTF-8本地化，需先安装或替换为系统支持的对应本地化标识。

方法3：手动拆分处理（适配特殊格式） #

如果价格格式固定，可手动拆分字符串提取有效数值：

def clean_price(price_str):
    # 移除$，按小数点拆分取整数部分，再移除逗号
    price_part = price_str.replace('$', '').split('.')[0]
    return int(price_part.replace(',', ''))

car_sales['Price'] = car_sales['Price'].apply(clean_price)

内容的提问来源于stack exchange，提问作者n0cuous