pandas 系列的矢量化格式功能

问题描述

假设我从系列未格式化的电话号码（字符串）开头，我想将它们格式化为（XXX）YYY-ZZZZ。

我可以使用正则表达式获取输入的子组件， str.match 或 str.extract 。我可以使用以下任一结果执行格式化：

  ser = pd.Series（data = ['1234567890'，' （r'（\d {3}）（\d {3}）（\d {4}）（$'$'
 $ b matched = ser.str.match '）
 
 extract = ser.astype（str）.str.extract（r'（？P< first> \d {3}）（？P< second> \d {3} ）（？P  \ d {4}）'）
 
 formatmatched = matched.apply（lambda x：'（{0}）{1}  -  {2}'。format * b $ b print'formatmatched'
 print formatmatched 
 
 formatextracted = extracted.apply（lambda x：'（{first}）{second}  -  {third}'。格式（** x.to_dict（）），axis = 1）
 print'formatextracted'
 print formatextracted 
  

结果：

$ p $ formatmatched
0（123）456-7890
1（234）567-8901
2（345）678-9012
dtype：object
formatextracted
0（123）456-7890
1（234）567 -8901
2（345）678-9012
dtype：object


是否有向量化的方式在上下文中应用格式化命令？

解决方案

$

  In [47]：s = pandas.Series（[1234567890，5552348866，13434]）
 
在[49]中：s 
 Out [49]：
 0 1234567890 
 1 5552348866 
 2 13434 
 dtype：object 
 
 In [50]：s.str.replace（r（\d {3}）（\ d {3}）（\ d {4}），r（\ 1）\ 2-\\ （3）
 Out [50]：
 0（123）456-7890 
 1（555）234-8866 
 2 13434 
 dtype：object 
  

您也可以想象首先执行另一个转换来移除任何非数字字符。 b

Say I start with a Series of unformatted phone numbers (as strings), and I would like to format them as (XXX) YYY-ZZZZ.

I can get the sub-components of my input using regular expressions and str.match or str.extract. And I can perform the formatting using the result of either:

ser = pd.Series(data=['1234567890', '2345678901', '3456789012'])

matched = ser.str.match(r'(\d{3})(\d{3})(\d{4})')

extracted = ser.astype(str).str.extract(r'(?P<first>\d{3})(?P<second>\d{3})(?P<third>\d{4})')

formatmatched = matched.apply(lambda x: '({0}) {1}-{2}'.format(*x))
print 'formatmatched'
print formatmatched

formatextracted = extracted.apply(lambda x: '({first}) {second}-{third}'.format(**x.to_dict()), axis=1)
print 'formatextracted'
print formatextracted

Results:

formatmatched
0    [phone]
1    [phone]
2    [phone]
dtype: object
formatextracted
0    [phone]
1    [phone]
2    [phone]
dtype: object

Is there a vectorized way to apply that formatting command in either context?

解决方案

You can do this directly with Series.str.replace():

In [47]: s = pandas.Series(["1234567890", "5552348866", "13434"])

In [49]: s
Out[49]:
0    1234567890
1    5552348866
2         13434
dtype: object

In [50]: s.str.replace(r"(\d{3})(\d{3})(\d{4})", r"(\1) \2-\3")
Out[50]:
0    [phone]
1    [phone]
2             13434
dtype: object

You could also imagine doing another transformation first to remove any non-digit characters.

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