Question

假设我的代码与此类似：

import pandas as pd

df=pd.DataFrame({'Name': [ 'Jay Leno', 'JayLin', 'Jay-Jameson', 'LinLeno', 'Lin Jameson', 'Python Leno', 'Python Lin', 'Python Jameson', 'Lin Jay', 'Python Monte'],
                 'Class': ['Rat','L','H','L','L','H', 'H','L','L','Circus']})
df['status']=''

pattern1=['^Jay(\s|-)?(Leno|Lin|Jameson)$','^Python(\s|-)?(Jay|Leno|Lin|Jameson|Monte)$','^Lin(\s|-)?(Leno|Jay|Jameson|Monte)$' ]
pattern2=['^Python(\s|-)?(Jay|Leno|Lin|Jameson|Monte)$' ]
pattern3=['^Lin(\s|-)?(Leno|Jay|Jameson|Monte)$' ]

for i in range(len(pattern1)):
    df.loc[df.Name.str.contains(pattern1[i]),'status'] = 'A'

for i in range(len(pattern2)):
    df.loc[df.Name.str.contains(pattern2[i]),'status'] = 'B'

for i in range(len(pattern3)):
    df.loc[df.Name.str.contains(pattern3[i]),'status'] = 'C'

print (df)

打印哪些：

C:\Python33\lib\site-packages\pandas\core\strings.py:184: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
  " groups, use str.extract.", UserWarning)
    Class            Name status
0     Rat        Jay Leno      A
1       L          JayLin      A
2       H     Jay-Jameson      A
3       L         LinLeno      C
4       L     Lin Jameson      C
5       H     Python Leno      B
6       H      Python Lin      B
7       L  Python Jameson      B
8       L         Lin Jay      C
9  Circus    Python Monte      B

[10 rows x 3 columns]

我的问题是如何删除错误，是否有办法通过更少的代码更有效地循环？我知道有一些叫做列表推导的东西，但我对如何使用它们很困惑。

我知道

可以抑制错误

pd.options.mode.chained_assignment = None

Answer 1

使用non-capturing parentheses (?:...)：

pattern1=['^Jay(?:\s|-)?(?:Leno|Lin|Jameson)$','^Python(?:\s|-)?(?:Jay|Leno|Lin|Jameson|Monte)$','^Lin(?:\s|-)?(?:Leno|Jay|Jameson|Monte)$' ]
pattern2=['^Python(?:\s|-)?(?:Jay|Leno|Lin|Jameson|Monte)$' ]
pattern3=['^Lin(?:\s|-)?(?:Leno|Jay|Jameson|Monte)$' ]

警告来自this code：

    if regex.groups > 0:
        warnings.warn("This pattern has match groups. To actually get the"
                      " groups, use str.extract.", UserWarning)

所以只要没有团体，就没有警告。

Python pandas有效地删除UserWarning和循环

1 个答案: