如何从 Pandas 数据框中特定列中的所有值中删除所有非数字字符？
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How can I remove all non-numeric characters from all the values in a particular column in pandas dataframe?
提问by ag14
I have a dataframe which looks like this:
我有一个如下所示的数据框：
A B C
1 red78 square big235
2 green circle small123
3 blue45 triangle big657
I need to be able to remove the non-numeric characters from all the rows in column C so that my dataframe looks like:
我需要能够从 C 列中的所有行中删除非数字字符，以便我的数据框看起来像：
A B C
1 red78 square 235
2 green circle 123
3 blue45 triangle 657
I tried using the following but get the error expected string or buffer:
我尝试使用以下方法但得到错误预期的字符串或缓冲区：
import re
dfOutput.imgID = dfOutput.imgID.apply(re.sub('[^0-9]','', dfOutput.imgID), axis = 0)
What should I do instead?
我应该怎么做？
Code to create dataframe:
创建数据框的代码：
dfObject = pd.DataFrame()
dfObject.set_value(1, 'A', 'red78')
dfObject.set_value(1, 'B', 'square')
dfObject.set_value(1, 'C', 'big235')
dfObject.set_value(2, 'A', 'green')
dfObject.set_value(2, 'B', 'circle')
dfObject.set_value(2, 'C', 'small123')
dfObject.set_value(3, 'A', 'blue45')
dfObject.set_value(3, 'B', 'triangle')
dfObject.set_value(3, 'C', 'big657')
回答by EdChum
Use str.extractand pass a regex pattern to extract just the numeric parts:
使用str.extract并传递正则表达式模式以仅提取数字部分：
In[40]:
dfObject['C'] = dfObject['C'].str.extract('(\d+)', expand=False)
dfObject
Out[40]:
A B C
1 red78 square 235
2 green circle 123
3 blue45 triangle 657
If needed you can cast to int:
如果需要，您可以投射到int：
dfObject['C'] = dfObject['C'].astype(int)
回答by Scott Boston
You can use .str.replacewith a regex:
您可以使用.str.replace正则表达式：
dfObject['C'] = dfObject.C.str.replace(r"[a-zA-Z]",'')
output:
输出：
A B C
1 red78 square 235
2 green circle 123
3 blue45 triangle 657
回答by Wiktor Stribi?ew
To remove all non-digit characters from strings in a Pandas column you should use str.replacewith \D+or [^0-9]+patterns:
要从 Pandas 列中的字符串中删除所有非数字字符，您应该使用str.replacewith\D+或[^0-9]+patterns：
dfObject['C'] = dfObject['C'].str.replace(r'\D+', '')
Or, since in Python 3, \Dis fully Unicode-aware by default and thus does not match non-ASCII digits (like ?????????, see proof) you should consider
或者，由于在 Python 3 中，\D默认情况下完全识别Unicode，因此不匹配非 ASCII 数字（如?????????，请参阅proof），您应该考虑
dfObject['C'] = dfObject['C'].str.replace(r'[^0-9]+', '')
So,
所以，
import re
print ( re.sub( r'\D+', '', '1?????????0') ) # => 1?????????0
print ( re.sub( r'[^0-9]+', '', '1?????????0') ) # => 10
回答by jpp
You can also do this via a lambdafunction with str.isdigit:
你也可以通过一个lambda函数来做到这一点str.isdigit：
import pandas as pd
df = pd.DataFrame({'Name': ['John5', 'Tom 8', 'Ron 722']})
df['Name'] = df['Name'].map(lambda x: ''.join([i for i in x if i.isdigit()]))
# Name
# 0 5
# 1 8
# 2 722
回答by MEdwin
After 2 years, to help others, I actually think that you were very close to the answer. I have used your logic but made it work. basically you create a function that does the clean up and then apply it to the column C.
2年后，帮助别人，其实我觉得你已经很接近答案了。我已经使用了你的逻辑，但使它起作用。基本上，您创建一个执行清理工作的函数，然后将其应用于 column C。
import pandas as pd
import re
df = pd.DataFrame({
'A': ['red78', 'green', 'blue45'],
'B': ['square', 'circle', 'triangle'],
'C': ['big235', 'small123', 'big657']
})
def remove_chars(s):
return re.sub('[^0-9]+', '', s)
df['C'] = df['C'].apply(remove_chars)
df
Result below:
结果如下：
A B C
0 red78 square 235
1 green circle 123
2 blue45 triangle 657
