python str.strip()
时间: 2023-12-20 11:31:06 浏览: 71
`str.strip()`是Python中的一个字符串方法,用于去除字符串开头和结尾的空格或指定字符。具体来说,它会返回一个新的字符串,该字符串是原始字符串的副本,但开头和结尾的空格或指定字符已被删除。
以下是一些使用`str.strip()`的例子:
1. 去除字符串开头和结尾的空格:
```python
s = ' hello world '
print(s.strip()) # 输出:'hello world'
```
2. 去除字符串开头和结尾的指定字符:
```python
s = '---hello world---'
print(s.strip('-')) # 输出:'hello world'
```
3. 去除字符串开头的指定字符:
```python
s = '---hello world'
print(s.lstrip('-')) # 输出:'hello world'
```
4. 去除字符串结尾的指定字符:
```python
s = 'hello world---'
print(s.rstrip('-')) # 输出:'hello world'
```
相关问题
python str.strip
`str.strip()` 是 Python 字符串的方法,用于返回去掉字符串开头和结尾的空白字符(例如空格、制表符、换行符等)后的新字符串。
例如:
```python
s = " hello world "
print(s.strip()) # 输出:"hello world"
```
`str.strip()` 还可以接受一个参数,用于指定要去除的字符集。例如:
```python
s = "!---hello world---!"
print(s.strip("!-")) # 输出:"hello world"
```
在这个例子中,`strip()` 方法去除了字符串开头和结尾的 "!" 和 "-" 字符。
columns.str.strip
As an AI language model, I don't have enough context to understand the meaning of "columns.str.strip". However, assuming that it is related to Python programming language and pandas library, here's a brief explanation:
"columns.str.strip" is a pandas method that applies the strip function to all the string values in a pandas DataFrame or Series. The "columns" in this context refer to the column names in the DataFrame or Series. This method is used to remove leading and trailing whitespaces from the strings in the data.
For example, if you have a pandas DataFrame with column names 'Name', 'Age', and 'Location', and some of the values in the 'Name' column have leading or trailing spaces, you can use the "columns.str.strip" method to remove them as follows:
```
import pandas as pd
data = {'Name': [' John', 'Mary', 'Peter ', ' Jane'],
'Age': [25, 30, 35, 40],
'Location': ['New York', 'London', 'Paris', 'Sydney']}
df = pd.DataFrame(data)
df['Name'] = df['Name'].str.strip() # remove leading/trailing spaces in 'Name' column
```
After applying the "columns.str.strip" method, the 'Name' column in the DataFrame will have values 'John', 'Mary', 'Peter', and 'Jane' with no leading or trailing spaces.