英文:
Drop rows that contain a specific string in a column
问题
# 请尝试以下代码来删除包含四个连续的1的行：
data = df[~df["col1"].astype(str).str.contains("1111")]
英文:
I have a large pandas dataset in the format as below
col1
11111112322
15211114821
25482136522
45225625656
11125648121
I would like to drop all rows that contain 1111 (four consecutive ones) to have below results
25482136522
45225625656
11125648121
I tried this but did not work:
data = df[df["col1"].str.contains("1111")==False]
Traceback (most recent call last):
File "<pyshell#17>", line 1, in <module>
data1_1 = section1[section1["col1"].str.contains("111111")==False]
File "C:\Users\henry\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\generic.py", line 5575, in __getattr__
return object.__getattribute__(self, name)
File "C:\Users\henry\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\accessor.py", line 182, in __get__
accessor_obj = self._accessor(obj)
File "C:\Users\henry\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\strings\accessor.py", line 177, in __init__
self._inferred_dtype = self._validate(data)
File "C:\Users\henry\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\strings\accessor.py", line 231, in _validate
raise AttributeError("Can only use .str accessor with string values!")
AttributeError: Can only use .str accessor with string values!. Did you mean: 'std'?
答案1
得分: 1
问题是，正如错误代码所述，该列不是字符串列：
> AttributeError: 只能在字符串值上使用 .str 访问器！你是不是想使用 'std'？
因此，要对其执行字符串操作，您首先必须将该列转换为字符串，然后您的代码将起作用：
df[df["col1"].astype(str).str.contains("1111")==False]
输出：
col1
2 25482136522
3 45225625656
4 11125648121
英文:
The issue, is, as the error code states, that the column is not a column of strings:
>AttributeError: Can only use .str accessor with string values!. Did you mean: 'std'?
So to perform string actions on it, you have to first convert the column to strings, then your code will work:
df[df["col1"].astype(str).str.contains("1111")==False]
Output:
col1
2 25482136522
3 45225625656
4 11125648121
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