如何根据条件在 Pandas 数据帧上应用字符串拆分方法？

Question

I would like to replace some values in my dataframe that were entered in the wrong format. For example, 850/07-498745 should be 07-498745. Now, I used string split successfully to do so. However, it turns all previously correctly formatted strings into NaNs. I tried to base it on a condition, but still I have the same problem. How can I fix it ?

Example Input:

mylist = ['850/07-498745', '850/07-148465', '07-499015']
df = pd.DataFrame(mylist)
df.rename(columns={ df.columns[0]: "mycolumn" }, inplace = True)

My Attempt:

df['mycolumn'] = df[df.mycolumn.str.contains('/') == True].mycolumn.str.split('/', 1).str[1]
df

Output:

What I wanted:

Answer 1

You can use split with / and grab the last returning string from the list:

df['mycolumn'].str.split('/').str[-1]

0    07-498745
1    07-148465
2    07-499015
Name: mycolumn, dtype: object

Answer 2

This would also work, and may help you understand why your original attempt did not:

mask = df.mycolumn.str.contains('/')
df.mycolumn.loc[mask] = df.mycolumn[mask].str.split('/', 1).str[1]

You were doing df['mycolumn'] = ... , which I believe is just replacing the entire Series for that column with the new one you formed.

Answer 3

For a regex solution:

df.mycolumn.str.extract('(?:.*/)?(.*)$')[0]

Output:

0    07-498745
1    07-148465
2    07-499015
Name: 0, dtype: object