Python 熊猫替换值
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Pandas replace values
提问by user308827
I have the following dataframe:
我有以下数据框：
col
0 pre
1 post
2 a
3 b
4 post
5 pre
6 pre
I want to replace all rows in the dataframe which do not contain 'pre' to become 'nonpre', so dataframe looks like:
我想将数据框中不包含“pre”的所有行替换为“nonpre”，因此数据框如下所示：
col
0 pre
1 nonpre
2 nonpre
3 nonpre
4 nonpre
5 pre
6 pre
I can do this using a dictionary and pandas replace, however I want to just select the elements which are not 'pre' and replace them with 'nonpre'. is there a better way to do that without listing all possible col values in a dictionary?
我可以使用字典和熊猫替换来做到这一点，但是我只想选择不是“pre”的元素并将它们替换为“nonpre”。有没有更好的方法来做到这一点而不在字典中列出所有可能的 col 值？
采纳答案by Marius
As long as you're comfortable with the df.loc[condition, column]syntax that pandas allows, this is very easy, just do df['col'] != 'pre'to find all rows that should be changed:
只要您对df.loc[condition, column]Pandas 允许的语法感到满意，这很容易，只需df['col'] != 'pre'查找所有应更改的行即可：
df['col2'] = df['col']
df.loc[df['col'] != 'pre', 'col2'] = 'nonpre'
df
Out[7]:
col col2
0 pre pre
1 post nonpre
2 a nonpre
3 b nonpre
4 post nonpre
5 pre pre
6 pre pre
回答by Mike
df[df['col'].apply(lambda x: 'pre' not in x)] = 'nonpre'
