Python Pandas 用相反的符号替换值
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Python Pandas replace values by their opposite sign
提问by eleijonmarck
I am trying to "clean" some data. I have values which are negative, which they cannot be. And I would like to replace all values that are negative to their corresponding positive values.
我正在尝试“清理”一些数据。我有负面的价值观，但他们不能。我想将所有负值替换为相应的正值。
A | B | C
-1.9 | -0.2 | 'Hello'
1.2 | 0.3 | 'World'
I would like this to become
我希望这成为
A | B | C
1.9 | 0.2 | 'Hello'
1.2 | 0.3 | 'World'
As of now I have just begun writing the replace statement
截至目前，我刚刚开始编写替换语句
df.replace(df.loc[(df['A'] < 0) & (df['B'] < 0)],df * -1,inplace=True)
Please help me in the right direction
请帮助我朝着正确的方向前进
回答by EdChum
Just call abs:
只需致电abs：
In [349]:
df = df.abs()
df
Out[349]:
A B
0 1.9 0.2
1 1.2 0.3
Another method would be to create a boolean mask, drop the NaNrows, call locon the index and assign the negative values:
另一种方法是创建一个布尔掩码，删除NaN行，调用loc索引并分配负值：
df.loc[df[df<0].dropna().index] = -df
EDIT
编辑
For the situation where you have strings the following would work:
对于您有字符串的情况，以下将起作用：
In [399]:
df[df.columns[df.dtypes != np.object]] = df[df.columns[df.dtypes != np.object]].abs()
df
Out[399]:
A B C
0 1.9 0.2 Hello
1 1.2 0.3 World
回答by Akash Nayak
You can be use this way:
你可以这样使用：
first make column as a string:
首先将列作为字符串：
df['A']=df['A'].astype('str')
df['B']=df['B'].astype('str')
Then use replace function:
然后使用替换功能：
df['A']=df['A'].str.replace('-','')
df['B']=df['B'].str.replace('-','')
then make it as float data type:
然后将其设为浮点数据类型：
df['A']=df['A'].astype('float')
df['B']=df['B'].astype('float')
I think this will be help you in this problem.
我认为这会帮助你解决这个问题。
