pandas -替换列值 [英] Pandas - replacing column values

问题描述

我知道这个问题有很多主题，但是没有一种方法适合我，所以我要发布有关我的具体情况的信息

I know there are a number of topics on this question, but none of the methods worked for me so I'm posting about my specific situation

我有一个看起来像这样的数据框:

I have a dataframe that looks like this:

data = pd.DataFrame([[1,0],[0,1],[1,0],[0,1]], columns=["sex", "split"])
data['sex'].replace(0, 'Female')
data['sex'].replace(1, 'Male')
data

我想做的是将性别列中的全0替换为女性"，将所有1替换为男性"，但是当我使用上面的代码时，数据框中的值似乎没有改变

What I want to do is replace all 0's in the sex column with 'Female', and all 1's with 'Male', but the values within the dataframe don't seem to change when I use the code above

我错误地使用了replace()吗?还是有更好的方法可以有条件地替换值?

Am I using replace() incorrectly? Or is there a better way to do conditional replacement of values?

推荐答案

是的，您使用的是错误的

Yes, you are using it incorrectly, Series.replace() is not inplace operation by default, it returns the replaced dataframe/series, you need to assign it back to your dataFrame/Series for its effect to occur. Or if you need to do it inplace, you need to specify the inplace keyword argument as True Example -

data['sex'].replace(0, 'Female',inplace=True)
data['sex'].replace(1, 'Male',inplace=True)

此外，您还可以通过对to_replace参数和value参数使用list将以上内容组合为单个replace函数调用，示例-

Also, you can combine the above into a single replace function call by using list for both to_replace argument as well as value argument , Example -

data['sex'].replace([0,1],['Female','Male'],inplace=True)

示例/演示-

In [10]: data = pd.DataFrame([[1,0],[0,1],[1,0],[0,1]], columns=["sex", "split"])

In [11]: data['sex'].replace([0,1],['Female','Male'],inplace=True)

In [12]: data
Out[12]:
      sex  split
0    Male      0
1  Female      1
2    Male      0
3  Female      1

---

您还可以使用字典，例如-

---

You can also use a dictionary, Example -

In [15]: data = pd.DataFrame([[1,0],[0,1],[1,0],[0,1]], columns=["sex", "split"])

In [16]: data['sex'].replace({0:'Female',1:'Male'},inplace=True)

In [17]: data
Out[17]:
      sex  split
0    Male      0
1  Female      1
2    Male      0
3  Female      1

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