pandas 根据另一列的值在熊猫中创建新列
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Create new column in pandas based on value of another column
提问by Piyush
I have some dataset about genders of various individuals. Say, the dataset looks like this:
我有一些关于不同个体性别的数据集。假设数据集如下所示：
Male
Female
Male and Female
Male
Male
Female
Trans
Unknown
Male and Female
Some identify themselves as Male, some female and some identify themselves as both male and female.
有些人认为自己是男性，有些人认为自己是男性和女性。
Now, what I want to do is create a new column in Pandas which maps
现在，我想要做的是在 Pandas 中创建一个新的列来映射
Males to 1,
Females to 2,
Others to 3
I wrote some code
我写了一些代码
def gender(x):
if x.str.contains("Male")
return 1
elif x.str.contains("Female")
return 2
elif return 3
df["Gender Values"] = df["Gender"].apply(gender)
But I was getting errors that function doesn't contain any attribute contains. I tried removing str:
但是我收到了函数不包含任何属性包含的错误。我尝试删除 str：
x.contains("Male")
and I was getting same error
我遇到了同样的错误
Is there a better way to do this?
有一个更好的方法吗？
回答by jezrael
You can use:
您可以使用：
def gender(x):
if "Female" in x and "Male" in x:
return 3
elif "Male" in x:
return 1
elif "Female" in x:
return 2
else: return 4
df["Gender Values"] = df["Gender"].apply(gender)
print (df)
Gender Gender Values
0 Male 1
1 Female 2
2 Male and Female 3
3 Male 1
4 Male 1
5 Female 2
6 Trans 4
7 Unknown 4
8 Male and Female 3
回答by Batman
Create a mapping function, and use that to map the values.
创建一个映射函数，并使用它来映射值。
def map_identity(identity):
if gender.lower() == 'male':
return 1
elif gender.lower() == 'female':
return 2
else:
return 3
df["B"] = df["A"].map(map_identity)
回答by Rajarshi Das
If there is no specific requirement to use 1, 2, 3 to Males, Females and Others respectively in that order, you can try LabelEncoder from Scikit-Learn. It will randomly allocate a unique number to each unique category in that column.
如果没有具体要求按顺序分别使用 1、2、3 到男性、女性和其他，您可以尝试使用 Scikit-Learn 的 LabelEncoder。它将随机为该列中的每个唯一类别分配一个唯一编号。
from sklearn import preprocessing
encoder = preprocessing.LabelEncoder()
encoder.fit(df["gender"])
For details, you can check Label Encoderdocumentation.
有关详细信息，您可以查看标签编码器文档。
Hope this helps!
希望这可以帮助！
