有条件地替换 pandas 数据框列中的值 [英] Conditional Substitution of values in pandas dataframe columns
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问题描述
假设我有一个pandas数据框,其列值像df.age = {25,35,76,21,23,30}这样的年龄
suppose I've a pandas dataframe with column values as age like this df.age = {25, 35, 76, 21, 23, 30}
我想像这样进行就地替换:
I want to do an inplace replace like this:
如果df.age> = 25且df.age< = 35: 将该值替换为1 别的: 将该值替换为0
if df.age >=25 and df.age <= 35: replace that value with 1 else: replace that value with 0
我已经尝试过df [df.age> = 7.35和df.age< = 7.45,'age'] = 0 但似乎不起作用.
I've tried this df[df.age >= 7.35 and df.age <= 7.45, 'age'] = 0 but doesn't seem to work.
推荐答案
您还可以创建一个函数来检查您的条件并将其应用于数据框:
You can also create a function to check your conditions, and apply to the dataframe:
def condition(value):
if 25 <= value <= 35:
return 1
return 0
# stealing sample from @AnandSKumar because I'm lazy
In [32]: df
Out[32]:
age
0 25
1 35
2 76
3 21
4 23
5 30
In [33]: df['age'] = df['age'].apply(condition)
In [34]: df
Out[34]:
age
0 1
1 1
2 0
3 0
4 0
5 1
或使用带有lambda的衬纸:
Or using one liner with lambda:
df['age'] = df['age'].apply(lambda x: 1 if 25 <= x <= 35 else 0)
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