pandas 数据框列中值的条件替换
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Conditional Substitution of values in pandas dataframe columns
提问by Linus_30
suppose I've a pandas dataframe with column values as age like this df.age = {25, 35, 76, 21, 23, 30}
假设我有一个 Pandas 数据框，列值是这样的 df.age = {25, 35, 76, 21, 23, 30}
I want to do an inplace replace like this:
我想做这样的就地替换：
if df.age >=25 and df.age <= 35: replace that value with 1 else: replace that value with 0
如果 df.age >=25 和 df.age <= 35：将该值替换为 1 否则：将该值替换为 0
I've tried this df[df.age >= 7.35 and df.age <= 7.45, 'age'] = 0 but doesn't seem to work.
我试过这个 df[df.age >= 7.35 and df.age <= 7.45, 'age'] = 0 但似乎不起作用。
回答by Anzel
You can also create a function to check your conditions, and apply to the dataframe:
您还可以创建一个函数来检查您的条件，并应用于数据框：
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
Or using one liner with lambda:
或者使用一个带有 lambda 的衬垫：
df['age'] = df['age'].apply(lambda x: 1 if 25 <= x <= 35 else 0)
回答by Anand S Kumar
You can compare the series with the values (25/35) according to your condition, and then use astype(int)to convert the True/Falsevalues, to 1/0. Example -
您可以根据您的条件将系列与值 (25/35) 进行比较，然后使用astype(int)将True/False值转换为1/0. 例子 -
df['age'] = ((25 <= df['age']) & (df['age'] <= 35)).astype(int)
Demo -
演示 -
In [2]: df = pd.DataFrame([[25], [35], [76], [21], [23], [30]],columns=['age'])
In [3]: df
Out[3]:
age
0 25
1 35
2 76
3 21
4 23
5 30
In [6]: ((25 <= df['age']) & (df['age'] <= 35)).astype(int)
Out[6]:
0 1
1 1
2 0
3 0
4 0
5 1
Name: age, dtype: int32
