suppose I've a pandas dataframe with column values as age like this df.age = {25, 35, 76, 21, 23, 30}
I want to do an inplace replace like this:
if df.age >=25 and df.age <= 35: replace that value with 1 else: replace that value with 0
I've tried this df[df.age >= 7.35 and df.age <= 7.45, 'age'] = 0 but doesn't seem to work.
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
Or using one liner with lambda:
df['age'] = df['age'].apply(lambda x: 1 if 25 <= x <= 35 else 0)
You can compare the series with the values (25/35) according to your condition, and then use astype(int) to convert the True/False values, to 1/0. Example -
df['age'] = ((25 <= df['age']) & (df['age'] <= 35)).astype(int)
Demo -
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
(df.age >= 25) & (df.age <= 35).