Replacement Values into the integer on dataset columns-CodePudding

	House Number	Street	First Name	Surname	Age	Relationship to Head of House	Marital Status	Gender	Occupation	Infirmity	Religion
0	1	Smith Radial	Grace	Patel	46	Head	Widowed	Female	Petroleum engineer	None	Catholic
1	1	Smith Radial	Ian	Nixon	24	Lodger	Single	Male	Publishing rights manager	None	Christian
2	2	Smith Radial	Frederick	Read	87	Head	Divorced	Male	Retired TEFL teacher	None	Catholic
3	3	Smith Radial	Daniel	Adams	58	Head	Divorced	Male	Therapist, music	None	Catholic
4	3	Smith Radial	Matthew	Hall	13	Grandson	NaN	Male	Student	None	NaN
5	3	Smith Radial	Steven	Fletcher	9	Grandson	NaN	Male	Student	None	NaN
6	4	Smith Radial	Alison	Jenkins	38	Head	Single	Female	Physiotherapist	None	Catholic
7	4	Smith Radial	Kelly	Jenkins	12	Daughter	NaN	Female	Student	None	NaN
8	5	Smith Radial	Kim	Browne	69	Head	Married	Female	Retired Estate manager/land agent	None	Christian
9	5	Smith Radial	Oliver	Browne	69	Husband	Married	Male	Retired Merchandiser, retail	None	None

Hello,

I have a dataset that you can see below. When I tried to convert Age to int. I got that error: ValueError: invalid literal for int() with base 10: '43.54302670766108'

This means there is float data inside that data. I tried to replace '.' to '0' then tried to convert but I failed. Could you help me to do that?

df['Age'] = df['Age'].replace('.','0')
df['Age'] = df['Age'].astype('int')

I still got the same error. I think replace line is not working. Do you know why?

Thanks

CodePudding user response：

Try:

df['Age'] = df['Age'].replace('\..*$', '', regex=True).astype(int)

Or, more drastic:

df['Age'] = df['Age'].replace('^(?:.*\D.*)?$', '0', regex=True).astype(int)

CodePudding user response：

You do not need to manipulate the strings; you might first convert values to float then to int like:

df["Age"] = df["Age"].astype('float').astype('int')