After looking at several posts here, every post explains how to replace yes/no in a column with 1/0, but the datatype of those numbers remain 'object' and is not float or int (even after I use astype(int)), so I can't do further operation with them. My code is below. Anyone knows how to convert datatype now from object to float or int?
df['Inter Plan'].replace({'no': 0, 'yes': 1}).astype(int)
print(df['Inter Plan'].dtypes)
astype returns a pandas series, it is not done in place. Use:
df["Inter Plan"] = df['Inter Plan'].replace({'no': 0, 'yes': 1}).astype(int)
There is a copy option for astype which will allow you to do the operation in place, but because you are using replace as well, I don't think you'll be able to do it all in one line, so it is probably best to just use the above piece of code. In addition, the in-place option (which is setting copy to False) comes with a warning:
be very careful setting
copy=Falseas changes to values then may propagate to other pandas objects.
Try casting to str before replacing:
df['Inter Plan'] = df['Inter Plan'].str.replace({'no': 0, 'yes': 1}).astype(int)
One way to do this:
df['Inter Plan'].replace({'no': 0, 'yes': 1}, inplace=True).astype('int', copy=False)