Converting string objects to int/float using pandas

Question

import pandas as pd

path1 = \"/home/supertramp/Desktop/100&life_180_data.csv\"

mydf =  pd.read_csv(path1)

numcigar = {\"Never\":0 ,\"1-5 Cigarettes/day\" :1,\"10-         


        

Answer 1

Try using this function for all problems of this kind:

def get_series_ids(x):
    '''Function returns a pandas series consisting of ids, 
       corresponding to objects in input pandas series x
       Example: 
       get_series_ids(pd.Series(['a','a','b','b','c'])) 
       returns Series([0,0,1,1,2], dtype=int)'''

    values = np.unique(x)
    values2nums = dict(zip(values,range(len(values))))
    return x.replace(values2nums)

Answer 2

OK, first problem is you have embedded spaces causing the function to incorrectly apply:

fix this using vectorised str:

mydf['Cigarettes'] = mydf['Cigarettes'].str.replace(' ', '')

now create your new column should just work:

mydf['CigarNum'] = mydf['Cigarettes'].apply(numcigar.get).astype(float)

UPDATE

Thanks to @Jeff as always for pointing out superior ways to do things:

So you can call replace instead of calling apply:

mydf['CigarNum'] = mydf['Cigarettes'].replace(numcigar)
# now convert the types
mydf['CigarNum'] = mydf['CigarNum'].convert_objects(convert_numeric=True)

you can also use factorize method also.

Thinking about it why not just set the dict values to be floats anyway and then you avoid the type conversion?

So:

numcigar = {"Never":0.0 ,"1-5 Cigarettes/day" :1.0,"10-20 Cigarettes/day":4.0}

Version 0.17.0 or newer

convert_objects is deprecated since 0.17.0, this has been replaced with to_numeric

mydf['CigarNum'] = pd.to_numeric(mydf['CigarNum'], errors='coerce')

Here errors='coerce' will return NaN where the values cannot be converted to a numeric value, without this it will raise an exception