Convert latitude and longitude to float-CodePudding

I have a dataframe df like these 3 first rows are these

City	Longitude	Latitude
Toluca	W099.6569	N19.2925
Pune	E73.8562	N18.5167
Bloemfontain	E026.216667	S29.116667

For example Bloemfontain row should be like these at the end

City	Longitude	Latitude
Bloemfontain	-29.116667	26.216667

I used this code

pattern = r'(?P<d>[\d\.] ).*?(?P<m>[\d\.] ).*?(?P<s>[\d\.] )'
dms = df['Latitude'].str.extract(pattern).astype(float)
df['LATITUDE'] = dms['d']   dms['m'].div(60)   dms['s'].div(3600)
dms = df['Longitude'].str.extract(pattern).astype(float)
df['LONGITUDE'] = dms['d']   dms['m'].div(60)   dms['s'].div(3600)

but got not the right longitude and latitude

CodePudding user response：

I don't see where it is necessary to utilize a pattern search at all. Here is how I would do the task.

# function to perform the partitioning
def cnvrtCoord(val: str) -> float:
    sgn =  1
    if val[0] == 'S' or val[0] == 'W':
        sgn = -1
    return float(val[1:]) * sgn

Then to change the contents of the Longitude and Latitude columns use:

df['Longitude'] = [cnvrtCoord(x) for x in df['Longitude'].tolist()]
df['Latitude'] = [cnvrtCoord(x) for x in df['Latitude'].tolist()]

This would produce the dataframe shown below given your input sample:

    City    Longitude   Latitude
0   Toluca  -99.656900  19.292500
1   Pune    73.856200   18.516700
2   Bloemfontain    26.216667   -29.116667