Convert string columns in decimal latitude and longitude skipping nan values using pandas

Question

there is a pandas dataframe column with latitud values as strings

0       47º 58,46 N
1        48º 06,8 N
2               NaN
3        47º 58,1 N
4        48º 05,0 N

code:

parts = df["Latitud"].str.extract('(\d+)º\s(\d*.\d*).([N|S|E|W])', expand=True) #(\d+)º\s(\d*.\d*).(.)



df["latitude"] = (parts[0].astype(int) + parts[1].astype(float) / 60 ) * parts[3].map({'N':1, 'S':-1, 'E': 1, 'W':-1})

Error:

ValueError: cannot convert float NaN to integer

How do I do to skip NaN empty values?

Answer 1

Let's try skipping the NaN values as requested:

# skip them here
notna = df['Latitud'].notna()

# extract the parts
parts = df.loc[notna, "Latitud"].str.extract('(\d+)º\s(\d*.\d*).([N|S|E|W])', expand=True) 

# update the data
df.loc[notna, 'latitude'] = (parts[0].astype(int) + parts[1].str.replace(',','.').astype(float) / 60 ) * parts[2].map({'N':1, 'S':-1, 'E': 1, 'W':-1})

Output:

       Latitud   latitude
0  47º 58,46 N  47.974333
1   48º 06,8 N  48.113333
2          NaN        NaN
3   47º 58,1 N  47.968333
4   48º 05,0 N  48.083333