Pandas: apply a specific function to columns and create other columns

Question

I have a pandas dataframe df with geographical coordinates like this:

    lat         lon         
0   48.01025772 -6.15690851 
1   48.02164841 -6.10588741 
2   48.03302765 -6.05480051 
... ...         ...

I need to convert these coordinates into a different system, and have a dedicated function for this. I plan to create two new columns, df['N'] which is paired with lat, and df['E'] which is paired with lon.

It's not relevant what the function looks like, so for simplicity let's call it f. The function operates like this: E, N = f(float(lat), float(lon))

Is there a way I can iterate through all rows of df, extract the lat,lon pair, (compute their transformation) and assign the values to the relevant columns?

Answer 1

You can use apply on the df and pass axis=1, in your function you should return a Series and assign the 2 columns directly:

In [207]:
def foo(lat, lon):
    return pd.Series([lat + 10, lon * 100])
​
df[['new_lat','new_lon']] = df.apply(lambda x: foo(x['lat'], x['lon']), axis=1)
df

Out[207]:
         lat       lon    new_lat   new_lon
0  48.010258 -6.156909  58.010258 -615.6909
1  48.021648 -6.105887  58.021648 -610.5887
2  48.033028 -6.054801  58.033028 -605.4801

depending on what your function is doing using apply can and should be avoided