Use one .map call to change values for multiple Series/columns in dataframe using one dictionary

Question

If I have a dataframe like so:

id latitude longitude
 a      -99        48
 b      -97        44
 c      -96        52

and I have a dictionary mapping the ids to new latitude+longitude values

new_lat_lon = { 'a':(-99, 58), 'b':(...), 'c':(...) }

is there a quick and dirty way to use .map to change the latitude/longitude columns at once?

eg

df[['latitude', 'longitude']] = df['id'].map(new_lat_lon)

this doesn't work of course, but if there's a way I'd like to know. I am aware that I can simply separate the dictionary into two separate ones, but I am interested if there is a more compact solution. If I need to modify the dictionary a bit (eg change the tuples to lists or something) that's cool too, as long as it's one dictionary. Thank you!

Answer 1

Using update

pd.DataFrame.from_dict(new_lat_lon,'index').rename(columns={0:'latitude',1:'longitude'})
Out[128]: 
   latitude  longitude
a       -99         58
b         1          2
c         2          3
updateddf=pd.DataFrame.from_dict(new_lat_lon,'index').rename(columns={0:'latitude',1:'longitude'})
df=df.set_index('id')
df.update(updateddf)
df.reset_index()
Out[132]: 
  id  latitude  longitude
0  a       -99         58
1  b         1          2
2  c         2          3

Answer 2

You can also unpack using str.get

vals = df.id.map(d)
df.latitude, df.longitude = vals.str.get(0), vals.str.get(1)

Notice that this works if all values in id are in d

If they are not, can do

vals =df.id.map(d).fillna(df[['latitude', 'longitude']].agg(tuple,1))
df.latitude, df.longitude = vals.str.get(0), vals.str.get(1)