unpacking function return into pandas dataframe columns

Question

I have an existing dataframe ("radar_locations") which contains (among other things) latitude and longitude coordinates. To that information, I need to add a country and state column, so I've written a function that does a reverse geocode and returns the two values needed return geodata.state, geodata.country

When I try to assign the values into new columns in the dataframe, I get an error that there are too many values to unpack. But if I update the code so that the function returns a single value, I can successfully write that value into a new dataframe column.

If this just an eccentricity of pandas or is there something more fundamental I'm missing?

works

def reverse_geocode(lat, long):
    ...
    return geodata.country

radar_locations['Country'] = radar_locations.apply(lambda x: reverse_geocode(x[1], x[0]), axis=1)

works

def reverse_geocode(lat, long):
    ...
    return geodata.state, geodata.country

state, country = reverse_geocode(mylat, mylong)

fails

def reverse_geocode(lat, long):
    ...
    return geodata.state, geodata.country

radar_locations['State'], radar_locations['Country'] = radar_locations.apply(lambda x: reverse_geocode(x[1], x[0]), axis=1)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-28-82e3c63a2ecb> in <module>()
     19         raise
     20 
---> 21 radar_locations['State'], radar_locations['Country'] =   radar_locations.apply(lambda x: reverse_geocode(x[1], x[0]), axis=1)

ValueError: too many values to unpack (expected 2)

Answer 1

Use zip and the * operator to unzip and perform the assignment:

# A function that returns multiple things.
def some_func(x):
    return x+1, x+2

# Example DataFrame
df = pd.DataFrame({'A': range(5)})

# Example usage.
df['B'], df['C'] = zip(*df['A'].apply(some_func))

The resulting output:

   A  B  C
0  0  1  2
1  1  2  3
2  2  3  4
3  3  4  5
4  4  5  6

The issue with trying to assign directly from the apply is that when you return multiple values, you're actually returning a single column of tuples, not two separate columns, which is why the unzipping process is necessary:

df['A'].apply(some_func)

0    (1, 2)
1    (2, 3)
2    (3, 4)
3    (4, 5)
4    (5, 6)