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I am currently projecting the latitude, longitude coordinates to a cartesian plane in my pandas data frame. So, I have a method for projection as:

def convert_lat_long_xy(lat, lo):
    return x, y

So this returns a tuple and I can use this method on my dataframe as:

df.apply(lambda x: convert_lat_long_xy(x.latitude, x.longitude), axis=1))

Now, what I would like to do is create two extra columns in my data frame called 'x' and 'y' to hold these values. I know I can do something like:

df['proj'] = df.apply(lambda x: convert_lat_long_xy(x.latitude, x.longitude), axis=1))

But is it possible to add the values to two different columns?

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Yes, you need to convert the output of lambda into pd.Series. Here's an example:

In [1]: import pandas as pd 

In [2]: pd.DataFrame(["1,2", "2,3"], columns=["coord"])
Out[2]: 
  coord
0   1,2
1   2,3

In [3]: df = pd.DataFrame(["1,2", "2,3"], columns=["coord"])

In [4]: df.apply(lambda x: pd.Series(x["coord"].split(",")), axis=1)
Out[4]: 
   0  1
0  1  2
1  2  3

In [5]: df[["x", "y"]] = df.apply(lambda x: pd.Series(x["coord"].split(",")), axis=1)

In [6]: df
Out[6]: 
  coord  x  y
0   1,2  1  2
1   2,3  2  3

For your particular case, df.apply will become like this:

df[['x', 'y']] = df.apply(lambda x: pd.Series(convert_lat_long_xy(x.latitude, x.longitude)), axis=1))
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Thank you! This works. Wonder if converting to series for every row is making things slow and better to convert the result?

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