Creating a DataFrame by appying a function that returns a tuple

Question

I have a dataset that has the following features :

[coord_x, coord_y, coord_z, xdot, ydot, zdot]

    epoch                   sat_id  x               y               z               Vx          Vy          Vz
0   2014-01-01 00:00:00.000 0   -8855.823863    13117.780146    -20728.353233   -0.908303   -3.808436   -2.022083
1   2014-01-01 00:46:43.000 0   -10567.672384   1619.746066 -24451.813271   -0.302590   -4.272617   -0.612796
2   2014-01-01 01:33:26.001 0   -10578.684043   -10180.467460   -24238.280949   0.277435    -4.047522   0.723155
3   2014-01-01 02:20:09.001 0   -9148.251857    -20651.437460   -20720.381279   0.715600    -3.373762   1.722115
4   2014-01-01 03:06:52.002 0   -6719.092336    -28929.061629   -14938.907967   0.992507    -2.519732   2.344703

I defined a function that takes those coordinates and return a tuple as follows:

(a, e, i, w, Om, theta) = cart2kep(coord_x, coord_y, coord_z, xdot, ydot, zdot)

I would like to add a, e, i, w, Om, theta as extra features to the initial dataframe : How can I do it efficiently ? Currently, I am doing it as follows :

out = df.apply(lambda x: cart2kep(x[2], x[3], x[4], x[5], x[6], x[7]), axis = 1)
out = out.apply(pd.Series, index = ['a', 'e', 'i', 'w', 'Om', 'theta'])
df = df.join(out)

It takes too long to execute. Is there a more pythonic way of doing it ?

Thank you in advance

Mrml91 · Accepted Answer · 2020-03-20 12:40:08Z

1

Change your function so that it returns a list rather than a tuple. Then, use the result_type argument of pd.DataFrame.apply:

   df[<list_of_columns>].apply(lambda row: cart2kep(*row), axis=1, result_type='expand')

edited Mar 20, 2020 at 12:40

answered Mar 19, 2020 at 21:16

Mrml91

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1 Comment

cnemri Over a year ago

Thank you very much. This is exactly what I was looking for !

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