Data
I have a dataframe that contains 5 columns:
- Latitude and longitude of origin (
origin_lat,origin_lng) - Latitude and longitude of destination (
dest_lat,dest_lng) - A score which was computed from the other fields
I have a matrix M that contains pairs of origin and destination latitude/longitude. Some of these pairs exists in the dataframe, other do not.
Goal
My goal is two-fold:
- Select all pairs from
Mthat are not present in the first four column of the dataframe, apply a functionfuncto them (to calculate the score column), and append the results to the existing dataframe. Note: We should not recalculate the score for already existing rows. - After adding the missing rows, select all the rows defined by the selection matrix
Min a new dataframedfs.
Example code
# STEP 1: Generate example data
ctr_lat = 40.676762
ctr_lng = -73.926420
N = 12
N2 = 3
data = np.array([ctr_lat+np.random.random((N))/10,
ctr_lng+np.random.random((N))/10,
ctr_lat+np.random.random((N))/10,
ctr_lng+np.random.random((N))/10]).transpose()
# Example function - does not matter what it does
def func(x):
return np.random.random()
# Create dataframe
geocols = ['origin_lat','origin_lng','dest_lat','dest_lng']
df = pd.DataFrame(data,columns=geocols)
df['score'] = df.apply(func,axis=1)
Which gives me a dataframe df like this:
origin_lat origin_lng dest_lat dest_lng score
0 40.684887 -73.924921 40.758641 -73.847438 0.820080
1 40.703129 -73.885330 40.774341 -73.881671 0.104320
2 40.761998 -73.898955 40.767681 -73.865001 0.564296
3 40.736863 -73.859832 40.681693 -73.907879 0.605974
4 40.761298 -73.853480 40.696195 -73.846205 0.779520
5 40.712225 -73.892623 40.722372 -73.868877 0.628447
6 40.683086 -73.846077 40.730014 -73.900831 0.320041
7 40.726003 -73.909059 40.760083 -73.829180 0.903317
8 40.748258 -73.839682 40.713100 -73.834253 0.457138
9 40.761590 -73.923624 40.746552 -73.870352 0.867617
10 40.748064 -73.913599 40.746997 -73.894851 0.836674
11 40.771164 -73.855319 40.703426 -73.829990 0.010908
I can then artificially create the selection matrix M which contains 3 rows that exists in the dataframe, and 3 rows that do not.
# STEP 2: Generate data to select
# As an example, I select 3 rows that are part of the dataframe, and 3 that are not
data2 = np.array([ctr_lat+np.random.random((N2))/10,
ctr_lng+np.random.random((N2))/10,
ctr_lat+np.random.random((N2))/10,
ctr_lng+np.random.random((N2))/10]).transpose()
M = np.concatenate((data[4:7,:],data2))
The matrix M looks like this:
array([[ 40.7612977 , -73.85348031, 40.69619549, -73.84620489],
[ 40.71222463, -73.8926234 , 40.72237185, -73.86887696],
[ 40.68308567, -73.84607722, 40.73001434, -73.90083107],
[ 40.7588412 , -73.87128079, 40.76750639, -73.91945371],
[ 40.74686156, -73.84804047, 40.72378653, -73.92207075],
[ 40.6922673 , -73.88275402, 40.69708748, -73.87905543]])
From here, I do not know how to know which rows from M are not present in df and add them. I do not know either how to select all the rows from df that are in M.
Ideas
My idea was to identify the missing rows, append them to df with a nan score, and recompute the score for the nan rows only. However, I do not know how to select these rows efficiently without looping on each element of the matrix M.
Any suggestion? Thanks a lot for your help!
