I am trying to obtain a final 'process' array by filtering the raw 'hitdt' array for '-1'; the column that contains -1 in a particular row in hitdt would determine the values of that row in 'process'. I am using if statements in what I suspect a very laborious way, but could not find working methods.
Currently, running def frame() does not return any errors, but when I check the resulting 'process' array, new columns remain NaN. 'hitdt' is the input data frame with 4 column series (hitdt['t1'] to hitdt['t4']). 'process' is an empty output dataframe. previous appending of data into hitdt series columns not involving 'if' statements were fine.
Is there a way to determine which column of a certain row in a data frame == a value, then apply statements to that row only, and iterate through all rows?
def frame():
global hitdt, process
#v1
for i, row in hitdt.iterrows():
if -1 == i in hitdt['t3']:
process['tau1'] = hitdt['t2']-hitdt['t1']
process['tau2'] = hitdt['t4']-hitdt['t1']
process['xx'] = geom['x2']
process['yy'] = geom['y2']
process['rho1'] = sqrt(square(geom['x2']-geom['x1']) + square(geom['y2']-geom['y1']))
process['alpha'] = 2.357067
elif -1 == i in hitdt['t4']:
process['tau1'] = hitdt['t3']-hitdt['t2']
process['tau2'] = hitdt['t1']-hitdt['t2']
process['xx'] = geom['x3']
process['yy'] = geom['y3']
process['rho1'] = sqrt(square(x3-x2) + square(y3-y2))
process['alpha'] = 0.749619
elif -1 == i in hitdt['t1']:
process['tau1'] = hitdt['t4']-hitdt['t3']
process['tau2'] = hitdt['t2']-hitdt['t3']
process['xx'] = geom['x4']
process['yy'] = geom['y4']
process['rho1'] = sqrt(square(x3-x4) + square(y3-y4))
process['alpha'] = -0.800233
elif -1 == i in hitdt['t2']:
process['tau1'] = hitdt['t1']-hitdt['t4']
process['tau2'] = hitdt['t3']-hitdt['t4']
process['xx'] = geom['x1']
process['yy'] = geom['y1']
process['rho1'] = sqrt(square(geom['x1']-geom['x4']) + square(geom['y1']-geom['y4']))
process['alpha'] = -1.906772
...
[In]: process
[Out]:
jd frac tau1 tau2 rho1 xx yy alpha hits
0 2457754 0.501143 NaN NaN NaN NaN NaN NaN 3
1 2457754 0.508732 NaN NaN NaN NaN NaN NaN 3
2 2457754 0.512411 NaN NaN NaN NaN NaN NaN 3
3 2457754 0.513932 NaN NaN NaN NaN NaN NaN 3
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