Remove float duplicates from a list of tuples created by zip

I create a list of tuples by zipping three lists together, data pairs:

 XYZip = list(zip(XaData, Y1aData, Y2aData))

[
    (0.001625625, 4.782947316198166, -0.011032947316198166),
    (-2.5e-06, 4.783447358402665, 0.020216552641597337),
    (0.0008137499999999999, 4.782997384780477, -0.017282997384780476),
    (0.00081, 4.783247405882726, 0.020216752594117274),
    (0.001625625, 4.782066023993667, -0.011032066023993668), 
    (0.00324625, 4.780809700135795, 0.03271919029986421),
    ...,
    (19.4121325, 4.653511649011105, 1.1703464883509889)
]

I need to get rid of the tuples where the XaData value is a duplicate, like this one 0.001625625. The whole tuple (0.001625625, 4.782066023993667, -0.011032066023993668) needs to go. Order doesn't matter. I can sort in a second step. I tried set() to no avail.

Data will be fed to a scipy.CubicSpline function where x need to be strictly increasing and will not accept duplicates!

I tried...

XYZip = list(zip(XaData, Y1aData, Y2aData))
XYUnique = set(XYZip)
XYSorted = sorted(XYUnique)
XaData, Y1aData, Y2aData = zip(*XYSorted)

...obviously not removing the tuples with the duplicate XaData value.

This is what I need in the first step:

[
    (0.001625625, 4.782947316198166, -0.011032947316198166),
    (-2.5e-06, 4.783447358402665, 0.020216552641597337),
    (0.0008137499999999999, 4.782997384780477, -0.017282997384780476),
    (0.00081, 4.783247405882726, 0.020216752594117274),
    (0.00324625, 4.780809700135795, 0.03271919029986421),
    ...,
    (19.4121325, 4.653511649011105, 1.1703464883509889)
]

Why set of tuple is not return correct answer in this case

(0.001625625, 4.782947316198166, -0.011032947316198166)
            ,      |           ,          |
        same,      not same    ,          not same
            ,      |           ,          | 
(0.001625625, 4.782066023993667, -0.011032066023993668)

# so

(0.001625625, 4.782947316198166, -0.011032947316198166) 
# not equal 
(0.001625625, 4.782066023993667, -0.011032066023993668)

It is easy and straightforward if you use pandas instead.

Example:

x = 1,1,2,3,4,5
y = 1,2,3,4,5,6
z = 1,3,5,7,9,11

pd.DataFrame(zip(x, y, z), columns=["x","y","z"]).drop_duplicates(subset='x', keep="last")

step 1
create DataFrame

df = pd.DataFrame(zip(x, y, z), columns=["x","y","z"])

step 2
drop duplicates Reference

df = df.drop_duplicates(subset='x', keep="last")

drop 1, 1, 1 because keep="last"

Combine to your code

XYZip = list(zip(XaData, Y1aData, Y2aData))

# create data frame
df = pd.DataFrame(XYZip, columns=["XaData","Y1aData","Y2aData"])

# removing the duplicate on XaData value.
df = df.drop_duplicates(subset='XaData', keep="last")

# if you want to convert to list of tuple 

result = [tuple(i) for i in df.values]
# result = [(1, 2, 3), (2, 3, 5), (3, 4, 7), (4, 5, 9), (5, 6, 11)]

or

Use dictionary of tuple instead.

temp = {i_x: (i_y, i_z) for i_x, i_y, i_z in zip(x, y, z)}
[((i,)+temp[i]) for i in temp]

step 1
convert x y z to dictionary (key x because I need to delete duplicate on x)

temp = {i_x: (i_y, i_z) for i_x, i_y, i_z in zip(x, y, z)}

step 2
convert to list of tuple

[((i,)+temp[i]) for i in temp]

result

[(1, 2, 3), (2, 3, 5), (3, 4, 7), (4, 5, 9), (5, 6, 11)]

# drop (1,1,1) because (1, 1, 1) and (1, 2, 3) are same in first element.

I solved it by checking if value is already in list like

if x not in x_list:
    x_list.append(x)
    y_list.append(y)
    z_list.append(z)

That solved it. All constructs with set() failed and scipy spline interpolation threw an error about not strictly increasing x-values.