Best way to classify a series of data in Python
If my assumptions are true I don't see a reason for any complex classifier. I'd simply check if the angle always gets larger or always gets smaller. Everytime this rule is followed you add 1 to a quality counter. If the rule is broken you reduce the quality counter by 1. In the end you divide the quality counter by the total amount of measured angles -> and then you decide a threshold for a good quality ratio.
Sorry if I don't understand the issue any better - an actual image could help a lot.
I have been working on image processing problem and I have preprocessed a bunch of images to find the most prominent horizontal lines in those images. Based on this data, I want to classify if the image has a good perspective angle or a bad angle. 
The data points are angles of lines I was able to detect in a sequence of images. Based on the perspective of the image, I know this data sometimes represents a "good-angle" image, and in some other cases, it represents a "bad-angle" image.
I tried np.polyfit, finding slopes of lines, finding derivatives of slopes, and several other methods but unable to find a simple metric that is so obvious by just looking at this data.
These are examples of "Good angles". You can notice they start from positive ones, and later ones are are negative. 
Good angle data
[7.97, 7.99, 9.01, 5.07, 5.01, 14.81, 8.86, -2.11, -0.86, 1.06, 0.86, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.97, 0.92, -0.95, -2.05, -2.2, -2.78, -2.93, -2.8, -2.99, -2.88, -2.94, -2.81, -3.04, -3.07, -3.0]
[3.96, 4.12, 6.04, 6.03, 6.08, 5.99, 6.99, 6.81, 6.81, 6.1, 6.1, 4.06, 3.98, 4.03, 3.92, 3.95, 3.84, 3.94, 4.07, 3.95, 3.87, 2.65, 1.88, 0.0, 0.0, -0.94, -1.06, -1.81, -1.81, -3.95, -4.09, -4.0, -3.93]
[8.75, 10.06, 9.02, 9.96, 9.89, 10.08, 9.99, 10.0, 10.02, 9.95, 4.04, 4.03, 3.93, -1.18, -0.95, -1.12, -1.02, -1.76, -1.92, -2.06, -5.99, -5.83, -6.01, -4.96, -7.84, -7.67]
These are examples of "Bad Angle" images. You can notice they start from negative numbers, and later ones are positive. You can also notice that these are significantly larger numbers than 0.
Bad Angle Data
[-13.92, -14.93, -4.11, -4.04, -2.18, 17.12, 18.01, 16.91, 15.95, 16.75, 14.16, 14.04]
[-14.93, -14.93, -7.92, -4.04, -5.91, -4.98, 16.08, 16.26, 16.24]
[11.81, -9.77, -10.2, -9.96, -10.09, -6.81, 2.13, 3.02, 2.77, 3.01, 2.78, 5.92, 5.96, 5.93, 2.96, 3.06, 1.03, 2.94, 6.2, 5.81, 5.04, 7.13, 5.89, 5.09, 4.89, 3.91, 4.15, 17.99, 6.04, 5.67, 7.24, 16.34, 17.02, 16.92, 15.99, 16.93, 15.76]
As this is based off of data captured from real images, we do have some irregularities in the dataset. I would like to avoid any glitches and use a metric that can classify my arrays as Good angle or bad angles.