Python 3.5 Trying to plot PCA with sklearn and matplotlib - pandas

Question

Using the following code generates the error: TypeError: float() argument must be a string or a number, not 'Pred':
I am struggling to figure out what is causing this error to be thrown.
self.features is a list composed of three floats ex. [1.1, 1.2, 1.3]
an example of self.features:
[array([-1.67191985, 0.1 , 9.69981494]), array([-0.68486623, 0.05 , 9.99085024]), array([ -1.36 , 0.1 , 10.44720459]), array([-2.46918915, 0. , 3.5483372 ]), array([-0.835 , 0.1 , 4.02740479])]
This is the method where the error is being thrown.
def pca(self):
pca = PCA(n_components=2)
x_np = np.asarray(self.features)
pca.fit(x_np)
X_reduced = pca.transform(x_np)
plt.figure(figsize=(10, 8))
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap='RdBu')
plt.xlabel('First component')
plt.ylabel('Second component')
The full trace back is:
Traceback (most recent call last):
File "/Users/user/PycharmProjects/Post-Translational-Modification-
Prediction/pred.py", line 244, in <module>
y.generate_pca()
File "/Users/user/PycharmProjects/Post-Translational-Modification-
Prediction/pred.py", line 222, in generate_pca
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap='RdBu')
File "/usr/local/lib/python3.5/site-packages/matplotlib/pyplot.py",
line 3435, in scatter
edgecolors=edgecolors, data=data, **kwargs)
File "/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py",
line 1892, in inner
return func(ax, *args, **kwargs)
File "/usr/local/lib/python3.5/site-packages/matplotlib/axes/_axes.py", line 3976, in scatter
c_array = np.asanyarray(c, dtype=float)
File "/usr/local/lib/python3.5/site-packages/numpy/core/numeric.py", line 583, in asanyarray
return array(a, dtype, copy=False, order=order, subok=True)
TypeError: float() argument must be a string or a number, not 'Pred'

Answer 1

The suggested fix by #WhoIsJack is to add np.arange(len(self.features))
The functional code for those who run into similar issues is:
def generate_pca(self):
y= np.arange(len(self.features))
pca = PCA(n_components=2)
x_np = np.asarray(self.features)
pca.fit(x_np)
X_reduced = pca.transform(x_np)
plt.figure(figsize=(10, 8))
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, cmap='RdBu')
plt.xlabel('First component')
plt.ylabel('Second component')
plt.show()