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def doPCA(data):
  from sklearn.decomposition import PCA
  pca = PCA(n_components=2)
  pca.fit(data)
  return pca
  
pca = doPCA(data)

# Percentage of variance explained by each of the selected components
print(pca.explained_variance_ratio)

#Principal axes in feature space, representing the directions of maximum variance in the data
first_pc = pca.components_[0]
second_pc = pca.components_[1]

#Apply dimensionality reduction
transformed_data = pca.transform(data)

# plot

for ii, jj in zip(transformed_data, data):
  plt.scatter(first_pc[0]*ii[0], first_pc[1]ii[0],color='r')
  plt.scatter(second_pc[0]*ii[1], second_pc[1]ii[1],color='c')
  plt.scatter(jj[0], jj[1], color='b')
  
plt.show()