diff --git a/useful_scripts/principal_eigenvector.py b/useful_scripts/principal_eigenvector.py
new file mode 100644
index 0000000..913cf62
--- /dev/null
+++ b/useful_scripts/principal_eigenvector.py
@@ -0,0 +1,20 @@
+# Select a principal eigenvector via NumPy
+# to be used as a template (copy & paste) script
+
+import numpy as np
+
+# set A to be your matrix
+A = np.array([[1, 2, 3],
+              [4, 5, 6],
+              [7, 8, 9]])
+
+
+eig_vals, eig_vecs = np.linalg.eig(A)
+idx = np.absolute(eig_vals).argsort()[::-1]  # decreasing order
+sorted_eig_vals = eig_vals[idx]
+sorted_eig_vecs = eig_vecs[:, idx]
+
+principal_eig_vec = sorted_eig_vecs[:, 0]  # eigvec with largest eigval
+
+normalized_pr_eig_vec = np.real(principal_eig_vec / np.sum(principal_eig_vec))
+print(normalized_pr_eig_vec)  # eigvec that sums up to one