K-Means Implementation in Python
Before answering which is better, here is a quick reminder of the algorithm:

"Choose" the number of clusters K
Initiate your first centroids
For each point, find the closest centroid
according to a distance function D
When all points are attributed to a cluster, calculate the barycenter of the cluster which become its new centroid
Repeat step 3. and step 4. until convergence

As stressed previously, the algorithm depends on various parameters:

The number of clusters
Your initial centroid positions 
A distance function to calculate distance between any point and centroid
A function to calculate the barycenter of each new cluster
A convergence metric
...

If none of the above is familiar to you, and you want to understand the role of each parameter, I would recommend to re-implement it on low-dimensional data-sets. Moreover, the implemented Python libraries might not match your specific requirements - even though they provide good tuning possibilities.
If your point is to use it quickly with a big-picture understanding, you can use existing implementation - scikit-learn would be a good choice.
Is it better to implement my own K-means Algorithm in Python or use the pre-implemented K-mean Algorithm in Python libraries like for example Scikit-Learn?