Wrong Graph Plot using K-Means in Python
Well, there are some issues:


Dimension vs K: Before talking about visualization I would like to address some clustering concept. Your data is in 7 dimensions but it does not mean that you have 7 clusters! Be careful here. For instance I have two features of people let's say salary and number of years they have working experience. Here I have two features but does it mean that there necessary two categories inside the data? sure not!
Visualization: Your data is in 7 dimension which is not visualizable. So you decided to reduce this to two which is a correct approach but you did a wrong thing for this correct approach. You can not take the first two features to visualize 7 dimensions, you need to REDUCE it to two features using Dimensionality Reduction algorithms like PCA, NMF, etc. What you did is actually IGNORING 5 dimensions of the points which are extremely informative for placing them in a 7-dimensional space.


Solution

Everything is right. Just add a PCA to your code like this:

From sklearn.decomposition import PCA
Model = PCA(n_components=2)
X_new = Model.fit_transform(X)
... Use X_new instead of X for K-means procedure


Please note that I wrote this relying on my memory so better to check the documentation if I had a typo or smth. In case you have more question you can comment here.

Good Luck!