How to create correct scatter plot for PCA?

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How to create correct scatter plot for PCA? - LK91 - Dec-11-2019

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I clustered my data (using kmeans) with high dimensions in Python and after I wanted to build scatter plot with using PCA. But my plot is very strange and I don't understand why? (image in attachment) Also I found that PCA components have negative values. Can someone advise how to build correct scatter plot?
My main steps :
1.normalize data
2.Kmeans clustering
3.create scatter plot

My code:

#Normalize data
scaler = MinMaxScaler()
new2 = pd.DataFrame(scaler.fit_transform(dd))
#Kmeans
kmeans = KMeans(n_clusters=5)
kmeans.fit(new2)
clusters = kmeans.predict(new2)
#PCA and scatter plot
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(new2)
principalDf = pd.DataFrame(data = principalComponents
             , columns = ['principal component 1', 'principal component 2'])
finalDf = pd.concat([principalDf, new2[['Cluster']]], axis = 1)

fig = plt.figure(figsize = (10,10))
ax = fig.add_subplot(1,1,1) 
ax.set_xlabel('Principal Component 1', fontsize = 15)
ax.set_ylabel('Principal Component 2', fontsize = 15)
ax.set_title('2 component PCA', fontsize = 20)
targets = ['0','1','2','3','4']
colors = ['red','blue','black','pink','green']
for target, color in zip(targets,colors):
    indicesToKeep = finalDf['Cluster'] == target
    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']
               , finalDf.loc[indicesToKeep, 'principal component 2']
               , c = color
               , s = 50)
ax.legend(targets)
ax.grid()