this is my code:

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

from sklearn.datasets import load_breast_cancer

cancer=load_breast_cancer()

cancer.keys()

df=pd.DataFrame(cancer['data'],columns=cancer['feature_names'])

df.head(5)

from sklearn.preprocessing import StandardScaler

scaler=StandardScaler()

scaler.fit(df)

scaled_data=scaler.transform(df)

scaled_data

from sklearn.decomposition import PCA

pca=PCA(n_components=2)

pca.fit(scaled_data)

x_pca=pca.transform(scaled_data)

scaled_data.shape

x_pca.shape

print (scaled_data)

x_pca

from sklearn.preprocessing import StandardScaler

scaler=StandardScaler()#instantiate

scaler.fit(cancer.data) # compute the mean and standard which will be used in the next command

X_scaled=scaler.transform(cancer.data)#

from sklearn.decomposition import PCA

pca=PCA(n_components=2)

pca.fit(X_scaled)

X_pca=pca.transform(X_scaled)

ex_variance=np.var(X_pca,axis=0)

ex_variance_ratio = ex_variance/np.sum(ex_variance)

print (ex_variance_ratio)

Xax=X_pca[:,0]

Yax=X_pca[:,1]

labels=cancer.target

cdict={0:'blue',1:'orange'}

labl={0:'Malignant',1:'Benign'}

marker={0:'^',1:'o'}

alpha={0:.3, 1:.5}

fig,ax=plt.subplots(figsize=(7,5))

fig.patch.set_facecolor('white')

for l in np.unique(labels):

ix=np.where(labels==l)

ax.scatter(Xax[ix],Yax[ix],c=cdict[l],s=40,

label=labl[l],marker=marker[l],alpha=alpha[l])

plt.xlabel("First Principal Component",fontsize=14)

plt.ylabel("Second Principal Component",fontsize=14)

plt.legend()

plt.show()

from mpl_toolkits.mplot3d import Axes3D

from sklearn.cluster import KMeans

from sklearn.datasets import load_iris

np.random.seed(5)

centers = [[1, 1], [-1, -1], [1, -1]]

iris = load_iris()

X = iris.data

y = iris.target

estimators = {'k_means_iris_3': KMeans(n_clusters=3),

'k_means_iris_8': KMeans(n_clusters=8),

'k_means_iris_bad_init': KMeans(n_clusters=3, n_init=1,

init='random')}

fignum = 1

for name, est in estimators.items():

#fig = plt.figure(fignum, figsize=(4, 3))

fig = plt.figure(fignum)

plt.clf()

ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

plt.cla()

est.fit(X)

labels = est.labels_

ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))

Can someone please help me make these match as best they can? I have attached what it should look like