import pandas as pd
import matplotlib.pyplot as plt
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

load dataset into Pandas DataFrame

df = pd.read_csv(url, names=['sepal length','sepal width','petal length','petal width','target'])

from sklearn.preprocessing import StandardScaler

features = ['sepal length', 'sepal width', 'petal length', 'petal width']

Separating out the features

x = df.loc[:, features].values

Separating out the target

y = df.loc[:,['target']].values

Standardizing the features

x = StandardScaler().fit_transform(x)

from sklearn.preprocessing import StandardScaler
features = ['sepal length', 'sepal width', 'petal length', 'petal width']

Separating out the features

x = df.loc[:, features].values

Separating out the target

y = df.loc[:,['target']].values

Standardizing the features

x = StandardScaler().fit_transform(x)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
, columns = ['principal component 1', 'principal component 2'])

finalDf = pd.concat([principalDf, df[['target']]], axis = 1)

fig = plt.figure(figsize = (8,8))
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 = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
colors = ['r', 'g', 'b']
for target, color in zip(targets,colors):
indicesToKeep = finalDf['target'] == target
ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']
, finalDf.loc[indicesToKeep, 'principal component 2']
, c = color
, s = 50)
ax.legend(targets)
ax.grid()