Menghilangan Attribute Species Pada Iris Dengan Python

  Umum
from sklearn import tree
import pandas as pd
import numpy as np

iris = pd.read_csv("/home/mfahri/Documents/data mining/data/iris.csv")

sl = np.array(iris["sepal_length xx"])
sw = np.array(iris["sepal_width"])
pl = np.array(iris["petal_length"])
pw = np.array(iris["petal_width"])
species = np.array(iris["species"])

semua = np.array(iris)

X = np.delete(semua,np.s_[4],1)


print(X)

Output

/home/mfahri/python/venv/bin/python /home/mfahri/python/sl.py
[[5.1 3.5 1.4 0.2]
 [4.9 3.0 1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.0 3.6 1.4 0.2]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.0 3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.0 1.4 0.1]
 [4.3 3.0 1.1 0.1]
 [5.8 4.0 1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.0 0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.0 3.0 1.6 0.2]
 [5.0 3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.0 3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.1 1.5 0.1]
 [4.4 3.0 1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.0 3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.0 3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.0 1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.0 3.3 1.4 0.2]
 [7.0 3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.0 1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1.0]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.0 2.0 3.5 1.0]
 [5.9 3.0 4.2 1.5]
 [6.0 2.2 4.0 1.0]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.0 4.5 1.5]
 [5.8 2.7 4.1 1.0]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.0 1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.0 4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.0 5.0 1.7]
 [6.0 2.9 4.5 1.5]
 [5.7 2.6 3.5 1.0]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1.0]
 [5.8 2.7 3.9 1.2]
 [6.0 2.7 5.1 1.6]
 [5.4 3.0 4.5 1.5]
 [6.0 3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.0 4.1 1.3]
 [5.5 2.5 4.0 1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.0 4.6 1.4]
 [5.8 2.6 4.0 1.2]
 [5.0 2.3 3.3 1.0]
 [5.6 2.7 4.2 1.3]
 [5.7 3.0 4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.0 1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.0 2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.0 5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.0 5.8 2.2]
 [7.6 3.0 6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2.0]
 [6.4 2.7 5.3 1.9]
 [6.8 3.0 5.5 2.1]
 [5.7 2.5 5.0 2.0]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.0 5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.0 2.2 5.0 1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2.0]
 [7.7 2.8 6.7 2.0]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.0 1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.0 4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.0 5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2.0]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.0 6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.0 3.0 4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.0 5.2 2.3]
 [6.3 2.5 5.0 1.9]
 [6.5 3.0 5.2 2.0]
 [6.2 3.4 5.4 2.3]
 [5.9 3.0 5.1 1.8]]

Process finished with exit code 0

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