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Python | Create Test DataSets using Sklearn
  • Difficulty Level : Medium
  • Last Updated : 26 May, 2021

Python’s Sklearn library provides a great sample dataset generator which will help you to create your own custom dataset. It’s fast and very easy to use. Following are the types of samples it provides.
For all the above methods you need to import sklearn.datasets.samples_generator
 

Python3




# importing libraries
from sklearn.datasets import make_blobs
 
# matplotlib for ploting
from matplotlib import pyplot as plt
from matplotlib import style

sklearn.datasets.make_blobs

Python3




# Creating Test DataSets using sklearn.datasets.make_blobs
from sklearn.datasets import make_blobs
from matplotlib import pyplot as plt
from matplotlib import style
 
style.use("fivethirtyeight")
 
X, y = make_blobs(n_samples = 100, centers = 3,
               cluster_std = 1, n_features = 2)
 
plt.scatter(X[:, 0], X[:, 1], s = 40, color = 'g')
plt.xlabel("X")
plt.ylabel("Y")
 
plt.show()
plt.clf()

Output: 
 

make_blobs with 3 centers

 



sklearn.datasets.make_moon

 

Python3




# Creating Test DataSets using sklearn.datasets.make_moon
from sklearn.datasets import make_moon
from matplotlib import pyplot as plt
from matplotlib import style
 
X, y = make_moons(n_samples = 1000, noise = 0.1)
plt.scatter(X[:, 0], X[:, 1], s = 40, color ='g')
plt.xlabel("X")
plt.ylabel("Y")
 
plt.show()
plt.clf()

Output: 
 

make_moons with 1000 data points

 

sklearn.datasets.make_circle

 

Python3




# Creating Test DataSets using sklearn.datasets.make_circles
from sklearn.datasets import make_circles
from matplotlib import pyplot as plt
from matplotlib import style
 
style.use("fivethirtyeight")
 
X, y = make_circles(n_samples = 100, noise = 0.02)
plt.scatter(X[:, 0], X[:, 1], s = 40, color ='g')
plt.xlabel("X")
plt.ylabel("Y")
 
plt.show()
plt.clf()

Output: 
 

make _circle with 100 data points

 

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