Python | Create Test DataSets using Sklearn

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_moons
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