Last Updated : 15 Jul, 2020
With the help of choice() method, we can get the random samples of one dimensional array and return the random samples of numpy array.
Syntax : numpy.random.choice(a, size=None, replace=True, p=None)
Parameters:
1) a - 1-D array of numpy having random samples.
2) size - Output shape of random samples of numpy array.
3) replace - Whether the sample is with or without replacement.
4) p - The probability attach with every samples in a.
Output : Return the numpy array of random samples.
Example #1 :
In this example we can see that by using choice() method, we are able to get the random samples of numpy array, it can generate uniform or non-uniform samples by using this method.
Python3
# import choice
import numpy as np
import matplotlib.pyplot as plt
# Using choice() method
gfg = np.random.choice(13, 5000)
count, bins, ignored = plt.hist(gfg, 25, density = True)
plt.show()
Output :
Example #2 :
Python3
# import choice
import numpy as np
import matplotlib.pyplot as plt
# Using choice() method
gfg = np.random.choice(5, 1000, p =[0.2, 0.1, 0.3, 0.4, 0])
count, bins, ignored = plt.hist(gfg, 14, density = True)
plt.show()
Output :
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4