{"id":14314,"date":"2021-03-31T16:22:02","date_gmt":"2021-03-31T16:22:02","guid":{"rendered":"https:\/\/www.askpython.com\/?p=14314"},"modified":"2021-03-31T16:22:04","modified_gmt":"2021-03-31T16:22:04","slug":"random-sampling-in-numpy","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python\/random-sampling-in-numpy","title":{"rendered":"4 Ways to Perform Random Sampling in NumPy"},"content":{"rendered":"\n

Hello, readers! In this article, we will be focusing on 4 Easy Ways to Perform Random Sampling<\/strong> in Python NumPy<\/a>.<\/p>\n\n\n\n

So, let us get started! \ud83d\ude42<\/p>\n\n\n\n

Random Sampling, to give an overview, is actually selecting random values from the defined type of data and present them to be in further use.<\/p>\n\n\n\n

In the course of this topic, we will be having a look at the below functions–<\/p>\n\n\n\n

  1. NumPy random_sample() method<\/strong><\/li>
  2. NumPy ranf() method<\/strong><\/li>
  3. NumPy random_integers() method<\/strong><\/li>
  4. NumPy randint() method<\/strong><\/li><\/ol>\n\n\n\n
    \n\n\n\n

    <\/span><\/span>1. NumPy random_sample() method for Random Sampling<\/h2>\n\n\n\n

    With random_sample() method<\/strong>, we can sample the data values and choose random data fat ease. It selects random samples between [0.0 – 1.0] only. We can build a single sample as well as an entire array based on random values.<\/p>\n\n\n\n

    Have a look at the below syntax!<\/p>\n\n\n

    \nrandom.random_sample()\n<\/pre><\/div>\n\n\n
    Example:<\/strong><\/p>\n\n\n\n
    In the below example, at first, we have performed random sampling and generated a single random value. Further, we have created a 2-dimensional array with random values by passing size as a parameter to the random_sample() function.<\/p>\n\n\n\n
    Note it that the random values would range between 0.0 to 1.0 only. Plus, random_sample() function generates random values of float type.<\/p>\n\n\n
    \nimport numpy as np\n\nran_val = np.random.random_sample()\nprint ("Random value : ", ran_val)\n\nran_arr = np.random.random_sample(size =(2, 4))\nprint ("Array filled with random float values: ", ran_arr) \n<\/pre><\/div>\n\n\n
    Output:<\/strong><\/p>\n\n\n
    \nRandom value :  0.3733413809567606\nArray filled with random float values:  [[0.45421908 0.34993556 0.79641287 0.56985183]\n                                        [0.88683577 0.91995939 0.16168328 0.35923753]]\n<\/pre><\/div>\n\n\n
    \n\n\n\n
    <\/span><\/span>2. The random_integers() function<\/h2>\n\n\n\n
    With random_integers() function<\/strong>, we can generate random values or even a multi-dimensional array of random value of type integer. That it, it generates random values of type Integer. Further, it gives us the liberty to choose the range of integer values from which the random numbers would be selected.<\/p>\n\n\n\n
    Syntax:<\/strong><\/p>\n\n\n
    \nrandom_integers(low, high, size)\n<\/pre><\/div>\n\n\n