{"id":20082,"date":"2023-02-13T17:54:20","date_gmt":"2023-02-13T17:54:20","guid":{"rendered":"https:\/\/machinelearningplus.com\/?p=20082"},"modified":"2023-02-16T10:29:59","modified_gmt":"2023-02-16T10:29:59","slug":"numpy-random-randint-in-python","status":"publish","type":"post","link":"https:\/\/machinelearningplus.com\/python\/numpy-random-randint-in-python\/","title":{"rendered":"Numpy.random.randint() in python"},"content":{"rendered":"

numpy.random.randint function is used to get random integers from low to high values. The low value is included while the high value is excluded in the calculations. The output values are taken from the discrete uniform<\/code> distribution of the range values. <\/strong><\/p>\n

random.randint(low, high=None, size=None, dtype=int)<\/h3>\n
    \n
  • Purpose:<\/strong> The numpy random randint function used for creating a numpy array with random integers from low to high interval.<\/p>\n<\/li>\n
  • \n

    Parameteres:<\/strong><\/p>\n

      \n
    • low:<\/strong> int or array-like of ints:<\/em> Lowest integers are drawn from the random values.<\/li>\n
    • high:<\/strong> int or array-like of ints, optional:<\/em> Larger or highest integers are drawn from the random values.<\/li>\n
    • size:<\/strong> int or tuple of ints, optional:<\/em> The shape of the array you want to create.<\/li>\n
    • dtype:<\/strong> dtype, optional<\/em> The type of the output you want.<\/li>\n<\/ul>\n<\/li>\n
    • Returns:<\/strong> out:int or ndarray of ints<\/em> Returns an array with specified shape along with random integer values in it.<\/p>\n<\/li>\n<\/ul>\n
      # Import Packages\nimport numpy as np\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n<\/code><\/pre>\n
      1.Construct 1-D array using randint() function<\/h2>\n
      In this example you will understand how to construct a 1-D array using np.random.randint() function<\/p>\n
      Example 1: Create a 1-D array using randint()<\/h3>\n
      # create a 1-D array\na = np.random.randint(low = 1, size = 5)\n<\/code><\/pre>\n
      # print the newly created array\nprint(a)\n<\/code><\/pre>\n
      [0 0 0 0 0]\n<\/code><\/pre>\n
        \n
      • when low = 1 then randint() takes integer as 0.<\/li>\n
      • The size = 5 which creates a 1-D array<\/li>\n
      • Now a 1-D array is created with integer as 0.<\/li>\n<\/ul>\n
        Example 2: Create a 1-D array using randint()<\/h3>\n
        # create a 1-D array\na = np.random.randint(low = 1, high = 6, size = 8)\n<\/code><\/pre>\n
        # print the newly created array\nprint(a)\n<\/code><\/pre>\n
        [2 1 1 1 3 3 3 1]\n<\/code><\/pre>\n
          \n
        • when low = 1 and high = 6 randint() takes integers between 1 to 5.<\/li>\n
        • The size = 8 which creates a 1-D array<\/li>\n
        • Now a 1-D array is created with integers between 1-5.<\/li>\n<\/ul>\n
          2.Construct 2-D array using randint() function<\/h2>\n
          In this example you will understand how to construct a 2-D array using np.random.randint() function<\/p>\n
          Example 1: Create a 2-D array using randint()<\/h3>\n
          # create a 2-D array\na = np.random.randint(low = 2, size = (2,3))\n<\/code><\/pre>\n
          # print newly created array\nprint(a)\n<\/code><\/pre>\n
          [[0 1 0]\n [0 0 0]]\n<\/code><\/pre>\n
            \n
          • when low = 2 then the randint() takes integers between 0 to 1.<\/li>\n
          • As you set the size as (2,3), which creates a 2-D array with 2 rows and 3 columns <\/li>\n
          • Now the 2-D array is created with integers between 0-1<\/li>\n<\/ul>\n
            Example 2: Create a 2-D array using randint()<\/h3>\n
            # create a 2-D array\na = np.random.randint(low = 0, high = 6, size = (3,4))\n<\/code><\/pre>\n
            # print newly created array\nprint(a)\n<\/code><\/pre>\n
            [[3 2 3 5]\n [1 4 5 2]\n [3 5 1 2]]\n<\/code><\/pre>\n
              \n
            • The low = 0 and high = 6 then the randint() takes integers between 0 to 5.<\/li>\n
            • As you set the size as (3,4), which creates a 2-D array with 3 rows and 4 columns.<\/li>\n
            • Now the 2-D array is created with integers between 0-5<\/li>\n<\/ul>\n
              3.Construct 3-D array using randint() function<\/h2>\n
              In this example you will understand how to construct a 3-D array using np.random.randint() function<\/p>\n
              Example 1: Create a 3-D array using randint()<\/h3>\n
              # create a 3-D array\na = np.random.randint(low = 0, high = 5, size = (2,3,4))\n<\/code><\/pre>\n
              # print newly created array\nprint(a)\n<\/code><\/pre>\n
              [[[0 0 2 4]\n  [3 2 3 4]\n  [1 3 3 1]]\n\n [[3 3 0 3]\n  [0 0 1 1]\n  [2 4 2 3]]]\n<\/code><\/pre>\n
              Note<\/strong> When creating a 3-D array you have to specify both high and low parameters. If you specify only low parameter it returns a error.<\/p>\n
              Try the above scenario to know what kind of error it returns.<\/p>\n
                \n
              • When is set to low = 0 and high = 5 the randint() takes integers between 0 to 4.<\/li>\n
              • As you set the size as (2,3,4) which creates a 3-D array. with two sub arrays, three rows and four columns for each sub array.<\/li>\n
              • Now the 3-D array is created with integers between 0-4<\/li>\n<\/ul>\n
                4.Test your knowledge<\/h2>\n
                Q1:<\/strong> Can you create a 3-D arrays without defining high parameter?
                \nAns:<\/strong> No, you cannot create a 3-D array without defining a high parameter. That returns a value error.<\/p>\n","protected":false},"excerpt":{"rendered":"
                numpy.random.randint function is used to get random integers from low to high values. The low value is included while the high value is excluded in the calculations. The output values are taken from the discrete uniform distribution of the range values. random.randint(low, high=None, size=None, dtype=int) Purpose: The numpy random randint function used for creating a […]<\/p>\n","protected":false},"author":1,"featured_media":20086,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[21],"tags":[26,22],"class_list":["post-20082","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-numpy","tag-python"],"acf":[],"yoast_head":"\nNumpy.random.randint() in python - machinelearningplus<\/title>\n<meta name=\"description\" content=\"Numpy.random.randint is used to get random integers from low to high values. 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