Reduction functions in NumPy aggregate the elements of an array into a single value. These functions are useful for tasks such as calculating statistics, finding minimum or maximum values, and computing sums or products.<\/p>\n\n\n\n
Sum:<\/strong> Calculates the sum of all elements in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Product:<\/strong> Calculates the product of all elements in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Minimum:<\/strong> Finds the minimum value in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Maximum:<\/strong> Finds the maximum value in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Mean:<\/strong> Calculates the average value of the elements in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Median:<\/strong> Finds the median value of the elements in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Standard Deviation:<\/strong> Calculates the standard deviation of the elements in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n Variance:<\/strong> Calculates the variance of the elements in an array.<\/p>\n\n\n\n Python<\/p>\n\n\n\n For multidimensional arrays, reduction functions can be applied along specific axes. This allows you to calculate statistics for rows, columns, or other dimensions.<\/p>\n\n\n\n Python<\/p>\n\n\n\n You can also create custom reduction functions using NumPy’s Python<\/p>\n\n\n\n By effectively using reduction functions, you can efficiently analyze and summarize data in NumPy, gaining valuable insights into your datasets.<\/p>\n","protected":false},"excerpt":{"rendered":" Reduction functions in NumPy aggregate the elements of an array into a single value. These functions are useful for tasks such as calculating statistics, finding minimum or maximum values, and computing sums or products. Common Reduction Functions Sum: Calculates the sum of all elements in an array. Python Product: Calculates the product of all elements…<\/p>\n","protected":false},"author":16,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-136401","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nimport numpy as np\n\narr = np.array([1, 2, 3, 4, 5])\nresult = np.sum(arr)<\/code><\/pre>\n\n\n\nresult = np.prod(arr)<\/code><\/pre>\n\n\n\nresult = np.min(arr)<\/code><\/pre>\n\n\n\nresult = np.max(arr)<\/code><\/pre>\n\n\n\nresult = np.mean(arr)<\/code><\/pre>\n\n\n\nresult = np.median(arr)<\/code><\/pre>\n\n\n\nresult = np.std(arr)<\/code><\/pre>\n\n\n\nresult = np.var(arr)<\/code><\/pre>\n\n\n\nAxis-Wise Reductions<\/strong><\/h3>\n\n\n\n
arr2d = np.array([[1, 2, 3], [4, 5, 6]])\n\n# Sum along the rows\nrow_sum = np.sum(arr2d, axis=0)\n\n# Sum along the columns\ncol_sum = np.sum(arr2d, axis=1)\n<\/code><\/pre>\n\n\n\nCustom Reduction Functions<\/strong><\/h3>\n\n\n\n
ufunc<\/code> objects. These functions can be applied element-wise to arrays and combined with other reduction functions.<\/p>\n\n\n\ndef my_func(x):\n return x**2\n\nresult = np.sum(my_func(arr))\n<\/code><\/pre>\n\n\n\n