{"id":4342,"date":"2022-08-10T01:08:21","date_gmt":"2022-08-10T01:08:21","guid":{"rendered":"https:\/\/www.pythontutorial.net\/?page_id=4342"},"modified":"2022-08-10T04:11:38","modified_gmt":"2022-08-10T04:11:38","slug":"numpy-prod","status":"publish","type":"page","link":"https:\/\/www.pythontutorial.net\/python-numpy\/numpy-prod\/","title":{"rendered":"NumPy prod()"},"content":{"rendered":"\n

Summary<\/strong>: in this tutorial, you’ll learn how to use the numpy prod()<\/code> function to calculate the product of numbers in an array.<\/p>\n\n\n\n

Introduction to to the NumPy prod() function #<\/a><\/h2>\n\n\n\n

Suppose you have three numbers n, m, and k. The product of the three numbers is nxmxk. For example, the product of 2, 3, and 4 is 2x3x4 = 24. <\/p>\n\n\n\n

To calcualte the products of numbers in an aray, you use the numpy prod()<\/code> function:<\/p>\n\n\n

numpy.prod(a, axis=None<\/span>, dtype=None<\/span>, out=None<\/span>, keepdims=<no value>, initial=<no value>, where=<no value>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
NumPy prod() function examples #<\/a><\/h2>\n\n\n\n
Let’s take some example of using the numpy prod()<\/code> function.<\/p>\n\n\n\n
1) Using the numpy prod() function with 1-D array example #<\/a><\/h3>\n\n\n\n
The following example uses the prod()<\/code> to calculate the products of numbers in a 1-D array:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.arange(1<\/span>, 5<\/span>)\nresult = np.prod(a)\n\nprint(a)\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
[1<\/span> 2<\/span> 3<\/span> 4<\/span>]\nresult=24<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
How it works.<\/p>\n\n\n\n
First, create an array that has 4 numbers from 1 to 4 using the arange()<\/code> function.<\/p>\n\n\n
a = np.arange(1<\/span>, 5<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Second, calculate the products of all the numbers in the array a:<\/p>\n\n\n
result = np.prod(a)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Third, display the numbers of the array a and their product:<\/p>\n\n\n
print(a)\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Note that you can pass an array like object to the prod()<\/code> function e.g., a list. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.prod([1<\/span>, 2<\/span>, 3<\/span>, 4<\/span>, 5<\/span>])\n\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
result=120<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
2) Using the numpy prod() function with multidimensional array examples #<\/a><\/h3>\n\n\n\n
The following example uses the prod()<\/code> to calcualte products of all numbers in a 2-D array:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.prod([\n    [1<\/span>, 2<\/span>],\n    [3<\/span>, 4<\/span>]\n])\n\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
result=24<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
To calcualte product of numbers of an axis, you can specify the axis argument. For example, the following uses the prod()<\/code> to calculate the product of numbers on axis 0:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.prod([\n    [1<\/span>, 2<\/span>],\n    [3<\/span>, 4<\/span>]\n], axis=0<\/span>)\n\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
result=[3<\/span> 8<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
\"\"<\/figure>\n\n\n\n
Similarly, you can calculate the product of numbers on axis 1:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.prod([\n    [1<\/span>, 2<\/span>],\n    [3<\/span>, 4<\/span>]\n], axis=1<\/span>)\n\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
result=[ 2<\/span> 12<\/span>]<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
\"\"<\/figure>\n\n\n\n
3) Selecting numbers to include in the product #<\/a><\/h3>\n\n\n\n
To select specific number to include in the product, you use the where argument. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\na = np.array([np.nan, 3<\/span>, 4<\/span>])\nresult = np.prod(a, where=[False<\/span>, True<\/span>, True<\/span>])\nprint(result)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
12.0<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
In this example, the array contains three elements np.nan, 3, and 4. <\/p>\n\n\n\n
The where argument uses a boolean list to specify which element in the array a should be included in the product.<\/p>\n\n\n\n
If the value of the where<\/code> list is True<\/code>, the corresponding element of the input array will be included in the product.<\/p>\n\n\n\n
4) Special cases #<\/a><\/h3>\n\n\n\n
Note that if you pass an array of integers to the prod()<\/code><\/code> function that causes an overflow, the prod()<\/code><\/code> won’t raise the error. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.prod(np.arange(1<\/span>, 100<\/span>))\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
result=0<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
The prod()<\/code> function returns 1 if the array is empty. For example:<\/p>\n\n\n
import<\/span> numpy as<\/span> np\n\nresult = np.prod(np.array([]))\n\nprint(f'result={result}<\/span>'<\/span>)<\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Output:<\/p>\n\n\n
1.0<\/span><\/code><\/span>Code language:<\/span> Python<\/span> (<\/span>python<\/span>)<\/span><\/small><\/pre>\n\n\n
Summary #<\/a><\/h2>\n\n\n\n
  • Use numpy prod()<\/code> function to calculate the product of numbers in an array.<\/li><\/ul>\n
    \n\t
    \n\t\t
    Was this tutorial helpful ?<\/div>\n\t\t
    \n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t Yes <\/span>\n\t\t\t<\/button>\n\n\t\t\t\n\t\t\t\t\n\t\t\t\t\t<\/path>\n\t\t\t\t<\/svg>\n\t\t\t\t No <\/span>\n\t\t\t<\/button>\n\t\t<\/div>\n\t<\/header>\n\n\t
    \n\t\t
    \n\t\t\t
    <\/div>\n\t\t\t