{"id":38155,"date":"2022-12-16T18:29:33","date_gmt":"2022-12-16T18:29:33","guid":{"rendered":"https:\/\/www.askpython.com\/?p=38155"},"modified":"2023-02-16T19:56:27","modified_gmt":"2023-02-16T19:56:27","slug":"numpy-float_power","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python-modules\/numpy\/numpy-float_power","title":{"rendered":"NumPy float_power"},"content":{"rendered":"\n

Hello and welcome to this tutorial on Numpy float_power<\/strong>. In this tutorial, we will be learning about the NumPy float_power() <\/strong>method and also seeing a lot of examples regarding the same. So let us begin!

Also Read: NumPy Power \u2013 Raising another number to the power of<\/a><\/em><\/strong><\/p>\n\n\n\n

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What is NumPy float_power?<\/h2>\n\n\n\n

The\u00a0float_power()<\/code>\u00a0method in NumPy is a function that returns an array calculated by raising the elements in one array to the power corresponding to the value in the second array. <\/p>\n\n\n\n

If x1<\/em> and x2<\/em> are two arrays, then float_power(x1, x2)<\/code> computes the output element-wise, i.e. by raising each value in x1<\/em> to the value in x2<\/em> at the corresponding position. As the name of the function itself suggests, its default return type is float. We will see the examples for this function in the upcoming section of this tutorial.<\/p>\n\n\n\n

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Syntax of NumPy float_power<\/h2>\n\n\n
\nnumpy.float_power(x1, x2, \/, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n<\/pre><\/div>\n\n\n
Parameter<\/strong><\/td>Description<\/strong><\/td>Required\/Optional<\/strong><\/td><\/tr>
x1 (array_like)<\/td>Base array.<\/td>Required<\/td><\/tr>
x2 (array_like)<\/td>Power\/Exponent array.<\/td>Required<\/td><\/tr>
out<\/td>An alternative output array in which to place the result. It must have the same shape as the expected output.<\/td>Optional<\/td><\/tr>
where<\/td>Takes an array-like object. At locations where it is True, the out<\/code> array will be set to the ufunc<\/code> result. Elsewhere, the out<\/code> array will retain its original value.<\/td>Optional<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Returns:<\/strong> 
An array containing the result of x<\/em>1 raised to x2<\/em>, element-wise. If x1<\/em> and x2 <\/em>are scalar, then the result is also a scalar.<\/p>\n\n\n\n
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Examples of Numpy float_power()<\/h2>\n\n\n\n
Lets now get right into the examples and understand how the float_power<\/code> method actually works.<\/p>\n\n\n\n
When both inputs are scalars<\/h3>\n\n\n
\nimport numpy as np\n\na = 5\nb = 3\n# using the float_power method \nans = np.float_power(a, b)\n\nprint("a = ", a, "\\nb =", b)\nprint("Result = ", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =  5 \nb = 3\nResult =  125.0\n<\/pre><\/div>\n\n\n
A simple example where the result is calculated as<\/p>\n\n\n
\n5^3 = 125\n<\/pre><\/div>\n\n\n
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When one input is a scalar and the other a 1-dimensional array<\/h3>\n\n\n
\nimport numpy as np\n\na = [0, -2, 4, -6, 8]\nb = 3\n# using the float_power method \nans = np.float_power(a, b)\n\nprint("a = ", a, "\\nb =", b)\nprint("Result = ", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =  [0, -2, 4, -6, 8] \nb = 3\nResult =  [   0.   -8.   64. -216.  512.]\n<\/pre><\/div>\n\n\n
Here, each element in the array a<\/em> is raised to the power b<\/em> and the output is calculated as<\/p>\n\n\n
\nans[0] = a[0] ^ b = 0 ^ 3 = 0\nans[1] = a[1] ^ b = -2 ^ 3 = -8\nans[2] = a[2] ^ b = 4 ^ 3 = 64\nans[3] = a[3] ^ b = -6 ^ 3 = -216\nans[4] = a[4] ^ b = 8 ^ 3 = 512\n<\/pre><\/div>\n\n\n
From the output, we can see that the function also handles negative values.<\/p>\n\n\n\n
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When both the input arrays are 1-dimensional<\/h3>\n\n\n
\nimport numpy as np\n\na = [3, 1, 4, 2.5]\nb = [0, 2, 2.7, 4]\n# using the float_power method \nans = np.float_power(a, b)\n\nprint("a = ", a, "\\nb =", b)\nprint("Result = ", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =  [3, 1, 4, 2.5] \nb = [0, 2, 2.7, 4]\nResult =  [ 1.          1.         42.22425314 39.0625    ]\n<\/pre><\/div>\n\n\n
Here, each element in a<\/em> is raised to the power of the corresponding element in b<\/em> and the output is calculated as:<\/p>\n\n\n
\nans[0] = a[0] ^ b[0] = 3 ^ 0 = 1\nans[1] = a[1] ^ b[1] = 1 ^ 2 = 1\nans[2] = a[2] ^ b[2] = 4 ^ 2.7 = 42.22425314\nans[3] = a[3] ^ b[3] = 2.5 ^ 4 = 39.0625\n<\/pre><\/div>\n\n\n
Observe that even floating point numbers are handled by the float_power()<\/code> method.<\/p>\n\n\n\n
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When both the input arrays are 2-dimensional<\/h3>\n\n\n
\nimport numpy as np\n\na = [[1, 2], [6, 3]]\nb = [[2, 5], [2, 3]]\n# using the float_power method \nans = np.float_power(a, b)\n\nprint("a = ", a, "\\nb =", b)\nprint("Result = \\n", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =  [[1, 2], [6, 3]] \nb = [[2, 5], [2, 3]]\nResult = \n [[ 1. 32.]\n [36. 27.]]\n<\/pre><\/div>\n\n\n
Similar to the above example,<\/p>\n\n\n
\nans[0][0] = a[0][0] ^ b[0][0] = 1 ^ 2 = 1\nans[0][1] = a[0][1] ^ b[0][1] = 2 ^ 5 = 32\n \nans[1][0] = a[1][0] ^ b[1][0] = 6 ^ 2 = 36\nans[1][1] = a[1][1] ^ b[1][1] = 3 ^ 3 = 27\n<\/pre><\/div>\n\n\n
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Conclusion<\/h2>\n\n\n\n
That\u2019s all! In this tutorial, we learned about the\u00a0Numpy float_power\u00a0<\/strong>method and practiced different types of examples using the same. \u00a0<\/p>\n\n\n\n
If you want to learn more about NumPy, feel free to go through our\u00a0NumPy tutorials<\/a>.<\/p>\n\n\n\n
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Reference<\/h2>\n\n\n\n