{"id":37466,"date":"2022-11-28T07:22:52","date_gmt":"2022-11-28T07:22:52","guid":{"rendered":"https:\/\/www.askpython.com\/?p=37466"},"modified":"2023-02-16T19:56:31","modified_gmt":"2023-02-16T19:56:31","slug":"numpy-exp","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python-modules\/numpy\/numpy-exp","title":{"rendered":"NumPy exp – A Complete Guide"},"content":{"rendered":"\n

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

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

The exp<\/code> method in NumPy is a function that returns the exponential of all the elements of the input array. This means that it calculates e^x <\/em>for each x <\/em>in the input array. Here, e<\/strong><\/em> is the Euler’s constant and has a value of approximately 2.718281. <\/p>\n\n\n\n

It can be said that np.exp(i)<\/strong> is approximately equal to e**i<\/strong>, where ‘**’ is the power operator. 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 exp method<\/h2>\n\n\n
\nnumpy.exp(x, \/, 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<\/td><\/tr>
x<\/td>Input array\/value.<\/td>Required<\/td><\/tr>
out<\/td>An alternative output array in which to place the result. By default, a new array is created.<\/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>
**kwargs<\/td>For other keyword-only arguments<\/td>Optional<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Returns:<\/strong> An array containing the element-wise exponential of x<\/em>. If x<\/em> is a scalar, then the result is also a scalar.<\/p>\n\n\n\n
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Using the Numpy exp() method <\/h2>\n\n\n\n
Let’s check out how to use the numpy exp method through different examples. <\/p>\n\n\n\n
1. Exponential of a scalar value using numpy exp()<\/h3>\n\n\n
\nimport numpy as np\n\n# positive scalar\na = 6\nans = np.exp(a)\nprint("a =", a)\nprint("Exponential =", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na = 6\nExponential = 403.4287934927351\n<\/pre><\/div>\n\n\n
The answer is calculated as e^6 i.e. (2.718281)^6 = 403.4287934927351.<\/p>\n\n\n
\nimport numpy as np\n\n# negative scalar\na = -6\nans = np.exp(a)\nprint("a =", a)\nprint("Exponential of the array =", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na = -6\nExponential of the array = 0.0024787521766663585\n<\/pre><\/div>\n\n\n
In this case, since a is a negative number, the exponential of a is (e)^(-6) i.e. 1\/(e)^6 = 1\/(2.718281)^6 = 0.0024787521766663585.<\/p>\n\n\n\n
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2. Exponential of a 1-dimensional array using numpy exp()<\/h3>\n\n\n
\nimport numpy as np\n\na = [0, 3, -2, 1]\nans = np.exp(a)\nprint("a =", a)\nprint("Exponential of the array =", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na = [0, 3, -2, 1]\nExponential of the array = [ 1.         20.08553692  0.13533528  2.71828183]\n<\/pre><\/div>\n\n\n
Here, the result array contains the exponential of e for each value in the input array. That is, the and contains the values, e^0, e^3, e^-2 and e^1 in order of the input values.<\/p>\n\n\n\n
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3. Exponential of a 2-dimensional array using numpy exp()<\/h3>\n\n\n
\nimport numpy as np\n\na = [[2, -4, 1], \n     [0, 1, 5]]\nans = np.exp(a)\nprint("a =\\n", a)\nprint("Exponential of the array =\\n", ans)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =\n [[2, -4, 1], [0, 1, 5]]\nExponential of the array =\n [[7.38905610e+00 1.83156389e-02 2.71828183e+00]\n [1.00000000e+00 2.71828183e+00 1.48413159e+02]]\n<\/pre><\/div>\n\n\n
Similar to the above example, the resulting array contains an exponential of e for each value in the input array in order.<\/p>\n\n\n\n
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4. Plotting the graph of np.exp() using numpy exp()<\/h3>\n\n\n\n
Let us now plot the graph of the np.exp()<\/code> function against some input values using the Matplotlib <\/a>library in Python.<\/p>\n\n\n
\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# input\nx = np.linspace(0, 5, 100)\n# output\ny = np.exp(x)\n\n\n# changing the size of figure to 8x8\nplt.figure(figsize=(8, 8))\ndisplay(plt.plot(x, y))\nplt.grid()\n# title of the graph\nplt.title("Graph of e^x")\nplt.xlabel("x")\nplt.ylabel("e^x")\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n\n
\"Graph
Graph Of Exponential<\/figcaption><\/figure>\n\n\n\n
In this example, we have created an evenly spaced array of numbers (x<\/em>) from 0 to 5 having 100 values in total. Then this array is passed to the np.exp()<\/code> function and stored in the result in y. At last, we plot the graph of y v\/s x<\/strong> and get the above plot as the result.<\/p>\n\n\n\n
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Summary<\/h2>\n\n\n\n
That\u2019s all! In this tutorial, we learned about the Numpy exp<\/strong> method and practiced different types of examples using the same. If you want to learn more about NumPy, feel free to go through our NumPy tutorials<\/a>.<\/p>\n\n\n\n
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Reference<\/h2>\n\n\n\n