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!

## What is NumPy exp?

The `exp`

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 *for each *x *in the input array. Here, * e* is the Euler’s constant and has a value of approximately 2.718281.

It can be said that **np.exp(i)** is approximately equal to **e**i**, where ‘**’ is the power operator. We will see the examples for this function in the upcoming section of this tutorial.

## Syntax of NumPy exp method

```
numpy.exp(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
```

Parameter | Description | Required/Optional |

x | Input array/value. | Required |

out | An alternative output array in which to place the result. By default, a new array is created. | Optional |

where | Takes an array-like object. At locations where it is True, the `out` array will be set to the `ufunc` result. Elsewhere, the `out` array will retain its original value. | Optional |

**kwargs | For other keyword-only arguments | Optional |

**Returns:** An array containing the element-wise exponential of *x*. If *x* is a scalar, then the result is also a scalar.

## Using the Numpy exp() method

Let’s check out how to use the numpy exp method through different examples.

### 1. Exponential of a scalar value using numpy exp()

```
import numpy as np
# positive scalar
a = 6
ans = np.exp(a)
print("a =", a)
print("Exponential =", ans)
```

**Output:**

```
a = 6
Exponential = 403.4287934927351
```

The answer is calculated as e^6 i.e. (2.718281)^6 = 403.4287934927351.

```
import numpy as np
# negative scalar
a = -6
ans = np.exp(a)
print("a =", a)
print("Exponential of the array =", ans)
```

**Output:**

```
a = -6
Exponential of the array = 0.0024787521766663585
```

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.

### 2. Exponential of a 1-dimensional array using numpy exp()

```
import numpy as np
a = [0, 3, -2, 1]
ans = np.exp(a)
print("a =", a)
print("Exponential of the array =", ans)
```

**Output:**

```
a = [0, 3, -2, 1]
Exponential of the array = [ 1. 20.08553692 0.13533528 2.71828183]
```

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.

### 3. Exponential of a 2-dimensional array using numpy exp()

```
import numpy as np
a = [[2, -4, 1],
[0, 1, 5]]
ans = np.exp(a)
print("a =\n", a)
print("Exponential of the array =\n", ans)
```

**Output:**

```
a =
[[2, -4, 1], [0, 1, 5]]
Exponential of the array =
[[7.38905610e+00 1.83156389e-02 2.71828183e+00]
[1.00000000e+00 2.71828183e+00 1.48413159e+02]]
```

Similar to the above example, the resulting array contains an exponential of e for each value in the input array in order.

### 4. Plotting the graph of np.exp() using numpy exp()

Let us now plot the graph of the `np.exp()`

function against some input values using the Matplotlib library in Python.

```
import numpy as np
import matplotlib.pyplot as plt
# input
x = np.linspace(0, 5, 100)
# output
y = np.exp(x)
# changing the size of figure to 8x8
plt.figure(figsize=(8, 8))
display(plt.plot(x, y))
plt.grid()
# title of the graph
plt.title("Graph of e^x")
plt.xlabel("x")
plt.ylabel("e^x")
```

**Output:**

In this example, we have created an evenly spaced array of numbers (*x*) from 0 to 5 having 100 values in total. Then this array is passed to the `np.exp()`

function and stored in the result in y. At last, we plot the graph of **y v/s x** and get the above plot as the result.

## Summary

That’s all! In this tutorial, we learned about the **Numpy exp** 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.