# NumPy exp – A Complete Guide 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])
```

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.