This article will talk about the Numpy i0 function. **The year was 1817.** It was a fine evening when a German astronomer by the name of *Friedrich Wilhelm Bessel* was taking a closer look at the movement of planets. Well, what else could he have referred to rather than the most advanced source available around that time â€“ *Keplerâ€™s equations of planetary motion*!* *While he went deep into investigating those equations, it was only a matter of time before he, himself discovered something.

An equation that explains the motion of fluids around a cylinder. Thus was born *Besselâ€™s equation. *In the later years, it will be used to provide mathematical solutions to the problems involving,

- The flow of heat in a solid cylinder
- The flow of electric current in a solid cylinder
- The motion of electromagnetic waves around a wire
- Deformation in an elastic body

When the above differential equation is solved, it results in *Besselâ€™s function*. Saving us from the tedious task of deriving the results through each step, Python helps us to jump straight to the results through an exclusive function â€“ the numpy.i0 (i-zero; not â€˜oâ€™). This is a modified form of *Besselâ€™s function *that is of the first kind and orders zero.

We would be exploring this very function in this article, but before that, there is something that needs to be done. Importing the *numpy *library. The following code can be used to serve the purpose.

```
import numpy as np
```

Thereafter, we shall explore further the *numpy.i0( ) *function through each of the following sections.

**Syntax ofÂ the***Numpy i0Â*function**Using***Numpy i0*on N-Dimensional Arrays**Creating plots for***Numpy i0*results

## Syntax ofÂ the Numpy i0Â function

The syntax of this function is pretty straightforward and is given below. Mathematically *Besselâ€™s function *of the first kind, order zero is denoted by I_{0}.

```
numpy.i0(x)
```

where,

Scalar or N-dimensional array which is the argument for the*x â€“**Besselâ€™s function*

## Using Numpy i0 on N-Dimensional Arrays

Let us construct an array of dimensions 4×1 and subject it to the Numpy *i0( ) *function to see what happens.

```
ar1 = np.array([-1, 0, -7, 9])
np.i0(ar1)
```

Following are the results for the above code.

One can observe that the results are returned in an array of the same dimensions as that of the input array â€˜ar1â€™. Each element in the output array is the result of the *Besselâ€™s function *when its corresponding value from the input is being processed.

The *i0( ) *function does not hold well when complex numbers are given as its inputs. The same is demonstrated below using an input array with complex numbers.

```
ar2= np.array([1.3, -2.5j])
np.i0(ar2)
```

The below error shall appear stating that the *i0( ) *function is not compatible with the complex numbers.

```
TypeError: i0 not supported for complex values
```

**Creating plots for ***numpy.i0( ) *results

*numpy.i0( )*results

A mathematical function does not come a full circle till efforts are not taken to convert them into a plot. We shall do just that in this section. For this, we need to import the *pyplot* from the *matplotlib *library using the following code.

```
import matplotlib.pyplot as plt
```

Once done, let us use the results of the ar1 as the coordinates for the plot.

```
ar1 = np.array([-1, 0, 2, 5])
r1 = np.i0(ar1)
```

Then the results shall be plotted against the x-axis in red using the below code.

```
x = np.arange(0, 28, 7)
plt.plot(x, r1, 'r')
```

**Conclusion**

Now that we have reached the end of this article, hope it has elaborated on how to use theÂ *i0( )Â *function from theÂ *numpyÂ *library. Hereâ€™s another article that details the usage of the* positive( ) *function from theÂ *numpyÂ *library in Python. There are numerous other enjoyable and equally informative articles in AskPython that might be of great help to those who are looking to level up in Python.Â *Audere est faucere*!