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

## What is NumPy mod?

The `mod()`

method in NumPy returns the element-wise remainder of the division of two given arrays. The `%`

operator in Python also returns the remainder of division, similar to the `mod()`

function.

We will see the examples demonstrating the use of this function in the upcoming sections of this tutorial.

## Syntax of NumPy mod

```
numpy.mod(x1, x2, out=None)
```

Parameter | Description | Required/Optional |

x1 (array_like) | Dividend array. | Required |

x2 (array_like) | Divisor array. | Required |

out | An alternative output array in which to place the result. It must have the same shape as the expected output. | Optional |

**Returns:** Returns the element-wise remainder of the division. If both *x1* and *x2* are scalars, then the result is also a scalar value.

## Examples

Letâ€™s now get right into using the numpy.mod method so we can understand the outputs.

### When both the elements are scalar

```
import numpy as np
dividend = 15
divisor = 7
ans = np.mod(dividend, divisor)
print("Dividend =", dividend, "\nDivisor =", divisor)
print(dividend, "%", divisor, "=", ans)
```

**Output:**

```
Dividend = 15
Divisor = 7
15 % 7 = 1
```

A simple case where both the elements are scalar. 7*2=14 and 7*3=21, so 15 is not completely divisible by 7 and the remainder comes out to be 1 here.

### Modulus of a scalar and an array using numpy.mod()

```
import numpy as np
dividend = [13, 8, 16]
divisor = 7
ans = np.mod(dividend, divisor)
print("Dividend =", dividend, "\nDivisor =", divisor)
print(dividend, "%", divisor, "=", ans)
```

**Output:**

```
Dividend = [13, 8, 16]
Divisor = 7
[13, 8, 16] % 7 = [6 1 2]
```

In this case, all the elements in the **dividend** array are one-by-one divided by the divisor and the remainder for each of these divisions is stored in the result array.

The output is computed as:

13%7 = 6

8%7 = 1

16%7 = 2

We can also reverse the elements as follows:

```
import numpy as np
dividend = 7
divisor = [7, 5, 3]
ans = np.mod(dividend, divisor)
print("Dividend =", dividend, "\nDivisor =", divisor)
print(dividend, "%", divisor, "=", ans)
```

**Output:**

```
Dividend = 7
Divisor = [7, 5, 3]
7 % [7, 5, 3] = [0 2 1]
```

Here, each element in the **divisor** array divides the **dividend** i.e. 7 and the remainder of it is stored in the output array. Hence, the output is computed as:

7%7 = 0

7%5 = 2

7%3 = 1

### Modulus when both the elements are 1-dimensional arrays

```
import numpy as np
dividend = [30, 58, 35]
divisor = [5, 9, 4]
ans = np.mod(dividend, divisor)
print("Dividend =", dividend, "\nDivisor =", divisor)
print(dividend, "%", divisor, "=", ans)
```

**Output:**

```
Dividend = [30, 58, 35]
Divisor = [5, 9, 4]
[30, 58, 35] % [5, 9, 4] = [0 4 3]
```

Here, the elements at the same places in both arrays undergo the division operation and the remainder is calculated. That is, dividend[0] is divided by divisor[0] and so on. This is nothing but element-wise division.

The output is computed as:

```
dividend[0] % divisor[0] = 30%5 = 0
dividend[1] % divisor[1] = 58%9 = 4
dividend[2] % divisor[2] = 35%4 = 3
```

### When both the elements are 2-dimensional arrays

```
import numpy as np
dividend = [[16, 15], [24, 23]]
divisor = [[4, 7], [10, 9]]
ans = np.mod(dividend, divisor)
print("Dividend =", dividend, "\nDivisor =", divisor)
print(dividend, "%", divisor, "=\n", ans)
```

**Output:**

```
Dividend = [[16, 15], [24, 23]]
Divisor = [[4, 7], [10, 9]]
[[16, 15], [24, 23]] % [[4, 7], [10, 9]] =
[[0 1]
[4 5]]
```

Same as the above example of 1-dimensional arrays, here as well the element-wise division takes place and the remainder is calculated as:

Row 1:

```
dividend[0][0] % divisor[0][0] = 16%4 = 0
dividend[0][1] % divisor[0][1] = 15%7 = 1
```

Row 2:

```
dividend[1][0] % divisor[1][0] = 24%10 = 4
dividend[1][1] % divisor[1][1] = 23%9 = 5
```

## Conclusion

Thatâ€™s all! In this tutorial, we learned about the **Numpy mod** 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.