## NumPy subtract()

One of the essential operations in NumPy is subtraction, which allows you to subtract elements from one array to another or a constant value from an array. In this blog post, we'll explore NumPy's subtract function through step-by-step examples.

## Getting Started with NumPy

Before we dive into the examples, you need to ensure that you have NumPy installed.

```
pip install numpy
```

```
import numpy as np
```

Now, let's start exploring NumPy's subtract function with step-by-step examples.

## Example 1: Subtracting Two Arrays

The most straightforward use of the np.subtract function is to subtract one array from another element-wise. Here's how you can do it:

```
import numpy as np
# Create two NumPy arrays
array1 = np.array([10, 21, 30, 40])
array2 = np.array([5, 16, 17, 18])
# Subtract array2 from array1
result = np.subtract(array1, array2)
print(result)
```

In this example, NumPy subtracts the corresponding elements of array1 from array2, resulting in a new array result. The output will be [ 5 5 13 22]

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## Example 2: Subtracting a Constant Value

You can also use the np.subtract function to subtract a constant value from all elements of an array. Here's an example:

```
import numpy as np
array = np.array([20, 40, 30, 40])
# Subtract a constant value (e.g., 5) from all elements
result = np.subtract(array, 5)
print(result)
```

In this example, we subtract 5 from all elements of the array, resulting in a new array result with values [15 35 25 35]

## Example 3: In-Place Subtraction

NumPy also allows you to perform in-place subtraction, which means modifying an existing array with the subtraction results. You can do this using the '-=' operator:

```
import numpy as np
# Create a NumPy array
array = np.array([20, 40, 30, 40])
# In-place subtraction of 5 from all elements
array -= 5
print(array)
```

In this case, the original array is modified, and the output will be [15 35 25 35].

NumPy supports broadcasting, which means you can perform subtraction operations between arrays of different shapes as long as they are compatible. For example, you can subtract a one-dimensional array from a two-dimensional array:

```
import numpy as np
# Create a two-dimensional array
matrix = np.array([[11, 22, 13], [42, 51, 63], [71, 83, 91]])
# Create a one-dimensional array
row = np.array([1, 2, 3])
# Subtract the one-dimensional array from each row of the two-dimensional array
result = matrix - row
print(result)
```

The output will be a new two-dimensional array where each row has been subtracted by the row array.

```
[[10 20 10]
[41 49 60]
[70 81 88]]
```

NumPy's 'np.subtract' function is a versatile tool for performing subtraction operations on arrays, whether it's subtracting two arrays, a constant value, or performing in-place subtraction. With broadcasting, you can efficiently work with arrays of different shapes. Understanding these capabilities of NumPy will be valuable for various scientific and data analysis tasks in Python.