Padding in numpy array numpy functions python numpy tutorial

## padding in numpy arrays

padding in numpy refers to the process of adding extra elements to an array, usually to alter its dimensions, shape, or to prepare it for certain operations (like convolution in image processing). this can be useful in various scenarios, such as ensuring that arrays have the same dimensions for mathematical operations, or to prevent loss of data during transformations.

### types of padding

1. **constant padding**: adds a constant value (like 0) around the array.
2. **edge padding**: repeats the edge values of the array.
3. **reflective padding**: mirrors the values along the edge of the array.
4. **symmetric padding**: similar to reflective padding but includes the edge values.

### functions for padding in numpy

- **array**: input array to pad.
- **pad_width**: the number of values to pad. it can be a single integer (for all dimensions), or a sequence of tuples (for each dimension).
- **mode**: the type of padding to apply (default is 'constant').
- **kwargs**: additional arguments depending on the mode.

### example code

#### 1. constant padding

**output:**

#### 2. edge padding

**output:**

#### 3. reflective padding

**output:**

#### 4. symmetric padding

**output:**

### conclusion

feel free to try out these examples and modify the padding parameters to see how the output changes!

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