A Beginner’s Guide to reshape, transpose, and axis in NumPy

# Import NumPy library

import numpy as np

# Example for `reshape`

b = np.array([1, 2, 3])

reshaped_b = b.reshape(3, 1)

print(reshaped_b)

**Output**:

[[1]

 [2]

 [3]]

**Explanation**:

- The original array `b` is a 1D array with shape `(3,)`.

- `reshape(3, 1)` converts it into a 2D array with shape `(3, 1)`, resulting in three rows and one column.

**Key Point**: `reshape` is used to change the dimensions of an array while preserving its data, as long as the new shape has the same number of elements as the original shape.

# Example for `transpose`

b = np.array([[1, 2, 3], [4, 5, 6]])

transposed_b = b.T

print(transposed_b)

**Output**:

[[1 4]

 [2 5]

 [3 6]]

**Explanation**:

- The original array `b` is a 2D array with shape `(2, 3)` (2 rows and 3 columns).

- The `transpose()` method (or `b.T`) switches rows and columns, converting it to a shape of `(3, 2)`.

**Key Point**: `transpose` is used to swap the dimensions of an array. For 2D arrays, it effectively flips the array over its diagonal.

# Example for `axis` with operations like sum

b = np.array([[1, 2, 3], [4, 5, 6]])

sum_along_axis0 = np.sum(b, axis=0)

sum_along_axis1 = np.sum(b, axis=1)

print("Sum along axis 0:", sum_along_axis0)

print("Sum along axis 1:", sum_along_axis1)

**Output**:

Sum along axis 0: [5 7 9]

Sum along axis 1: [ 6 15]

**Explanation**:

- `axis=0` refers to the vertical axis (columns), so summing along `axis=0` sums each column.

- `axis=1` refers to the horizontal axis (rows), so summing along `axis=1` sums each row.

**Key Point**: The concept of `axis` is not about reshaping or transposing but about how operations are performed across dimensions of the array.

### Summary:

- **`reshape`**: Changes the shape of an array while keeping the same data. Useful for reformatting arrays without altering their content.

- **`transpose`**: Swaps the dimensions of the array. For 2D arrays, it converts rows to columns and vice versa.

- **`axis`**: Defines the direction along which operations (like sum, mean) are applied. It’s crucial for performing operations that aggregate or reduce data along specific dimensions.

### Visualizing the Difference:

For a 2D array like `[[1, 2, 3], [4, 5, 6]]`:

- **`reshape(3, 2)`**: Converts it to `[[1, 2], [3, 4], [5, 6]]`, rearranging the elements into a new shape.

- **`transpose()`**: Converts it to `[[1, 4], [2, 5], [3, 6]]`, switching rows with columns.

Each of these operations is useful in different contexts, depending on what you need to achieve with the array's structure.