In this tutorial, we will learn how to use the Transpose function (T) in Python with the help of the popular library, NumPy.

The transpose function is used to change the orientation of a given matrix or ndarray. Specifically, it reverses the axes of the input array, significantly helping with linear algebra and data manipulation tasks.

### Step 1: Installing NumPy

To use transpose in Python, we need to have the NumPy library installed. If you don’t have it installed already, you can install it using pip:

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pip install numpy |

### Step 2: Importing NumPy and Creating an Array

After installing NumPy, you need to import it and create the input array, which can be a list or a multi-dimensional array. For this example, let’s create a 2×3 array:

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import numpy as np # Create a 2x3 array arr = np.array([[1, 2, 3], [4, 5, 6]]) |

### Step 3: Using the T Attribute

Now we can use the **T** attribute to obtain the transpose of the created array. The code below demonstrates its usage:

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# Get the transpose of the array transpose_arr = arr.T # Print the original and transposed arrays print("Original Array:") print(arr) print("Transposed Array:") print(transpose_arr) |

When the code is executed, you will see the original 2×3 array and its Transpose displayed as a 3×2 array:

**Original Array:**

[[1 2 3] [4 5 6]]

**Transposed Array:**

[[1 4] [2 5] [3 6]]

## Full Code Example

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import numpy as np # Create a 2x3 array arr = np.array([[1, 2, 3], [4, 5, 6]]) # Get the transpose of the array transpose_arr = arr.T # Print the original and transposed arrays print("Original Array:") print(arr) print("Transposed Array:") print(transpose_arr) |

This simple example demonstrated how to use the **T** attribute in Python to obtain the transpose of a given array using NumPy. This technique can be very useful when working with linear algebra and manipulating data in a variety of applications.

## Conclusion

The Transpose function (T) in Python can be a handy tool when dealing with multi-dimensional arrays in machine learning, data analysis, or linear algebra tasks. Utilizing the NumPy library, transposing arrays becomes a straightforward task.

Now you know how to implement it in your projects and take advantage of its capabilities.