Introduction to NumPy ndarray.T
We may build a Transpose of an array with a size larger than or equal to 2 using the Numpy ndarray.T object.
Return: It returns Transpose of an array
Code Examples of NumPy ndarray.T
Example 01: We can see in this example that we may convert an array with the help of the ndarray.T object.
# welcome to softhunt.net # import the important module in python import numpy as np # make an array with numpy arr = np.array([[1, 3, 5], [2, 4, 6]]) # applying ndarray.T object softhunt = arr.T print(softhunt)
[[1 2] [3 4] [5 6]]
# welcome to softhunt.net # import the important module in python import numpy as np # make an array with numpy arr = np.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]) # applying ndarray.T object softhunt = arr.T print(softhunt)
[[ 1 2 3] [ 4 5 6] [ 7 8 9] [10 11 12]]
Note: These programs will not run in online IDEs. Please test them on your systems to see how they operate.
What is capital T in Python OR String capitalize() in Python?
The capitalise() function in Python produces a duplicate of the original string with the first character converted to a capital (uppercase) letter and all remaining characters in the string converted to lowercase letters.
string_name: It is the name of a string whose first character we want to capitalize.
Parameters: The capitalize() function does not take any parameter.
Return: The capitalize() function returns a string with the first character in the capital.
How do I transpose in NumPy?
We may do the simple operation of transposing inside one line by utilizing Numpy’s numpy.transpose() function. Although it can transpose 2-D arrays, it has no effect on 1-D arrays. The 2-D NumPy array is transposed using this approach.
What is a NumPy Ndarray?
The ndarray, which is a shorthand term for N-dimensional array, is the most important data structure in NumPy. The data in a ndarray is simply referred to as an array when dealing with NumPy. It’s a memory array with data of the same type, such as integers or floating-point values, that’s fixed in size.
NumPy ndarray.T: Conclusion