NumPy Array Reshaping tutorial in Hindi | Part 87
NumPy Array Reshaping tutorial in Hindi @arvindprogramming
NumPy array reshaping refers to the process of changing the shape or dimensions of an existing array object without changing its data. Reshaping can be done by using the `reshape()` function in NumPy. The reshaping operation creates a new view of the original array with a different shape, allowing for different operations and calculations to be performed on the data.
Reshaping can involve changing the number of dimensions of an array or altering the size of a specific dimension. The only requirement is that the total number of elements in the original array remains the same after reshaping.
For example, a 1-dimensional array with 12 elements can be reshaped into a 3x4 2-dimensional array or a 2x2x3 3-dimensional array. The reshaped array may be stored in a new variable or assigned back to the original variable.
Reshaping arrays can be useful in various scenarios, such as preparing data for machine learning algorithms that have specific input shape requirements, converting between different representations of data, or simply modifying the structure of an array to facilitate specific computations.
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