Numpy Array Reshaping: A Beginner's Guide to reshape and flatten

This article introduces two practical methods for array reshaping in Numpy: `reshape` and `flatten`, which are used to meet different data processing needs. The core premise is that the total number of array elements before and after reshaping must be consistent. The `reshape` method can change the array shape (e.g., 1D to 2D). Its syntax is `arr.reshape(new_shape)`, which supports specifying the shape with a tuple. Using `-1` allows automatic calculation of the missing dimension (e.g., if the number of rows is 3, the number of columns is automatically calculated). It returns a new array without modifying the original array. The `flatten` method flattens a multi-dimensional array into a 1D array and returns a new array (a copy), avoiding modification of the original array. Unlike `ravel` (which returns a view), `flatten` is recommended for priority use. A common error is "mismatched element count", where it is necessary to ensure that the product of the `reshape` parameters equals the size of the original array (`original_array.size`). In summary, `reshape` flexibly adjusts the shape, and `flatten` safely flattens to 1D. Mastering both methods enables efficient array reshaping and lays the foundation for data processing (e.g., in machine learning).