The ravel() function is used for returning a 1D array containing all the elements of the n-dimensional input array. If you want to flatten the array, use the numpy ravel() function.
np.ravel
The np.ravel() function helps us create multidimensional arrays and derive other mathematical statistics. To flatten an array, use the numpy ravel() function. For example, the ravel(array, order = ‘C’) function returns a contiguous flattened array(1D array with all the input-array elements and with the same type as it). A copy is made only if needed.
Syntax
numpy.ravel(a, order='C')
Parameters
- a: This parameter depicts the Input array in which the elements are read in the order specified by an order which gets further packed as a 1D array.
- order: The order argument can be either C_contiguous or F_contiguous, where C order operates row-rise on the array, and F order operates the column-wise operations.
Return Value
Numpy ravel() function returns the 1D array containing all the elements of input array with shape (a.size ()).
Examples
Program to show the working of ravel function.
import numpy as np
arr = np.arange(6).reshape(3, 2)
print('The original array:')
print(arr, "\n")
print('After applying ravel function:')
print(arr.ravel())
#Maintaining F order
print('ravel function in F-style ordering:')
print(arr.ravel(order='F'))
#K-order preserving the ordering
print("\nnumpy.ravel() function in K-style ordering: ", arr.ravel(order='K'))
Output
The original array: [[0 1] [2 3] [4 5]] After applying ravel function: [0 1 2 3 4 5] ravel function in F-style ordering: [0 2 4 1 3 5] numpy.ravel() function in K-style ordering: [0 1 2 3 4 5]
Explanation
Here, in the above code, the 1st function was used to create a 1D array in which no order was given due to which K type of order was used by default.
2nd function was used to create a 1D array in which F-style ordering was used in which elements are inserted in the array column-wise.
3rd function was used to create a 1D array in which F-style ordering was used in which elements are inserted in the array row-wise.
See the following second code.
import numpy as np
arr = np.arange(6).reshape(3, 2)
print("Array: \n", arr)
# calling the numpy.ravel() function
print("\nravel() value: ", arr.ravel())
# ravel() is equivalent to reshape(-1, order=order).
print("\nnumpy.ravel() == numpy.reshape(-1)")
print("Reshaping array : ", arr.reshape(-1))
Output
Array: [[0 1] [2 3] [4 5]] ravel() value: [0 1 2 3 4 5] numpy.ravel() == numpy.reshape(-1) Reshaping array : [0 1 2 3 4 5]
Explanation
Here, in the above code, the 1st function was used to create a 1D array in which no order was given due to which K type of order was used by default.
2nd function was used to create a 1D array using reshape function in which (-1) was passed as an argument that behaves equally as of K type order in the ravel function.
Conclusion
Numpy ravel() function returns the flattened one-dimensional array. The copy is made only if needed. The returned array will have the same type as that of an input array.
