Numpy Array Manipulation operations

Author: OmkarPathak

Numpy/P05_NumpyArrayManipulation.py

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+# Author: OMKAR PATHAK
+
+# This example shows various array manipulation operations
+import numpy as np
+
+# numpy.reshape(array_to_reshape, tuple_of_new_shape) gives new shape (dimension) to our array
+myArray = np.arange(0, 30, 2)
+print(myArray)              # [ 0  2  4  6  8 10 12 14 16 18 20 22 24 26 28]
+
+myArrayReshaped = myArray.reshape(5, 3)
+print(myArrayReshaped)
+
+# [[ 0  2  4]
+#  [ 6  8 10]
+#  [12 14 16]
+#  [18 20 22]
+#  [24 26 28]]
+
+# numpy.ndarray.flat() returns an 1-D iterator
+print(myArray.flat[5])      # 10
+
+# numpy.ndarray.flatten() restores the reshaped array into a 1-D array
+print(myArrayReshaped.flatten())
+
+# numpy.tranpose() this helps to find the tranpose of the given array
+print(myArrayReshaped.transpose())
+
+# [[ 0  6 12 18 24]
+#  [ 2  8 14 20 26]
+#  [ 4 10 16 22 28]]
+
+# numpy.swapaxes(array, axis1, axis2) interchanges the two axes of an array
+originalArray = np.arange(8).reshape(2,2,2)
+print(originalArray)
+
+# [[[0 1]
+#   [2 3]]
+#  
+#  [[4 5]
+#   [6 7]]]
+
+print(np.swapaxes(originalArray, 2, 0))
+
+# [[[0 4]
+#   [2 6]]
+#  
+#  [[1 5]
+#   [3 7]]]
+
+# numpy.rollaxis(arr, axis, start) rolls the specified axis backwards, until it lies in a specified position
+print(np.rollaxis(originalArray, 2))
+
+# [[[0 2]
+#   [4 6]]
+#  
+#  [[1 3]
+#   [5 7]]]
+
+# numpy.resize(arr, shape) returns a new array with the specified size. If the new size is greater than
+# the original, the repeated copies of entries in the original are contained
+
+myArray = np.array([[1,2,3],[4,5,6]])
+print(myArray)
+
+# [[1 2 3]
+#  [4 5 6]]
+
+print(np.resize(myArray, (3, 2)))
+
+# [[1 2]
+#  [3 4]
+#  [5 6]]
+
+# numpy.append(array, values, axis)
+myArray = np.array([[1,2,3],[4,5,6]])
+print(myArray)
+
+# [[1 2 3]
+#  [4 5 6]]
+
+print(np.append(myArray, [7, 8, 9]))
+
+# [1 2 3 4 5 6 7 8 9]