Added Transpose task to section Transposing, Sorting and Concatenatin…

Numpy/Transposing, Sorting, Concatenating/Transpose/task-info.yaml

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+type: edu
+files:
+- name: task.py
+  visible: true
+  placeholders:
+  - offset: 147
+    length: 7
+    placeholder_text: '# TODO'
+  - offset: 204
+    length: 17
+    placeholder_text: '# TODO'
+  - offset: 227
+    length: 42
+    placeholder_text: '# TODO'
+- name: tests/test_task.py
+  visible: false
+- name: __init__.py
+  visible: false
+- name: tests/__init__.py
+  visible: false

Numpy/Transposing, Sorting, Concatenating/Transpose/task.md

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+## Transpose
+
+When you transpose an array, the row and column indices of every 
+element are switched. Item `[0, 1]`, for example, becomes item `[1, 0]`.
+[`numpy.ndarray.transpose`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.transpose.html#numpy.ndarray.transpose) returns a view of the array with axes transposed.
+Alternatively you can also use the attribute [`a.T`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.T.html#numpy.ndarray.T).
+
+This method accepts `None` or a tuple of `int`s. In the case of `None` or no argument it reverses the order of the axes.
+In the case of a tuple of `int`s: `i` in the `j`-th place in the tuple means that `a`’s `i`-th axis becomes `a.transpose()`’s `j`-th axis.
+To illustrate:
+
+```python
+a = np.arange(15).reshape(3, 5)
+print(a.transpose())
+print(a.T)
+print(a.transpose(1, 0))
+```
+Output (all print the same thing):
+```text
+[[ 0  5 10]
+ [ 1  6 11]
+ [ 2  7 12]
+ [ 3  8 13]
+ [ 4  9 14]]
+```
+
+> <i>Method [`numpy.ndarray.reshape`](course://Numpy/Array Basics/Reshape) we encountered earlier
+gives a new shape to an array without changing its data and can also be used for transposing.</i>
+
+### Task 
+1. Transpose the array `a`.
+2. Transform array `b` so that it looks like this:
+```text
+   [[0 1 2]
+   [3 4 5]
+   [6 7 8]]
+```
+3. Use numpy methods `arange`, `reshape` and `transpose` to acquire a 3-D array `c` that looks like this:
+```text
+[[[ 0  4  8]
+  [ 2  6 10]]
+
+ [[ 1  5  9]
+  [ 3  7 11]]]
+```
+
+<div class="hint">You can use either <code>a.T</code> or <code>a.transpose()</code> for the first two arrays.</div>
+
+<div class="hint">In #3 use the methods in the suggested order. You can do it in one line of code.</div>

Numpy/Transposing, Sorting, Concatenating/Transpose/task.py

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+import numpy as np
+
+a = np.array([['clear', 'usually', 'of'],
+              ['conscience', 'the', 'bad'],
+              ['is', 'sign', 'memory']])
+a = a.T
+
+b = np.array([[0, 3, 6], [1, 4, 7], [2, 5, 8]])
+b = b.transpose()
+
+c = np.arange(12).reshape(3, 2, 2).transpose()
+
+
+if __name__ == '__main__':
+    print(a)
+    print(b)
+    print(c)
+

Numpy/Transposing, Sorting, Concatenating/Transpose/tests/test_task.py

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+import unittest
+import numpy as np
+
+from task import a, b, c
+
+
+class TestCase(unittest.TestCase):
+    def test_arrays(self):
+        b_test = np.array([[0, 3, 6], [1, 4, 7], [2, 5, 8]]).transpose()
+        c_test = np.arange(12).reshape(3, 2, 2).transpose()
+        a_test = np.array([['clear', 'conscience', 'is'],
+                           ['usually', 'the', 'sign'],
+                           ['of', 'bad', 'memory']])
+        np.testing.assert_array_equal(a, a_test, err_msg='Array `a` is off!')
+        np.testing.assert_array_equal(b, b_test, err_msg='Array `b` is off!')
+        np.testing.assert_array_equal(c, c_test, err_msg='Array `c` is off!')
+

Numpy/Transposing, Sorting, Concatenating/lesson-info.yaml

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+content:
+- Transpose

Numpy/section-info.yaml

@@ -2,3 +2,4 @@ content:
 - Introduction
 - Array Basics
 - Array Indexing and Slicing
+- Transposing, Sorting, Concatenating