Merge pull request #15 from jetbrains-academy/sofia/element-wise_comparison

Added task element-wise comparison

Author: sofiiako

Numpy/Compare, Search/Element-wise Comparison/__init__.py

@@ -0,0 +1,17 @@
+type: edu
+files:
+- name: task.py
+  visible: true
+  placeholders:
+  - offset: 56
+    length: 19
+    placeholder_text: '# TODO'
+  - offset: 90
+    length: 14
+    placeholder_text: '# TODO'
+- name: tests/test_task.py
+  visible: false
+- name: __init__.py
+  visible: false
+- name: tests/__init__.py
+  visible: false

Numpy/Compare, Search/Element-wise Comparison/task-info.yaml

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+## Element-wise comparison
+
+Element-wise comparison is when you compare each element of one array to an element of another array.
+[`numpy.equal`](https://numpy.org/doc/stable/reference/generated/numpy.equal.html) is the function that returns `(x1 == x2)` element-wise.
+It accepts two input arrays, which must either have equal `shape` or be broadcastable to a common shape 
+(which becomes the shape of the output). Elements of the output array are typically boolean values.
+
+Example:
+```python
+print(np.equal([0, 1, 3], np.arange(3)))
+```
+Output:
+```text
+[ True,  True, False]
+```
+What is compared are values, not types. So an int (1) and an array of length one can evaluate as `True`:
+```python
+print(np.equal(1, np.ones(1)))
+```
+Output:
+```text
+[ True]
+```
+The `==` operator can be used as a shorthand for `np.equal` on ndarrays.
+
+### `numpy.array_equal`
+
+[`numpy.array_equal`](https://numpy.org/doc/stable/reference/generated/numpy.array_equal.html) is the
+function that returns `True` if two input arrays have **the same shape and elements**, and `False` otherwise.
+
+Examples:
+```python
+print(np.array_equal([1, 2], [1, 2]))
+print(np.array_equal(np.array([1, 2]), np.array([1, 2])))
+print(np.array_equal([1, 2], [1, 2, 3]))
+print(np.array_equal([1, 2], [1, 4]))
+```
+Output:
+```text
+True
+True
+False
+False
+```
+
+### Task
+Define a **1-D** array `b`, such that by comparing it element-wise with the array `a` you can 
+get the array `compare_a_b`, which is `array_equal` (has the same elements and shape) to the array in the last
+`print` statement. When you run the script, the last `print` statement has to print `True`.
+
+<div class="hint">Array <code>b</code> should have be of shape <code>(5,)</code>. Use
+<code>np.arange</code> with <code>start</code>, <code>stop</code> and <code>step</code> arguments.
+By looking an the resulting boolean array you can figure out which integer has to be in 
+what position in <code>b</code> and then come up with suitable arguments for the <code>np.arange</code>
+function.</div>
+
+<div class="hint">To get the array <code>compare_a_b</code>, use the <code>np.equal</code> function.</div>

Numpy/Compare, Search/Element-wise Comparison/task.md

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+import numpy as np
+
+a = np.arange(20).reshape(4, 5)
+b = np.arange(0, 25, 6)
+compare_a_b = np.equal(a, b)
+
+
+if __name__ == '__main__':
+    print(a)
+    print(b)
+    print(np.array_equal(compare_a_b, np.array([[True, False, False, False, False],
+                                                [False, True, False, False, False],
+                                                [False, False, True, False, False],
+                                                [False, False, False, True, False]])))
+

Numpy/Compare, Search/Element-wise Comparison/task.py

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+import unittest
+import numpy as np
+
+from task import *
+
+
+class TestCase(unittest.TestCase):
+    def test_array_b(self):
+        test_b = np.arange(0, 25, 6)
+        self.assertEqual(b.shape, test_b.shape, msg='Shape of array b has to be (5,).')
+
+    def test_array_c(self):
+        test_b = np.arange(0, 25, 6)
+        test_c = np.equal(a, test_b)
+        np.testing.assert_array_equal(compare_a_b, test_c, err_msg='Boolean arrays do not match.')

Numpy/Compare, Search/Element-wise Comparison/tests/__init__.py

@@ -1,2 +1,3 @@
 content:
 - Compare with a scalar
+- Element-wise Comparison