Merge pull request #18 from jetbrains-academy/sofia/numpy_search

Added task Search to Compare and Search

Author: sofiiako

NumPy/Compare Search/Search/__init__.py

@@ -0,0 +1,23 @@
+type: edu
+files:
+- name: task.py
+  visible: true
+  placeholders:
+  - offset: 194
+    length: 8
+    placeholder_text: '# TODO'
+  - offset: 226
+    length: 38
+    placeholder_text: '# TODO'
+  - offset: 277
+    length: 23
+    placeholder_text: '# TODO'
+  - offset: 209
+    length: 7
+    placeholder_text: '# TODO'
+- name: tests/test_task.py
+  visible: false
+- name: __init__.py
+  visible: false
+- name: tests/__init__.py
+  visible: false

NumPy/Compare Search/Search/task-info.yaml

@@ -0,0 +1,77 @@
+## Search
+
+You can search an array for a certain value, or for values that satisfy a condition, 
+and return the **indices** that get a match.
+
+You can use the [`numpy.nonzero`](https://numpy.org/doc/stable/reference/generated/numpy.nonzero.html) method:
+
+```python
+a = np.array([0, 10, 4, 11, 7, 3])
+print(np.nonzero(a < 5))
+print(np.nonzero(a == 7))
+print(a < 5)
+```
+Output:
+```text
+(array([0, 2, 5]),)
+(array([4]),)
+[ True False  True False False  True]
+```
+`numpy.nonzero` returns the indices of the elements that are non-zero.
+A common use for it is to find the indices of an array, where a condition is 
+`True`. For the array `a`, the condition `a < 5` is a boolean array (see example above). 
+`False` is interpreted as `0`, therefore `np.nonzero(a < 5)` yields the indices of 
+the `a` where the condition is true. Using the result to index `a` (`a[np.nonzero(a < 5)]`) is equivalent to 
+using the mask directly (`a[a < 5]`), and the latter is the preferred spelling.
+
+`np.nonzero(a < 5)` is equivalent to `np.where(a < 5)`:
+
+```python
+print(np.where(a < 5))
+```
+Output:
+```text
+(array([0, 2, 5]),)
+```
+However, [`numpy.where`](https://numpy.org/doc/stable/reference/generated/numpy.where.html) can also be used in a different way: if provided with a condition
+and two input arrays, `numpy.where(condition[, x, y])` returns **elements** (not indices) chosen from 
+`x` or `y` depending on the condition:
+
+```python
+a = np.array([0, 0, 0, 0, 0, 5, 6, 7, 8, 9])
+b = a * 10
+print(np.where(a < 5, a, b))
+```
+Output:
+```text
+[ 0  0  0  0  0 50 60 70 80 90]
+```
+
+In fact, the first argument accepted by this function is also an array - a boolean array, `a < 5` in this case.
+Therefore, it can also be used like this:
+
+```python
+result = np.where([False, True, False], [1, 1, 1], [7, 8, 9])
+print(result)
+```
+Output:
+```text
+[7 1 9]
+```
+`x`, `y` and `condition` arrays need to be broadcastable to some shape.
+
+### Task
+
+You are given an array of random integers between 0 and 25 representing temperatures registered in
+certain days of the week. Using the functionality you just learned about, generate an array `result`, 
+which contains strings, either `Low` or `High`, in the order depending on whether the corresponding day of the 
+week was warmer than 15 degrees (`High`) or not (`Low`). You will probably need to define two additional arrays, `high` and `low`, 
+containing strings that will be used for the `result`.
+
+In addition, `warm_days` should contain the names of the warm days (with temperatures above 15).
+
+<div class="hint"><code>result</code> should look something like <code>['High' 'Low' 'Low' 'Low' 'High' 'High' 'High']</code>.</div>
+
+<div class="hint">Use <code>numpy.where</code> with a condition about temperatures and two input arrays <code>high</code> and <code>low</code> to get the <code>result</code>.</div>
+
+<div class="hint">For the last part, use <code>numpy.nonzero</code> or a direct mask.</div>

NumPy/Compare Search/Search/task.md

@@ -0,0 +1,17 @@
+import numpy as np
+rng = np.random.default_rng()
+
+temperatures = rng.integers(25, size=7)
+days = np.array(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])
+
+high = ['High']
+low = ['Low']
+result = np.where(temperatures > 15, high, low)
+warm_days = days[temperatures > 15]
+
+if __name__ == '__main__':
+    print(temperatures)
+    print(result)
+    print(warm_days)
+
+

NumPy/Compare Search/Search/task.py

@@ -0,0 +1,19 @@
+import unittest
+import numpy as np
+
+from task import temperatures, days, high, low, result, warm_days
+
+
+class TestCase(unittest.TestCase):
+    def test_week(self):
+        high_test = ['High']
+        low_test = ['Low']
+        np.testing.assert_array_equal(result, np.where(temperatures > 15, high_test, low_test),
+                                      err_msg='Your `result` array '
+                                              'does not contain the values we expected.')
+        self.assertEqual(result.shape, days.shape, msg='Shape of the array `result` should match the shape of `days`.')
+
+    def test_names(self):
+        np.testing.assert_array_equal(warm_days, days[temperatures > 15],
+                                      err_msg='`warm_days` array is off. It should contain '
+                                              'the names of days when temperature was higher than 15')

NumPy/Compare Search/Search/tests/__init__.py

@@ -3,3 +3,4 @@ content:
 - Compare with a scalar
 - Element-wise Comparison
 - Find maximum
+- Search