Simplify sudoku.is_completed() using builtin all() (#1608)

* Simplify sudoku.is_completed() using builtin all()

Simplify __sudoku.is_completed()__ using Python builtin function [__all()__](https://docs.python.org/3/library/functions.html#all).

* fixup! Format Python code with psf/black push

* Update sudoku.py

* fixup! Format Python code with psf/black push

* Old style exception -> new style for Python 3

* updating DIRECTORY.md

* Update convex_hull.py

* fixup! Format Python code with psf/black push

* e.args[0] = "msg"

* ValueError: could not convert string to float: 'pi'

* Update convex_hull.py

* fixup! Format Python code with psf/black push

backtracking/sudoku.py

@@ -1,5 +1,4 @@
 """
-
     Given a partially filled 9×9 2D array, the objective is to fill a 9×9
     square grid with digits numbered 1 to 9, so that every row, column, and
     and each of the nine 3×3 sub-grids contains all of the digits.
@@ -9,9 +8,7 @@
     function on the next column to see if it returns True. if yes, we
     have solved the puzzle. else, we backtrack and place another number
     in that cell and repeat this process.
-
 """
-
 # assigning initial values to the grid
 initial_grid = [
     [3, 0, 6, 5, 0, 8, 4, 0, 0],
@@ -24,6 +21,7 @@ initial_grid = [
     [0, 0, 0, 0, 0, 0, 0, 7, 4],
     [0, 0, 5, 2, 0, 6, 3, 0, 0],
 ]
+
 # a grid with no solution
 no_solution = [
     [5, 0, 6, 5, 0, 8, 4, 0, 3],
@@ -44,9 +42,7 @@ def is_safe(grid, row, column, n):
     column, and the 3x3 subgrids contain the digit 'n'.
     It returns False if it is not 'safe' (a duplicate digit
     is found) else returns True if it is 'safe'
-
     """
-
     for i in range(9):
         if grid[row][i] == n or grid[i][column] == n:
             return False
@@ -62,26 +58,29 @@ def is_safe(grid, row, column, n):
 def is_completed(grid):
     """
     This function checks if the puzzle is completed or not.
-    it is completed when all the cells are assigned with a number(not zero)
-    and There is no repeating number in any column, row or 3x3 subgrid.
+    it is completed when all the cells are assigned with a non-zero number.
 
+    >>> is_completed([[0]])
+    False
+    >>> is_completed([[1]])
+    True
+    >>> is_completed([[1, 2], [0, 4]])
+    False
+    >>> is_completed([[1, 2], [3, 4]])
+    True
+    >>> is_completed(initial_grid)
+    False
+    >>> is_completed(no_solution)
+    False
     """
-
-    for row in grid:
-        for cell in row:
-            if cell == 0:
-                return False
-
-    return True
+    return all(all(cell != 0 for cell in row) for row in grid)
 
 
 def find_empty_location(grid):
     """
     This function finds an empty location so that we can assign a number
     for that particular row and column.
-
     """
-
     for i in range(9):
         for j in range(9):
             if grid[i][j] == 0:
@@ -129,9 +128,7 @@ def print_solution(grid):
     """
     A function to print the solution in the form
     of a 9x9 grid
-
     """
-
     for row in grid:
         for cell in row:
             print(cell, end=" ")
@@ -139,7 +136,6 @@ def print_solution(grid):
 
 
 if __name__ == "__main__":
-
     # make a copy of grid so that you can compare with the unmodified grid
     for grid in (initial_grid, no_solution):
         grid = list(map(list, grid))

divide_and_conquer/convex_hull.py

@@ -28,7 +28,7 @@ class Point:
     Examples
     --------
     >>> Point(1, 2)
-    (1, 2)
+    (1.0, 2.0)
     >>> Point("1", "2")
     (1.0, 2.0)
     >>> Point(1, 2) > Point(0, 1)
@@ -200,8 +200,7 @@ def _validate_input(points):
                 )
         elif not hasattr(points, "__iter__"):
             raise ValueError(
-                "Expecting an iterable object "
-                f"but got an non-iterable type {points}"
+                "Expecting an iterable object " f"but got an non-iterable type {points}"
             )
     except TypeError as e:
         print("Expecting an iterable of type Point, list or tuple.")

graphs/dijkstra_algorithm.py

@@ -104,9 +104,7 @@ class Graph:
         # u -> v(w)
         for u in self.adjList:
             print(
-                u,
-                "->",
-                " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]),
+                u, "->", " -> ".join(str(f"{v}({w})") for v, w in self.adjList[u]),
             )
 
     def dijkstra(self, src):

machine_learning/sequential_minimum_optimization.py

@@ -499,9 +499,7 @@ def test_cancel_data():
     for i in range(test_tags.shape[0]):
         if test_tags[i] == predict[i]:
             score += 1
-    print(
-        f"\r\nall: {test_num}\r\nright: {score}\r\nfalse: {test_num - score}"
-    )
+    print(f"\r\nall: {test_num}\r\nright: {score}\r\nfalse: {test_num - score}")
     print(f"Rough Accuracy: {score / test_tags.shape[0]}")