requirements.txt: Unpin numpy (TheAlgorithms#2287)

* requirements.txt: Unpin numpy

* fixup! Format Python code with psf/black push

* Less clutter

* fixup! Format Python code with psf/black push

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

graphs/karger.py

@@ -5,20 +5,19 @@
 import random
 from typing import Dict, List, Set, Tuple
 
-
 # Adjacency list representation of this graph:
 # https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
 TEST_GRAPH = {
-    '1': ['2', '3', '4', '5'],
-    '2': ['1', '3', '4', '5'],
-    '3': ['1', '2', '4', '5', '10'],
-    '4': ['1', '2', '3', '5', '6'],
-    '5': ['1', '2', '3', '4', '7'],
-    '6': ['7', '8', '9', '10', '4'],
-    '7': ['6', '8', '9', '10', '5'],
-    '8': ['6', '7', '9', '10'],
-    '9': ['6', '7', '8', '10'],
-    '10': ['6', '7', '8', '9', '3']
+    "1": ["2", "3", "4", "5"],
+    "2": ["1", "3", "4", "5"],
+    "3": ["1", "2", "4", "5", "10"],
+    "4": ["1", "2", "3", "5", "6"],
+    "5": ["1", "2", "3", "4", "7"],
+    "6": ["7", "8", "9", "10", "4"],
+    "7": ["6", "8", "9", "10", "5"],
+    "8": ["6", "7", "9", "10"],
+    "9": ["6", "7", "8", "10"],
+    "10": ["6", "7", "8", "9", "3"],
 }
 
 
@@ -61,8 +60,9 @@ def partition_graph(graph: Dict[str, List[str]]) -> Set[Tuple[str, str]]:
         for neighbor in uv_neighbors:
             graph_copy[neighbor].append(uv)
 
-        contracted_nodes[uv] = {contracted_node for contracted_node in
-                                contracted_nodes[u].union(contracted_nodes[v])}
+        contracted_nodes[uv] = {
+            node for node in contracted_nodes[u].union(contracted_nodes[v])
+        }
 
         # Remove nodes u and v.
         del graph_copy[u]
@@ -75,8 +75,12 @@ def partition_graph(graph: Dict[str, List[str]]) -> Set[Tuple[str, str]]:
 
     # Find cutset.
     groups = [contracted_nodes[node] for node in graph_copy]
-    return {(node, neighbor) for node in groups[0]
-            for neighbor in graph[node] if neighbor in groups[1]}
+    return {
+        (node, neighbor)
+        for node in groups[0]
+        for neighbor in graph[node]
+        if neighbor in groups[1]
+    }
 
 
 if __name__ == "__main__":

other/scoring_algorithm.py

@@ -1,4 +1,4 @@
-'''
+"""
 developed by: markmelnic
 original repo: https://github.com/markmelnic/Scoring-Algorithm
 
@@ -23,17 +23,17 @@
 
 >>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
 [[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
-'''
+"""
 
 
-def procentual_proximity(source_data : list, weights : list) -> list:
+def procentual_proximity(source_data: list, weights: list) -> list:
 
-    '''
+    """
     weights - int list
     possible values - 0 / 1
     0 if lower values have higher weight in the data set
     1 if higher values have higher weight in the data set
-    '''
+    """
 
     # getting data
     data_lists = []

requirements.txt

@@ -5,7 +5,7 @@ flake8
 keras
 matplotlib
 mypy
-numpy>=1.17.4
+numpy
 opencv-python
 pandas
 pillow

web_programming/world_covid19_stats.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
 
-'''
+"""
 Provide the current worldwide COVID-19 statistics.
 This data is being scrapped from 'https://www.worldometers.info/coronavirus/'.
-'''
+"""
 
 import requests
 from bs4 import BeautifulSoup
@@ -13,8 +13,8 @@ def world_covid19_stats(url: str = "https://www.worldometers.info/coronavirus")
     """
     Return a dict of current worldwide COVID-19 statistics
     """
-    soup = BeautifulSoup(requests.get(url).text, 'html.parser')
-    keys = soup.findAll('h1')
+    soup = BeautifulSoup(requests.get(url).text, "html.parser")
+    keys = soup.findAll("h1")
     values = soup.findAll("div", {"class": "maincounter-number"})
     keys += soup.findAll("span", {"class": "panel-title"})
     values += soup.findAll("div", {"class": "number-table-main"})