diff --git a/.github/workflows/autoblack.yml b/.github/workflows/autoblack.yml
index 25a04f2767c8..25d291c6ffbb 100644
--- a/.github/workflows/autoblack.yml
+++ b/.github/workflows/autoblack.yml
@@ -17,7 +17,7 @@ jobs:
         if: failure()
         run: |
           black .
-          isort --profile black --recursive .
+          isort --profile black .
           git config --global user.name github-actions
           git config --global user.email '${GITHUB_ACTOR}@users.noreply.github.com'          
           git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
diff --git a/DIRECTORY.md b/DIRECTORY.md
index 34ee376be1c3..17940c6a333b 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -253,6 +253,7 @@
   * [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
   * [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
   * [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
+  * [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
   * [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
   * [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
   * [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
@@ -596,6 +597,7 @@
 
 ## Searches
   * [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
+  * [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
   * [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
   * [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
   * [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
diff --git a/arithmetic_analysis/in_static_equilibrium.py b/arithmetic_analysis/in_static_equilibrium.py
index addaff888f7f..dd7fa706143e 100644
--- a/arithmetic_analysis/in_static_equilibrium.py
+++ b/arithmetic_analysis/in_static_equilibrium.py
@@ -6,9 +6,10 @@
 mypy : passed
 """
 
-from numpy import array, cos, sin, radians, cross  # type: ignore
 from typing import List
 
+from numpy import array, cos, cross, radians, sin  # type: ignore
+
 
 def polar_force(
     magnitude: float, angle: float, radian_mode: bool = False
diff --git a/arithmetic_analysis/newton_raphson.py b/arithmetic_analysis/newton_raphson.py
index a3e8bbf0fc21..890cff060ec8 100644
--- a/arithmetic_analysis/newton_raphson.py
+++ b/arithmetic_analysis/newton_raphson.py
@@ -2,9 +2,9 @@
 # Author: Syed Haseeb Shah (github.com/QuantumNovice)
 # The Newton-Raphson method (also known as Newton's method) is a way to
 # quickly find a good approximation for the root of a real-valued function
-
 from decimal import Decimal
 from math import *  # noqa: F401, F403
+
 from sympy import diff
 
 
diff --git a/ciphers/hill_cipher.py b/ciphers/hill_cipher.py
index 82382d873baf..0014c8693bc6 100644
--- a/ciphers/hill_cipher.py
+++ b/ciphers/hill_cipher.py
@@ -35,8 +35,8 @@
 https://www.youtube.com/watch?v=4RhLNDqcjpA
 
 """
-
 import string
+
 import numpy
 
 
diff --git a/ciphers/playfair_cipher.py b/ciphers/playfair_cipher.py
index 030fe8155a69..33b52906fb05 100644
--- a/ciphers/playfair_cipher.py
+++ b/ciphers/playfair_cipher.py
@@ -1,5 +1,5 @@
-import string
 import itertools
+import string
 
 
 def chunker(seq, size):
diff --git a/compression/burrows_wheeler.py b/compression/burrows_wheeler.py
index 2a08c6092cda..03912f80e1a7 100644
--- a/compression/burrows_wheeler.py
+++ b/compression/burrows_wheeler.py
@@ -10,7 +10,7 @@
 original character. The BWT is thus a "free" method of improving the efficiency
 of text compression algorithms, costing only some extra computation.
 """
-from typing import List, Dict
+from typing import Dict, List
 
 
 def all_rotations(s: str) -> List[str]:
diff --git a/computer_vision/harriscorner.py b/computer_vision/harriscorner.py
index 35302f01411b..fb7f560f7873 100644
--- a/computer_vision/harriscorner.py
+++ b/computer_vision/harriscorner.py
@@ -1,5 +1,5 @@
-import numpy as np
 import cv2
+import numpy as np
 
 """
 Harris Corner Detector
diff --git a/data_structures/binary_tree/non_recursive_segment_tree.py b/data_structures/binary_tree/non_recursive_segment_tree.py
index 97851af937d9..cdcf1fa8dd2d 100644
--- a/data_structures/binary_tree/non_recursive_segment_tree.py
+++ b/data_structures/binary_tree/non_recursive_segment_tree.py
@@ -35,7 +35,7 @@
 >>> st.query(0, 2)
 [1, 2, 3]
 """
-from typing import List, Callable, TypeVar
+from typing import Callable, List, TypeVar
 
 T = TypeVar("T")
 
diff --git a/data_structures/binary_tree/segment_tree_other.py b/data_structures/binary_tree/segment_tree_other.py
index c3ab493d5f4f..df98eeffb3c6 100644
--- a/data_structures/binary_tree/segment_tree_other.py
+++ b/data_structures/binary_tree/segment_tree_other.py
@@ -3,9 +3,8 @@
 allowing queries to be done later in log(N) time
 function takes 2 values and returns a same type value
 """
-
-from queue import Queue
 from collections.abc import Sequence
+from queue import Queue
 
 
 class SegmentTreeNode(object):
diff --git a/data_structures/hashing/double_hash.py b/data_structures/hashing/double_hash.py
index ce4454db0bef..9c6c8fed6a00 100644
--- a/data_structures/hashing/double_hash.py
+++ b/data_structures/hashing/double_hash.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
-
 from hash_table import HashTable
-from number_theory.prime_numbers import next_prime, check_prime
+from number_theory.prime_numbers import check_prime, next_prime
 
 
 class DoubleHash(HashTable):
diff --git a/data_structures/hashing/hash_table_with_linked_list.py b/data_structures/hashing/hash_table_with_linked_list.py
index 48d93bbc5cff..94934e37b65e 100644
--- a/data_structures/hashing/hash_table_with_linked_list.py
+++ b/data_structures/hashing/hash_table_with_linked_list.py
@@ -1,6 +1,7 @@
-from hash_table import HashTable
 from collections import deque
 
+from hash_table import HashTable
+
 
 class HashTableWithLinkedList(HashTable):
     def __init__(self, *args, **kwargs):
diff --git a/digital_image_processing/convert_to_negative.py b/digital_image_processing/convert_to_negative.py
index cba503938aec..7df44138973c 100644
--- a/digital_image_processing/convert_to_negative.py
+++ b/digital_image_processing/convert_to_negative.py
@@ -1,8 +1,7 @@
 """
     Implemented an algorithm using opencv to convert a colored image into its negative
 """
-
-from cv2 import imread, imshow, waitKey, destroyAllWindows
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 def convert_to_negative(img):
diff --git a/digital_image_processing/dithering/burkes.py b/digital_image_processing/dithering/burkes.py
index 0de21f4c009b..2bf0bbe03225 100644
--- a/digital_image_processing/dithering/burkes.py
+++ b/digital_image_processing/dithering/burkes.py
@@ -1,8 +1,8 @@
 """
 Implementation Burke's algorithm (dithering)
 """
-from cv2 import destroyAllWindows, imread, imshow, waitKey
 import numpy as np
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 class Burkes:
diff --git a/digital_image_processing/edge_detection/canny.py b/digital_image_processing/edge_detection/canny.py
index 477ffcb9bbb1..295b4d825c12 100644
--- a/digital_image_processing/edge_detection/canny.py
+++ b/digital_image_processing/edge_detection/canny.py
@@ -1,5 +1,6 @@
 import cv2
 import numpy as np
+
 from digital_image_processing.filters.convolve import img_convolve
 from digital_image_processing.filters.sobel_filter import sobel_filter
 
diff --git a/digital_image_processing/filters/bilateral_filter.py b/digital_image_processing/filters/bilateral_filter.py
index 753d6ddb7a3f..76ae4dd20345 100644
--- a/digital_image_processing/filters/bilateral_filter.py
+++ b/digital_image_processing/filters/bilateral_filter.py
@@ -9,11 +9,11 @@
 Output:
     img:A 2d zero padded image with values in between 0 and 1
 """
+import math
+import sys
 
 import cv2
 import numpy as np
-import math
-import sys
 
 
 def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:
diff --git a/digital_image_processing/filters/convolve.py b/digital_image_processing/filters/convolve.py
index ec500d940366..299682010da6 100644
--- a/digital_image_processing/filters/convolve.py
+++ b/digital_image_processing/filters/convolve.py
@@ -1,8 +1,8 @@
 # @Author  : lightXu
 # @File    : convolve.py
 # @Time    : 2019/7/8 0008 下午 16:13
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-from numpy import array, zeros, ravel, pad, dot, uint8
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+from numpy import array, dot, pad, ravel, uint8, zeros
 
 
 def im2col(image, block_size):
diff --git a/digital_image_processing/filters/gaussian_filter.py b/digital_image_processing/filters/gaussian_filter.py
index 79026ddcc57a..87fa67fb65ea 100644
--- a/digital_image_processing/filters/gaussian_filter.py
+++ b/digital_image_processing/filters/gaussian_filter.py
@@ -1,10 +1,11 @@
 """
 Implementation of gaussian filter algorithm
 """
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8
 from itertools import product
 
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+from numpy import dot, exp, mgrid, pi, ravel, square, uint8, zeros
+
 
 def gen_gaussian_kernel(k_size, sigma):
     center = k_size // 2
diff --git a/digital_image_processing/filters/median_filter.py b/digital_image_processing/filters/median_filter.py
index 151ef8a55df1..174018569d62 100644
--- a/digital_image_processing/filters/median_filter.py
+++ b/digital_image_processing/filters/median_filter.py
@@ -1,9 +1,8 @@
 """
 Implementation of median filter algorithm
 """
-
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
-from numpy import zeros_like, ravel, sort, multiply, divide, int8
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+from numpy import divide, int8, multiply, ravel, sort, zeros_like
 
 
 def median_filter(gray_img, mask=3):
diff --git a/digital_image_processing/filters/sobel_filter.py b/digital_image_processing/filters/sobel_filter.py
index 822d49fe38a1..33284a32f424 100644
--- a/digital_image_processing/filters/sobel_filter.py
+++ b/digital_image_processing/filters/sobel_filter.py
@@ -2,7 +2,8 @@
 # @File    : sobel_filter.py
 # @Time    : 2019/7/8 0008 下午 16:26
 import numpy as np
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
+
 from digital_image_processing.filters.convolve import img_convolve
 
 
diff --git a/digital_image_processing/histogram_equalization/histogram_stretch.py b/digital_image_processing/histogram_equalization/histogram_stretch.py
index d4a6c08418ee..0288a2c1fcf5 100644
--- a/digital_image_processing/histogram_equalization/histogram_stretch.py
+++ b/digital_image_processing/histogram_equalization/histogram_stretch.py
@@ -6,10 +6,9 @@
 import copy
 import os
 
-import numpy as np
-
 import cv2
-import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import pyplot as plt
 
 
 class contrastStretch:
diff --git a/digital_image_processing/resize/resize.py b/digital_image_processing/resize/resize.py
index f33e80e580de..4836521f9f58 100644
--- a/digital_image_processing/resize/resize.py
+++ b/digital_image_processing/resize/resize.py
@@ -1,6 +1,6 @@
 """ Multiple image resizing techniques """
 import numpy as np
-from cv2 import imread, imshow, waitKey, destroyAllWindows
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 class NearestNeighbour:
diff --git a/digital_image_processing/rotation/rotation.py b/digital_image_processing/rotation/rotation.py
index 37b45ca39897..2951f18fc0ec 100644
--- a/digital_image_processing/rotation/rotation.py
+++ b/digital_image_processing/rotation/rotation.py
@@ -1,6 +1,6 @@
-from matplotlib import pyplot as plt
-import numpy as np
 import cv2
+import numpy as np
+from matplotlib import pyplot as plt
 
 
 def get_rotation(
diff --git a/digital_image_processing/sepia.py b/digital_image_processing/sepia.py
index b97b4c0ae1bc..dfb5951676aa 100644
--- a/digital_image_processing/sepia.py
+++ b/digital_image_processing/sepia.py
@@ -1,8 +1,7 @@
 """
     Implemented an algorithm using opencv to tone an image with sepia technique
 """
-
-from cv2 import imread, imshow, waitKey, destroyAllWindows
+from cv2 import destroyAllWindows, imread, imshow, waitKey
 
 
 def make_sepia(img, factor: int):
diff --git a/digital_image_processing/test_digital_image_processing.py b/digital_image_processing/test_digital_image_processing.py
index fe8890de9a31..40f2f7b83b6d 100644
--- a/digital_image_processing/test_digital_image_processing.py
+++ b/digital_image_processing/test_digital_image_processing.py
@@ -1,21 +1,21 @@
 """
 PyTest's for Digital Image Processing
 """
-
-import digital_image_processing.edge_detection.canny as canny
-import digital_image_processing.filters.gaussian_filter as gg
-import digital_image_processing.filters.median_filter as med
-import digital_image_processing.filters.sobel_filter as sob
-import digital_image_processing.filters.convolve as conv
-import digital_image_processing.change_contrast as cc
-import digital_image_processing.convert_to_negative as cn
-import digital_image_processing.sepia as sp
-import digital_image_processing.dithering.burkes as bs
-import digital_image_processing.resize.resize as rs
-from cv2 import imread, cvtColor, COLOR_BGR2GRAY
+from cv2 import COLOR_BGR2GRAY, cvtColor, imread
 from numpy import array, uint8
 from PIL import Image
 
+from digital_image_processing import change_contrast as cc
+from digital_image_processing import convert_to_negative as cn
+from digital_image_processing import sepia as sp
+from digital_image_processing.dithering import burkes as bs
+from digital_image_processing.edge_detection import canny as canny
+from digital_image_processing.filters import convolve as conv
+from digital_image_processing.filters import gaussian_filter as gg
+from digital_image_processing.filters import median_filter as med
+from digital_image_processing.filters import sobel_filter as sob
+from digital_image_processing.resize import resize as rs
+
 img = imread(r"digital_image_processing/image_data/lena_small.jpg")
 gray = cvtColor(img, COLOR_BGR2GRAY)
 
diff --git a/dynamic_programming/fractional_knapsack.py b/dynamic_programming/fractional_knapsack.py
index 15210146bf66..c74af7ef8fc5 100644
--- a/dynamic_programming/fractional_knapsack.py
+++ b/dynamic_programming/fractional_knapsack.py
@@ -1,5 +1,5 @@
-from itertools import accumulate
 from bisect import bisect
+from itertools import accumulate
 
 
 def fracKnapsack(vl, wt, W, n):
diff --git a/dynamic_programming/max_sub_array.py b/dynamic_programming/max_sub_array.py
index 284edb5841e4..1ca4f90bbaa2 100644
--- a/dynamic_programming/max_sub_array.py
+++ b/dynamic_programming/max_sub_array.py
@@ -73,9 +73,10 @@ def max_sub_array(nums: List[int]) -> int:
     A random simulation of this algorithm.
     """
     import time
-    import matplotlib.pyplot as plt
     from random import randint
 
+    from matplotlib import pyplot as plt
+
     inputs = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
     tim = []
     for i in inputs:
diff --git a/dynamic_programming/optimal_binary_search_tree.py b/dynamic_programming/optimal_binary_search_tree.py
index e6f93f85ef0f..0d94c1b61d39 100644
--- a/dynamic_programming/optimal_binary_search_tree.py
+++ b/dynamic_programming/optimal_binary_search_tree.py
@@ -16,9 +16,7 @@
 # frequencies will be placed near the root of the tree while the nodes
 # with low frequencies will be placed near the leaves of the tree thus
 # reducing search time in the most frequent instances.
-
 import sys
-
 from random import randint
 
 
diff --git a/fuzzy_logic/fuzzy_operations.py b/fuzzy_logic/fuzzy_operations.py
index 795a8cde653f..0f573f158663 100644
--- a/fuzzy_logic/fuzzy_operations.py
+++ b/fuzzy_logic/fuzzy_operations.py
@@ -9,7 +9,6 @@
 import numpy as np
 import skfuzzy as fuzz
 
-
 if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
@@ -45,7 +44,7 @@
     # max-product composition
 
     # Plot each set A, set B and each operation result using plot() and subplot().
-    import matplotlib.pyplot as plt
+    from matplotlib import pyplot as plt
 
     plt.figure()
 
diff --git a/geodesy/lamberts_ellipsoidal_distance.py b/geodesy/lamberts_ellipsoidal_distance.py
index 969e70befd0b..4a318265e5e6 100644
--- a/geodesy/lamberts_ellipsoidal_distance.py
+++ b/geodesy/lamberts_ellipsoidal_distance.py
@@ -1,4 +1,5 @@
 from math import atan, cos, radians, sin, tan
+
 from haversine_distance import haversine_distance
 
 
diff --git a/graphics/bezier_curve.py b/graphics/bezier_curve.py
index 512efadf86ee..48755647e02d 100644
--- a/graphics/bezier_curve.py
+++ b/graphics/bezier_curve.py
@@ -1,7 +1,7 @@
 # https://en.wikipedia.org/wiki/B%C3%A9zier_curve
 # https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
-
 from typing import List, Tuple
+
 from scipy.special import comb
 
 
@@ -78,7 +78,7 @@ def plot_curve(self, step_size: float = 0.01):
             step_size: defines the step(s) at which to evaluate the Bezier curve.
             The smaller the step size, the finer the curve produced.
         """
-        import matplotlib.pyplot as plt
+        from matplotlib import pyplot as plt
 
         to_plot_x: List[float] = []  # x coordinates of points to plot
         to_plot_y: List[float] = []  # y coordinates of points to plot
diff --git a/graphs/basic_graphs.py b/graphs/basic_graphs.py
index 1cbd82a2bd08..6e3c63251527 100644
--- a/graphs/basic_graphs.py
+++ b/graphs/basic_graphs.py
@@ -1,6 +1,5 @@
 from collections import deque
 
-
 if __name__ == "__main__":
     # Accept No. of Nodes and edges
     n, m = map(int, input().split(" "))
diff --git a/graphs/breadth_first_search_2.py b/graphs/breadth_first_search_2.py
index 0c87b5d8bf3d..293a1012f61f 100644
--- a/graphs/breadth_first_search_2.py
+++ b/graphs/breadth_first_search_2.py
@@ -12,8 +12,7 @@
             mark w as explored
             add w to Q (at the end)
 """
-
-from typing import Set, Dict
+from typing import Dict, Set
 
 G = {
     "A": ["B", "C"],
diff --git a/graphs/depth_first_search.py b/graphs/depth_first_search.py
index f3e0ed4ebaa6..1e2231907b78 100644
--- a/graphs/depth_first_search.py
+++ b/graphs/depth_first_search.py
@@ -11,8 +11,7 @@
         if v unexplored:
             DFS(G, v)
 """
-
-from typing import Set, Dict
+from typing import Dict, Set
 
 
 def depth_first_search(graph: Dict, start: str) -> Set[int]:
diff --git a/graphs/directed_and_undirected_(weighted)_graph.py b/graphs/directed_and_undirected_(weighted)_graph.py
index 0312e982a9e0..267111a3a401 100644
--- a/graphs/directed_and_undirected_(weighted)_graph.py
+++ b/graphs/directed_and_undirected_(weighted)_graph.py
@@ -1,7 +1,7 @@
-from collections import deque
-import random as rand
 import math as math
+import random as rand
 import time
+from collections import deque
 
 # the default weight is 1 if not assigned but all the implementation is weighted
 
diff --git a/graphs/multi_heuristic_astar.py b/graphs/multi_heuristic_astar.py
index 386aab695bb0..77ca5760d5f0 100644
--- a/graphs/multi_heuristic_astar.py
+++ b/graphs/multi_heuristic_astar.py
@@ -1,4 +1,5 @@
 import heapq
+
 import numpy as np
 
 
diff --git a/greedy_method/test_knapsack.py b/greedy_method/test_knapsack.py
index 71a259a1ec46..f3ae624ea7aa 100644
--- a/greedy_method/test_knapsack.py
+++ b/greedy_method/test_knapsack.py
@@ -1,4 +1,5 @@
 import unittest
+
 import greedy_knapsack as kp
 
 
diff --git a/hashes/sha1.py b/hashes/sha1.py
index 26069fb9b7fb..04ecdd788039 100644
--- a/hashes/sha1.py
+++ b/hashes/sha1.py
@@ -23,10 +23,9 @@
 the final hash.
 Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
 """
-
 import argparse
-import struct
 import hashlib  # hashlib is only used inside the Test class
+import struct
 import unittest
 
 
diff --git a/linear_algebra/src/test_linear_algebra.py b/linear_algebra/src/test_linear_algebra.py
index 21fed9529ac0..be6245747f87 100644
--- a/linear_algebra/src/test_linear_algebra.py
+++ b/linear_algebra/src/test_linear_algebra.py
@@ -6,9 +6,16 @@
 
 This file contains the test-suite for the linear algebra library.
 """
-
 import unittest
-from lib import Matrix, Vector, axpy, squareZeroMatrix, unitBasisVector, zeroVector
+
+from lib import (
+    Matrix,
+    Vector,
+    axpy,
+    squareZeroMatrix,
+    unitBasisVector,
+    zeroVector,
+)
 
 
 class Test(unittest.TestCase):
diff --git a/machine_learning/gaussian_naive_bayes.py b/machine_learning/gaussian_naive_bayes.py
index 24c884adb98a..c200aa5a4d2d 100644
--- a/machine_learning/gaussian_naive_bayes.py
+++ b/machine_learning/gaussian_naive_bayes.py
@@ -1,10 +1,9 @@
 # Gaussian Naive Bayes Example
-
-from sklearn.naive_bayes import GaussianNB
-from sklearn.metrics import plot_confusion_matrix
+from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
 from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
+from sklearn.naive_bayes import GaussianNB
 
 
 def main():
diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py
index 9f6c65f58fd6..071c58db256b 100644
--- a/machine_learning/k_means_clust.py
+++ b/machine_learning/k_means_clust.py
@@ -52,11 +52,12 @@
 
 
 """
+import warnings
+
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
 from sklearn.metrics import pairwise_distances
-import warnings
 
 warnings.filterwarnings("ignore")
 
@@ -193,7 +194,7 @@ def kmeans(
 
 # Mock test below
 if False:  # change to true to run this test case.
-    import sklearn.datasets as ds
+    from sklearn import datasets as ds
 
     dataset = ds.load_iris()
     k = 3
diff --git a/machine_learning/k_nearest_neighbours.py b/machine_learning/k_nearest_neighbours.py
index 481a8e1dbcd0..e90ea09a58c1 100644
--- a/machine_learning/k_nearest_neighbours.py
+++ b/machine_learning/k_nearest_neighbours.py
@@ -1,5 +1,6 @@
-import numpy as np
 from collections import Counter
+
+import numpy as np
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 
diff --git a/machine_learning/knn_sklearn.py b/machine_learning/knn_sklearn.py
index c36a530736cd..9a9114102ff3 100644
--- a/machine_learning/knn_sklearn.py
+++ b/machine_learning/knn_sklearn.py
@@ -1,5 +1,5 @@
-from sklearn.model_selection import train_test_split
 from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
 
 # Load iris file
diff --git a/machine_learning/linear_discriminant_analysis.py b/machine_learning/linear_discriminant_analysis.py
index 01be288ea64a..b26da2628982 100644
--- a/machine_learning/linear_discriminant_analysis.py
+++ b/machine_learning/linear_discriminant_analysis.py
@@ -41,11 +41,9 @@
 
     Author: @EverLookNeverSee
 """
-
 from math import log
 from os import name, system
-from random import gauss
-from random import seed
+from random import gauss, seed
 
 
 # Make a training dataset drawn from a gaussian distribution
diff --git a/machine_learning/linear_regression.py b/machine_learning/linear_regression.py
index 29bb7c48589e..8c0dfebbca9d 100644
--- a/machine_learning/linear_regression.py
+++ b/machine_learning/linear_regression.py
@@ -7,8 +7,8 @@
 fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
 Rating). We try to best fit a line through dataset and estimate the parameters.
 """
-import requests
 import numpy as np
+import requests
 
 
 def collect_dataset():
diff --git a/machine_learning/logistic_regression.py b/machine_learning/logistic_regression.py
index 1c1906e8e6b2..48d88ef61185 100644
--- a/machine_learning/logistic_regression.py
+++ b/machine_learning/logistic_regression.py
@@ -14,14 +14,12 @@
 Coursera ML course
 https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
 """
-
 import numpy as np
-import matplotlib.pyplot as plt
+from matplotlib import pyplot as plt
+from sklearn import datasets
 
 # get_ipython().run_line_magic('matplotlib', 'inline')
 
-from sklearn import datasets
-
 
 # In[67]:
 
diff --git a/machine_learning/lstm/lstm_prediction.py b/machine_learning/lstm/lstm_prediction.py
index fbf802f4d8ee..5452f0443f62 100644
--- a/machine_learning/lstm/lstm_prediction.py
+++ b/machine_learning/lstm/lstm_prediction.py
@@ -4,14 +4,12 @@
     * http://colah.github.io/posts/2015-08-Understanding-LSTMs
     * https://en.wikipedia.org/wiki/Long_short-term_memory
 """
-
-from keras.layers import Dense, LSTM
-from keras.models import Sequential
 import numpy as np
 import pandas as pd
+from keras.layers import LSTM, Dense
+from keras.models import Sequential
 from sklearn.preprocessing import MinMaxScaler
 
-
 if __name__ == "__main__":
     """
     First part of building a model is to get the data and prepare
diff --git a/machine_learning/multilayer_perceptron_classifier.py b/machine_learning/multilayer_perceptron_classifier.py
index d78d2a9ed8eb..604185cef677 100644
--- a/machine_learning/multilayer_perceptron_classifier.py
+++ b/machine_learning/multilayer_perceptron_classifier.py
@@ -1,6 +1,5 @@
 from sklearn.neural_network import MLPClassifier
 
-
 X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
 y = [0, 1, 0, 0]
 
diff --git a/machine_learning/polymonial_regression.py b/machine_learning/polymonial_regression.py
index f781141f169c..374c35f7f905 100644
--- a/machine_learning/polymonial_regression.py
+++ b/machine_learning/polymonial_regression.py
@@ -1,5 +1,5 @@
-import matplotlib.pyplot as plt
 import pandas as pd
+from matplotlib import pyplot as plt
 from sklearn.linear_model import LinearRegression
 
 # Splitting the dataset into the Training set and Test set
diff --git a/machine_learning/random_forest_classifier.py b/machine_learning/random_forest_classifier.py
index e7acd91346a1..6370254090f7 100644
--- a/machine_learning/random_forest_classifier.py
+++ b/machine_learning/random_forest_classifier.py
@@ -1,10 +1,9 @@
 # Random Forest Classifier Example
-
+from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import plot_confusion_matrix
-import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
 
 
 def main():
diff --git a/machine_learning/random_forest_regressor.py b/machine_learning/random_forest_regressor.py
index f78b6bbd0f42..0aade626b038 100644
--- a/machine_learning/random_forest_regressor.py
+++ b/machine_learning/random_forest_regressor.py
@@ -1,10 +1,8 @@
 # Random Forest Regressor Example
-
 from sklearn.datasets import load_boston
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import mean_absolute_error
-from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from sklearn.model_selection import train_test_split
 
 
 def main():
diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py
index 3583b18ab2e6..98ce05c46cff 100644
--- a/machine_learning/sequential_minimum_optimization.py
+++ b/machine_learning/sequential_minimum_optimization.py
@@ -36,9 +36,9 @@
 import sys
 import urllib.request
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from matplotlib import pyplot as plt
 from sklearn.datasets import make_blobs, make_circles
 from sklearn.preprocessing import StandardScaler
 
diff --git a/machine_learning/support_vector_machines.py b/machine_learning/support_vector_machines.py
index 3bf54a69128d..c5e5085d8748 100644
--- a/machine_learning/support_vector_machines.py
+++ b/machine_learning/support_vector_machines.py
@@ -1,5 +1,5 @@
-from sklearn.datasets import load_iris
 from sklearn import svm
+from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 
 
diff --git a/maths/3n_plus_1.py b/maths/3n_plus_1.py
index d3684561827f..baaa74f89bf5 100644
--- a/maths/3n_plus_1.py
+++ b/maths/3n_plus_1.py
@@ -1,4 +1,4 @@
-from typing import Tuple, List
+from typing import List, Tuple
 
 
 def n31(a: int) -> Tuple[List[int], int]:
diff --git a/maths/fibonacci.py b/maths/fibonacci.py
index d1e8cf7775fd..e6519035401e 100644
--- a/maths/fibonacci.py
+++ b/maths/fibonacci.py
@@ -7,10 +7,10 @@
     reference-->Su, Francis E., et al. "Fibonacci Number Formula." Math Fun Facts.
     <http://www.math.hmc.edu/funfacts>
 """
-import math
 import functools
+import math
 import time
-from decimal import getcontext, Decimal
+from decimal import Decimal, getcontext
 
 getcontext().prec = 100
 
diff --git a/maths/gamma.py b/maths/gamma.py
index 9193929baa28..69cd819ef186 100644
--- a/maths/gamma.py
+++ b/maths/gamma.py
@@ -1,6 +1,7 @@
 import math
-from scipy.integrate import quad
+
 from numpy import inf
+from scipy.integrate import quad
 
 
 def gamma(num: float) -> float:
diff --git a/maths/gaussian.py b/maths/gaussian.py
index edd52d1a4b2c..5d5800e00989 100644
--- a/maths/gaussian.py
+++ b/maths/gaussian.py
@@ -5,7 +5,7 @@
 python : 3.7.3
 
 """
-from numpy import pi, sqrt, exp
+from numpy import exp, pi, sqrt
 
 
 def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
diff --git a/maths/line_length.py b/maths/line_length.py
index 0b1ddb5b7866..6df0a916efe6 100644
--- a/maths/line_length.py
+++ b/maths/line_length.py
@@ -1,5 +1,5 @@
-from typing import Callable, Union
 import math as m
+from typing import Callable, Union
 
 
 def line_length(
diff --git a/maths/mobius_function.py b/maths/mobius_function.py
index df0f66177501..4fcf35f21813 100644
--- a/maths/mobius_function.py
+++ b/maths/mobius_function.py
@@ -5,8 +5,8 @@
 flake8 : True
 """
 
-from maths.prime_factors import prime_factors
 from maths.is_square_free import is_square_free
+from maths.prime_factors import prime_factors
 
 
 def mobius(n: int) -> int:
diff --git a/maths/newton_raphson.py b/maths/newton_raphson.py
index 7b16d2dd9b2e..d8a98dfa6703 100644
--- a/maths/newton_raphson.py
+++ b/maths/newton_raphson.py
@@ -7,7 +7,6 @@
     limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
     is raised. If iteration limit is reached, try increasing maxiter.
     """
-
 import math as m
 
 
@@ -42,7 +41,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
 
 
 if __name__ == "__main__":
-    import matplotlib.pyplot as plt
+    from matplotlib import pyplot as plt
 
     f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     solution, error, steps = newton_raphson(
diff --git a/maths/prime_numbers.py b/maths/prime_numbers.py
index 1b2a29f1a203..38bebddeee41 100644
--- a/maths/prime_numbers.py
+++ b/maths/prime_numbers.py
@@ -1,5 +1,5 @@
-from typing import Generator
 import math
+from typing import Generator
 
 
 def slow_primes(max: int) -> Generator[int, None, None]:
diff --git a/maths/relu.py b/maths/relu.py
index 38a937decb0c..89667ab3e73f 100644
--- a/maths/relu.py
+++ b/maths/relu.py
@@ -9,9 +9,9 @@
 Script inspired from its corresponding Wikipedia article
 https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
 """
+from typing import List
 
 import numpy as np
-from typing import List
 
 
 def relu(vector: List[float]):
diff --git a/maths/volume.py b/maths/volume.py
index 04283743d71f..41d2331db3cb 100644
--- a/maths/volume.py
+++ b/maths/volume.py
@@ -3,8 +3,8 @@
 
 Wikipedia reference: https://en.wikipedia.org/wiki/Volume
 """
-from typing import Union
 from math import pi, pow
+from typing import Union
 
 
 def vol_cube(side_length: Union[int, float]) -> float:
diff --git a/maths/zellers_congruence.py b/maths/zellers_congruence.py
index 9c13c29210b1..8608b32f3ee3 100644
--- a/maths/zellers_congruence.py
+++ b/maths/zellers_congruence.py
@@ -1,5 +1,5 @@
-import datetime
 import argparse
+import datetime
 
 
 def zeller(date_input: str) -> str:
diff --git a/matrix/tests/test_matrix_operation.py b/matrix/tests/test_matrix_operation.py
index bf049b32116e..3500dfeb0641 100644
--- a/matrix/tests/test_matrix_operation.py
+++ b/matrix/tests/test_matrix_operation.py
@@ -6,11 +6,13 @@
 -vv -m mat_ops -p no:cacheprovider
 """
 
+import logging
+
 # standard libraries
 import sys
+
 import numpy as np
 import pytest
-import logging
 
 # Custom/local libraries
 from matrix import matrix_operation as matop
diff --git a/neural_network/back_propagation_neural_network.py b/neural_network/back_propagation_neural_network.py
index c771dc46afc2..43e796e77be3 100644
--- a/neural_network/back_propagation_neural_network.py
+++ b/neural_network/back_propagation_neural_network.py
@@ -17,9 +17,8 @@
 Date: 2017.11.23
 
 """
-
 import numpy as np
-import matplotlib.pyplot as plt
+from matplotlib import pyplot as plt
 
 
 def sigmoid(x):
diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py
index ac0eddeb5cb6..d821488025ef 100644
--- a/neural_network/convolution_neural_network.py
+++ b/neural_network/convolution_neural_network.py
@@ -14,8 +14,9 @@
     - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
 import pickle
+
 import numpy as np
-import matplotlib.pyplot as plt
+from matplotlib import pyplot as plt
 
 
 class CNN:
diff --git a/other/least_recently_used.py b/other/least_recently_used.py
index e1b5ab5bd380..1b901d544b8d 100644
--- a/other/least_recently_used.py
+++ b/other/least_recently_used.py
@@ -1,5 +1,5 @@
-from abc import abstractmethod
 import sys
+from abc import abstractmethod
 from collections import deque
 
 
diff --git a/other/sierpinski_triangle.py b/other/sierpinski_triangle.py
index e27db3a2e8c4..cf41ffa5f190 100644
--- a/other/sierpinski_triangle.py
+++ b/other/sierpinski_triangle.py
@@ -27,8 +27,8 @@
 http://www.riannetrujillo.com/blog/python-fractal/
 
 """
-import turtle
 import sys
+import turtle
 
 PROGNAME = "Sierpinski Triangle"
 
diff --git a/project_euler/problem_07/sol3.py b/project_euler/problem_07/sol3.py
index 9b02ea87ec49..602eb13b623d 100644
--- a/project_euler/problem_07/sol3.py
+++ b/project_euler/problem_07/sol3.py
@@ -5,8 +5,8 @@
 
 We can see that the 6th prime is 13. What is the Nth prime number?
 """
-import math
 import itertools
+import math
 
 
 def primeCheck(number):
diff --git a/project_euler/problem_42/solution42.py b/project_euler/problem_42/solution42.py
index 2380472153c6..1e9bb49c7a06 100644
--- a/project_euler/problem_42/solution42.py
+++ b/project_euler/problem_42/solution42.py
@@ -15,7 +15,6 @@
 """
 import os
 
-
 # Precomputes a list of the 100 first triangular numbers
 TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
 
diff --git a/scheduling/shortest_job_first.py b/scheduling/shortest_job_first.py
index 8aa642a8b25b..6a2fdeeecc5a 100644
--- a/scheduling/shortest_job_first.py
+++ b/scheduling/shortest_job_first.py
@@ -3,9 +3,9 @@
 Please note arrival time and burst
 Please use spaces to separate times entered.
 """
+from typing import List
 
 import pandas as pd
-from typing import List
 
 
 def calculate_waitingtime(
diff --git a/scripts/validate_filenames.py b/scripts/validate_filenames.py
index f22fda4149f3..01712e8f7707 100755
--- a/scripts/validate_filenames.py
+++ b/scripts/validate_filenames.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
-
 import os
+
 from build_directory_md import good_file_paths
 
 filepaths = list(good_file_paths())
diff --git a/searches/hill_climbing.py b/searches/hill_climbing.py
index 324097ef5a24..70622ebefb4e 100644
--- a/searches/hill_climbing.py
+++ b/searches/hill_climbing.py
@@ -150,7 +150,7 @@ def hill_climbing(
             solution_found = True
 
     if visualization:
-        import matplotlib.pyplot as plt
+        from matplotlib import pyplot as plt
 
         plt.plot(range(iterations), scores)
         plt.xlabel("Iterations")
diff --git a/searches/simulated_annealing.py b/searches/simulated_annealing.py
index b12303274b14..8535e5419a4a 100644
--- a/searches/simulated_annealing.py
+++ b/searches/simulated_annealing.py
@@ -84,7 +84,7 @@ def simulated_annealing(
             current_state = next_state
 
     if visualization:
-        import matplotlib.pyplot as plt
+        from matplotlib import pyplot as plt
 
         plt.plot(range(iterations), scores)
         plt.xlabel("Iterations")
diff --git a/searches/tabu_search.py b/searches/tabu_search.py
index f0c3241dbe19..24d0dbf6f1c2 100644
--- a/searches/tabu_search.py
+++ b/searches/tabu_search.py
@@ -24,9 +24,8 @@
     -s size_of_tabu_search
 e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
 """
-
-import copy
 import argparse
+import copy
 
 
 def generate_neighbours(path):
diff --git a/sorts/external_sort.py b/sorts/external_sort.py
index e5b2c045fa95..060e67adf827 100644
--- a/sorts/external_sort.py
+++ b/sorts/external_sort.py
@@ -3,8 +3,8 @@
 #
 # Sort large text files in a minimum amount of memory
 #
-import os
 import argparse
+import os
 
 
 class FileSplitter:
diff --git a/sorts/random_normal_distribution_quicksort.py b/sorts/random_normal_distribution_quicksort.py
index 5da9337ee080..73eb70bea07f 100644
--- a/sorts/random_normal_distribution_quicksort.py
+++ b/sorts/random_normal_distribution_quicksort.py
@@ -1,5 +1,6 @@
 from random import randint
 from tempfile import TemporaryFile
+
 import numpy as np
 
 
diff --git a/web_programming/crawl_google_results.py b/web_programming/crawl_google_results.py
index 7d2be7c03c22..a33a3f3bbe5c 100644
--- a/web_programming/crawl_google_results.py
+++ b/web_programming/crawl_google_results.py
@@ -1,10 +1,9 @@
 import sys
 import webbrowser
 
+import requests
 from bs4 import BeautifulSoup
 from fake_useragent import UserAgent
-import requests
-
 
 if __name__ == "__main__":
     print("Googling.....")
diff --git a/web_programming/get_imdbtop.py b/web_programming/get_imdbtop.py
index 522e423b4eab..669e7f89824b 100644
--- a/web_programming/get_imdbtop.py
+++ b/web_programming/get_imdbtop.py
@@ -1,5 +1,5 @@
-from bs4 import BeautifulSoup
 import requests
+from bs4 import BeautifulSoup
 
 
 def imdb_top(imdb_top_n):