isort --profile black --recursive . (TheAlgorithms#2170)

* isort --profile black --recursive .

* Update codespell.yml

* typo: vertices

* typo: Explanation

* typo: Explanation

* Fix typos

Author: cclauss

.github/workflows/autoblack.yml

@@ -11,12 +11,13 @@ jobs:
     steps:
       - uses: actions/checkout@v1  # Use v1, NOT v2
       - uses: actions/setup-python@v2
-      - run: pip install black
+      - run: pip install black isort
       - run: black --check .
       - name: If needed, commit black changes to a new pull request
         if: failure()
         run: |
           black .
+          isort --profile black --recursive .
           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

.github/workflows/codespell.yml

@@ -13,5 +13,5 @@ jobs:
           SKIP="./.*,./other/dictionary.txt,./other/words,./project_euler/problem_22/p022_names.txt"
           codespell -L ans,fo,hist,iff,secant,tim --skip=$SKIP --quiet-level=2
       - name: Codespell comment
-          if: ${{ failure() }}
-          uses: plettich/python_codespell_action@master
+        if: ${{ failure() }}
+        uses: plettich/python_codespell_action@master

dynamic_programming/max_non_adjacent_sum.py

@@ -1,4 +1,4 @@
-# Video Explaination: https://www.youtube.com/watch?v=6w60Zi1NtL8&feature=emb_logo
+# Video Explanation: https://www.youtube.com/watch?v=6w60Zi1NtL8&feature=emb_logo
 
 from typing import List
 

dynamic_programming/minimum_cost_path.py

@@ -1,4 +1,4 @@
-# Youtube Explaination: https://www.youtube.com/watch?v=lBRtnuxg-gU
+# Youtube Explanation: https://www.youtube.com/watch?v=lBRtnuxg-gU
 
 from typing import List
 

graphs/connected_components.py

@@ -12,7 +12,7 @@
 
 def dfs(graph: dict, vert: int, visited: list) -> list:
     """
-    Use depth first search to find all vertexes
+    Use depth first search to find all vertices
     being in the same component as initial vertex
     >>> dfs(test_graph_1, 0, 5 * [False])
     [0, 1, 3, 2]

machine_learning/k_means_clust.py

@@ -250,7 +250,7 @@ def ReportGenerator(
     df["dummy"] = 1
     numeric_cols = df.select_dtypes(np.number).columns
     report = (
-        df.groupby(["Cluster"])[  # constract report dataframe
+        df.groupby(["Cluster"])[  # construct report dataframe
             numeric_cols
         ]  # group by cluster number
         .agg(
@@ -289,14 +289,14 @@ def ReportGenerator(
 
     clustersize = report[
         (report["Features"] == "dummy") & (report["Type"] == "count")
-    ]  # caclulating size of cluster(count of clientID's)
+    ]  # calculate the size of cluster(count of clientID's)
     clustersize.Type = (
         "ClusterSize"  # rename created cluster df to match report column names
     )
     clustersize.Features = "# of Customers"
     clusterproportion = pd.DataFrame(
         clustersize.iloc[:, 2:].values
-        / clustersize.iloc[:, 2:].values.sum()  # caclulating proportion of cluster
+        / clustersize.iloc[:, 2:].values.sum()  # calculating the proportion of cluster
     )
     clusterproportion[
         "Type"