From c94722f8b7ae34f08628753b65bd6130264ca211 Mon Sep 17 00:00:00 2001
From: JOHNMWASHUMA <jmwashuma@live.com>
Date: Thu, 6 Feb 2025 09:27:00 +0300
Subject: [PATCH] Remove ununsed command

---
 .../commands/process_results_csv.py           | 73 -------------------
 1 file changed, 73 deletions(-)
 delete mode 100644 tally_ho/apps/tally/management/commands/process_results_csv.py

diff --git a/tally_ho/apps/tally/management/commands/process_results_csv.py b/tally_ho/apps/tally/management/commands/process_results_csv.py
deleted file mode 100644
index 765bd1df0..000000000
--- a/tally_ho/apps/tally/management/commands/process_results_csv.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from django.core.management.base import BaseCommand
-import duckdb
-from datetime import datetime
-from django.utils.translation import gettext_lazy
-
-results_path = 'data/results.csv'
-
-class Command(BaseCommand):
-    help = gettext_lazy("Process results.")
-
-    def handle(self, *args, **kwargs):
-        self.process_candidates_votes_with_duckdb()
-
-    def process_candidates_votes_with_duckdb(self):
-        """
-        Process a CSV file to sum votes for duplicate candidates and
-        recalculate total votes using DuckDB.
-
-        Returns:
-            str: The path to the output CSV file.
-        """
-        # Output file will be named with a timestamp
-        date_time = datetime.now().strftime('%Y%m%d_%H%M%S')
-        output_file = f"processed_candidates_{date_time}.csv"
-
-        try:
-            # Create an in-memory DuckDB connection
-            con = duckdb.connect(database=':memory:')
-
-            # Load the CSV file into DuckDB
-            sql =\
-                str(
-                    "CREATE TABLE candidates AS ",
-                    f"SELECT * FROM read_csv_auto('{results_path}')"
-                )
-            con.execute(sql)
-
-            # Validate the existence of required columns
-            table_info =\
-                con.execute("PRAGMA table_info('candidates')").fetchdf()
-            required_columns = table_info['name'].tolist()
-
-            if 'Name' in required_columns and 'Votes' in required_columns:
-                # Aggregate votes by candidate name
-                aggregated_df = con.execute("""
-                    SELECT
-                        "Name" AS candidate_name,
-                        SUM(Votes) AS votes,
-                        "Total Votes" AS total_votes,
-                        "Status" as status,
-                    FROM candidates
-                    GROUP BY "Name", "Total Votes", "Status"
-                """).fetchdf()
-
-                # Recalculate total votes
-                total_votes = aggregated_df['votes'].sum()
-                print(f"Total Votes: {total_votes}")
-
-                # Add a new column with the total votes for all rows
-                aggregated_df['total_votes'] = total_votes
-
-                # Save the result to a new CSV file
-                aggregated_df.to_csv(output_file, index=False)
-                print(f"Processed data saved to {output_file}")
-
-                return output_file
-            else:
-                raise KeyError(
-                    str("The required columns ('Candidate Name' and 'Votes') "
-                        "are missing from the CSV file."))
-        except Exception as e:
-            print(f"An error occurred: {e}")
-            return None