Write results to an Excel file

shell.nix

@@ -3,5 +3,10 @@
 with pkgs;
 
 mkShell {
-  buildInputs = [ python39 python39Packages.numpy python39Packages.pandas ];
+  buildInputs = [
+    python39
+    python39Packages.numpy
+    python39Packages.pandas
+    python39Packages.XlsxWriter
+  ];
 }

src/execution.py

@@ -1,8 +1,9 @@
 from glob import glob
 from subprocess import run
 from sys import executable
+
 from numpy import mean, std
-from pandas import DataFrame
+from pandas import DataFrame, ExcelWriter
 
 
 def file_list(path):
@@ -32,12 +33,12 @@ def populate_dataframes(greedy, local, greedy_list, local_list, dataset):
     greedy_results = process_output(greedy_list)
     local_results = process_output(local_list)
     greedy_dict = {
-        "dataset": dataset,
+        "dataset": dataset.removeprefix("data/"),
         "distancia": mean(greedy_results),
         "desviacion": std(greedy_results),
     }
     local_dict = {
-        "dataset": dataset,
+        "dataset": dataset.removeprefix("data/"),
         "distancia": mean(local_results),
         "desviacion": std(local_results),
     }
@@ -68,11 +69,20 @@ def script_execution(filenames, greedy, local, iterations=3):
 
 
 def export_results(greedy, local):
-    greedy.to_excel()
-    local.to_excel()
+    dataframes = {"Greedy": greedy, "Local search": local}
+    writer = ExcelWriter(path="docs/algorithm-results.xlsx", engine="xlsxwriter")
+    for name, df in dataframes.items():
+        df.to_excel(writer, sheet_name=name, index=False)
+        worksheet = writer.sheets[name]
+        for index, column in enumerate(df):
+            series = df[column]
+            max_length = series.astype(str).str.len().max()
+            worksheet.set_column(index, index, width=max_length + 5)
+    writer.save()
 
 
 if __name__ == "__main__":
     datasets = file_list(path="data/*.txt")
     greedy, local = create_dataframes()
     populated_greedy, populated_local = script_execution(datasets, greedy, local)
+    export_results(populated_greedy, populated_local)