klassen in File speichern

Übersicht in Excel

main.py

@@ -34,6 +34,7 @@ ox_quota_sus = 5120         # oxUserQuota LuL
 #######################
 # MAIN FUNCTION START #
 
+# .venv\Scripts\activate
 
 if __name__ == "__main__":
 
@@ -89,10 +90,17 @@ if __name__ == "__main__":
     # ---------------------------------------------------- #
     # Step 2 - auf name, vorname reduzieren und abgleichen #
 
+    # Klassen Übersicht leeren
+    with open('./output/klassen.txt', 'w') as file:
+        file.write('')
+
+    lul_exel_path = 'output/stats_lul.xlsx'
+    sus_exel_path = 'output/stats_sus.xlsx'
+
     print("\n Lehrer:innen:")
-    lul_matched, new_lul = step2.compare_data(lul_new, lul_sys, len(lul_testuser_df))
+    lul_matched, new_lul = step2.compare_data(lul_new, lul_sys, len(lul_testuser_df), lul_exel_path)
     print("\n Schüler:innen:")
-    sus_matched, new_sus = step2.compare_data(sus_new, sus_sys, len(sus_testuser_df))
+    sus_matched, new_sus = step2.compare_data(sus_new, sus_sys, len(sus_testuser_df), sus_exel_path)
 
     # ----------------------------------------------------------------------------------------#
     # Step 3 - Import Data generieren - klasse, uuids, weiteres in einer Liste zusammenführen #

output/log.txt

@@ -12,24 +12,6 @@ Anzahl Test- / Systemuser SuS: 1
 
 Eintr<EFBFBD>ge in System Liste: 101
 Eintr<EFBFBD>ge in Import Liste: 89
-['L_L' nan '9c-24' '5e-24' '9d-24' 'Ec-24' 'Ef-24' 'Eg-24' '6c-24' '6a-24'
- '7d-24' 'Q2f-24' '10b-24' '8b-24' '7a-24' 'Q2b-24' '7b-24' '8a-24'
- '9e-24' 'Ed-24' '10d-24' '9b-24' 'SB' '6b-24' 'Q2c-24' '8c-24' '5b-24'
- '5c-24' '10a-24' '9a-24' 'Q2e-23' '10c-24' '6d-24' '7c-24' '8d-24'
- 'Eb-24' 'Ee-24' 'Q2d-24' 'Q2a-24' '5d-24' '5a-24' 'Q2g-24' 'Q1a-24'
- 'Ea-24' 'Sek' '8e-23']
-['HL0705102-L_L' 'HL0705102-6d-23' 'HL0705102-Q2e-23' 'HL0705102-5c-23'
- 'HL0705102-5a-23' 'HL0705102-10e-23' 'HL0705102-8b-23' 'HL0705102-Q1f-23'
- 'HL0705102-9b-23' 'HL0705102-7b-23' 'HL0705102-8a-23' 'HL0705102-9d-23'
- 'HL0705102-Q2a-23' 'HL0705102-10a-23' 'HL0705102-Q1c-23'
- 'HL0705102-8e-23' 'HL0705102-6a-23' 'HL0705102-Q2d-23' 'HL0705102-DaZ-23'
- 'HL0705102-Q1a-23' 'HL0705102-Q1g-23' 'HL0705102-Q2b-23'
- 'HL0705102-7c-23' 'HL0705102-10b-23' 'HL0705102-9a-23' 'HL0705102-10d-23'
- 'HL0705102-Ea-23' 'HL0705102-Q2c-23' 'HL0705102-5b-23' 'HL0705102-8d-23'
- 'HL0705102-9c-23' 'HL0705102-Q1e-23' 'HL0705102-7a-23' 'HL0705102-6b-23'
- 'HL0705102-6c-23' 'HL0705102-8c-23' 'HL0705102-Q1b-23' 'HL0705102-5d-23'
- 'HL0705102-10c-23' 'HL0705102-7d-23' 'HL0705102-Sek' 'HL0705102-Q1d-23'
- nan 'HL0705102-testgruppe' 'HL0705102-Dummy']
 
 Anzahl neuer Nutzer: 4
 Anzahl <20>bereinstimmungen: 87
@@ -42,24 +24,6 @@ M
 
 Eintr<EFBFBD>ge in System Liste: 973
 Eintr<EFBFBD>ge in Import Liste: 1011
-['Q2f-24' '10a-24' 'Ef-24' '10b-24' '10c-24' '10d-24' '5a-24' '5b-24'
- '5c-24' '5d-24' '5E-24' '6a-24' '6b-24' '6c-24' '6d-24' '7a-24' '7b-24'
- '7c-24' '7d-24' '8a-24' '8b-24' '8c-24' '8d-24' '9a-24' '9b-24' '9c-24'
- '9d-24' '9E-24' 'Ea-24' 'Eb-24' 'Ec-24' 'Ed-24' 'Ee-24' 'Eg-24' 'Q1a-24'
- 'Q2a-24' 'Q2b-24' 'Q2c-24' 'Q2d-24' 'Q2e-24' 'Q2g-24' nan]
-['HL0705102-10b-23' 'HL0705102-10e-23' 'HL0705102-10a-23'
- 'HL0705102-10c-23' 'HL0705102-10d-23' 'HL0705102-9a-23'
- 'HL0705102-Q1b-23' 'HL0705102-Q1a-23' 'HL0705102-Q1d-23'
- 'HL0705102-Q1f-23' 'HL0705102-Q1g-23' 'HL0705102-Q1c-23'
- 'HL0705102-Ea-23' 'HL0705102-Q1e-23' 'HL0705102-Q2d-23'
- 'HL0705102-Q2a-23' 'HL0705102-Q2c-23' 'HL0705102-Q2b-23'
- 'HL0705102-Q2e-23' 'HL0705102-9d-23' 'HL0705102-9b-23' 'HL0705102-9c-23'
- 'HL0705102-8a-23' 'HL0705102-8b-23' 'HL0705102-Dummy' 'HL0705102-8c-23'
- 'HL0705102-8e-23' 'HL0705102-8d-23' 'HL0705102-7c-23' 'HL0705102-7b-23'
- 'HL0705102-7d-23' 'HL0705102-7a-23' 'HL0705102-Ed-22' 'HL0705102-DaZ-23'
- 'HL0705102-6a-23' 'HL0705102-6b-23' 'HL0705102-6c-23' 'HL0705102-6d-23'
- 'HL0705102-Q2c-22' 'HL0705102-5d-23' 'HL0705102-5c-23' 'HL0705102-5b-23'
- 'HL0705102-5a-23' 'HL0705102-testgruppe']
 
 Anzahl neuer Nutzer: 168
 Anzahl <20>bereinstimmungen: 844

src/step2.py

@@ -1,8 +1,9 @@
+import numpy as np
 import pandas as pd
 from Levenshtein import distance
 
 
-def compare_data(new, sys, count_test):
+def compare_data(new, sys, count_test, path):
     print(f"\nEinträge in System Liste: {len(sys)}")
     print(f"Einträge in Import Liste: {len(new)}")
 
@@ -25,7 +26,7 @@ def compare_data(new, sys, count_test):
     only_new = merged_df[merged_df['_merge'] == 'left_only'].drop(columns=['_merge'])
     only_sys = merged_df[merged_df['_merge'] == 'right_only'].drop(columns=['_merge'])
 
-    print_status(matches, only_new, only_sys, count_test)
+    print_status(matches, only_new, only_sys, count_test, path)
     search_typos(only_new[['name', 'vorname']], only_sys[['name', 'vorname']])
 
     return matches, only_new
@@ -50,10 +51,17 @@ def unique_classes(df):
     df['klasse'] = df['klasse'].str.split(',')
     df = df.explode('klasse')
     eindeutige_klassen = df['klasse'].unique()
-    print(eindeutige_klassen)
+    with open('./output/klassen.txt', 'a') as file:
+        file.write(np.array_str(eindeutige_klassen))
+    # print(eindeutige_klassen)
 
 
-def print_status(matches, new, old, count_test):
+def print_status(matches, new, old, count_test, path):
     print("\nAnzahl neuer Nutzer:", len(new))
     print("Anzahl Übereinstimmungen:", len(matches) + count_test)
     print("Anzahl veralteter Nutzer:", len(old) - count_test)
+    with pd.ExcelWriter(path, engine='openpyxl') as writer:
+
+        matches.to_excel(writer, sheet_name='Matches', index=False)
+        new.to_excel(writer, sheet_name='Neu', index=False)
+        old.to_excel(writer, sheet_name='Alt', index=False)