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c547a74bba
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import pandas as pd
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def check_headers_predelibfile(df):
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# Check if the headers are already in the column names (first row)
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if 'Achternaam' in df.columns and 'Voornaam' in df.columns:
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print("Headers found in first row - file already processed, returning unchanged")
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return df # Return the dataframe unchanged
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else:
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# Find the row index where 'Achternaam' and 'Voornaam' appear as headers
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header_row = None
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for i, row in df.iterrows():
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if 'Achternaam' in row.values and 'Voornaam' in row.values:
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header_row = i
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break
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if header_row is not None:
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# Delete all rows before the header row
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df = df.iloc[header_row:].reset_index(drop=True)
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# Set the first row as column headers
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df.columns = df.iloc[0]
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df = df.iloc[1:].reset_index(drop=True)
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# Define the columns to keep
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columns_to_keep = [
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'ID', 'Achternaam', 'Voornaam', 'E-mail', 'Loopbaan',
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'Drempelteller omschrijving', 'Programma status omschrijving',
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'OO Periode', 'OO Studiegidsnummer', 'OO Lange omschrijving',
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'OO Eenheden', 'OO Sessie', 'OO Credit (Y/N)', 'OO Periode credit',
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'OO Programma code', 'OO Programma korte omschr.', 'Totaal aantal SP',
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'Aantal SP vereist', 'Aantal SP zonder VZP', 'Adviesrapport code',
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'Waarschuwing', 'Lijsttype'
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]
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# Keep only the specified columns (only if they exist in the dataframe)
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existing_columns = [col for col in columns_to_keep if col in df.columns]
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df = df[existing_columns]
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print(f"Deleted {header_row} rows, set proper headers, and kept {len(existing_columns)} columns")
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return df
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else:
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print("Headers 'Achternaam' and 'Voornaam' not found in the file")
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return df
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def check_headers_dashboard_inschrijvingenfile(df):
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# Check if the headers are already in the column names (first row)
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if 'Naam' in df.columns and 'Voornaam' in df.columns:
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print("Headers found in first row of dashboard_inschrijvingen - no need to search for header row")
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header_row = -1 # Indicates headers are already set
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else:
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# Find the row index where 'Naam' and 'Voornaam' appear as headers
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header_row = None
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for i, row in df.iterrows():
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if 'Naam' in row.values and 'Voornaam' in row.values:
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header_row = i
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break
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if header_row is not None:
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# Delete all rows before the header row
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df = df.iloc[header_row:].reset_index(drop=True)
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# Set the first row as column headers
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df.columns = df.iloc[0]
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df = df.iloc[1:].reset_index(drop=True)
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if header_row is not None and header_row >= 0:
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print(f"Deleted {header_row} rows in dashboard_file, set proper headers")
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elif header_row == -1:
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print(f"Headers were already correct in dashboard_file.")
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return df
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else:
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print("Headers 'Achternaam' and 'Voornaam' not found in the file")
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return df
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if __name__ == "__main__":
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# Read the Excel files
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df_predelib = pd.read_excel('db.xlsx')
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df_dashboard = pd.read_excel('dashboard_inschrijvingen.xlsx')
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# Process the dataframes
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processed_predelib_df = check_headers_predelibfile(df_predelib)
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processed_dashboard_df = check_headers_dashboard_inschrijvingenfile(df_dashboard)
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import pandas as pd
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def compare_sp_values(predelib_df, dashboard_df):
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"""
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Compare 'Totaal aantal SP' from predelib_df with 'Ingeschr. SP (intern)' from dashboard_df
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for matching IDs between the two dataframes.
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Args:
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predelib_df (pandas.DataFrame): Dataframe from predeliberation file with 'ID' and 'Totaal aantal SP' columns
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dashboard_df (pandas.DataFrame): Dataframe from dashboard file with 'ID' and 'Ingeschr. SP (intern)' columns
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Returns:
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list: List of dictionaries containing mismatches, or empty list if all match
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"""
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if 'ID' not in predelib_df.columns:
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print("Warning: 'ID' column not found in predelib dataframe")
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return []
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if 'ID' not in dashboard_df.columns:
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print("Warning: 'ID' column not found in dashboard dataframe")
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return []
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if 'Totaal aantal SP' not in predelib_df.columns:
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print("Warning: 'Totaal aantal SP' column not found in predelib dataframe")
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return []
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if 'Ingeschr. SP (intern)' not in dashboard_df.columns:
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print("Warning: 'Ingeschr. SP (intern)' column not found in dashboard dataframe")
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return []
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# Find matching IDs
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# First, let's debug the ID columns
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print(f"Predelib ID column type: {predelib_df['ID'].dtype}")
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print(f"Dashboard ID column type: {dashboard_df['ID'].dtype}")
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print(f"Sample predelib IDs: {list(predelib_df['ID'].head())}")
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print(f"Sample dashboard IDs: {list(dashboard_df['ID'].head())}")
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# Convert IDs to strings to ensure consistent comparison
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predelib_ids = set(str(x) for x in predelib_df['ID'] if pd.notna(x))
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dashboard_ids = set(str(x) for x in dashboard_df['ID'] if pd.notna(x))
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matching_ids = predelib_ids.intersection(dashboard_ids)
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print(f"Found {len(matching_ids)} matching IDs between the two dataframes")
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if len(matching_ids) == 0:
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print("No matching IDs found between the dataframes")
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print(f"Total predelib IDs: {len(predelib_ids)}")
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print(f"Total dashboard IDs: {len(dashboard_ids)}")
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return []
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# Compare SP values for matching IDs
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mismatches = []
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for id_val in matching_ids:
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# Convert back to original type for filtering (try both string and original)
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predelib_matches = predelib_df[predelib_df['ID'].astype(str) == id_val]
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dashboard_matches = dashboard_df[dashboard_df['ID'].astype(str) == id_val]
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if len(predelib_matches) == 0 or len(dashboard_matches) == 0:
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continue
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predelib_sp = predelib_matches['Totaal aantal SP'].iloc[0]
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dashboard_sp = dashboard_matches['Ingeschr. SP (intern)'].iloc[0]
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if predelib_sp != dashboard_sp:
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mismatches.append({
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'ID': id_val,
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'Predelib_SP': predelib_sp,
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'Dashboard_SP': dashboard_sp
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})
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if len(mismatches) == 0:
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print("All SP values match between the two dataframes!")
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else:
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print(f"Found {len(mismatches)} mismatches:")
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for mismatch in mismatches:
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print(f" ID {mismatch['ID']}: Predelib={mismatch['Predelib_SP']}, Dashboard={mismatch['Dashboard_SP']}")
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return mismatches
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if __name__ == "__main__":
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# Example usage - can be used for testing
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from checkheaders import check_headers_predelibfile, check_headers_dashboard_inschrijvingenfile
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# Read the Excel files
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df_predelib = pd.read_excel('db.xlsx')
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df_dashboard = pd.read_excel('dashboard_inschrijvingen.xlsx')
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# Process the dataframes
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processed_predelib_df = check_headers_predelibfile(df_predelib)
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processed_dashboard_df = check_headers_dashboard_inschrijvingenfile(df_dashboard)
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# Compare SP values between the two processed dataframes
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print("\nComparing SP values between predelib and dashboard files:")
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mismatches = compare_sp_values(processed_predelib_df, processed_dashboard_df)
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import pandas as pd
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from checkheaders import check_headers_dashboard_inschrijvingenfile, check_headers_predelibfile
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from compare_sp import compare_sp_values
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# Read the Excel file
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df_predelib = pd.read_excel('db.xlsx')
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df_dashboard = pd.read_excel('dashboard_inschrijvingen.xlsx')
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processed_predelib_df = check_headers_predelibfile(df_predelib)
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processed_dashboard_df = check_headers_dashboard_inschrijvingenfile(df_dashboard)
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# Further processing can be done with processed_predelib_df and processed_dashboard_df
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print("Processed Predelib DataFrame:")
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print(processed_predelib_df)
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print("\nProcessed Dashboard DataFrame:")
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print(processed_dashboard_df)
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compare_sp_values(processed_predelib_df, processed_dashboard_df)
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print("\nComparison of the predelib file with the dashboard file on SP values complete.")
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extracurriculaire vakken komen niet uit de wizard. aparte excel voor nodig, nl dashboard inschrijvingen.
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Voor de check of ze extra keuzevakken opnemen en de vereiste dus hoger moet komen te staan: vgl de kollomen Totaal aantal SP Aantal SP vereist
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deze kolom moet ook worden gechecked als ze eigenlijk minder opnemen; dus die moeten altijd aan elkaar gelijk zijn.
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