Add Measles_File_Prep script

Measles_File_Prep.py

@@ -0,0 +1,47 @@
+import os
+from Bio import SeqIO
+import pandas as pd
+
+# --- User Input Section ---
+input_csv = input("Enter the path to Measles_Metadata.csv: ")
+
+
+# --- CSV Processing Function (Second Block) ---
+def process_data(input_csv, output_tsv):
+    """
+    Reads a CSV, filters rows without required accession or date, and saves
+    the accession number and date columns to a TSV file.
+
+    Args:
+        input_csv (str): The path to the input CSV file.
+        output_tsv (str): The path to the output TSV file.
+    """
+    try:
+        # 1. Read the CSV file into a pandas DataFrame.
+        # Assuming 'UPHL_lab_accession' and 'patient_disease_onset_date' are the column names.
+        df = pd.read_csv(input_csv, dtype={'UPHL_lab_accession': str})
+
+        # 2. Remove rows where the required columns are null or missing.
+        df_filtered = df.dropna(subset=['UPHL_lab_accession'])
+        df_filtered = df_filtered.dropna(subset=['patient_disease_onset_date'])
+
+        # 3. Select only the "Accession" and "Date" columns.
+        df_filtered = df_filtered[['UPHL_lab_accession', 'patient_disease_onset_date']]
+
+        # 4. Rename the columns to "Accession" and "Date".
+        df_final = df_filtered.rename(columns={'UPHL_lab_accession': 'Accession', 'patient_disease_onset_date': 'Date'})
+
+        # 5. Save the new DataFrame to a TSV file.
+        df_final.to_csv(output_tsv, sep='\t', index=False)
+
+        print(f"Successfully processed data. Filtered data saved to '{output_tsv}'.")
+
+    except FileNotFoundError:
+        print(f"Error: The file '{input_csv}' was not found.")
+    except KeyError as e:
+        print(f"Error: A required column was not found. Please check your column names. Missing column: {e}")
+    except Exception as e:
+        print(f"An unexpected error occurred during CSV processing: {e}")
+
+# Run the CSV processing block
+process_data(input_csv, 'measles_dates.txt')