Untitled
unknown
plain_text
2 months ago
1.7 kB
4
Indexable
import pandas as pd # Load the files file1 = pd.read_csv("file1.csv") # Replace with actual file path file2 = pd.read_csv("file2.csv") # Replace with actual file path file3 = pd.read_csv("file3.csv") # Replace with actual file path # Ensure relevant columns are treated as strings to avoid issues file1["GP_LGD_CODE"] = file1["GP_LGD_CODE"].astype(str) file2["GP_LGD_CODE"] = file2["GP_LGD_CODE"].astype(str) file2["pincode"] = file2["pincode"].astype(str) file3["GP_LGD_CODE"] = file3["GP_LGD_CODE"].astype(str) file3["25k pin code"] = file3["25k pin code"].astype(str) # Initialize the remarks column file1["Remarks"] = "" for index, row in file1.iterrows(): gp_lgd_code = row["GP_LGD_CODE"] # Step 1: Filter file2 based on GP_LGD_CODE filtered_file2 = file2[file2["GP_LGD_CODE"] == gp_lgd_code] count_file2 = len(filtered_file2) if count_file2 == 0: continue # Skip if no matching GP_LGD_CODE found in file2 # Step 2: Search in file3 for the same GP_LGD_CODE filtered_file3 = file3[file3["GP_LGD_CODE"] == gp_lgd_code] if filtered_file3.empty: continue # Skip if no match found in file3 # Step 3: Get pincode from file3 pincode = filtered_file3.iloc[0]["25k pin code"] # Step 4: Search in file2 based on pincode filtered_by_pincode = file2[file2["pincode"] == pincode] count_pincode = len(filtered_by_pincode) # Step 5: Compute the difference and generate remark reduced_count = count_file2 - count_pincode remark = f"Reduced count by {reduced_count} acc to pincode {count_pincode}" file1.at[index, "Remarks"] = remark # Save the output file1.to_csv("output.csv", index=False) print("Processing complete. Output saved as output.csv")
Editor is loading...
Leave a Comment