SmartDataCleaner is a professional tool to clean, normalize, and prepare CSV data. In this tutorial, we’ll go through building it step by step using Python, Pandas, and Tkinter with ttkbootstrap.
Step 1: Project Setup
We’ll need a few libraries:
pip install pandas numpy ttkbootstrap reportlab
Import the essentials:
import os, sys, threading, json
from datetime import datetime
import tkinter as tk
from tkinter import filedialog
import pandas as pd
import numpy as np
import re
import ttkbootstrap as tb
from ttkbootstrap.constants import *
Explanation:
pandas & numpy for data handling.
tkinter & ttkbootstrap for the GUI.
threading for background data cleaning.
json for exporting results.
Step 2: Globals and Utilities
Define global variables and helper functions:
stop_event = threading.Event()
cleanup_results = {}
log_file = os.path.join(os.getcwd(), "datacleaner.log")
def log_error(msg):
with open(log_file, "a", encoding="utf-8") as f:
f.write(f"[{datetime.now().isoformat()}] {msg}\n")
def clean_column_name(name):
name = name.strip().lower()
name = re.sub(r"[^\w\s]", "", name)
name = re.sub(r"\s+", "_", name)
return name
Explanation:
stop_event allows stopping the cleanup mid-process.
log_error writes errors to a log file.
clean_column_name converts column names to snake_case.
Step 3: GUI – Root Window
Create the main window:
app = tb.Window(themename="darkly")
app.title("SmartDataCleaner v2.0.0")
app.geometry("1100x650")
Explanation:
ttkbootstrap.Window provides a modern look.
darkly theme gives a dark interface.
Step 4: File Selection Section
Allow the user to select a CSV file:
file_path = tk.StringVar()
row1 = tb.Labelframe(app, text="Select CSV File", padding=10)
row1.pack(fill="x", padx=10, pady=6)
tb.Label(row1, text="File:", width=10).pack(side="left")
tb.Entry(row1, textvariable=file_path, width=60).pack(side="left", padx=6)
tb.Button(
row1,
text="📄 CSV File",
bootstyle="secondary",
command=lambda: file_path.set(filedialog.askopenfilename(filetypes=[("CSV Files", "*.csv")]))
).pack(side="left", padx=4)
Explanation:
Users can browse their system to pick a CSV.
The selected path is stored in file_path.
Step 5: Cleanup Controls
Add Start and Stop buttons:
row2 = tb.Labelframe(app, text="Cleanup Controls", padding=10)
row2.pack(fill="x", padx=10, pady=6)
start_btn = tb.Button(row2, text="🧹 CLEAN DATA", bootstyle="success")
stop_btn = tb.Button(row2, text="🛑 STOP", bootstyle="danger-outline", state="disabled")
start_btn.pack(side="left", padx=6)
stop_btn.pack(side="left", padx=6)
Explanation:
start_btn will start the cleaning process.
stop_btn can halt a running cleanup safely.
Step 6: Display Results
Use a Treeview to show column analysis:
row3 = tb.Labelframe(app, text="Cleanup Results & Suggestions", padding=10)
row3.pack(fill="both", expand=True, padx=10, pady=6)