Jubilant Data Analyzer Python GUI

import tkinter as tk
from tkinter import ttk, filedialog, messagebox
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import io
import base64
from PIL import Image, ImageTk

class DataAnalyzerApp:
    def __init__(self, root):
        self.root = root
        self.root.title("Jubilant Data Analyzer")
        self.root.geometry("800x600")

        self.df = None

        # --- Main Frame ---
        self.main_frame = ttk.Frame(self.root, padding=10)
        self.main_frame.pack(fill=tk.BOTH, expand=True)

        # --- Load Data Button ---
        self.load_button = ttk.Button(self.main_frame, text="Load CSV Data", command=self.load_data)
        self.load_button.pack(pady=10)

        # --- Data Display ---
        self.data_tree = ttk.Treeview(self.main_frame, columns=('Column 1'), show='headings')
        self.data_tree.pack(fill=tk.BOTH, expand=True)

        # --- Analysis Options ---
        self.analysis_frame = ttk.LabelFrame(self.main_frame, text="Analysis Options", padding=10)
        self.analysis_frame.pack(fill=tk.BOTH, pady=10)

        self.summary_button = ttk.Button(self.analysis_frame, text="Show Summary Statistics", command=self.show_summary)
        self.summary_button.pack(side=tk.LEFT, padx=5)

        self.plot_button = ttk.Button(self.analysis_frame, text="Generate Plots", command=self.generate_plots)
        self.plot_button.pack(side=tk.LEFT, padx=5)

        # --- Details Button ---
        self.details_button = ttk.Button(self.main_frame, text="Details", command=self.show_details)
        self.details_button.pack(pady=10)

    def load_data(self):
        file_path = filedialog.askopenfilename(filetypes=[("CSV files", "*.csv")])
        if file_path:
            try:
                self.df = pd.read_csv(file_path)
                self.populate_treeview()
            except Exception as e:
                messagebox.showerror("Error", f"Could not load data: {e}")

    def populate_treeview(self):
        # Clear existing data
        for item in self.data_tree.get_children():
            self.data_tree.delete(item)

        # Configure columns
        self.data_tree['columns'] = list(self.df.columns)
        for col in self.df.columns:
            self.data_tree.heading(col, text=col)
            self.data_tree.column(col, width=100)  # Adjust width as needed

        # Insert data
        for index, row in self.df.iterrows():
            self.data_tree.insert('', 'end', values=list(row))

    def show_summary(self):
        if self.df is not None:
            try:
                summary = self.df.describe().to_string()
                messagebox.showinfo("Summary Statistics", summary)
            except Exception as e:
                 messagebox.showerror("Error", f"Could not generate summary: {e}")
        else:
            messagebox.showinfo("Info", "Please load data first.")

    def generate_plots(self):
        if self.df is not None:
            try:
                # Create a new window for plots
                plot_window = tk.Toplevel(self.root)
                plot_window.title("Data Plots")

                # Iterate through columns and create histograms
                for col in self.df.columns:
                    if pd.api.types.is_numeric_dtype(self.df[col]):
                        fig, ax = plt.subplots()
                        self.df[col].hist(ax=ax)
                        ax.set_title(f"Histogram of {col}")
                        ax.set_xlabel(col)
                        ax.set_ylabel("Frequency")

                        # Embed plot in Tkinter window
                        canvas = FigureCanvasTkAgg(fig, master=plot_window)
                        canvas.draw()
                        canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)

            except Exception as e:
                messagebox.showerror("Error", f"Could not generate plots: {e}")
        else:
            messagebox.showinfo("Info", "Please load data first.")


    def show_details(self):
        details_text = """
        Jubilant Data Analyzer is a versatile tool designed for quick data exploration and analysis.
        It allows users to load CSV files, display the data in a tabular format, and generate basic summary statistics and histograms.

        Features:
        - Load data from CSV files.
        - Display data in a scrollable Treeview.
        - Generate summary statistics (mean, median, etc.).
        - Create histograms for numerical columns.

        This application utilizes the following libraries:
        - tkinter: For the GUI.
        - pandas: For data manipulation and analysis.
        - matplotlib: For generating plots.
        """
        messagebox.showinfo("Details", details_text)

if __name__ == "__main__":
    root = tk.Tk()
    app = DataAnalyzerApp(root)
    root.mainloop()