Section 1.1: What is PySide6?

PySide6 is a Python library that enables the creation of GUI applications. It serves as a wrapper around Qt6, a powerful UI framework. With PySide6, you can develop fully functional applications compatible with Windows, Mac, Linux, and even mobile platforms.

To get started with PySide6, it's crucial to set up a virtual environment for the necessary library installations. Ensure you have Python installed and execute the following commands in your terminal:

python -m venv env

source env/bin/activate

pip install pyside6

Once installed, you can verify the PySide6 version to confirm everything is set up correctly.

Section 1.2: The "Hello World" Application

Remember the excitement of writing your first lines of code? I felt that thrill again when creating a simple "Hello World" application with PySide6. Here’s how to do it:

Create a file named hello-world.py and add the following code:

import sys

from PySide6.QtWidgets import QApplication, QLabel

app = QApplication(sys.argv)

label = QLabel("Hello World")

label.show()

sys.exit(app.exec())

Run the application using:

python hello-world.py

You should see a simple window displaying "Hello World."

Section 2.1: Selecting a Dataset

The National Retail Federation has conducted surveys on Valentine's Day spending for over ten years. We’ll utilize their data, available on Kaggle under a Creative Commons Public Domain license. Here’s how to load the datasets:

import pandas as pd

import matplotlib.pyplot as plt

historical_spending = pd.read_csv('data/historical_spending.csv')

gifts_gender = pd.read_csv('data/gifts_gender.csv')

gifts_age = pd.read_csv('data/gifts_age.csv')

This dataset includes:

• Historical Spending: Tracks spending percentages on various gift categories from 2010 to 2019.
• Gifts by Gender: Compares spending habits of men and women on Valentine's Day.
• Gifts by Age: Analyzes spending preferences across different age groups.

We can pose several interesting questions based on this data, such as:

• How has average spending changed over the last decade?
• What are the gender differences in gift preferences?
• How do spending preferences vary across age groups?

Section 2.2: Creating Visualizations

To analyze Valentine's Day spending trends, I decided to create a series of visualizations. Using Matplotlib, I plotted a line graph to illustrate historical spending patterns:

palette = plt.get_cmap('Set1')

for column in historical_spending.drop(['Year', 'PercentCelebrating'], axis=1):

plt.plot(historical_spending['Year'], historical_spending[column], color=palette(num), linewidth=2.5, label=column)

plt.title("Historical Spending Trends on Valentine's Day (2010-2019)")

plt.xlabel("Year")

plt.ylabel("Spending ($)")

plt.xticks(historical_spending['Year'])

plt.legend()

plt.show()

The visualization revealed significant trends, such as a spike in spending from 2017 to 2020.

Section 2.3: Wrapping Visualizations in a Desktop App

Having created visualizations, I wanted to integrate them into a user-friendly desktop application. Here’s a snippet of how the code looks:

from PySide6.QtWidgets import QApplication, QMainWindow, QPushButton, QVBoxLayout, QWidget

from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas

class MplCanvas(FigureCanvas):

def __init__(self):

self.fig = Figure()

super().__init__(self.fig)

self.load_data()

def load_data(self):

self.historical_spending = pd.read_csv('data/historical_spending.csv')

self.gifts_gender = pd.read_csv('data/gifts_gender.csv')

self.gifts_age = pd.read_csv('data/gifts_age.csv')

def plot_historical_spending(self):

# Plotting logic goes here

pass

class MainWindow(QMainWindow):

def __init__(self):

super().__init__()

self.canvas = MplCanvas()

# Create buttons and layout

pass

if __name__ == '__main__':

app = QApplication(sys.argv)

mainWin = MainWindow()

mainWin.show()

sys.exit(app.exec())

With this setup, users can interact with the application to visualize different data insights.