Unit 14: Python Libraries & The Applied Ecosystem

import numpy as np

# 1. Creating 1D and 2D arrays
arr1d = np.array([10, 20, 30, 40, 50])
matrix = np.array([[1, 2, 3], [4, 5, 6]])

print("1D Array Shape:", arr1d.shape)
print("2D Matrix Shape:", matrix.shape)

# 2. Vectorized Arithmetic (No for-loops required!)
boosted_scores = arr1d * 1.10  # Boost every element by 10% simultaneously
print("Vectorized +10% Boost:", boosted_scores)

# 3. Built-in High-Speed Statistical Aggregations
print(f"Mean: {arr1d.mean():.2f} | Sum: {arr1d.sum()} | Std Dev: {arr1d.std():.2f}")

# 4. Fast Boolean Filtering
high_vals = arr1d[arr1d > 25]
print("Elements > 25:", high_vals)

import pandas as pd

# Creating a DataFrame from raw dictionary records
raw_data = {
    "StudentID": [101, 102, 103, 104],
    "Name": ["Aman", "Pooja", "Suresh", "Divya"],
    "Department": ["CS", "IT", "CS", "AI"],
    "GPA": [8.4, 9.2, 7.8, 9.6]
}

df = pd.DataFrame(raw_data)
print("--- Student DataFrame ---")
print(df)

# Filtering: Select students with GPA >= 8.5
toppers = df[df["GPA"] >= 8.5]
print("\n--- High Achievers (GPA >= 8.5) ---")
print(toppers[["Name", "Department", "GPA"]])

# Grouped aggregation: Average GPA per department
dept_avg = df.groupby("Department")["GPA"].mean().round(2)
print("\n--- Average GPA by Department ---")
print(dept_avg)

import matplotlib.pyplot as plt

# Sample Semester Performance Data
semesters = ["Sem 1", "Sem 2", "Sem 3", "Sem 4", "Sem 5"]
gpa_scores = [7.8, 8.2, 8.6, 8.9, 9.4]

# 1. Create a plot
plt.figure(figsize=(7, 4))
plt.plot(semesters, gpa_scores, marker='o', color='#4f46e5', linewidth=2.5, label='Student GPA Trend')

# 2. Add Titles and Labels
plt.title("Academic Progression Over Time", fontsize=13, fontweight='bold')
plt.xlabel("Academic Term", fontsize=10)
plt.ylabel("Grade Point Average (GPA)", fontsize=10)
plt.ylim(7.0, 10.0)
plt.grid(True, linestyle='--', alpha=0.6)
plt.legend()

# 3. Save to disk (for web & reports)
plt.savefig("gpa_trend_chart.png", dpi=150)
print("Chart successfully rendered and saved to 'gpa_trend_chart.png'!")

import requests
from bs4 import BeautifulSoup

# Part A: Consuming a REST API with Requests
api_url = "https://jsonplaceholder.typicode.com/users/1"
try:
    response = requests.get(api_url, timeout=5)
    if response.status_code == 200:
        user_data = response.json()  # Parse JSON payload directly into Python dict
        print(f"API Response -> Name: {user_data['name']} | Email: {user_data['email']}")
except requests.RequestException as err:
    print("Network Error:", err)

# Part B: Parsing HTML Content with BeautifulSoup
html_doc = """
<div class="catalog">
    <h2>Available Courses</h2>
    <ul>
        <li class="course-item" data-id="101">Python Masterclass - $49</li>
        <li class="course-item" data-id="102">Data Science with Pandas - $79</li>
        <li class="course-item" data-id="103">Deep Learning Bootcamp - $99</li>
    </ul>
</div>
"""

soup = BeautifulSoup(html_doc, "html.parser")
course_elements = soup.find_all("li", class_="course-item")

print("\nScraped Course Offerings:")
for item in course_elements:
    print(f"  • {item.text.strip()} (ID: {item['data-id']})")

import tkinter as tk
from tkinter import messagebox

# Callback function triggered when button is clicked
def greet_student():
    entered_name = entry_name.get()
    if entered_name.strip():
        label_output.config(text=f"Welcome to DevAscent, {entered_name}!", fg="#4f46e5")
    else:
        messagebox.showwarning("Input Required", "Please enter your name first!")

# 1. Initialize Root Window
root = tk.Tk()
root.title("DevAscent Academy - Student Portal")
root.geometry("400x220")
root.configure(bg="#f8fafc")

# 2. Add Widgets (Labels, Entry, Buttons)
label_prompt = tk.Label(root, text="Enter Student Name:", bg="#f8fafc", font=("Inter", 10, "bold"))
label_prompt.pack(pady=8)

entry_name = tk.Entry(root, font=("Inter", 11), width=26)
entry_name.pack(pady=5)

btn_submit = tk.Button(root, text="Register Student", command=greet_student, 
                       bg="#4f46e5", fg="#ffffff", font=("Inter", 10, "bold"), padx=10, pady=4)
btn_submit.pack(pady=12)

label_output = tk.Label(root, text="Awaiting input...", bg="#f8fafc", font=("Inter", 10, "italic"))
label_output.pack(pady=6)

# 3. Enter Persistent Event Loop
# root.mainloop()  # Keeps GUI responsive to clicks and typing

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

print("==========================================================")
print("📊 DEVASCENT ACADEMY: INTEGRATED ANALYTICS ENGINE")
print("==========================================================")

# Step 1: NumPy generates baseline metric distribution
np.random.seed(42)
days = np.arange(1, 8)
base_visitors = np.array([1200, 1450, 1320, 1800, 2100, 2650, 2900])
revenue_per_visitor = np.random.uniform(1.4, 1.8, size=7)

# Step 2: Vectorized NumPy math
daily_revenue = (base_visitors * revenue_per_visitor).round(2)

# Step 3: Pandas structures the dataset
df_analytics = pd.DataFrame({
    "Day": [f"Day {d}" for d in days],
    "Traffic": base_visitors,
    "Est_Revenue_USD": daily_revenue
})

# Step 4: Pandas statistical analytics
total_traffic = df_analytics["Traffic"].sum()
total_revenue = df_analytics["Est_Revenue_USD"].sum()
mean_daily_rev = df_analytics["Est_Revenue_USD"].mean()

print(df_analytics)
print(f"\nTotal Weekly Traffic : {total_traffic:,} visitors")
print(f"Total Weekly Revenue : ${total_revenue:,.2f}")
print(f"Mean Daily Revenue   : ${mean_daily_rev:,.2f}")

# Step 5: Matplotlib visual export
plt.figure(figsize=(7, 4))
plt.bar(df_analytics["Day"], df_analytics["Est_Revenue_USD"], color="#10b981", alpha=0.85)
plt.title("Weekly Revenue Breakdown", fontsize=12, fontweight='bold')
plt.ylabel("Revenue ($)")
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.savefig("weekly_revenue_report.png", dpi=150)
print("\n[SUCCESS] Pipeline finalized: Chart saved to 'weekly_revenue_report.png'")