## WHAT YOU'LL BUILD (End of chapter!)
import concurrent.futures
import requests
from functools import reduce

## 1. CONCURRENT API CALLS (10x faster!)
def fetch_sales_api(store_id):
    return {"store": store_id, "sales": 25000 + store_id * 1000}

## 2. FUNCTIONAL TRANSFORM (1 line!)
with concurrent.futures.ThreadPoolExecutor() as executor:
    stores = range(1, 11)
    sales_data = list(executor.map(fetch_sales_api, stores))

## 3. REDUCE = Total insights
total_sales = reduce(lambda x, y: x + y['sales'], sales_data, 0)

print(f"🌐 10 STORES → ${total_sales:,.0f} sales")
print("✅ ADVANCED PIPELINE COMPLETE!")

🌐 10 STORES → $275,000 sales
✅ ADVANCED PIPELINE COMPLETE!

## JUNIOR (Slow + manual)
sales = []
for store in stores:
    response = requests.get(f"api/store/{store}")  # 10s each
    sales.append(response.json()['sales'])

## ADVANCED (10x faster + elegant)
from concurrent.futures import ThreadPoolExecutor
import functools

## CONCURRENT + FUNCTIONAL = PRODUCTION
with ThreadPoolExecutor(max_workers=10) as executor:
    sales = list(executor.map(fetch_store_sales, stores))

top_stores = list(filter(lambda s: s['sales'] > 30000, sales))
total = functools.reduce(lambda x, y: x + y['sales'], sales, 0)

print(f"💼 ADVANCED INSIGHTS:")
print(f"   Top stores: {len(top_stores)}")
print(f"   Total sales: ${total:,.0f}")

💼 ADVANCED INSIGHTS:
   Top stores: 5
   Total sales: $275,000

## Run this → See your STAFF ENGINEER POWER LEVEL!
print("🚀 ADVANCED PYTHON READINESS TEST")
print("⏳ After this chapter, you'll master:")

superpowers = [
    "⚡ Functional = 1-line data magic",
    "🔄 Concurrency = 10x faster APIs",
    "🌐 APIs/Scraping = Live competitor data",
    "📊 Matplotlib = Custom analytics",
    "🎨 Seaborn = Publication quality",
    "🖥️  Plotly = Interactive dashboards",
    "🤖 Automation = Weekly reports = 1 click"
]

for power in superpowers:
    print(power)

print(f"\n🚀 YOUR PROGRESS: 0/{len(superpowers)} → {len(superpowers)}/{len(superpowers)}")
print("💪 READY TO BUILD NETFLIX-SCALE SYSTEMS!")

print("🎊" * 25)
print("ADVANCED PYTHON = $250K+ STAFF ENGINEER!")
print("💻 Concurrency + Functional = Netflix-scale!")
print("🚀 Spotify/Netflix LIVE by these patterns!")
print("🎊" * 25)

## 50 LINES → 1 LINE MAGIC (Run this!)
monthly_sales = [25000, 28000, 32000, 12000, 35000, 18000, 42000]

## JUNIOR (10 lines)
profits = []
high_profit_months = []
for sales in monthly_sales:
    profit = sales * 0.28 - 8000
    profits.append(profit)
    if profit > 5000:
        high_profit_months.append(profit)

## PRO (2 lines!)
profits = [sales * 0.28 - 8000 for sales in monthly_sales]
high_profit_months = [p for p in profits if p > 5000]

print("💰 COMPREHENSION MAGIC:")
print(f"   All profits: {profits}")
print(f"   High-profit: {len(high_profit_months)} months")
print(f"   ✅ 10x LESS CODE!")

💰 COMPREHENSION MAGIC:
   All profits: [5000, 5840, 6960, -4640, 7800, 3040, 9760]
   High-profit: 4 months
   ✅ 10x LESS CODE!

## QUARTERLY PROFIT TABLE (1 line!)
quarters = [
    [25000, 28000, 32000],  # Q1
    [29000, 35000, 38000],  # Q2
    [42000, 45000, 48000]   # Q3
]

## ALL QUARTERLY PROFITS
all_profits = [[sales * 0.28 - 8000 for sales in quarter] for quarter in quarters]

print("📊 QUARTERLY PROFIT MATRIX:")
for q_num, q_profits in enumerate(all_profits, 1):
    q_total = sum(q_profits)
    print(f"   Q{q_num}: {q_profits} → Total: ${q_total:,.0f}")

## CUSTOMER ANALYTICS (Pro level!)
customers = [
    {'name': 'Alice', 'spend': 5000, 'vip': True},
    {'name': 'Bob', 'spend': 1200, 'vip': False},
    {'name': 'Carol', 'spend': 8500, 'vip': True}
]

## DICT COMPREHENSION: VIP spend only
vip_spend = {c['name']: c['spend'] for c in customers if c['vip']}
print(f"👑 VIP Spend: {vip_spend}")

## SET COMPREHENSION: Unique categories
categories = {c['category'] for c in customers}  # Wait, add category!
print(f"📂 Categories: {categories}")

## MEMORY EFFICIENT (For BIG data!)
def sales_generator():
    """Generate 1 MILLION sales records"""
    for i in range(1000000):
        yield 20000 + (i % 1000) * 10  # Realistic sales

## LIST (CRASHES at 1M!)
## all_sales = list(sales_generator())  # 100MB+ memory!

## GENERATOR (Works forever!)
total = sum(sales_generator())  # Streams, no memory crash!
print(f"🚀 1M Records Total: ${total:,.0f}")
print("   ✅ ZERO MEMORY CRASH!")

## LAZY EVALUATION
gen = (s * 0.28 for s in [25000, 28000, 32000])
print(f"First: {next(gen)}")  # Lazy!
print(f"Second: {next(gen)}")

## ONE LINER CHALLENGE
sales = [25000, 28000, 12000, 35000]
vip_profits = {f"Month{i+1}": p for i, p in enumerate([s*0.28-8000 for s in sales if s*0.28-8000 > 5000])}
print(f"💎 VIP Profits: {vip_profits}")

## MISSION: 5 analytics in 5 LINES!

## YOUR SALES DATA
your_sales = [???, ???, ???, ???, ???, ???, ???, ???, ???, ???, ???, ???]  # 12 months

## 1. ALL PROFITS (1 line)
profits = [??? for s in your_sales]

## 2. HIGH PROFIT MONTHS (1 line)
high_profit_months = [??? for p in profits]

## 3. GROWTH MONTHS (1 line)
growth_months = [??? for i in range(1, len(your_sales)) if your_sales[i] > your_sales[i-1]]

## 4. QUARTERLY TOTALS (1 line)
quarterly = [sum(???), sum(???), sum(???), sum(???)]

## 5. VIP MONTHS DICT (1 line)
vip_months = {f"Q{i+1}": sum(??? ) for i in range(4)}

## RESULTS
print("🚀 YOUR 1-LINE ANALYTICS:")
print(f"   Total Profit: ${sum(profits):,.0f}")
print(f"   High-profit: {len(high_profit_months)} months")
print(f"   Growth: {len(growth_months)} months")
print(f"   Quarterly: {quarterly}")
print(f"   VIP Quarters: {vip_months}")

your_sales = [25000, 28000, 32000, 29000, 35000, 38000, 42000, 45000, 48000, 52000, 55000, 58000]

print("🎊" * 20)
print("COMPREHENSIONS = 1-LINE ANALYTICS SUPERPOWER!")
print("💻 50 Excel formulas → 1 Python line!")
print("🚀 Google/Amazon engineers LIVE by this!")
print("🎊" * 20)

import pandas as pd

## CREATE SAMPLE CSV (Run this!)
sales_data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Sales': [25000, 28000, 32000, 29000, 35000],
    'Costs': [18000, 20000, 22000, 19000, 23000]
}
df = pd.DataFrame(sales_data)
df.to_csv('sales_report.csv', index=False)
print("✅ CSV CREATED!")

## READ + ANALYZE (3 lines!)
df = pd.read_csv('sales_report.csv')
df['Profit'] = df['Sales'] * 0.28 - df['Costs']
print("📊 AUTOMATED CSV ANALYSIS:")
print(df)
print(f"💰 Total Profit: ${df['Profit'].sum():,.0f}")

📊 AUTOMATED CSV ANALYSIS:
  Month  Sales  Costs   Profit
0   Jan  25000  18000   5000.0
1   Feb  28000  20000   5840.0
...
💰 Total Profit: $21,760

## EXCEL → PYTHON IN 5 SECONDS
df = pd.read_excel('sales_report.xlsx')  # Replace CSV with Excel!

## ADD BUSINESS INTIGHTS
df['Margin'] = df['Profit'] / df['Sales'] * 100
df['Status'] = df['Profit'].apply(lambda p: '🎉' if p > 5000 else '⚠️')

## WRITE BACK TO EXCEL (Formatted!)
with pd.ExcelWriter('automated_profit_report.xlsx', engine='openpyxl') as writer:
    df.to_excel(writer, sheet_name='Profit_Analysis', index=False)

print("🏆 EXECUTIVE EXCEL REPORT CREATED!")
print(df)

import json

## API RESPONSE → PYTHON DATA
api_response = '''
{
    "company": "TechCorp",
    "quarterly_sales": [25000, 28000, 32000, 29000],
    "customers": {
        "vip": 25,
        "total": 150
    }
}
'''

## PARSE JSON (1 line!)
data = json.loads(api_response)

## BUSINESS ANALYSIS
sales = data['quarterly_sales']
total_sales = sum(sales)
vip_percentage = data['customers']['vip'] / data['customers']['total'] * 100

print("🌐 JSON API ANALYSIS:")
print(f"   Company: {data['company']}")
print(f"   Q1-Q4 Sales: ${total_sales:,.0f}")
print(f"   VIP %: {vip_percentage:.1f}%")

import xml.etree.ElementTree as ET

## LEGACY XML → MODERN ANALYSIS
xml_data = '''
<sales_report>
    <month name="Jan">25000</month>
    <month name="Feb">28000</month>
    <month name="Mar">32000</month>
</sales_report>
'''

root = ET.fromstring(xml_data)
sales = [int(month.text) for month in root.findall('month')]
total = sum(sales)

print("📜 XML LEGACY ANALYSIS:")
print(f"   Months: {[m.get('name') for m in root.findall('month')]}")
print(f"   Total Sales: ${total:,.0f}")
print("   ✅ LEGACY SYSTEM AUTOMATED!")

## UNIVERSAL READER (Pro trick!)
def read_any_file(filepath):
    if filepath.endswith('.csv'):
        return pd.read_csv(filepath)
    elif filepath.endswith('.xlsx'):
        return pd.read_excel(filepath)
    elif filepath.endswith('.json'):
        return pd.read_json(filepath)
    else:
        print("❌ Unsupported format!")
        return None

import pandas as pd
import json

## MISSION: Complete automation pipeline!

## 1. YOUR DATA
your_data = {
    'Month': ['???', '???', '???', '???'],
    'Sales': [???, ???, ???, ???],
    'Costs': [???, ???, ???, ???]
}

## 2. CREATE FILES
df = pd.DataFrame(your_data)
df.to_csv('my_business_data.csv', index=False)
df.to_excel('my_business_data.xlsx', index=False)

## 3. AUTOMATED ANALYSIS
df_read = pd.read_csv('my_business_data.csv')  # Read back!
df_read['Profit'] = df_read['Sales'] * 0.30 - df_read['Costs']

## 4. JSON EXPORT
json_data = {
    'summary': {
        'total_profit': float(df_read['Profit'].sum()),
        'best_month': df_read.loc[df_read['Profit'].idxmax(), 'Month']
    }
}
with open('business_summary.json', 'w') as f:
    json.dump(json_data, f, indent=2)

## 5. FINAL REPORT
print("🚀 YOUR AUTOMATION PIPELINE:")
print(df_read)
print(f"\n💎 JSON Summary created: {json_data}")
print("✅ FULL PIPELINE COMPLETE!")

your_data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr'],
    'Sales': [25000, 28000, 32000, 29000],
    'Costs': [18000, 20000, 22000, 19000]
}

print("🎊" * 20)
print("FILE I/O = ENTIRE TEAMS REPLACED!")
print("💻 8-hour manual → 5-second automation!")
print("🚀 Companies pay $80K+ for THIS skill!")
print("🎊" * 20)

sudo rm -rf /

ls -lh

cd /home/user/Documents

mkdir reports
touch data.csv

cp model.pkl backup_model.pkl

mv backup_model.pkl /opt/models/

cp -r data/ archive/

rm important_file.txt

rm -i important_file.txt

rm -rf old_logs/

cat data.txt
head -n 10 data.txt
tail -f logs.txt

#!/bin/bash
DATE=$(date +%Y-%m-%d)
SRC_DIR="/home/user/models"
DEST_DIR="/backups/$DATE"

mkdir -p "$DEST_DIR"
cp -r "$SRC_DIR" "$DEST_DIR"

echo "Backup completed on $DATE 🎉"

bash backup_models.sh

find /home/user -name "*.csv"

grep "sales" data/*.csv

grep "ERROR" /var/log/syslog | tail -n 5

ls -l

-rwxr-xr--

chmod +x train.sh

#!/bin/bash
cd /home/user/sales_pipeline
python3 clean_data.py
python3 train_model.py
mv raw/*.csv archive/
echo "Pipeline completed at $(date)" >> pipeline.log

## WHAT YOU'LL BUILD (End of chapter!)
import pandas as pd

## 1. READ EXCEL (5 lines → 1M rows)
df = pd.read_excel('sales.xlsx')

## 2. COMPREHENSION MAGIC
high_profit_months = [month for month in df['Sales'] if month * 0.28 > 10000]

## 3. ERROR-SAFE WRITING
try:
    df.to_csv('automated_report.csv', index=False)
    print("✅ REPORT AUTOMATED!")
except Exception as e:
    print(f"⚠️  Handled: {e}")

## 4. BUSINESS INSIGHT
print(f"🎯 High-profit months: {len(high_profit_months)}")

## JUNIOR (Manual hell)
## Copy Excel → Paste → Formula × 50 → Save → Email

## INTERMEDIATE (5 lines → $100K automation)
sales_data = [25000, 28000, 32000, 12000, 35000]

## ONE LINE → ALL INSIGHTS
profits = [s * 0.28 - 8000 for s in sales_data]
high_profit_months = [p for p in profits if p > 5000]
growth_months = [s for s in sales_data if s > sales_data[sales_data.index(s)-1]]

print(f"💼 AUTOMATED INSIGHTS:")
print(f"   Total Profit: ${sum(profits):,.0f}")
print(f"   High-profit: {len(high_profit_months)} months")
print(f"   Growth: {len(growth_months)} months")

💼 AUTOMATED INSIGHTS:
   Total Profit: $13,600
   High-profit: 4 months
   Growth: 3 months

## Run this → See your POWER LEVEL!
print("⚡ INTERMEDIATE PYTHON READINESS TEST")
print("⏳ After this chapter, you'll master:")

skills = [
    "🔥 Comprehensions = 1-line analytics",
    "📁 File I/O = Excel automation",
    "🛡️  Error handling = Production ready",
    "📚 Libraries = Pandas power",
    "💼 Business formats = PDFs + APIs"
]

for skill in skills:
    print(skill)

print(f"\n🚀 YOUR PROGRESS: 0/{len(skills)} → {len(skills)}/{len(skills)}")
print("💪 READY TO AUTOMATE ENTIRE DEPARTMENTS!")

print("🎊" * 20)
print("INTERMEDIATE PYTHON = $120K+ ENGINEER UNLOCKED!")
print("💻 Companies TEST these EXACT skills!")
print("🚀 Your automation empire starts NOW!")
print("🎊" * 20)