Python Foundations for Analysts

💡 Key concepts

• Setup: python -m venv .venv → activate → pip install pandas openpyxl python-docx matplotlib. Keep a requirements.txt.
• Tools: VS Code with the Python extension. Jupyter notebooks for exploration, .py scripts for repeatable automation.
• Core syntax mapped to Excel thinking:

  Excel idea	Python
  Cell value	variable cpi = 0.93
  Range / column	list [0.93, 1.02, 0.88]
  Lookup table	dict {"PRG-001": "KESTREL", ...}
  IF / IFS	if / elif / else
  Fill down a formula	for loop or list comprehension
  LAMBDA	def function

• f-strings: f"{program}: CPI {cpi:.2f}, CV ${cv:,.0f}"
• pathlib for file paths: Path("Data") / "milestones.csv"
• Errors: read tracebacks bottom-up. try/except for expected failures (missing file).

🛠 Hands-on exercise: EVM Functions in Python

Data: a hard-coded list of dicts for 5 programs: {"program": "PRG-001", "bcwp": 4_200_000, "acwp": 4_650_000, "bcws": 4_500_000, "bac": 18_000_000}.

1. Write functions cpi(ev, ac), spi(ev, pv), eac_cpi(bac, ev, ac), rag(idx) that handle divide-by-zero (return None).
2. Loop over programs and print a formatted line per program.
3. Write the results to a CSV with the csv module.
4. Wrap the file write in try/except and print a helpful message if the folder doesn't exist.

📦 Mini-project: Milestone Countdown Script

A script that reads milestones.csv with the csv module (no pandas yet), finds incomplete milestones forecast within the next 60 days, sorts them by date, and prints a clean text report grouped by program, with days remaining and slip vs. baseline.