After finishing this lesson, you will be able to confidently do the following three things.

• ▸✅ Why Python became the standard language for AI/data
• ▸✅ Setting up venv + requirements.txt for Python 3.x
• ▸✅ Four built-in functions: print / input / type / dir

Keep the learning goals as a checklist, and close the lesson once you can answer all of them.

NumPy = the standard for numerical computation. Arrays at C speed. PyTorch, TensorFlow, and Pandas are all built on NumPy.

1. Install + Create Arrays

$ pip install numpy

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print(a)             # [1 2 3 4 5]
print(a.shape)       # (5,)
print(a.dtype)       # int64

# Common creations
np.zeros(5)          # [0. 0. 0. 0. 0.]
np.ones((2, 3))      # Fill 2x3 with 1s
np.arange(0, 10, 2)  # [0 2 4 6 8]
np.linspace(0, 1, 5) # [0. 0.25 0.5 0.75 1.]

2. Vector Operations — In One Line

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)         # [5 7 9]      ← Element-wise +
print(a * b)         # [4 10 18]    ← Element-wise *
print(a * 2)         # [2 4 6]      ← All elements × 2
print(a ** 2)        # [1 4 9]      ← Square
print(a.sum())       # 6
print(a.mean())      # 2.0

The biggest difference from a list — [1,2,3] + [4,5,6] = [1,2,3,4,5,6] (concatenation). NumPy performs mathematical operations.

3. 2D Arrays (Matrices)

A = np.array([[1, 2, 3],
              [4, 5, 6]])

print(A.shape)       # (2, 3)
print(A[0, 1])       # 2 (row 0, col 1)
print(A[:, 0])       # [1 4]  (all rows, col 0)
print(A.T)           # Transpose — [[1 4], [2 5], [3 6]]

4. Statistics

data = np.array([85, 92, 78, 90, 88])

print(data.mean())      # 86.6 (mean)
print(data.std())       # 5.16 (standard deviation)
print(data.max())       # 92
print(data.argmax())    # 1 (index of max value)
print(np.median(data))  # 88

5. Conditions — Boolean Indexing

a = np.array([1, -2, 3, -4, 5])

positives_only = a[a > 0]                    # [1 3 5]
a[a < 0] = 0                          # [-value → 0]
print(a)                              # [1 0 3 0 5]

6. Speed — Compared to Python Lists

import time

# Python list
lst = list(range(10_000_000))
start = time.time()
result = [x * 2 for x in lst]
print(f"list: {time.time()-start:.2f}s")     # approx 0.5s

# NumPy
arr = np.arange(10_000_000)
start = time.time()
result = arr * 2
print(f"numpy: {time.time()-start:.2f}s")    # approx 0.02s (25x faster)

One-Line Summary

np.array() + vector operations + boolean indexing + statistics = the starting point for data analysis.

1. Implemented in C, so extremely fast
2. Vectorization: operations without loops
3. Broadcasting: automatic size alignment

Python features concise, readable syntax and is used across a wide range of fields. As an interpreted language, it can be executed immediately in a REPL environment. It follows the PEP 8 coding style guide and supports automatic formatting with Black/autopep8. Type hints improve code readability and IDE support. Use pip for package management and venv/conda for virtual environment setup.

Knowing the concepts in this lesson lets you give specific instructions to AI. Instead of a vague 'fix this,' make vocabulary-driven requests — that is where token savings begin.

• ▸'Convert this for loop into a numpy vector operation'
• ▸'Refactor this data cleaning into pandas method chaining'

Why This Reduces Tokens

When you don't know the concepts, even after receiving an AI response you have to ask 'What does that mean?' again. That follow-up question is what consumes tokens. Learn the concept once, and the conversation finishes in a single exchange.