Engineering Python · NumPy & pandas Basics

NumPy Arrays

In Engineering Python because ML labs pass features as arrays — ndarray, shape and dtype are the vocabulary those labs assume.

NumPy's `ndarray` is a typed, shaped block of numbers. Create arrays from lists, inspect `.shape` and `.dtype`, and prefer array ops over Python loops for numeric work.

Engineering Python
Medium level
4 concepts

1ndarray from lists

`np.array([...])` builds an array. Homogeneous dtype (often float64 or int64) lets NumPy store values compactly and compute fast.

Figure. np.array([1.0, 2.0, 3.0]) stores those three values as one homogeneous block — one dtype for every cell.

np.array

import numpy as np
a = np.array([1.0, 2.0, 3.0])
print(a)

What library provides ndarray?

NumPy
json
io

import numpy as np

2shape and dtype

`.shape` is a tuple of sizes along each axis. `.dtype` is the element type. Rank-1 shape `(n,)` is a vector; `(n, m)` is a matrix.

Figure. X = [[1, 2], [3, 4]] has two rows and two columns, so .shape is the tuple (2, 2). .dtype is the shared element type of those four cells.

Takeaway

Idea`.shape` is a tuple of sizes along each axis. `.dtype` is the element type. Rank-1 shape `(n,)` is a vector; `(n, m)` is a matrix.

shape dtype

import numpy as np
X = np.array([[1, 2], [3, 4]])
print(X.shape, X.dtype)

What does arr.shape return for a 2D NumPy array with 3 rows and 4 columns?

The tuple (3, 4)
The integer 12
The string "3x4"
The list [4, 3]

NumPy ndarray.shape returns a tuple of array dimensions, (n_rows, n_cols).

3Why arrays before ML

Feature matrices are 2-D arrays: rows are examples, columns are features. Getting shape right is half of debugging an ML lab.

Figure. A feature matrix is two-dimensional: each row is one example, each column is one feature. Shape is that row-by-column geometry.

Takeaway

IdeaFeature matrices are 2-D arrays: rows are examples, columns are features. Getting shape right is half of debugging an ML lab.

4Lab: make an array

Create an array and print its shape.

Figure. np.array([[1, 2, 3], [4, 5, 6]]) is two rows by three columns, so X.shape prints (2, 3).

Takeaway

IdeaCreate an array and print its shape.

Coding lab. Create an ndarray runs in the app, with checks on your output.

Notes

In Engineering Python because ML labs pass features as arrays — ndarray, shape and dtype are the vocabulary those labs assume.
`np.array([...])` builds an array. Homogeneous dtype (often float64 or int64) lets NumPy store values compactly and compute fast.
`.shape` is a tuple of sizes along each axis. `.dtype` is the element type. Rank-1 shape `(n,)` is a vector; `(n, m)` is a matrix.

Exam traps & shortcuts

Run one cell at a time and read stdout before changing more lines.
Names are labels for values; rebinding a name does not rewrite old prints.

Recap

ndarrays have shape and dtype; build them from lists; rows×cols matter for ML.

ndarray from lists: `np.array([...])` builds an array. Homogeneous dtype (often float64 or int64) lets NumPy store values compactly and compute fast.
shape and dtype: `.shape` is a tuple of sizes along each axis. `.dtype` is the element type. Rank-1 shape `(n,)` is a vector; `(n, m)` is a matrix.
Why arrays before ML: Feature matrices are 2-D arrays: rows are examples, columns are features. Getting shape right is half of debugging an ML lab.
Lab: make an array: Create an array and print its shape.

Practise NumPy Arrays

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