#set document(title: "15.2 NumPy", author: "OpenStax / XYZ Homework") #set page(width: 8.5in, height: auto, margin: 1in) #import "@preview/cetz:0.5.2" #set text(font: ("STIX Two Text", "Libertinus Serif", "New Computer Modern"), size: 10.5pt, lang: "en") #show math.equation: set text(font: ("STIX Two Math", "New Computer Modern Math")) #set par(justify: true, leading: 0.62em, spacing: 0.9em) #set enum(spacing: 1.1em) // room between list items so tall inline fractions don't collide #set list(spacing: 1.1em) #set table(stroke: 0.5pt + rgb("#c7ccd3")) #let BLUE = rgb("#183B6F") // brand navy — section bars + example/solution labels (white on navy 11.09:1) #let ORANGE = rgb("#A94509") // brand primary-700 — AA-safe deep orange for TEXT (5.93:1 on white; raw brand #F37021 is 2.94:1 and must never carry text) #let RED = rgb("#DC2626") // brand error-600 #let GREEN = rgb("#059669") // brand success-600 (decoration only; small green text uses green-text #007942) #show heading.where(level: 1): it => block(width: 100%, above: 0pt, below: 16pt, fill: gradient.linear(BLUE, rgb("#2C5AA0")), inset: (x: 14pt, y: 12pt), radius: 3pt, text(fill: white, weight: "bold", size: 19pt, it.body)) #show heading.where(level: 2): it => block(width: 100%, above: 18pt, below: 10pt, fill: BLUE, inset: (x: 10pt, y: 6pt), radius: 2pt, text(fill: white, weight: "bold", size: 12pt, it.body)) #show heading.where(level: 3): it => text(fill: ORANGE, weight: "bold", size: 12.5pt, it.body) #show heading.where(level: 4): it => text(fill: BLUE, weight: "bold", size: 10.5pt, it.body) #let examplebox(label, title, body) = block(width: 100%, breakable: true, fill: rgb("#EFF1F5"), stroke: 0.5pt + rgb("#CFDDF0"), radius: 4pt, inset: 10pt, above: 12pt, below: 12pt)[ #block(below: 6pt)[#box(fill: BLUE, inset: (x: 6pt, y: 2pt), radius: 2pt, text(fill: white, weight: "bold", size: 8.5pt, label)) #h(0.4em) #strong[#title]] #body] // rail = decorative left rule (raw brand token); labelcolor = AA-safe label text shade #let notebox(label, rail, labelcolor, tint, body) = block(width: 100%, breakable: true, fill: tint, stroke: (left: 3pt + rail), inset: (left: 10pt, rest: 8pt), radius: (right: 4pt), above: 11pt, below: 11pt)[ #text(fill: labelcolor, weight: "bold", size: 7.5pt, tracking: 0.5pt)[#upper(label)] #linebreak() #body] #let solutionbox(body) = block(above: 4pt, below: 8pt)[ #text(fill: BLUE, weight: "bold", size: 8.5pt)[Solution] #linebreak() #body] #let figph(msg) = block(width: 100%, height: 60pt, fill: rgb("#f6f7f9"), stroke: (paint: rgb("#c7ccd3"), dash: "dashed"), radius: 4pt, inset: 10pt)[ #align(center + horizon, text(fill: rgb("#889"), style: "italic", size: 9pt, msg))] // Standardize inlined figure sizes: measure the natural CeTZ canvas, then scale to a // consistent envelope (aspect-aware; see build_typst.py FIG_* constants). Unlike the // print preamble, dimensions are FLOORED: in an editor a user can trim a figure to a // degenerate 1-D shape (a bare line), and w/h or tw/w would then divide by zero. #let _STD_W = 3.5 #let _WIDE_W = 5.6 #let _MAX_H = 3.4 #let _ASPECT_WIDE = 2.2 #let _UPSCALE_MAX = 1.15 #let stdfig(body) = context { let m = measure(body) let w = calc.max(m.width / 1in, 0.01) let h = calc.max(m.height / 1in, 0.01) let tw = if w / h > _ASPECT_WIDE { _WIDE_W } else { _STD_W } let s = calc.min(tw / w, _MAX_H / h, _UPSCALE_MAX) align(center, box(scale(x: s * 100%, y: s * 100%, reflow: true, body))) } #show figure: set block(breakable: false) #set figure(gap: 8pt) #show figure.caption: set text(size: 8.5pt, fill: rgb("#555")) == 15.2#h(0.6em)NumPy === Learning objectives By the end of this section you should be able to - Describe the NumPy library. - Create a NumPy array object. - Choose appropriate NumPy functions to process arrays of numerical data. === NumPy library #strong[NumPy] (Numerical Python) is a Python library that provides support for efficient numerical operations on large, multi-dimensional arrays and serves as a fundamental building block for data analysis in Python. The conventional alias for importing NumPy is np. In other words, NumPy is imported as import numpy as np. NumPy implements the #strong[ndarray] object, which allows the creation of a multi-dimensional array of homogeneous data types (columns with the same data type) and efficient data processing. An ndarray object can have any number of dimensions and can store elements of various numeric data types. To create a NumPy ndarray object, one of the following options can be used: - Creating an ndarray by converting a Python list or tuple using the np.array() function. - Using built-in functions like np.zeros() and np.ones() for creating an array of all 0's or all 1's, respectively. - Generating an array with random numbers using np.random.rand(n, m), where n and m are the number of rows and columns, respectively. - Loading data from a file. Ex: np.genfromtxt('data.csv', delimiter=','). #notebox("Note", rgb("#8a94a6"), rgb("#556666"), rgb("#f7f8fa"))[ #emph[Creating an ndarray object] #link("https://www.openstax.org/r/dictionary-object")[More creating a dictionary object; ch 15, video 3] ] #notebox("Note", rgb("#8a94a6"), rgb("#556666"), rgb("#f7f8fa"))[ #emph[NumPy library] ] === NumPy operations In addition to the ndarray data type, NumPy's operations provide optimized performance for large-scale computation. The key features of NumPy include: - Mathematical operations: NumPy provides a range of mathematical functions and operations that can be applied to entire arrays or specific elements. These operations include arithmetic, trigonometric, exponential, and logarithmic functions. - Array manipulation: NumPy provides various functions to manipulate the shape, size, and structure of arrays. These include reshaping, transposing, concatenating, splitting, and adding or removing elements from arrays. - Linear algebra operations: NumPy offers a set of linear algebra functions for matrix operations, like matrix multiplication, matrix inversion, eigenvalues, and eigenvectors. #notebox("Note", rgb("#8a94a6"), rgb("#556666"), rgb("#f7f8fa"))[ #emph[NumPy array operations] #link("https://www.openstax.org/r/NumPy-operations")[NumPy array operations; ch 15, video 4] ] #notebox("Note", rgb("#8a94a6"), rgb("#556666"), rgb("#f7f8fa"))[ #emph[NumPy operations] ] #notebox("Note", rgb("#8a94a6"), rgb("#556666"), rgb("#f7f8fa"))[ #emph[Exploring further] Please refer to the NumPy user guide for more information about the NumPy library. - #link("https://openstax.org/r/100numpyguide")[NumPy User Guide] ] === Programming practice with Google Use the Google Colaboratory document below to practice NumPy functionalities to extract statistical insights from a dataset. #link("https://openstax.org/r/100numpycolab")[Google Colaboratory document]