1.3.1. Introduction to Python

Python is a general-purpose, high-level programming language. It is very popular.

Our first set of readings, from Computational and Inferential Thinking [ADW21], introduce the first programming concepts and how one does them in python.

• 3. Programming in Python

  • 3.1. Expressions

  • 3.2. Names

  • 3.3. Call Expressions

• 4. Data Types

  • 4.1. Numbers

  • 4.2. Strings

  • 4.3. Comparisons

Reading Question

• What is the difference between 2, "2", two, and "two"?

Our second set of readings is also from Computational and Inferential Thinking [ADW21], and introduces sequence and table data types. The examples here use the python datascience library, which was created for the DATA 8 The Foundations of Data Science course at the University of California at Berkeley.

The datascience package is an open source Python package that helps make programming more accessible to all students, regardless of background. As a pedagogical aid, the package is designed to help students more intuitively conduct data science techniques without first spending considerable time directly learning more complex tools such as pandas or matplotlib. At Berkeley, these other packages are introduced in further upper-division coursework such as Data 100.

source

If you want to try the examples in these readings, first do

from datascience import *

• 3. Programming in Python

  • 3.4. Introduction to Tables

• 5. Sequences

  • 5.1. Arrays

  • 5.2. Ranges

  • 5.3. More on Arrays

Reading Question

• How would you use np.arange to create array([ 1.5,  1. ,  0.5,  0. , -0.5])?

Further Resource

• From Python Programming for Data Science [Beu21]:

  • Chapter 1: Python Basics

    • 2. Basic Python Data Types

    • 3. Lists and Tuples

    • 4. String Methods

    • 5. Dictionaries

    • 6. Empties

    • 7. Conditionals

  • Chapter 2: Loops & Functions

    • 1. for Loops

    • 2. while loops

    • 3. Comprehensions

    • 5. Functions

  • Chapter 5: Introduction to NumPy

    • 2. NumPy Arrays

    • 3. Array Operations and Broadcasting

    • 4. Indexing and slicing

• From the Python Data Science Handbook [Van16]

  • 2. Introduction to NumPy

    • Understanding Data Types in Python

    • The Basics of NumPy Arrays

    • Computation on NumPy Arrays: Universal Functions

    • Aggregations: Min, Max, and Everything In Between

    • Sorting Arrays

• From python.org:

  • Python for Beginners

  • The Python Tutorial

  • Python Documentation