Python

This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data as well as substantial material on machine learning.

The book is based on an MIT course and was developed for use not only in a conventional classroom but in a massive open online course (MOOC). It contains material suitable for a two-semester introductory computer science sequence.

This third edition has expanded the initial explanatory material, making it a gentler introduction to programming for the beginner, with more programming examples and many more “finger exercises.” A new chapter shows how to use the Pandas package for analyzing time series data. All the code has been rewritten to make it stylistically consistent with the PEP 8 standards. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. The book also includes a Python 3 quick reference guide.

John V. Guttag is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT.

1 GETTING STARTED
2 INTRODUCTION TO PYTHON 
3 SOME SIMPLE NUMERICAL PROGRAMS 
4 FUNCTIONS, SCOPING,AND ABSTRACTION 
5 STRUCTURED TYPES AND MUTABILITY
6 RECURSION AND GLOBAL VARIABLES
7 MODULES AND FILES 
8 TESTING AND DEBUGGING 
9 EXCEPTIONS AND ASSERTIONS 
10 CLASSES AND OBJECT-ORIENTED PROGRAMMING
11 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY
12 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES
13 PLOTTING AND MORE ABOUT CLASSES
14 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS 
15 DYNAMIC PROGRAMMING 
16 RANDOM WALKS ANDMORE ABOUT DATA VISUALIZATION 
17 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS 
18 MONTE CARLO SIMULATION 
19 SAMPLING AND CONFIDENCE 
20 UNDERSTANDING EXPERIMENTAL DATA 
21 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING 
22 LIES, DAMNED LIES, AND STATISTICS 
23 EXPLORING DATA WITH PANDAS 
24 A QUICK LOOK AT MACHINE LEARNING 
25 CLUSTERING 
26 CLASSIFICATIONMETHODS
PYTHON 3.8 QUICK REFERENCE