├── README.md ├── exams ├── 2018-19 │ ├── 2019-01-23 │ │ ├── 2019-01-23.ipynb │ │ ├── 2019-01-23_solution.ipynb │ │ ├── README.txt │ │ └── dataset.csv │ ├── 2019-02-21 │ │ ├── 2019-02-21.ipynb │ │ ├── 2019-02-21_solution.ipynb │ │ ├── README.txt │ │ ├── corpus.txt │ │ └── dataset.csv │ ├── 2019-06-20 │ │ ├── 2019-06-20.ipynb │ │ ├── 2019-06-20_solution.ipynb │ │ ├── README.txt │ │ ├── corpus.txt │ │ └── dataset.csv │ └── 2019-07-15 │ │ ├── 2019-07-15.ipynb │ │ ├── 2019-07-15_solution.ipynb │ │ ├── README.txt │ │ └── dataset.csv └── README.md └── lectures ├── notebooks ├── Lecture_00_Preliminaries.ipynb ├── Lecture_01_Introduction_And_Environment_Setup.ipynb ├── Lecture_02_Python_Basics.ipynb ├── Lecture_03_Python_Data_Types_1.ipynb ├── Lecture_04_Python_Data_Types_2.ipynb ├── Lecture_05_Functions_IO.ipynb ├── Lecture_06_NumPy.ipynb ├── Lecture_06b_Linear_Algebra_Basics.ipynb ├── Lecture_07_Introduction_To_Pandas.ipynb ├── Lecture_08_Pandas_IO.ipynb ├── Lecture_09_Pandas_Data_Preparation.ipynb ├── Lecture_10_Matplotlib_Data_Visualization.ipynb ├── Lecture_11_A_Machine_Learning_Primer.ipynb ├── Lecture_12_The_Regression_Problem_Example_(Part_1).ipynb ├── Lecture_12_The_Regression_Problem_Example_(Part_2).ipynb ├── Lecture_13_The_Classification_Problem_Example_(Part_1).ipynb ├── Lecture_13_The_Classification_Problem_Example_(Part_2).ipynb ├── data │ ├── auto-mpg-regression │ │ ├── README.txt │ │ ├── dataset.tsv │ │ └── dataset.txt │ ├── iris_dataset.csv │ ├── loan-prediction │ │ ├── README.txt │ │ └── dataset.csv │ ├── sample.json │ ├── sample.txt │ ├── sample_df.json │ ├── sample_out.json │ ├── tmp.txt │ ├── user_occupations.pickle │ ├── user_occupations.txt │ └── user_occupations_no_header.txt └── img │ ├── abstraction-layers.png │ ├── architecture.png │ ├── arithmetic_ops.png │ ├── associative_array.png │ ├── binary.png │ ├── boolean_ops.png │ ├── built_in_types.png │ ├── c-c++.png │ ├── code_cell.png │ ├── code_point.png │ ├── comparisons.png │ ├── computers.png │ ├── cpu-decode.png │ ├── cpu-execute.png │ ├── cpu-fetch.png │ ├── cpu.png │ ├── data_science.jpg │ ├── decimal.png │ ├── encoding.png │ ├── file_methods.png │ ├── file_open_mode.png │ ├── function_call.png │ ├── hash_table.png │ ├── hello_world.png │ ├── input-device.png │ ├── ipython_kernel_and_terminal.png │ ├── java.jpg │ ├── join_types.jpg │ ├── jupyter_logo.svg │ ├── launch_python.png │ ├── markdown_cell.png │ ├── markdown_cell_1.png │ ├── markdown_cell_2.png │ ├── markdown_cell_3.png │ ├── markdown_cell_4.png │ ├── matplotlib_savefig.png │ ├── matplotlib_subplots.png │ ├── ml_bias_variance.png │ ├── ml_dataset_splitting.png │ ├── ml_feature_engineering.png │ ├── ml_labeled_dataset_1.png │ ├── ml_labeled_dataset_2.png │ ├── ml_models.png │ ├── ml_mse_convex.png │ ├── ml_overview.png │ ├── ml_preprocessing_catcon.png │ ├── ml_preprocessing_collinearity.png │ ├── ml_preprocessing_imbalance.png │ ├── ml_preprocessing_na.png │ ├── ml_preprocessing_outliers.png │ ├── ml_preprocessing_scale.png │ ├── ml_preprocessing_sparsity.png │ ├── ndarray_vs_list.png │ ├── notebook_dashboard.png │ ├── notebook_interface.png │ ├── np_binary_ufuncs.png │ ├── np_creation.png │ ├── np_dtypes_1.png │ ├── np_dtypes_2.png │ ├── np_indexing.png │ ├── np_lin_alg_funcs.png │ ├── np_random_funcs.png │ ├── np_set_funcs.png │ ├── np_slicing.png │ ├── np_stat_funcs.png │ ├── np_unary_ufuncs_1.png │ ├── np_unary_ufuncs_2.png │ ├── other_kernels.png │ ├── output-device.png │ ├── pandas.png │ ├── pandas_plot_1.png │ ├── pandas_plot_2.png │ ├── pandas_plot_df.png │ ├── pd_aggregate_stats_1.png │ ├── pd_aggregate_stats_2.png │ ├── pd_arithmetics.png │ ├── pd_concat.png │ ├── pd_dataframe_1.png │ ├── pd_dataframe_2.png │ ├── pd_freq.png │ ├── pd_index.png │ ├── pd_index_methods.png │ ├── pd_indexing.png │ ├── pd_io_read_1.png │ ├── pd_io_read_2.png │ ├── pd_io_read_csv_1.png │ ├── pd_io_read_csv_2.png │ ├── pd_merge_args_1.png │ ├── pd_merge_args_2.png │ ├── pd_merge_how.png │ ├── ram-address.png │ ├── ram-bit.png │ ├── ram-cell-4.png │ ├── ram-cell.png │ ├── ram.png │ ├── rebinding.png │ ├── reference.png │ ├── reference2.png │ ├── scalar_vs_simd.png │ ├── sequence_ops.png │ ├── set_ops_1.png │ ├── set_ops_2.png │ ├── skewness.jpeg │ ├── slicing.png │ ├── vonneumann-architecture.png │ ├── vonneumann.png │ ├── xkcd_ml_1.png │ └── xkcd_ml_2.png └── slides ├── Lecture_00_Preliminaries.pdf ├── Lecture_01_Introduction_And_Environment_Setup.pdf ├── Lecture_02_Python_Basics.pdf ├── Lecture_03_Python_Data_Types_1.pdf ├── Lecture_04_Python_Data_Types_2.pdf ├── Lecture_05_Functions_IO.pdf ├── Lecture_06_NumPy.pdf ├── Lecture_06b_Linear_Algebra_Basics.pdf ├── Lecture_07_Introduction_To_Pandas.pdf ├── Lecture_08_Pandas_IO.pdf ├── Lecture_10_Matplotlib_Data_Visualization.pdf ├── Lecture_11_A_Machine_Learning_Primer.pdf └── Lecture_14_Deep_Dive_On_Logistic_Regression.pdf /README.md: -------------------------------------------------------------------------------- 1 | # Python Programming for Data Science 2 | 3 | ## General Info 4 | 5 | Welcome to Python Programming for Data Science! 6 | 7 | This is a first-year course of the [MSc in Data Science of the University of Padova](https://datascience.math.unipd.it/). Indeed, it is one of the three modules which the course "_Fundamentals of Information Systems_" is made of. 8 | 9 | This repository contains lecture materials (in the form of Jupyter Notebook and PDF slides) as well as exercises from the 2018-19 examination sessions (with solutions). 10 | 11 | 12 | ## Course Goal 13 | 14 | The goal of this module is to teach the basics of the Python programming language along with a special focus on Data Science. In particular, students will become familiar with Python packages that are widely used by the community of data scientists and machine learning practicioners, such as ```numpy```, ```scipy```, ```pandas```, ```seaborn```, and ```scikit-learn```, just to name a few.
15 | Eventually, at the end of this module students are expected to be able to implement all the stages of a typical machine learning pipeline: from collecting data to building predictive models for solving either a regression or a classification problem.
16 | A full detailed description of the course is available [here](https://en.didattica.unipd.it/off/2018/LM/SC/SC2377/000ZZ/SCP7078720/N0). 17 | 18 | ## Course Syllabus 19 | Python Programming for Data Science provides students with the foundational coding skills they need as data scientists. 20 | 21 | We start our journey with an exhaustive tutorial on how to properly set up your environment, which is used throughout the class. Essentially, this consists of: 22 | 23 | - Installing **Python 3.x** (we will be using Python 3.6 installed via [Anaconda](https://www.anaconda.com/) in this class) 24 | - Installing and setting up [Jupyter Notebook](https://jupyter.org/) 25 | 26 | Then, we move to discussing the basics of the Python programming language: 27 | 28 | - Python object model 29 | - built-in data types 30 | - fuctions 31 | - I/O 32 | 33 | Finally, we will dig into a set of the most up-to-date data science Python packages, such as: 34 | 35 | - ```numpy```/```scipy``` (for numerical/scientific computing) 36 | - ```pandas``` (for data manipulation) 37 | - ```matplotlib```/```seaborn``` (for data visualization) 38 | - ```scikit-learn``` (for machine learning tasks like regression and classification). 39 | 40 | ## Class Schedule 41 | 42 | | Lecture \# | Topics | Class Material | 43 | |------------|-----------------------------------------------|----------------| 44 | | Lecture 0 | Preliminary computer science concepts | [Notebook](./lectures/notebooks/Lecture_00_Preliminaries.ipynb), [Slides](./lectures/slides/Lecture_00_Preliminaries.pdf) | 45 | | Lecture 1 | Introduction and environment setup | [Notebook](./lectures/notebooks/Lecture_01_Introduction_And_Environment_Setup.ipynb), [Slides](./lectures/slides/Lecture_01_Introduction_And_Environment_Setup.pdf) | 46 | | Lecture 2 | Python basics | [Notebook](./lectures/notebooks/Lecture_02_Python_Basics.ipynb), [Slides](./lectures/slides/Lecture_02_Python_Basics.pdf) | 47 | | Lecture 3 | Python's built-in data types (Part I) | [Notebook](./lectures/notebooks/Lecture_03_Python_Data_Types_1.ipynb), [Slides](./lectures/slides/Lecture_03_Python_Data_Types_1.pdf) | 48 | | Lecture 4 | Python's built-in data types (Part II) | [Notebook](./lectures/notebooks/Lecture_04_Python_Data_Types_2.ipynb), [Slides](./lectures/slides/Lecture_04_Python_Data_Types_2.pdf) | 49 | | Lecture 5 | Functions & I/O | [Notebook](./lectures/notebooks/Lecture_05_Functions_IO.ipynb), [Slides](./lectures/slides/Lecture_05_Functions_IO.pdf) | 50 | | Lecture 6 | ```numpy``` package | [Notebook](./lectures/notebooks/Lecture_06_NumPy.ipynb), [Slides](./lectures/slides/Lecture_06_NumPy.pdf) | 51 | | Lecture 6b | Review of linear algebra basics | [Notebook](./lectures/notebooks/Lecture_06b_Linear_Algebra_Basics.ipynb), [Slides](./lectures/slides/Lecture_06b_Linear_Algebra_Basics.pdf) | 52 | | Lecture 7 | Introduction to ```pandas``` package | [Notebook](./lectures/notebooks/Lecture_07_Introduction_To_Pandas.ipynb), [Slides](./lectures/slides/Lecture_07_Introduction_To_Pandas.pdf) | 53 | | Lecture 8 | I/O with ```pandas``` | [Notebook](./lectures/notebooks/Lecture_08_Pandas_IO.ipynb), [Slides](./lectures/slides/Lecture_08_Pandas_IO.pdf) | 54 | | Lecture 9 | Data preparation with ```pandas``` | [Notebook](./lectures/notebooks/Lecture_09_Pandas_Data_Preparation.ipynb), [Slides](./lectures/slides/Lecture_09_Pandas_Data_Preparation.pdf) | 55 | | Lecture 10 | Data visualization with ```matplotlib``` | [Notebook](./lectures/notebooks/Lecture_10_Matplotlib_Data_Visualization.ipynb), [Slides](./lectures/slides/Lecture_10_Matplotlib_Data_Visualization.pdf) | 56 | | Lecture 11 | A Machine Learning Primer (seminar) | [Notebook](./lectures/notebooks/Lecture_11_A_Machine_Learning_Primer.ipynb), [Slides](./lectures/slides/Lecture_11_A_Machine_Learning_Primer.pdf) | 57 | | Lecture 12 | The Regression Problem: Example (Part I) | [Notebook](./lectures/notebooks/Lecture_12_The_Regression_Problem_Example_(Part_1).ipynb)| 58 | | Lecture 13 | The Regression Problem: Example (Part II) | [Notebook](.lectures/notebooks/Lecture_12_The_Regression_Problem_Example_(Part_2).ipynb)| 59 | | Lecture 14 | The Classification Problem: Example (Part I) | [Notebook](./lectures/notebooks/Lecture_13_The_Classification_Problem_Example_(Part_1).ipynb)| 60 | | Lecture 15 | The Classification Problem: Example (Part II) | [Notebook](./lectures/notebooks/Lecture_13_The_Classification_Problem_Example_(Part_2).ipynb)| 61 | | Lecture 16 | Logistic Regression Demystified (seminar) | [Slides](./lectures/slides/Lecture_14_Deep_Dive_On_Logistic_Regression.pdf) | 62 | 63 | -------------------------------------------------------------------------------- /exams/2018-19/2019-01-23/README.txt: -------------------------------------------------------------------------------- 1 | This is a sample of 1,000 instances from the data which was originally extracted from the census bureau database found at http://www.census.gov/ftp/pub/DES/www/welcome.html 2 | Donor: Ronny Kohavi and Barry Becker, 3 | Data Mining and Visualization 4 | Silicon Graphics. 5 | e-mail: ronnyk@sgi.com for questions. 6 | --------------------------------------------------------------------------------------------------------- 7 | Description of 14 features: 8 | age: [continuous]. 9 | workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. 10 | fnlwgt: a demographic indicator computed from the census bureau database [continuous]. 11 | education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. 12 | education_num: [continuous]. 13 | marital_status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. 14 | occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. 15 | relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. 16 | race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. 17 | sex: Female, Male. 18 | capital_gain: [continuous]. 19 | capital_loss: [continuous]. 20 | hours_per_week: weekly working hours [continuous]. 21 | native_country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. 22 | --------------------------------------------------------------------------------------------------------- 23 | Binary label: 24 | income_greater_than_50k: +1/-1 25 | -------------------------------------------------------------------------------- /exams/2018-19/2019-02-21/README.txt: -------------------------------------------------------------------------------- 1 | About this Dataset 2 | 3 | This data set contains 721 unique Pokemons, including their number (ID), name, first and second type, and basic stats: HP, Attack, Defense, Special Attack, Special Defense, and Speed. It has been of great use when teaching statistics to kids. With certain types you can also give a geeky introduction to machine learning. 4 | 5 | This are the raw attributes that are used for calculating how much damage an attack will do in the games. This dataset is about the pokemon games (NOT pokemon cards or Pokemon Go). 6 | 7 | The data as described by Myles O'Neill is: 8 | 9 | #: ID for each pokemon 10 | Name: Name of each pokemon 11 | Type 1: Each pokemon has a type, this determines weakness/resistance to attacks 12 | Type 2: Some pokemon are dual type and have 2 13 | Total: sum of all stats that come after this, a general guide to how strong a pokemon is 14 | HP: hit points, or health, defines how much damage a pokemon can withstand before fainting 15 | Attack: the base modifier for normal attacks (e.g., Scratch, Punch) 16 | Defense: the base damage resistance against normal attacks 17 | SP Atk: special attack, the base modifier for special attacks (e.g., fire blast, bubble beam) 18 | SP Def: the base damage resistance against special attacks 19 | Speed: determines which pokemon attacks first each round 20 | 21 | The data for this table has been acquired from several different sites, including: 22 | 23 | pokemon.com 24 | pokemondb 25 | bulbapedia -------------------------------------------------------------------------------- /exams/2018-19/2019-06-20/README.txt: -------------------------------------------------------------------------------- 1 | About this Dataset 2 | 3 | This data set contains 1781 unique anonymised URLs, along with a set of 18 features and a binary class label (TYPE), which indicates whether the corresponding URL is malicious (1) or not (0). 4 | 5 | Below is a detailed description of this dataset. 6 | 7 | - URL: it is the anonimous identification of the URL analyzed in the study. 8 | - URL_LENGTH: it is the number of characters in the URL. 9 | - NUMBER_SPECIAL_CHARACTERS: it is number of special characters identified in the URL, such as, "/", "%", "#", "&", ". ", "=". 10 | - CHARSET: it is a categorical value and its meaning is the character encoding standard (also called character set). 11 | - SERVER: it is a categorical value and its meaning is the operative system of the server got from the packet response. 12 | - CONTENT_LENGTH: it represents the content size (in bytes) of the HTTP header. 13 | - WHOIS_COUNTRY: it is a categorical variable, its values are the countries we got from the server response (specifically, our script used the API of Whois). 14 | - WHOIS_STATEPRO: it is a categorical variable, its values are the states we got from the server response (specifically, our script used the API of Whois). 15 | - WHOIS_REGDATE: Whois provides the server registration date, so this variable has date values with format DD/MM/YYY HH:MM 16 | - WHOIS_UPDATED_DATE: through the Whois we got the last update date from the server analyzed. 17 | - TCP_CONVERSATION_EXCHANGE: this variable is the number of TCP packets exchanged between the server and our honeypot client. 18 | - DIST_REMOTE_TCP_PORT: it is the number of the ports detected and different to TCP. 19 | - REMOTE_IPS: this variable has the total number of IPs connected to the honeypot. 20 | - APP_BYTES: this is the number of bytes transfered. 21 | - SOURCE_APP_PACKETS: packets sent from the honeypot to the server. 22 | - REMOTE_APP_PACKETS: packets received from the server. 23 | - APP_PACKETS: this is the total number of IP packets generated during the communication between the honeypot and the server. 24 | - DNS_QUERY_TIMES: this is the number of DNS packets generated during the communication between the honeypot and the server. 25 | - TYPE: this is a categorical variable, its values represent the type of web page analyzed, specifically, 1 is for malicious websites and 0 is for benign websites. -------------------------------------------------------------------------------- /exams/2018-19/2019-07-15/README.txt: -------------------------------------------------------------------------------- 1 | About this Dataset 2 | 3 | This data set contains 1781 unique anonymised URLs, along with a set of 18 features and a binary class label (TYPE), which indicates whether the corresponding URL is malicious (1) or not (0). 4 | 5 | Below is a detailed description of this dataset. 6 | 7 | - URL: it is the anonimous identification of the URL analyzed in the study. 8 | - URL_LENGTH: it is the number of characters in the URL. 9 | - NUMBER_SPECIAL_CHARACTERS: it is number of special characters identified in the URL, such as, "/", "%", "#", "&", ". ", "=". 10 | - CHARSET: it is a categorical value and its meaning is the character encoding standard (also called character set). 11 | - SERVER: it is a categorical value and its meaning is the operative system of the server got from the packet response. 12 | - CONTENT_LENGTH: it represents the content size (in bytes) of the HTTP header. 13 | - WHOIS_COUNTRY: it is a categorical variable, its values are the countries we got from the server response (specifically, our script used the API of Whois). 14 | - WHOIS_STATEPRO: it is a categorical variable, its values are the states we got from the server response (specifically, our script used the API of Whois). 15 | - WHOIS_REGDATE: Whois provides the server registration date, so this variable has date values with format DD/MM/YYY HH:MM 16 | - WHOIS_UPDATED_DATE: through the Whois we got the last update date from the server analyzed. 17 | - TCP_CONVERSATION_EXCHANGE: this variable is the number of TCP packets exchanged between the server and our honeypot client. 18 | - DIST_REMOTE_TCP_PORT: it is the number of the ports detected and different to TCP. 19 | - REMOTE_IPS: this variable has the total number of IPs connected to the honeypot. 20 | - APP_BYTES: this is the number of bytes transfered. 21 | - SOURCE_APP_PACKETS: packets sent from the honeypot to the server. 22 | - REMOTE_APP_PACKETS: packets received from the server. 23 | - APP_PACKETS: this is the total number of IP packets generated during the communication between the honeypot and the server. 24 | - DNS_QUERY_TIMES: this is the number of DNS packets generated during the communication between the honeypot and the server. 25 | - TYPE: this is a categorical variable, its values represent the type of web page analyzed, specifically, 1 is for malicious websites and 0 is for benign websites. -------------------------------------------------------------------------------- /exams/README.md: -------------------------------------------------------------------------------- 1 | # Examination Sessions 2 | 3 | These folders contain exercises as part of 2018-19 examination sessions. 4 | -------------------------------------------------------------------------------- /lectures/notebooks/Lecture_00_Preliminaries.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "# Fundamentals of Information Systems\n", 12 | "\n", 13 | "## Python Programming (for Data Science)\n", 14 | "\n", 15 | "### Master's Degree in Data Science\n", 16 | "\n", 17 | "#### Gabriele Tolomei\n", 18 | "gtolomei@math.unipd.it
\n", 19 | "University of Padua, Italy
\n", 20 | "2018/2019
\n", 21 | "October 8, 2018" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": { 27 | "slideshow": { 28 | "slide_type": "slide" 29 | } 30 | }, 31 | "source": [ 32 | "# Lecture 0: Preliminaries" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": { 38 | "slideshow": { 39 | "slide_type": "slide" 40 | } 41 | }, 42 | "source": [ 43 | "# Course Structure\n", 44 | "\n", 45 | "- This course is made of **3** distinct modules, each one covering a specific set of topics:\n", 46 | "\n", 47 | " - **Python Programming (for Data Science)** (40 hours, taught by **Dr. Gabriele Tolomei**);\n", 48 | " - **Database Technologies** (24 hours, taught by **Dr. Nicolò Navarin**);\n", 49 | " - **Computer Networking** (32 hours, taught by **Dr. Armir Bujari**). " 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "metadata": { 55 | "slideshow": { 56 | "slide_type": "slide" 57 | } 58 | }, 59 | "source": [ 60 | "# Course Info\n", 61 | "\n", 62 | "- We use **Moodle** for sharing communications and materials for this course (lectures, exercises, etc.).\n", 63 | "- **NOTE**: If you haven't already done it, please subscribe to the Moodle's class page at the following address: https://elearning.unipd.it/math/course/view.php?id=321\n", 64 | "- You are encouraged to ask for a meeting with the teacher in case you need any clarification: just drop us an email, and we will do our best to satisfy your request (provided you do it with a _reasonable_ notice!)." 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": { 70 | "slideshow": { 71 | "slide_type": "slide" 72 | } 73 | }, 74 | "source": [ 75 | "# _This_ Module's Objectives\n", 76 | "\n", 77 | "- This module is meant to teach you the **fundamental skills of Python programming** with a special focus on data science tasks.\n", 78 | "\n", 79 | "- Firstly, you will learn the **basics** of Python programming :)\n", 80 | "\n", 81 | "- On top of the above, you will learn the \"nuts and bolts\" of _manipulating_, _processing_, _cleaning_, and _crunching_ **data with Python**.\n", 82 | "\n", 83 | "- Ultimately, you will be equipped with the toolbox you need to become a _real_ **data scientist**!" 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "metadata": { 89 | "slideshow": { 90 | "slide_type": "slide" 91 | } 92 | }, 93 | "source": [ 94 | "# Course Prerequisites\n", 95 | "\n", 96 | "- Fundamentals of (von Neumann) **computer architecture** (*CPU*, *memory hierarchy*) and **operating systems** (*program* vs. *process*).\n", 97 | "\n", 98 | "- Very basic **coding** skills (not necessarily in Python): _variables_, _assignment_, _function call_, etc.\n", 99 | "\n", 100 | "- Some familiarity with **Unix-like shell** commands.\n", 101 | "\n", 102 | "- A laptop! (If some of you don't have one, please let me know and we will find out a solution)." 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": { 108 | "slideshow": { 109 | "slide_type": "slide" 110 | } 111 | }, 112 | "source": [ 113 | "# Plus\n", 114 | "\n", 115 | "- Later on, we may occasionally need to refer to common data science concepts, methodologies, and techniques:\n", 116 | " - **Probability and Statistics**: probability distributions, random variables, expectation, mean, variance, sampling, tests of statistical significance, etc.\n", 117 | " - **Machine Learning**: supervised/unsupervised learning, training/test set, bias-variance tradeoff, learning algorithms, etc." 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": { 123 | "slideshow": { 124 | "slide_type": "slide" 125 | } 126 | }, 127 | "source": [ 128 | "# Exams\n", 129 | "\n", 130 | "- A **single**, **unified** written test divided into **3** sections, i.e., one for each module. \n", 131 | "- How each section is organized depends on the module it refers to;\n", 132 | "- For instance, concerning **_this_** module you may expect to be asked to solve **coding exercises** and, possibly, answer **theoretical questions**.\n", 133 | "- **Don't worry now**! There will be time to discuss about exams many times in the future!" 134 | ] 135 | }, 136 | { 137 | "cell_type": "markdown", 138 | "metadata": { 139 | "slideshow": { 140 | "slide_type": "slide" 141 | } 142 | }, 143 | "source": [ 144 | "# (Quick) Recap\n", 145 | "\n", 146 | "- How computer works?\n", 147 | " - von Neumann computing model: **CPU** + **RAM** + **I/O**\n", 148 | "- Abstraction layers (from the \"physical\" machine)\n", 149 | " - from machine language to higher-level languages (e.g., **C/C++**, **Java**, **Python**)\n", 150 | "- What does _actually_ mean programming a computer?" 151 | ] 152 | }, 153 | { 154 | "cell_type": "markdown", 155 | "metadata": { 156 | "slideshow": { 157 | "slide_type": "slide" 158 | } 159 | }, 160 | "source": [ 161 | "# Computers are _everywhere_, seriously!\n", 162 | "\n", 163 | "
\n", 164 | "
![](./img/computers.png)
" 165 | ] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": { 170 | "slideshow": { 171 | "slide_type": "slide" 172 | } 173 | }, 174 | "source": [ 175 | "# Conceptually, they are all the same!\n", 176 | "\n", 177 | "
\n", 178 | "They all follow the same architectural model introduced by **John von Neumann** back in 1945\n", 179 | "
\n", 180 | "
![](./img/vonneumann.png)
" 181 | ] 182 | }, 183 | { 184 | "cell_type": "markdown", 185 | "metadata": { 186 | "slideshow": { 187 | "slide_type": "slide" 188 | } 189 | }, 190 | "source": [ 191 | "# von Neumann's Computing Architecture\n", 192 | "\n", 193 | "
\n", 194 | "
![](./img/vonneumann-architecture.png)
\n", 195 | "
Source: [Wikipedia](https://en.wikipedia.org/wiki/Von_Neumann_architecture#/media/File:Von_Neumann_Architecture.svg)
" 196 | ] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": { 201 | "slideshow": { 202 | "slide_type": "slide" 203 | } 204 | }, 205 | "source": [ 206 | "# von Neumann's Computing Architecture: Input\n", 207 | "\n", 208 | "
\n", 209 | "
![](./img/input-device.png)
" 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": { 215 | "slideshow": { 216 | "slide_type": "slide" 217 | } 218 | }, 219 | "source": [ 220 | "# von Neumann's Computing Architecture: Output\n", 221 | "\n", 222 | "
\n", 223 | "
![](./img/output-device.png)
" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": { 229 | "slideshow": { 230 | "slide_type": "slide" 231 | } 232 | }, 233 | "source": [ 234 | "# von Neumann's Computing Architecture: CPU\n", 235 | "\n", 236 | "- The **C**entral **P**rocessing **U**nit (**CPU**) is meant of executing _sequences of instructions_, one instruction by the other.\n", 237 | "- Each instruction encodes basic arithmetic and logic computations, using CPU's internal registers.\n", 238 | "
![](./img/cpu.png)
" 239 | ] 240 | }, 241 | { 242 | "cell_type": "markdown", 243 | "metadata": { 244 | "slideshow": { 245 | "slide_type": "slide" 246 | } 247 | }, 248 | "source": [ 249 | "# von Neumann's Computing Architecture: RAM\n", 250 | "\n", 251 | "- **R**andom **A**ccess **M**emory (**RAM**) contains _instructions_ and _data_, which instructions operate on\n", 252 | "
![](./img/ram.png)
" 253 | ] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "metadata": { 258 | "slideshow": { 259 | "slide_type": "slide" 260 | } 261 | }, 262 | "source": [ 263 | "# A Closer Look into Main Memory (RAM)\n", 264 | "\n", 265 | "- Represented as a sequence (i.e., array) of contiguous **cells**, a.k.a. **locations**.\n", 266 | "\n", 267 | "- Each memory cell is logically organized into groups of **8 bits** (**1 byte**) each, or multiple of it (e.g., 32 bits = 4 bytes).\n", 268 | "\n", 269 | "- Each cell is uniquely identified by its own **memory address**.\n", 270 | "\n", 271 | "- CPU and I/O units may read from/write to main memory by specifying memory address.\n", 272 | "\n", 273 | "- Addressing is usually performed at the single byte level." 274 | ] 275 | }, 276 | { 277 | "cell_type": "markdown", 278 | "metadata": { 279 | "slideshow": { 280 | "slide_type": "slide" 281 | } 282 | }, 283 | "source": [ 284 | "# Binary Digit (Bit)\n", 285 | "\n", 286 | "- Can take on **2** possible values: **0** or **1**.\n", 287 | "\n", 288 | "- Suitable encoding to represent the smallest amount of information (e.g., voltage of digital circuits).\n", 289 | "\n", 290 | "
![](./img/ram-bit.png)
" 291 | ] 292 | }, 293 | { 294 | "cell_type": "markdown", 295 | "metadata": { 296 | "slideshow": { 297 | "slide_type": "slide" 298 | } 299 | }, 300 | "source": [ 301 | "# Memory Cell/Location\n", 302 | "\n", 303 | "
\n", 304 | "
![](./img/ram-cell.png)
" 305 | ] 306 | }, 307 | { 308 | "cell_type": "markdown", 309 | "metadata": { 310 | "slideshow": { 311 | "slide_type": "slide" 312 | } 313 | }, 314 | "source": [ 315 | "# Memory Cell/Location\n", 316 | "\n", 317 | "
\n", 318 | "
![](./img/ram-cell-4.png)
" 319 | ] 320 | }, 321 | { 322 | "cell_type": "markdown", 323 | "metadata": { 324 | "slideshow": { 325 | "slide_type": "slide" 326 | } 327 | }, 328 | "source": [ 329 | "# Memory Address\n", 330 | "\n", 331 | "
\n", 332 | "
![](./img/ram-address.png)
" 333 | ] 334 | }, 335 | { 336 | "cell_type": "markdown", 337 | "metadata": { 338 | "slideshow": { 339 | "slide_type": "slide" 340 | } 341 | }, 342 | "source": [ 343 | "# CPU Interpreter\n", 344 | "\n", 345 | "- The CPU realizes an **interpreter** which cyclically does the following **3** operations:\n", 346 | "\n", 347 | " - **Fetch**: retrieve from main memory an instruction which is stored at a specific address whose value is contained in a dedicated CPU register, called **Program Counter**;\n", 348 | " - **Decode**: decode the retrieved instruction;\n", 349 | " - **Execute**: execute the decoded instruction." 350 | ] 351 | }, 352 | { 353 | "cell_type": "markdown", 354 | "metadata": { 355 | "slideshow": { 356 | "slide_type": "slide" 357 | } 358 | }, 359 | "source": [ 360 | "# Machine Language\n", 361 | "\n", 362 | "- Defines a set of (elementary) instructions which the CPU interpreter is able to execute directly.\n", 363 | "\n", 364 | "- Such a language is expressed using **binary numerical system**.\n", 365 | "\n", 366 | "- In other words, each instruction of a specific machine language must be encoded as a **sequence of bits**." 367 | ] 368 | }, 369 | { 370 | "cell_type": "markdown", 371 | "metadata": { 372 | "slideshow": { 373 | "slide_type": "slide" 374 | } 375 | }, 376 | "source": [ 377 | "# Binary vs. Decimal Numerical System\n", 378 | "\n", 379 | "- In the **decimal numerical system** (base 10), each digit can take only one out of **10** possible values: **0**, **1**, ..., **9**.\n", 380 | "
![](./img/decimal.png)
\n", 381 | " \n", 382 | "- In the **binary numerical system** (base 2), each digit is a bit:\n", 383 | "
![](./img/binary.png)
" 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": { 389 | "slideshow": { 390 | "slide_type": "slide" 391 | } 392 | }, 393 | "source": [ 394 | "# Machine Language: Specifications\n", 395 | "\n", 396 | "- Instructions defined by a machine language are composed of **2** parts:\n", 397 | " - An **operator** (**op code**);\n", 398 | " - One or more **operands** representing either CPU's internal registers or main memory addresses.\n", 399 | "- The collection of instructions defined by a certain machine language (i.e., **instruction set**) is specific to a hardware implementation: e.g., _Intel x86_, _ARM_, _Sparc_, _MIPS_.\n", 400 | "\n", 401 | "- In a nutshell, machine language indicates the number of bits each instruction dedicates to the operator and operands." 402 | ] 403 | }, 404 | { 405 | "cell_type": "markdown", 406 | "metadata": { 407 | "slideshow": { 408 | "slide_type": "slide" 409 | } 410 | }, 411 | "source": [ 412 | "# CPU Cycle: 1. Fetch\n", 413 | "\n", 414 | "
\n", 415 | "
![](./img/cpu-fetch.png)
" 416 | ] 417 | }, 418 | { 419 | "cell_type": "markdown", 420 | "metadata": { 421 | "slideshow": { 422 | "slide_type": "slide" 423 | } 424 | }, 425 | "source": [ 426 | "# CPU Cycle: 2. Decode\n", 427 | "\n", 428 | "
\n", 429 | "
![](./img/cpu-decode.png)
" 430 | ] 431 | }, 432 | { 433 | "cell_type": "markdown", 434 | "metadata": { 435 | "slideshow": { 436 | "slide_type": "slide" 437 | } 438 | }, 439 | "source": [ 440 | "# CPU Cycle: 3. Execute\n", 441 | "\n", 442 | "
\n", 443 | "
![](./img/cpu-execute.png)
" 444 | ] 445 | }, 446 | { 447 | "cell_type": "markdown", 448 | "metadata": { 449 | "slideshow": { 450 | "slide_type": "slide" 451 | } 452 | }, 453 | "source": [ 454 | "# Instructions vs. Data\n", 455 | "\n", 456 | "- According to the von Neumann's architecture, memory cells contains **both** instructions and data.\n", 457 | "\n", 458 | "- CPU wouldn't be able to distinguish between those by simply \"reading\" a bit sequence stored at a given memory address.\n", 459 | "\n", 460 | "- The **Program Counter** register allows separating instructions from data as it _always_ contains the memory address of an instruction." 461 | ] 462 | }, 463 | { 464 | "cell_type": "markdown", 465 | "metadata": { 466 | "slideshow": { 467 | "slide_type": "slide" 468 | } 469 | }, 470 | "source": [ 471 | "# Back to Machine Language\n", 472 | "\n", 473 | "- (Binary) Machine language indicates how to write a (representation of a) **program** which is the closest to the bare metal machine (hardware).\n", 474 | "\n", 475 | "- Theoretically, we could write programs using machine language instructions, directly.\n", 476 | "\n", 477 | "- In practice, though, this would be totally insane! (Think about how complex are programs running on our computers, smartphones, etc.).\n", 478 | "\n", 479 | "- **SOLUTION**: abstracting low-level machine language using higher-level languages that are closer to our natural language." 480 | ] 481 | }, 482 | { 483 | "cell_type": "markdown", 484 | "metadata": { 485 | "slideshow": { 486 | "slide_type": "slide" 487 | } 488 | }, 489 | "source": [ 490 | "# Abstraction Layers\n", 491 | "\n", 492 | "
\n", 493 | "
![](./img/abstraction-layers.png)
" 494 | ] 495 | }, 496 | { 497 | "cell_type": "markdown", 498 | "metadata": { 499 | "slideshow": { 500 | "slide_type": "slide" 501 | } 502 | }, 503 | "source": [ 504 | "# Abstraction Layers\n", 505 | "\n", 506 | "- Each layer is associated with a **language** (which is adopted by _that_ layer).\n", 507 | "\n", 508 | "- Every functionality (of the language) of a layer is implemented by a **program** which is written using language(s) of the layer(s) below.\n", 509 | "\n", 510 | "- **PRO**:\n", 511 | " - Separation between **_what_** has to be done (_specifics_) and **_how_** this has to be done (_implementation_)\n", 512 | "\n", 513 | "- **CON**:\n", 514 | " - The more we abstract from the physical machine the less will be the control we will have over it (delegated to lower layers)" 515 | ] 516 | }, 517 | { 518 | "cell_type": "markdown", 519 | "metadata": { 520 | "slideshow": { 521 | "slide_type": "slide" 522 | } 523 | }, 524 | "source": [ 525 | "# What Does Computer Programming Mean?\n", 526 | "\n", 527 | "- Generally speaking, it means writing a program using a high-level language to solve a given problem/task (e.g., find the minimum element of a set of integers).\n", 528 | "\n", 529 | "- In this module, we will be using **Python** as the high-level language to write down our programs.\n", 530 | "\n", 531 | "- Code written using a high-level language is usually referred to as **source code**, and it **_cannot_** be directly executed by the computer.\n", 532 | "\n", 533 | "- **REMEMBER:** The CPU (interpreter) can only directly execute instructions that are defined by a specific (binary) machine language." 534 | ] 535 | }, 536 | { 537 | "cell_type": "markdown", 538 | "metadata": { 539 | "slideshow": { 540 | "slide_type": "slide" 541 | } 542 | }, 543 | "source": [ 544 | "# From High-Level Source Code to Low-Level Machine Code\n", 545 | "\n", 546 | "- Different ways of achieving this, which depends on the _implementation_ of the high-level language:\n", 547 | "\n", 548 | " - **Compilation**\n", 549 | " - **Interpretation**\n", 550 | " - **Hybrid**: Compilation + Interpretation" 551 | ] 552 | }, 553 | { 554 | "cell_type": "markdown", 555 | "metadata": { 556 | "slideshow": { 557 | "slide_type": "slide" 558 | } 559 | }, 560 | "source": [ 561 | "# Compilation\n", 562 | "\n", 563 | "- Uses a special computer program, called **compiler**.\n", 564 | "\n", 565 | "- Takes as input a program written in some language (**source code**) and translates it into another language (**target code**): e.g., from C/C++ to assembly.\n", 566 | "\n", 567 | "- Results of compilation is **not** directly executable by the CPU interpreter (although the compiler is tied to a specific CPU): e.g., assembly needs to be further transformed to binary (object) code via **assembler**.\n", 568 | " \n", 569 | "- Eventually, another program, called **linker**, combines multiple object codes and external libraries into a **single** machine executable code." 570 | ] 571 | }, 572 | { 573 | "cell_type": "markdown", 574 | "metadata": { 575 | "slideshow": { 576 | "slide_type": "slide" 577 | } 578 | }, 579 | "source": [ 580 | "# Example: C/C++\n", 581 | "\n", 582 | "
\n", 583 | "
![](./img/c-c++.png)
" 584 | ] 585 | }, 586 | { 587 | "cell_type": "markdown", 588 | "metadata": { 589 | "slideshow": { 590 | "slide_type": "slide" 591 | } 592 | }, 593 | "source": [ 594 | "# Interpretation\n", 595 | "\n", 596 | "- Uses a special computer program, called **interpreter**.\n", 597 | "\n", 598 | "- Also interpreters \"translates\" a high-level language into a low-level one, but it does so at the moment the program is run, one instruction at a time.\n", 599 | "\n", 600 | "- The easiest example of an interpreter is the CPU, which realizes an interpreter of machine language via the **3**-phase cylce: _fetch_, _decode_, _execute_.\n", 601 | "\n", 602 | "- Purely interpreted implementations of high-level languages are now rare (**Smalltalk**, 1980), due to performance reasons." 603 | ] 604 | }, 605 | { 606 | "cell_type": "markdown", 607 | "metadata": { 608 | "slideshow": { 609 | "slide_type": "slide" 610 | } 611 | }, 612 | "source": [ 613 | "# Hybrid: Compilation + Interpretation\n", 614 | "\n", 615 | "- Tries to take advantage of both worlds.\n", 616 | "\n", 617 | "- High-level language is firstly compiled into an **intermediate** language (usually, referred to as **bytecode**) \n", 618 | "\n", 619 | "- Bytecode is not tied to a specific hardware/CPU and can be interpreted (i.e., executed directly) by a so-called **virtual machine** hosted on top of the physical machine\n", 620 | "\n", 621 | "
bytecode : virtual machine = machine code : CPU
\n", 622 | "\n", 623 | "- Notable examples of high-level languages whose major implementations mix compilation and interpretation: **Java**, **Python**, **Lisp**, etc." 624 | ] 625 | }, 626 | { 627 | "cell_type": "markdown", 628 | "metadata": { 629 | "slideshow": { 630 | "slide_type": "slide" 631 | } 632 | }, 633 | "source": [ 634 | "# Example: Java\n", 635 | "\n", 636 | "
\n", 637 | "
![](./img/java.jpg)
" 638 | ] 639 | }, 640 | { 641 | "cell_type": "markdown", 642 | "metadata": { 643 | "slideshow": { 644 | "slide_type": "slide" 645 | } 646 | }, 647 | "source": [ 648 | "# Considerations on Hybrid Approach\n", 649 | "\n", 650 | "- The bytecode interpreter of a virtual machine is itself a program. \n", 651 | "\n", 652 | " - Oracle's HotSpot JVM interpreter for Java compiled bytecode (implemented in C++ and assembly)\n", 653 | " - CPython interpreter for Python compiled bytecode (implemented in C)\n", 654 | " \n", 655 | "- Hybrid implementations allow **portability**.\n", 656 | "\n", 657 | "- The same Java code can be compiled on a machine (e.g., Windows/Intel x86) and run everywhere (e.g., Linux/SPARC) as long as there is an implementation of the JVM bytecode interpreter." 658 | ] 659 | }, 660 | { 661 | "cell_type": "markdown", 662 | "metadata": { 663 | "slideshow": { 664 | "slide_type": "slide" 665 | } 666 | }, 667 | "source": [ 668 | "# Take Away Message\n", 669 | "\n", 670 | "- Computational model based on CPU + Main Memory (+ I/O).\n", 671 | "\n", 672 | "- Abstraction layers eases writing computer programs.\n", 673 | "\n", 674 | "- Low-level binary machine language vs. high-level programming languages.\n", 675 | "\n", 676 | "- Different language implementations (even for the same language specifications)." 677 | ] 678 | } 679 | ], 680 | "metadata": { 681 | "celltoolbar": "Slideshow", 682 | "kernelspec": { 683 | "display_name": "Python 3", 684 | "language": "python", 685 | "name": "python3" 686 | }, 687 | "language_info": { 688 | "codemirror_mode": { 689 | "name": "ipython", 690 | "version": 3 691 | }, 692 | "file_extension": ".py", 693 | "mimetype": "text/x-python", 694 | "name": "python", 695 | "nbconvert_exporter": "python", 696 | "pygments_lexer": "ipython3", 697 | "version": "3.6.4" 698 | } 699 | }, 700 | "nbformat": 4, 701 | "nbformat_minor": 2 702 | } 703 | -------------------------------------------------------------------------------- /lectures/notebooks/Lecture_06b_Linear_Algebra_Basics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "# Fundamentals of Information Systems\n", 12 | "\n", 13 | "## Python Programming (for Data Science)\n", 14 | "\n", 15 | "### Master's Degree in Data Science\n", 16 | "\n", 17 | "#### Gabriele Tolomei\n", 18 | "gtolomei@math.unipd.it
\n", 19 | "University of Padua, Italy
\n", 20 | "2018/2019
\n", 21 | "November, 12 2018" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": { 27 | "slideshow": { 28 | "slide_type": "slide" 29 | } 30 | }, 31 | "source": [ 32 | "# Lecture 6 (Extra): Basics of Linear Algebra" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": { 38 | "slideshow": { 39 | "slide_type": "slide" 40 | } 41 | }, 42 | "source": [ 43 | "## What is a Matrix?\n", 44 | "\n", 45 | "- A bidimensional array which is the building block of linear algebra. \n", 46 | "\n", 47 | "- Linear algebra is used quite a bit in advanced statistics, largely because it provides two benefits:\n", 48 | " - Compact notation for describing sets of data and sets of equations;\n", 49 | " - Efficient methods for manipulating sets of data and solving sets of equations." 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "metadata": { 55 | "slideshow": { 56 | "slide_type": "slide" 57 | } 58 | }, 59 | "source": [ 60 | "## Matrix Definition\n", 61 | "\n", 62 | "- A **matrix** is a rectangular array of numbers arranged in **rows** and **columns**.\n", 63 | "\n", 64 | "- The following is an example of a $3$-by-$4$ matrix $\\textbf{A}$:\n", 65 | "\n", 66 | "$$\n", 67 | "\\textbf{A} = \\begin{bmatrix}\n", 68 | " 1.2 & -0.7 & 3.1 & 2.8 \\\\\n", 69 | " -5.9 & 1.4 & 0.3 & -4.3 \\\\\n", 70 | " 0.0 & 1.0 & 12.7 & 6.5 \\\\\n", 71 | "\\end{bmatrix}\n", 72 | "$$" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": { 78 | "slideshow": { 79 | "slide_type": "slide" 80 | } 81 | }, 82 | "source": [ 83 | "## Matrix Definition\n", 84 | "\n", 85 | "- More generally, an $m$-by-$n$ matrix $\\textbf{A}$ can be represented as follows:\n", 86 | "\n", 87 | "$$\n", 88 | "\\textbf{A} = \\begin{bmatrix}\n", 89 | " a_{11} & a_{12} & a_{13} & \\dots & a_{1n} \\\\\n", 90 | " a_{21} & a_{22} & a_{23} & \\dots & a_{2n} \\\\\n", 91 | " \\dots & \\dots & \\dots & \\dots & \\dots\\\\\n", 92 | " a_{m1} & a_{m2} & a_{m3} & \\dots & a_{mn}\n", 93 | "\\end{bmatrix}\n", 94 | "$$\n", 95 | "- $a_{ij}$ refers to the element of $\\textbf{A}$ located at the $i$-th row and $j$-th column.\n", 96 | "- $m$ and $n$ are called **dimensions** of the matrix.\n", 97 | "- Sometimes, you specifiy dimensions when defining a matrix, e.g., $\\textbf{A}_{m,n}$.\n" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": { 103 | "slideshow": { 104 | "slide_type": "slide" 105 | } 106 | }, 107 | "source": [ 108 | "## Matrix Equality\n", 109 | "\n", 110 | "- Two matrices $\\textbf{A}$ and $\\textbf{B}$ are equal if **all three** of the following conditions are met:\n", 111 | " - Each matrix has the same number of rows;\n", 112 | " - Each matrix has the same number of columns;\n", 113 | " - Corresponding elements within each matrix are equal." 114 | ] 115 | }, 116 | { 117 | "cell_type": "markdown", 118 | "metadata": { 119 | "slideshow": { 120 | "slide_type": "slide" 121 | } 122 | }, 123 | "source": [ 124 | "## Transpose Matrix\n", 125 | "\n", 126 | "- The transpose of a matrix $\\textbf{A}_{m,n}$ is another matrix $\\textbf{A}^{T}_{n,m}$ that is obtained by using rows from the first matrix as columns in the second matrix.\n", 127 | "\n", 128 | "- For example, it is easy to see that the transpose of matrix $\\textbf{A}_{3,2}$ is $\\textbf{A}^{T}_{2,3}$:\n", 129 | "\n", 130 | "$$\n", 131 | "\\textbf{A} = \\begin{bmatrix}\n", 132 | " 1.2 & -0.7 \\\\\n", 133 | " -5.9 & 1.4 \\\\\n", 134 | " 0.0 & 1.0 \\\\\n", 135 | "\\end{bmatrix}\n", 136 | "~~~~~\n", 137 | "\\textbf{A}^T = \\begin{bmatrix}\n", 138 | " 1.2 & -5.9 & 0.0 \\\\\n", 139 | " -0.7 & 1.4 & 1.0 \\\\\n", 140 | "\\end{bmatrix}\n", 141 | "$$\n", 142 | "\n", 143 | "- Row 1 of matrix $\\textbf{A}$ becomes column 1 of $\\textbf{A}^T$, row 2 of $\\textbf{A}$ becomes column $\\textbf{A}^T$, and finally row 3 of $\\textbf{A}$ becomes column 3 of $\\textbf{A}^T$." 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": { 149 | "slideshow": { 150 | "slide_type": "slide" 151 | } 152 | }, 153 | "source": [ 154 | "## Vectors\n", 155 | "\n", 156 | "- Vectors are a \"special\" type of matrix, which have only one column or one row.\n", 157 | "\n", 158 | "- They come in **two** flavors: **column vectors** and **row vectors**. \n", 159 | "\n", 160 | "- For example, matrix $\\textbf{a}$ is a $3$-by-$1$ column vector, and matrix $\\textbf{a}^T$ is a $1$-by-$3$ row vector.\n", 161 | "\n", 162 | "$$\n", 163 | "\\textbf{a} = \\begin{bmatrix}\n", 164 | " 1.2 \\\\\n", 165 | " -5.9 \\\\\n", 166 | " 0.0 \\\\\n", 167 | "\\end{bmatrix}\n", 168 | "~~~~~\n", 169 | "\\textbf{a}^T = \\begin{bmatrix}\n", 170 | " 1.2 & -5.9 & 0.0 \\\\\n", 171 | "\\end{bmatrix}\n", 172 | "$$" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": { 178 | "slideshow": { 179 | "slide_type": "slide" 180 | } 181 | }, 182 | "source": [ 183 | "## Square Matrix\n", 184 | "\n", 185 | "- A square matrix is a matrix having the same number of rows and columns (i.e., an $n$-by-$n$ matrix). \n", 186 | "\n", 187 | "- Some kinds of square matrices are particularly interesting:\n", 188 | " - **Symmetric Matrix**\n", 189 | " - **Diagonal Matrix**\n", 190 | " - **Scalar Matrix**" 191 | ] 192 | }, 193 | { 194 | "cell_type": "markdown", 195 | "metadata": { 196 | "slideshow": { 197 | "slide_type": "slide" 198 | } 199 | }, 200 | "source": [ 201 | "## Symmetric Matrix\n", 202 | "\n", 203 | "- A matrix $\\textbf{A}_{n,n}$ is **symmetric** if its transpose $\\textbf{A}^{T}_{n,n}$ is equal to itself.\n", 204 | "\n", 205 | "- For example:\n", 206 | "\n", 207 | "$$\n", 208 | "\\textbf{A} = \\begin{bmatrix}\n", 209 | " 1.2 & -5.9 \\\\\n", 210 | " -5.9 & 1.2 \\\\\n", 211 | "\\end{bmatrix}\n", 212 | "=\n", 213 | "\\begin{bmatrix}\n", 214 | " 1.2 & -5.9 \\\\\n", 215 | " -5.9 & 1.2 \\\\\n", 216 | "\\end{bmatrix}\n", 217 | "= \\textbf{A}^T\n", 218 | "$$" 219 | ] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": { 224 | "slideshow": { 225 | "slide_type": "slide" 226 | } 227 | }, 228 | "source": [ 229 | "## Diagonal Matrix\n", 230 | "\n", 231 | "- A **diagonal** matrix $\\textbf{A}_{n,n}$ is a special type of **symmetric** matrix, in which it has zeros in the off-diagonal elements.\n", 232 | "\n", 233 | "- For example:\n", 234 | "\n", 235 | "$$\n", 236 | "\\textbf{A} = \\begin{bmatrix}\n", 237 | " 1.2 & 0 & 0 \\\\\n", 238 | " 0 & 2.7 & 0 \\\\\n", 239 | " 0 & 0 & -3.1 \\\\\n", 240 | "\\end{bmatrix}\n", 241 | "$$" 242 | ] 243 | }, 244 | { 245 | "cell_type": "markdown", 246 | "metadata": { 247 | "slideshow": { 248 | "slide_type": "slide" 249 | } 250 | }, 251 | "source": [ 252 | "## Scalar Matrix\n", 253 | "\n", 254 | "- A **scalar** matrix $\\textbf{A}_{n,n}$ is a special kind of **diagonal** matrix, in which it has equal-valued elements along the diagonal.\n", 255 | "\n", 256 | "- For example:\n", 257 | "\n", 258 | "$$\n", 259 | "\\textbf{A} = \\begin{bmatrix}\n", 260 | " 2.7 & 0 & 0 \\\\\n", 261 | " 0 & 2.7 & 0 \\\\\n", 262 | " 0 & 0 & 2.7 \\\\\n", 263 | "\\end{bmatrix}\n", 264 | "$$" 265 | ] 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "metadata": { 270 | "slideshow": { 271 | "slide_type": "slide" 272 | } 273 | }, 274 | "source": [ 275 | "# Matrix Operations" 276 | ] 277 | }, 278 | { 279 | "cell_type": "markdown", 280 | "metadata": { 281 | "slideshow": { 282 | "slide_type": "slide" 283 | } 284 | }, 285 | "source": [ 286 | "## Matrix Addition and Subtraction\n", 287 | "\n", 288 | "- Just like ordinary algebra, linear algebra has operations like addition and subtraction.\n", 289 | "\n", 290 | "- Two matrices can be added or subtracted **only if** they have the same dimensions, i.e., the same number of rows and columns.\n", 291 | "\n", 292 | "- Addition or subtraction is accomplished **element-wise**. For example, consider the following matrices $\\textbf{A}$ and $\\textbf{B}$.\n", 293 | "\n", 294 | "$$\n", 295 | "\\textbf{A} = \\begin{bmatrix}\n", 296 | " 1.2 & -0.7 & 9.8\\\\\n", 297 | " -5.9 & 1.4 & 6.2\\\\\n", 298 | "\\end{bmatrix}\n", 299 | "~~~~~\n", 300 | "\\textbf{B} = \\begin{bmatrix}\n", 301 | " -0.8 & -2.9 & 0.0 \\\\\n", 302 | " 1.6 & 1.4 & 1.0 \\\\\n", 303 | "\\end{bmatrix}\n", 304 | "$$" 305 | ] 306 | }, 307 | { 308 | "cell_type": "markdown", 309 | "metadata": { 310 | "slideshow": { 311 | "slide_type": "slide" 312 | } 313 | }, 314 | "source": [ 315 | "## Matrix Addition and Subtraction\n", 316 | "\n", 317 | "\n", 318 | "$$\n", 319 | "\\textbf{A} + \\textbf{B} = \\begin{bmatrix}\n", 320 | " 0.4 & -3.6 & 9.8\\\\\n", 321 | " -4.3 & 2.8 & 7.2\\\\\n", 322 | "\\end{bmatrix}\n", 323 | "~~~~~\n", 324 | "\\textbf{A} - \\textbf{B} = \\begin{bmatrix}\n", 325 | " 2.0 & 2.2 & 9.8 \\\\\n", 326 | " -7.5 & 0.0 & 5.2 \\\\\n", 327 | "\\end{bmatrix}\n", 328 | "$$\n", 329 | "\n", 330 | "- Note that addition is commutative (i.e., $\\textbf{A} + \\textbf{B} = \\textbf{B} + \\textbf{A}$), but subtraction in general is not." 331 | ] 332 | }, 333 | { 334 | "cell_type": "markdown", 335 | "metadata": { 336 | "slideshow": { 337 | "slide_type": "slide" 338 | } 339 | }, 340 | "source": [ 341 | "## Matrix Multiplication\n", 342 | "\n", 343 | "- In linear algebra, there are **two** kinds of matrix multiplication: \n", 344 | " - multiplication of a matrix by a scalar (i.e., a number);\n", 345 | " - multiplication of a matrix by another matrix." 346 | ] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "metadata": { 351 | "slideshow": { 352 | "slide_type": "slide" 353 | } 354 | }, 355 | "source": [ 356 | "## How to Multiply a Matrix by a Scalar\n", 357 | "\n", 358 | "- When you multiply a matrix $\\textbf{A}$ by a scalar, you multiply **every element** in the matrix by that same number. \n", 359 | "\n", 360 | "- This operation produces a new matrix, which is called a **scalar multiple**.\n", 361 | "\n", 362 | "- For example, consider the following:\n", 363 | "\n", 364 | "$$\n", 365 | "\\textbf{A} = \\begin{bmatrix}\n", 366 | " 1 & 9 & 4 \\\\\n", 367 | " 5 & 2 & 0 \\\\\n", 368 | " -1 & 3 & 3 \\\\\n", 369 | "\\end{bmatrix}\n", 370 | "~~~~~\n", 371 | "k \\cdot \\textbf{A} = \\begin{bmatrix}\n", 372 | " k & 9k & 4k \\\\\n", 373 | " 5k & 2k & 0 \\\\\n", 374 | " -1k & 3k & 3k \\\\\n", 375 | "\\end{bmatrix}\n", 376 | "~~~(k \\in \\mathbb{R})\n", 377 | "$$" 378 | ] 379 | }, 380 | { 381 | "cell_type": "markdown", 382 | "metadata": { 383 | "slideshow": { 384 | "slide_type": "slide" 385 | } 386 | }, 387 | "source": [ 388 | "## How to Multiply a Matrix by a Matrix\n", 389 | "\n", 390 | "- The product of a matrix $\\textbf{A}$ by another matrix $\\textbf{B}$, i.e., $\\textbf{A}\\cdot \\textbf{B}$ is defined **only** when the number of columns in $\\textbf{A}$ is equal to the number of rows in $\\textbf{B}$.\n", 391 | "\n", 392 | "- Analogously, $\\textbf{B}\\cdot \\textbf{A}$ is defined only when the number of columns in $\\textbf{B}$ is equal to the number of rows in $\\textbf{A}$.\n", 393 | "\n", 394 | "- More generally, if $\\textbf{A}$ is an $m$-by-$k$ matrix, and $\\textbf{B}$ is an $k$-by-$n$ matrix the matrix product $\\textbf{A}\\cdot \\textbf{B}$ is an $m$-by-$n$ matrix $\\textbf{C}$.\n", 395 | "\n", 396 | "- Each element of $\\textbf{C}$ can be therefore computed according to the following formula:\n", 397 | "$$\n", 398 | "c_{ij} = \\sum_{p=1}^k a_{ip}\\cdot b_{pj}\n", 399 | "$$\n" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": { 405 | "slideshow": { 406 | "slide_type": "slide" 407 | } 408 | }, 409 | "source": [ 410 | "## How to Multiply a Matrix by a Matrix\n", 411 | "\n", 412 | "- In the formula above we identify: \n", 413 | " - $c_{ij}$ as the element in row $i$ and column $j$ of the resulting matrix $\\textbf{C}$;\n", 414 | " - $a_{ip}$ as the element in row $i$ and column $p$ of the first operand matrix $\\textbf{A}$;\n", 415 | " - $b_{pj}$ as the element in row $p$ and column $j$ of the second operand matrix $\\textbf{B}$;\n", 416 | " - $\\sum_{p=1}^k$ indicates that $a_{ip}\\cdot b_{pj}$ must be summed over $p = 1\\ldots k$." 417 | ] 418 | }, 419 | { 420 | "cell_type": "markdown", 421 | "metadata": { 422 | "slideshow": { 423 | "slide_type": "slide" 424 | } 425 | }, 426 | "source": [ 427 | "## Matrix Multiplication: An Example\n", 428 | "\n", 429 | "- Let's work through an example to show how the above formula works. Suppose we want to compute $\\textbf{A}\\cdot \\textbf{B}$, given the matrices below:\n", 430 | "\n", 431 | "$$\n", 432 | "\\textbf{A} = \\begin{bmatrix}\n", 433 | " 0 & 1 & 2 \\\\\n", 434 | " 3 & 4 & 5 \\\\\n", 435 | "\\end{bmatrix}\n", 436 | "~~~\n", 437 | "\\textbf{B} = \\begin{bmatrix}\n", 438 | " 6 & 7 \\\\\n", 439 | " 8 & 9 \\\\\n", 440 | " 10 & 11\\\\\n", 441 | "\\end{bmatrix}\n", 442 | "$$\n", 443 | "- Let $\\textbf{C} = \\textbf{A}\\cdot \\textbf{B}$, which we know will be a $2$-by-$2$ matrix." 444 | ] 445 | }, 446 | { 447 | "cell_type": "markdown", 448 | "metadata": { 449 | "slideshow": { 450 | "slide_type": "slide" 451 | } 452 | }, 453 | "source": [ 454 | "## Matrix Multiplication: An Example\n", 455 | "\n", 456 | "$$\n", 457 | "c_{11} = \\sum_{p=1}^3 a_{1p}\\cdot b_{p1} = 0*6 + 1*8 +2*10 = 0 + 8 + 20 = 28\\\\\n", 458 | "c_{12} = \\sum_{p=1}^3 a_{1p}\\cdot b_{p2} = 0*7 + 1*9 +2*11 = 0 + 9 + 22 = 31\\\\\n", 459 | "c_{21} = \\sum_{p=1}^3 a_{2p}\\cdot b_{p1} = 3*6 + 4*8 +5*10 = = 18 + 32 + 50 = 100\\\\\n", 460 | "c_{22} = \\sum_{p=1}^3 a_{2p}\\cdot b_{p2} = 3*7 + 4*9 +5*11 = 21 + 36 +55 = 112\\\\\n", 461 | "$$" 462 | ] 463 | }, 464 | { 465 | "cell_type": "markdown", 466 | "metadata": { 467 | "slideshow": { 468 | "slide_type": "slide" 469 | } 470 | }, 471 | "source": [ 472 | "## Matrix Multiplication: An Example\n", 473 | "\n", 474 | "$$\n", 475 | "\\textbf{A} \\cdot \\textbf{B} =\n", 476 | "\\textbf{C} = \\begin{bmatrix}\n", 477 | " 28 & 31 \\\\\n", 478 | " 100 & 112 \\\\\n", 479 | "\\end{bmatrix}\n", 480 | "$$" 481 | ] 482 | }, 483 | { 484 | "cell_type": "markdown", 485 | "metadata": { 486 | "slideshow": { 487 | "slide_type": "slide" 488 | } 489 | }, 490 | "source": [ 491 | "## Multiplication Order\n", 492 | "\n", 493 | "- In some cases, matrix multiplication is defined for $\\textbf{A}\\cdot \\textbf{B}$, but not for $\\textbf{B}\\cdot \\textbf{A}$, and vice versa. \n", 494 | "\n", 495 | "- However, even when matrix multiplication is possible in both directions, results may be different. That is, $\\textbf{A}\\cdot \\textbf{B}$ is generally different from $\\textbf{B}\\cdot \\textbf{A}$.\n", 496 | "\n", 497 | "- The bottom line: when you multiply two matrices, order matters!" 498 | ] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "metadata": { 503 | "slideshow": { 504 | "slide_type": "slide" 505 | } 506 | }, 507 | "source": [ 508 | "## Identity Matrix\n", 509 | "\n", 510 | "- The **identity matrix** is an $n$-by-$n$ diagonal matrix with $1$'s in the diagonal and $0$'s everywhere else. \n", 511 | "\n", 512 | "- The identity matrix is often denoted by $\\textbf{I}$ (or $\\textbf{I}_{n,n}$ or $\\textbf{I}_{n}$).\n", 513 | "\n", 514 | "- The identity matrix has a nice property: Any matrix that can be multiplied by $\\textbf{I}$ remains the same, that is:\n", 515 | "\n", 516 | "$$\n", 517 | "\\textbf{A}\\cdot\\textbf{I} = \\textbf{I}\\cdot\\textbf{A} = \\textbf{A}\n", 518 | "$$\n", 519 | "\n", 520 | "- Of course, if $\\textbf{A}$ is not a square matrix, $\\textbf{I}$ will have different size depending on whether you do $\\textbf{A}\\cdot\\textbf{I}$ or $\\textbf{I}\\cdot\\textbf{A}$." 521 | ] 522 | }, 523 | { 524 | "cell_type": "markdown", 525 | "metadata": { 526 | "slideshow": { 527 | "slide_type": "slide" 528 | } 529 | }, 530 | "source": [ 531 | "## Vector Multiplication\n", 532 | "\n", 533 | "- The multiplication of a vector by a vector produces some interesting results.\n", 534 | "\n", 535 | "- One is known as the vector **inner product** (a.k.a. **dot product** or **scalar product**), whilst the other is called the vector **outer product**.\n" 536 | ] 537 | }, 538 | { 539 | "cell_type": "markdown", 540 | "metadata": { 541 | "slideshow": { 542 | "slide_type": "slide" 543 | } 544 | }, 545 | "source": [ 546 | "## Vector Inner Product (Dot Product)\n", 547 | "\n", 548 | "- Assume that $\\textbf{a}$ and $\\textbf{b}$ are vectors, each with the same number of elements $n$. Then, the **inner product** of $\\textbf{a}\\cdot \\textbf{b}$ is a scalar $s\\in \\mathbb{R}$.\n", 549 | "$$\n", 550 | "\\textbf{a}^T\\cdot \\textbf{b} = \\textbf{b}^T\\cdot \\textbf{a} = s\n", 551 | "$$\n", 552 | "\n", 553 | "- $\\textbf{a}$ and $\\textbf{b}$ are column vectors, each having $n$ elements;\n", 554 | "\n", 555 | "- $\\textbf{a}^T$ is the transpose of $\\textbf{a}$, which makes $\\textbf{a}^T$ a row vector;\n", 556 | "\n", 557 | "- $\\textbf{b}^T$ is the transpose of $\\textbf{b}$, which makes $\\textbf{b}^T$ a row vector;\n", 558 | "\n", 559 | "- $s$ is a scalar; that is, $s$ is a real number, **not** a matrix!\n", 560 | "\n", 561 | "- Note that the product of two matrices is usually another matrix. However, the inner product of two vectors is a real number!" 562 | ] 563 | }, 564 | { 565 | "cell_type": "markdown", 566 | "metadata": { 567 | "slideshow": { 568 | "slide_type": "slide" 569 | } 570 | }, 571 | "source": [ 572 | "## Vector Outer Product\n", 573 | "\n", 574 | "- Assume that $\\textbf{a}$ and $\\textbf{b}$ are vectors of $m$ and $n$ elements, respectively. Then, the **outer product** of $\\textbf{a}\\otimes \\textbf{b}$ is an $m$-by-$n$ matrix $\\textbf{C}$.\n", 575 | "\n", 576 | "$$\n", 577 | "\\textbf{a}\\otimes \\textbf{b}^T = \\textbf{C}\n", 578 | "$$\n", 579 | "\n", 580 | "- $\\textbf{a}$ is an $m$-by-$1$ column vector;\n", 581 | "\n", 582 | "- $\\textbf{b}^T$ is the transpose of $\\textbf{b}$, which makes $\\textbf{b}^T$ a $1$-by-$n$ row vector;\n", 583 | "\n", 584 | "- $\\textbf{C}$ is an $m$-by-$n$ matrix.\n", 585 | "\n", 586 | "- Let's see how this works!" 587 | ] 588 | }, 589 | { 590 | "cell_type": "markdown", 591 | "metadata": { 592 | "slideshow": { 593 | "slide_type": "slide" 594 | } 595 | }, 596 | "source": [ 597 | "## Vector Outer Product\n", 598 | "\n", 599 | "$$\n", 600 | "\\textbf{a} = \\begin{bmatrix}\n", 601 | " u \\\\\n", 602 | " v \\\\\n", 603 | "\\end{bmatrix}\n", 604 | "~~\n", 605 | "\\textbf{b} = \\begin{bmatrix}\n", 606 | " x \\\\\n", 607 | " y \\\\\n", 608 | " z \\\\\n", 609 | "\\end{bmatrix}\n", 610 | "~~~\n", 611 | "\\textbf{a}\\otimes \\textbf{b}^T = \\textbf{C} = \\begin{bmatrix}\n", 612 | " u\\cdot x & u\\cdot y & u\\cdot z \\\\\n", 613 | " v\\cdot x & v\\cdot y & v\\cdot z \\\\\n", 614 | "\\end{bmatrix}\n", 615 | "$$\n", 616 | "\n", 617 | "- Notice that the elements of matrix $\\textbf{C}$ consist of the product of elements from vector $\\textbf{a}$ \"crossed\" with elements from vector $\\textbf{b}$." 618 | ] 619 | }, 620 | { 621 | "cell_type": "markdown", 622 | "metadata": { 623 | "slideshow": { 624 | "slide_type": "slide" 625 | } 626 | }, 627 | "source": [ 628 | "## Norm of a Vector\n", 629 | "\n", 630 | "- A **norm** is a function that assigns a strictly positive length to a vector (in a vector space).\n", 631 | "\n", 632 | "- Given a vector $\\textbf{x} \\in \\mathbb{R}^n = (x_1, \\ldots, x_n)$ we define the $\\ell_p$-norm (a.k.a. the $p$-norm), with $p\\geq 1$ as follows:\n", 633 | "\n", 634 | "$$\n", 635 | "||\\textbf{x}||_p = \\Bigg(\\sum_{i=1}^n |x_i|^p \\Bigg)^{1/p}\n", 636 | "$$\n", 637 | "\n", 638 | "where $|x_i|$ is the **absolute value** of $x_i$, and $|x_i| = x_i$ iff $x_i \\geq 0$; $-x_i$, otherwise." 639 | ] 640 | }, 641 | { 642 | "cell_type": "markdown", 643 | "metadata": { 644 | "slideshow": { 645 | "slide_type": "slide" 646 | } 647 | }, 648 | "source": [ 649 | "## $\\ell_p$-norm\n", 650 | "\n", 651 | "- $\\ell_1$ ($p=1$) a.k.a. the **taxicab norm** or **Manhattan norm**:\n", 652 | " $$ \n", 653 | " ||\\textbf{x}||_1 = |\\textbf{x}| = \\sum_{i=1}^n |x_i|\n", 654 | " $$\n", 655 | "- $\\ell_2$ ($p=2$) a.k.a. the **Euclidean norm**:\n", 656 | " $$ \n", 657 | " ||\\textbf{x}||_2 = ||\\textbf{x}|| = \\sqrt{x_1^2 + \\ldots + x_n^2}\n", 658 | " $$\n", 659 | "- $\\ell_{\\infty}$ ($p=\\infty$) as $p$ approaches to $\\infty$ the $p$-norm approaches the **infinity norm** or **maximum norm**:\n", 660 | " $$ \n", 661 | " ||\\textbf{x}||_{\\infty} = \\max_i |x_i|\n", 662 | " $$" 663 | ] 664 | } 665 | ], 666 | "metadata": { 667 | "celltoolbar": "Slideshow", 668 | "kernelspec": { 669 | "display_name": "Python 3", 670 | "language": "python", 671 | "name": "python3" 672 | }, 673 | "language_info": { 674 | "codemirror_mode": { 675 | "name": "ipython", 676 | "version": 3 677 | }, 678 | "file_extension": ".py", 679 | "mimetype": "text/x-python", 680 | "name": "python", 681 | "nbconvert_exporter": "python", 682 | "pygments_lexer": "ipython3", 683 | "version": "3.6.4" 684 | } 685 | }, 686 | "nbformat": 4, 687 | "nbformat_minor": 2 688 | } 689 | -------------------------------------------------------------------------------- /lectures/notebooks/data/auto-mpg-regression/README.txt: -------------------------------------------------------------------------------- 1 | 1. Title: Auto-Mpg Data 2 | 3 | 2. Sources: 4 | (a) Origin: This dataset was taken from the StatLib library which is 5 | maintained at Carnegie Mellon University. The dataset was 6 | used in the 1983 American Statistical Association Exposition. 7 | (c) Date: July 7, 1993 8 | 9 | 3. Past Usage: 10 | - See 2b (above) 11 | - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. 12 | In Proceedings on the Tenth International Conference of Machine 13 | Learning, 236-243, University of Massachusetts, Amherst. Morgan 14 | Kaufmann. 15 | 16 | 4. Relevant Information: 17 | 18 | This dataset is a slightly modified version of the dataset provided in 19 | the StatLib library. In line with the use by Ross Quinlan (1993) in 20 | predicting the attribute "mpg", 8 of the original instances were removed 21 | because they had unknown values for the "mpg" attribute. The original 22 | dataset is available in the file "auto-mpg.data-original". 23 | 24 | "The data concerns city-cycle fuel consumption in miles per gallon, 25 | to be predicted in terms of 3 multivalued discrete and 5 continuous 26 | attributes." (Quinlan, 1993) 27 | 28 | 5. Number of Instances: 398 29 | 30 | 6. Number of Attributes: 9 including the class attribute 31 | 32 | 7. Attribute Information: 33 | 34 | 1. mpg: continuous 35 | 2. cylinders: multi-valued discrete 36 | 3. displacement: continuous 37 | 4. horsepower: continuous 38 | 5. weight: continuous 39 | 6. acceleration: continuous 40 | 7. model year: multi-valued discrete 41 | 8. origin: multi-valued discrete 42 | 9. car name: string (unique for each instance) 43 | 44 | 8. Missing Attribute Values: horsepower has 6 missing values 45 | 46 | -------------------------------------------------------------------------------- /lectures/notebooks/data/auto-mpg-regression/dataset.tsv: -------------------------------------------------------------------------------- 1 | 18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu" 2 | 15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320" 3 | 18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite" 4 | 16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst" 5 | 17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino" 6 | 15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500" 7 | 14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala" 8 | 14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii" 9 | 14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina" 10 | 15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl" 11 | 15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se" 12 | 14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340" 13 | 15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo" 14 | 14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)" 15 | 24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii" 16 | 22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster" 17 | 18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet" 18 | 21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick" 19 | 27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510" 20 | 26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan" 21 | 25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504" 22 | 24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls" 23 | 25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e" 24 | 26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002" 25 | 21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin" 26 | 10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250" 27 | 10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20" 28 | 11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200" 29 | 9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d" 30 | 27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510" 31 | 28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300" 32 | 25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona" 33 | 25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto" 34 | 19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin" 35 | 16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom" 36 | 17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu" 37 | 19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500" 38 | 18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador" 39 | 14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala" 40 | 14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham" 41 | 14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500" 42 | 14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii" 43 | 12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)" 44 | 13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)" 45 | 13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)" 46 | 18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)" 47 | 22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)" 48 | 19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird" 49 | 18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang" 50 | 23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000" 51 | 28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900" 52 | 30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304" 53 | 30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b" 54 | 31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200" 55 | 35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200" 56 | 27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111" 57 | 26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket" 58 | 24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop" 59 | 25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop" 60 | 23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3" 61 | 20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega" 62 | 21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout" 63 | 13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala" 64 | 14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina" 65 | 15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii" 66 | 14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500" 67 | 17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst" 68 | 11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis" 69 | 13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom" 70 | 12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale" 71 | 13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal" 72 | 19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe" 73 | 15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)" 74 | 13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)" 75 | 13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)" 76 | 14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)" 77 | 18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)" 78 | 22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)" 79 | 21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)" 80 | 26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)" 81 | 22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)" 82 | 28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)" 83 | 23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)" 84 | 28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)" 85 | 27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)" 86 | 13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350" 87 | 14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador" 88 | 13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu" 89 | 14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino" 90 | 15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom" 91 | 12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham" 92 | 13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic" 93 | 13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd" 94 | 14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan" 95 | 13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham" 96 | 12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom" 97 | 13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham" 98 | 18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant" 99 | 16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom" 100 | 18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet" 101 | 18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick" 102 | 23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster" 103 | 26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle" 104 | 11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala" 105 | 12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country" 106 | 13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb" 107 | 12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser" 108 | 18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin" 109 | 20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina" 110 | 21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega" 111 | 22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610" 112 | 18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3" 113 | 19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto" 114 | 21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6" 115 | 26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe" 116 | 15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s" 117 | 16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix" 118 | 29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128" 119 | 24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta" 120 | 20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls" 121 | 19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea" 122 | 15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom" 123 | 24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le" 124 | 20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii" 125 | 11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega" 126 | 20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster" 127 | 21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick" 128 | 19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet" 129 | 15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova" 130 | 31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210" 131 | 26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto" 132 | 32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200" 133 | 25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega" 134 | 16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic" 135 | 16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador" 136 | 18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring" 137 | 16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino" 138 | 13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)" 139 | 14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)" 140 | 14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)" 141 | 14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)" 142 | 29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox" 143 | 26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher" 144 | 26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta" 145 | 31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona" 146 | 32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710" 147 | 28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt" 148 | 24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128" 149 | 26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc" 150 | 24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic" 151 | 26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru" 152 | 31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9" 153 | 19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom" 154 | 18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova" 155 | 15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch" 156 | 15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick" 157 | 16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina" 158 | 15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air" 159 | 16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury" 160 | 14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd" 161 | 17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century" 162 | 16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu" 163 | 15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador" 164 | 18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury" 165 | 21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk" 166 | 20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" 167 | 13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" 168 | 29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" 169 | 23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" 170 | 20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" 171 | 23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" 172 | 24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" 173 | 25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" 174 | 24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" 175 | 18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" 176 | 29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" 177 | 19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" 178 | 23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" 179 | 23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" 180 | 22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" 181 | 25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" 182 | 33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" 183 | 28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" 184 | 25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" 185 | 25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" 186 | 26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" 187 | 27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" 188 | 17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" 189 | 16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" 190 | 15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" 191 | 14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" 192 | 22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" 193 | 22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" 194 | 24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" 195 | 22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" 196 | 29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" 197 | 24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" 198 | 29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" 199 | 33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" 200 | 20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" 201 | 18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" 202 | 18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" 203 | 17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" 204 | 29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" 205 | 32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" 206 | 28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" 207 | 26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" 208 | 20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" 209 | 13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" 210 | 19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" 211 | 19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" 212 | 16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" 213 | 16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" 214 | 13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" 215 | 13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" 216 | 13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" 217 | 31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" 218 | 30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" 219 | 36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" 220 | 25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" 221 | 33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" 222 | 17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" 223 | 17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" 224 | 15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" 225 | 15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" 226 | 17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" 227 | 20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" 228 | 19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" 229 | 18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" 230 | 16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" 231 | 15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" 232 | 15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" 233 | 16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" 234 | 29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" 235 | 24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" 236 | 26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" 237 | 25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" 238 | 30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" 239 | 33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" 240 | 30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" 241 | 30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" 242 | 22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" 243 | 21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" 244 | 21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" 245 | 43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" 246 | 36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" 247 | 32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" 248 | 39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" 249 | 36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" 250 | 19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" 251 | 19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" 252 | 20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" 253 | 19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" 254 | 20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" 255 | 20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" 256 | 25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" 257 | 20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" 258 | 19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" 259 | 20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" 260 | 20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" 261 | 18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" 262 | 18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" 263 | 19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" 264 | 17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" 265 | 18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" 266 | 17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" 267 | 30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" 268 | 27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" 269 | 27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" 270 | 30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" 271 | 21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" 272 | 23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" 273 | 23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" 274 | 23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" 275 | 20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" 276 | 17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" 277 | 21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" 278 | 16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" 279 | 31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" 280 | 29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" 281 | 21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" 282 | 19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" 283 | 22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" 284 | 20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" 285 | 20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" 286 | 17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" 287 | 17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" 288 | 16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" 289 | 18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" 290 | 16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" 291 | 15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" 292 | 19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" 293 | 18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" 294 | 31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" 295 | 34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" 296 | 35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" 297 | 27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" 298 | 25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" 299 | 23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" 300 | 27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" 301 | 23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" 302 | 34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" 303 | 34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" 304 | 31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" 305 | 37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" 306 | 28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" 307 | 28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" 308 | 26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" 309 | 33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" 310 | 41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" 311 | 38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" 312 | 32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" 313 | 37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" 314 | 28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" 315 | 26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" 316 | 24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" 317 | 19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" 318 | 34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" 319 | 29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" 320 | 31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" 321 | 37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" 322 | 32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" 323 | 46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" 324 | 27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" 325 | 40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" 326 | 44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" 327 | 43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" 328 | 36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" 329 | 30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" 330 | 44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" 331 | 40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" 332 | 33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" 333 | 29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" 334 | 32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" 335 | 23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" 336 | 35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" 337 | 23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" 338 | 32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" 339 | 27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" 340 | 26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" 341 | 25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" 342 | 23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" 343 | 30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" 344 | 39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" 345 | 39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" 346 | 35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" 347 | 32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" 348 | 37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" 349 | 37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" 350 | 34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" 351 | 34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" 352 | 34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" 353 | 29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" 354 | 33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" 355 | 34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" 356 | 33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" 357 | 32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" 358 | 32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" 359 | 31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" 360 | 28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" 361 | 30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" 362 | 25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" 363 | 24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" 364 | 22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" 365 | 26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" 366 | 20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" 367 | 17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" 368 | 28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" 369 | 27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" 370 | 34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" 371 | 31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" 372 | 29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" 373 | 27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" 374 | 24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" 375 | 23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl" 376 | 36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" 377 | 37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" 378 | 31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" 379 | 38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" 380 | 36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" 381 | 36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" 382 | 36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" 383 | 34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" 384 | 38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" 385 | 32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" 386 | 38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" 387 | 25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" 388 | 38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" 389 | 26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" 390 | 22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" 391 | 32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" 392 | 36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" 393 | 27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" 394 | 27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" 395 | 44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" 396 | 32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" 397 | 28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" 398 | 31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" 399 | -------------------------------------------------------------------------------- /lectures/notebooks/data/auto-mpg-regression/dataset.txt: -------------------------------------------------------------------------------- 1 | 18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu" 2 | 15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320" 3 | 18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite" 4 | 16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst" 5 | 17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino" 6 | 15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500" 7 | 14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala" 8 | 14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii" 9 | 14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina" 10 | 15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl" 11 | 15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se" 12 | 14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340" 13 | 15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo" 14 | 14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)" 15 | 24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii" 16 | 22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster" 17 | 18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet" 18 | 21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick" 19 | 27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510" 20 | 26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan" 21 | 25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504" 22 | 24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls" 23 | 25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e" 24 | 26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002" 25 | 21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin" 26 | 10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250" 27 | 10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20" 28 | 11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200" 29 | 9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d" 30 | 27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510" 31 | 28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300" 32 | 25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona" 33 | 25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto" 34 | 19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin" 35 | 16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom" 36 | 17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu" 37 | 19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500" 38 | 18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador" 39 | 14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala" 40 | 14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham" 41 | 14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500" 42 | 14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii" 43 | 12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)" 44 | 13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)" 45 | 13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)" 46 | 18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)" 47 | 22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)" 48 | 19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird" 49 | 18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang" 50 | 23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000" 51 | 28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900" 52 | 30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304" 53 | 30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b" 54 | 31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200" 55 | 35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200" 56 | 27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111" 57 | 26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket" 58 | 24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop" 59 | 25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop" 60 | 23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3" 61 | 20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega" 62 | 21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout" 63 | 13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala" 64 | 14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina" 65 | 15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii" 66 | 14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500" 67 | 17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst" 68 | 11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis" 69 | 13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom" 70 | 12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale" 71 | 13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal" 72 | 19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe" 73 | 15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)" 74 | 13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)" 75 | 13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)" 76 | 14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)" 77 | 18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)" 78 | 22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)" 79 | 21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)" 80 | 26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)" 81 | 22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)" 82 | 28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)" 83 | 23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)" 84 | 28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)" 85 | 27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)" 86 | 13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350" 87 | 14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador" 88 | 13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu" 89 | 14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino" 90 | 15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom" 91 | 12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham" 92 | 13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic" 93 | 13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd" 94 | 14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan" 95 | 13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham" 96 | 12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom" 97 | 13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham" 98 | 18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant" 99 | 16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom" 100 | 18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet" 101 | 18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick" 102 | 23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster" 103 | 26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle" 104 | 11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala" 105 | 12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country" 106 | 13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb" 107 | 12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser" 108 | 18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin" 109 | 20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina" 110 | 21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega" 111 | 22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610" 112 | 18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3" 113 | 19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto" 114 | 21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6" 115 | 26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe" 116 | 15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s" 117 | 16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix" 118 | 29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128" 119 | 24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta" 120 | 20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls" 121 | 19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea" 122 | 15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom" 123 | 24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le" 124 | 20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii" 125 | 11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega" 126 | 20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster" 127 | 21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick" 128 | 19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet" 129 | 15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova" 130 | 31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210" 131 | 26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto" 132 | 32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200" 133 | 25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega" 134 | 16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic" 135 | 16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador" 136 | 18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring" 137 | 16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino" 138 | 13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)" 139 | 14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)" 140 | 14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)" 141 | 14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)" 142 | 29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox" 143 | 26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher" 144 | 26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta" 145 | 31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona" 146 | 32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710" 147 | 28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt" 148 | 24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128" 149 | 26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc" 150 | 24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic" 151 | 26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru" 152 | 31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9" 153 | 19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom" 154 | 18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova" 155 | 15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch" 156 | 15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick" 157 | 16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina" 158 | 15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air" 159 | 16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury" 160 | 14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd" 161 | 17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century" 162 | 16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu" 163 | 15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador" 164 | 18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury" 165 | 21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk" 166 | 20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" 167 | 13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" 168 | 29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" 169 | 23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" 170 | 20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" 171 | 23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" 172 | 24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" 173 | 25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" 174 | 24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" 175 | 18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" 176 | 29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" 177 | 19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" 178 | 23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" 179 | 23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" 180 | 22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" 181 | 25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" 182 | 33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" 183 | 28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" 184 | 25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" 185 | 25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" 186 | 26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" 187 | 27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" 188 | 17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" 189 | 16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" 190 | 15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" 191 | 14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" 192 | 22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" 193 | 22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" 194 | 24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" 195 | 22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" 196 | 29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" 197 | 24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" 198 | 29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" 199 | 33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" 200 | 20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" 201 | 18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" 202 | 18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" 203 | 17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" 204 | 29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" 205 | 32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" 206 | 28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" 207 | 26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" 208 | 20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" 209 | 13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" 210 | 19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" 211 | 19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" 212 | 16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" 213 | 16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" 214 | 13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" 215 | 13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" 216 | 13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" 217 | 31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" 218 | 30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" 219 | 36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" 220 | 25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" 221 | 33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" 222 | 17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" 223 | 17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" 224 | 15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" 225 | 15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" 226 | 17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" 227 | 20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" 228 | 19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" 229 | 18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" 230 | 16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" 231 | 15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" 232 | 15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" 233 | 16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" 234 | 29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" 235 | 24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" 236 | 26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" 237 | 25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" 238 | 30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" 239 | 33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" 240 | 30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" 241 | 30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" 242 | 22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" 243 | 21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" 244 | 21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" 245 | 43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" 246 | 36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" 247 | 32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" 248 | 39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" 249 | 36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" 250 | 19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" 251 | 19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" 252 | 20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" 253 | 19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" 254 | 20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" 255 | 20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" 256 | 25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" 257 | 20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" 258 | 19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" 259 | 20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" 260 | 20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" 261 | 18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" 262 | 18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" 263 | 19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" 264 | 17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" 265 | 18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" 266 | 17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" 267 | 30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" 268 | 27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" 269 | 27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" 270 | 30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" 271 | 21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" 272 | 23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" 273 | 23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" 274 | 23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" 275 | 20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" 276 | 17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" 277 | 21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" 278 | 16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" 279 | 31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" 280 | 29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" 281 | 21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" 282 | 19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" 283 | 22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" 284 | 20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" 285 | 20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" 286 | 17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" 287 | 17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" 288 | 16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" 289 | 18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" 290 | 16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" 291 | 15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" 292 | 19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" 293 | 18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" 294 | 31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" 295 | 34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" 296 | 35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" 297 | 27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" 298 | 25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" 299 | 23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" 300 | 27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" 301 | 23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" 302 | 34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" 303 | 34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" 304 | 31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" 305 | 37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" 306 | 28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" 307 | 28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" 308 | 26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" 309 | 33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" 310 | 41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" 311 | 38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" 312 | 32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" 313 | 37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" 314 | 28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" 315 | 26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" 316 | 24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" 317 | 19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" 318 | 34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" 319 | 29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" 320 | 31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" 321 | 37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" 322 | 32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" 323 | 46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" 324 | 27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" 325 | 40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" 326 | 44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" 327 | 43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" 328 | 36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" 329 | 30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" 330 | 44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" 331 | 40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" 332 | 33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" 333 | 29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" 334 | 32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" 335 | 23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" 336 | 35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" 337 | 23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" 338 | 32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" 339 | 27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" 340 | 26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" 341 | 25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" 342 | 23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" 343 | 30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" 344 | 39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" 345 | 39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" 346 | 35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" 347 | 32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" 348 | 37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" 349 | 37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" 350 | 34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" 351 | 34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" 352 | 34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" 353 | 29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" 354 | 33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" 355 | 34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" 356 | 33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" 357 | 32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" 358 | 32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" 359 | 31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" 360 | 28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" 361 | 30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" 362 | 25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" 363 | 24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" 364 | 22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" 365 | 26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" 366 | 20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" 367 | 17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" 368 | 28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" 369 | 27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" 370 | 34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" 371 | 31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" 372 | 29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" 373 | 27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" 374 | 24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" 375 | 23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl" 376 | 36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" 377 | 37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" 378 | 31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" 379 | 38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" 380 | 36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" 381 | 36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" 382 | 36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" 383 | 34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" 384 | 38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" 385 | 32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" 386 | 38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" 387 | 25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" 388 | 38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" 389 | 26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" 390 | 22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" 391 | 32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" 392 | 36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" 393 | 27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" 394 | 27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" 395 | 44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" 396 | 32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" 397 | 28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" 398 | 31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" 399 | -------------------------------------------------------------------------------- /lectures/notebooks/data/iris_dataset.csv: -------------------------------------------------------------------------------- 1 | sepal_length_cm,sepal_width_cm,petal_length_cm,petal_width_cm,class 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5,3.4,1.5,NA,Iris-setosa 10 | 4.4,2.9,1.4,NA,Iris-setosa 11 | 4.9,3.1,1.5,NA,Iris-setosa 12 | 5.4,3.7,1.5,NA,Iris-setosa 13 | 4.8,3.4,1.6,NA,Iris-setosa 14 | 4.8,3,1.4,0.1,Iris-setosa 15 | 5.7,3,1.1,0.1,Iris-setosa 16 | 5.8,4,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setossa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5,3,1.6,0.2,Iris-setosa 28 | 5,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.1,Iris-setosa 37 | 5,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.1,1.5,0.1,Iris-setosa 40 | 4.4,3,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5,3.3,1.4,0.2,Iris-setosa 52 | 7,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5,2,3.5,1,Iris-versicolor 63 | 5.9,3,4.2,1.5,Iris-versicolor 64 | 6,2.2,4,1,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 0.067,3,5,1.7,Iris-versicolor 80 | 0.06,2.9,4.5,1.5,Iris-versicolor 81 | 0.057,2.6,3.5,1,Iris-versicolor 82 | 0.055,2.4,3.8,1.1,Iris-versicolor 83 | 0.055,2.4,3.7,1,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6,2.8,5.1,1.6,Iris-versicolor 86 | 5.4,3,4.5,1.5,Iris-versicolor 87 | 6,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4,1.2,Iris-versicolor 95 | 5,2.3,3.3,1,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3,5.8,2.2,Iris-virginica 107 | 7.6,3,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5,2,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6,2.2,5,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2,Iris-virginica 124 | 5.6,2.8,6.7,2,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6,3,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5,2.3,Iris-virginica 149 | 6.5,3,5.2,2,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /lectures/notebooks/data/loan-prediction/README.txt: -------------------------------------------------------------------------------- 1 | Variable Description 2 | Loan_ID Unique Loan ID 3 | Gender Male/Female 4 | Married Applicant married (Y/N) 5 | Dependents Number of dependents 6 | Education Applicant Education (Graduate/Not Graduate) 7 | Self_Employed Self employed (Y/N) 8 | ApplicantIncome Applicant income 9 | CoapplicantIncome Coapplicant income 10 | LoanAmount Loan amount in thousands 11 | Loan_Amount_Term Term of loan in months 12 | Credit_History Credit history meets guidelines (0/1) 13 | Property_Area Urban/Semi Urban/Rural 14 | Loan_Status Loan approved (Y/N) 15 | -------------------------------------------------------------------------------- /lectures/notebooks/data/sample.json: -------------------------------------------------------------------------------- 1 | {"name": "Wes", 2 | "places_lived": ["United States", "Spain", "Germany"], 3 | "pet": null, 4 | "siblings": [{"name": "Scott", "age": 29, "pets": ["Zeus", "Zuko"]}, 5 | {"name": "Katie", "age": 38, 6 | "pets": ["Sixes", "Stache", "Cisco"]}] 7 | } -------------------------------------------------------------------------------- /lectures/notebooks/data/sample.txt: -------------------------------------------------------------------------------- 1 | Sueña el rico en su riqueza, 2 | que más cuidados le ofrece; 3 | 4 | sueña el pobre que padece 5 | su miseria y su pobreza; 6 | 7 | sueña el que a medrar empieza, 8 | sueña el que afana y pretende, 9 | sueña el que agravia y ofende, 10 | 11 | y en el mundo, en conclusión, 12 | todos sueñan lo que son, 13 | aunque ninguno lo entiende. 14 | 15 | -------------------------------------------------------------------------------- /lectures/notebooks/data/sample_df.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "color": "red", 4 | "value": "#f00" 5 | }, 6 | { 7 | "color": "green", 8 | "value": "#0f0" 9 | }, 10 | { 11 | "color": "blue", 12 | "value": "#00f" 13 | }, 14 | { 15 | "color": "cyan", 16 | "value": "#0ff" 17 | }, 18 | { 19 | "color": "magenta", 20 | "value": "#f0f" 21 | }, 22 | { 23 | "color": "yellow", 24 | "value": "#ff0" 25 | }, 26 | { 27 | "color": "black", 28 | "value": "#000" 29 | } 30 | ] -------------------------------------------------------------------------------- /lectures/notebooks/data/sample_out.json: -------------------------------------------------------------------------------- 1 | {"name": "Wes", "places_lived": ["United States", "Spain", "Germany"], "pet": null, "siblings": [{"name": "Scott", "age": 29, "pets": ["Zeus", "Zuko"]}, {"name": "Katie", "age": 38, "pets": ["Sixes", "Stache", "Cisco"]}]} -------------------------------------------------------------------------------- /lectures/notebooks/data/tmp.txt: -------------------------------------------------------------------------------- 1 | Sueña el rico en su riqueza, 2 | que más cuidados le ofrece; 3 | sueña el pobre que padece 4 | su miseria y su pobreza; 5 | sueña el que a medrar empieza, 6 | sueña el que afana y pretende, 7 | sueña el que agravia y ofende, 8 | y en el mundo, en conclusión, 9 | todos sueñan lo que son, 10 | aunque ninguno lo entiende. 11 | -------------------------------------------------------------------------------- /lectures/notebooks/data/user_occupations.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gtolomei/python-for-datascience/38d0acaa36ac1766e20f2dfef364b667404c9a68/lectures/notebooks/data/user_occupations.pickle -------------------------------------------------------------------------------- /lectures/notebooks/data/user_occupations.txt: -------------------------------------------------------------------------------- 1 | user_id|age|gender|occupation|zip_code 2 | 1|24|M|technician|85711 3 | 2|53|F|other|94043 4 | 3|23|M|writer|32067 5 | 4|24|M|technician|43537 6 | 5|33|F|other|15213 7 | 6|42|M|executive|98101 8 | 7|57|M|administrator|91344 9 | 8|36|M|administrator|05201 10 | 9|29|M|student|01002 11 | 10|53|M|lawyer|90703 12 | 11|39|F|other|30329 13 | 12|28|F|other|06405 14 | 13|47|M|educator|29206 15 | 14|45|M|scientist|55106 16 | 15|49|F|educator|97301 17 | 16|21|M|entertainment|10309 18 | 17|30|M|programmer|06355 19 | 18|35|F|other|37212 20 | 19|40|M|librarian|02138 21 | 20|42|F|homemaker|95660 22 | 21|26|M|writer|30068 23 | 22|25|M|writer|40206 24 | 23|30|F|artist|48197 25 | 24|21|F|artist|94533 26 | 25|39|M|engineer|55107 27 | 26|49|M|engineer|21044 28 | 27|40|F|librarian|30030 29 | 28|32|M|writer|55369 30 | 29|41|M|programmer|94043 31 | 30|7|M|student|55436 32 | 31|24|M|artist|10003 33 | 32|28|F|student|78741 34 | 33|23|M|student|27510 35 | 34|38|F|administrator|42141 36 | 35|20|F|homemaker|42459 37 | 36|19|F|student|93117 38 | 37|23|M|student|55105 39 | 38|28|F|other|54467 40 | 39|41|M|entertainment|01040 41 | 40|38|M|scientist|27514 42 | 41|33|M|engineer|80525 43 | 42|30|M|administrator|17870 44 | 43|29|F|librarian|20854 45 | 44|26|M|technician|46260 46 | 45|29|M|programmer|50233 47 | 46|27|F|marketing|46538 48 | 47|53|M|marketing|07102 49 | 48|45|M|administrator|12550 50 | 49|23|F|student|76111 51 | 50|21|M|writer|52245 52 | 51|28|M|educator|16509 53 | 52|18|F|student|55105 54 | 53|26|M|programmer|55414 55 | 54|22|M|executive|66315 56 | 55|37|M|programmer|01331 57 | 56|25|M|librarian|46260 58 | 57|16|M|none|84010 59 | 58|27|M|programmer|52246 60 | 59|49|M|educator|08403 61 | 60|50|M|healthcare|06472 62 | 61|36|M|engineer|30040 63 | 62|27|F|administrator|97214 64 | 63|31|M|marketing|75240 65 | 64|32|M|educator|43202 66 | 65|51|F|educator|48118 67 | 66|23|M|student|80521 68 | 67|17|M|student|60402 69 | 68|19|M|student|22904 70 | 69|24|M|engineer|55337 71 | 70|27|M|engineer|60067 72 | 71|39|M|scientist|98034 73 | 72|48|F|administrator|73034 74 | 73|24|M|student|41850 75 | 74|39|M|scientist|T8H1N 76 | 75|24|M|entertainment|08816 77 | 76|20|M|student|02215 78 | 77|30|M|technician|29379 79 | 78|26|M|administrator|61801 80 | 79|39|F|administrator|03755 81 | 80|34|F|administrator|52241 82 | 81|21|M|student|21218 83 | 82|50|M|programmer|22902 84 | 83|40|M|other|44133 85 | 84|32|M|executive|55369 86 | 85|51|M|educator|20003 87 | 86|26|M|administrator|46005 88 | 87|47|M|administrator|89503 89 | 88|49|F|librarian|11701 90 | 89|43|F|administrator|68106 91 | 90|60|M|educator|78155 92 | 91|55|M|marketing|01913 93 | 92|32|M|entertainment|80525 94 | 93|48|M|executive|23112 95 | 94|26|M|student|71457 96 | 95|31|M|administrator|10707 97 | 96|25|F|artist|75206 98 | 97|43|M|artist|98006 99 | 98|49|F|executive|90291 100 | 99|20|M|student|63129 101 | 100|36|M|executive|90254 102 | 101|15|M|student|05146 103 | 102|38|M|programmer|30220 104 | 103|26|M|student|55108 105 | 104|27|M|student|55108 106 | 105|24|M|engineer|94043 107 | 106|61|M|retired|55125 108 | 107|39|M|scientist|60466 109 | 108|44|M|educator|63130 110 | 109|29|M|other|55423 111 | 110|19|M|student|77840 112 | 111|57|M|engineer|90630 113 | 112|30|M|salesman|60613 114 | 113|47|M|executive|95032 115 | 114|27|M|programmer|75013 116 | 115|31|M|engineer|17110 117 | 116|40|M|healthcare|97232 118 | 117|20|M|student|16125 119 | 118|21|M|administrator|90210 120 | 119|32|M|programmer|67401 121 | 120|47|F|other|06260 122 | 121|54|M|librarian|99603 123 | 122|32|F|writer|22206 124 | 123|48|F|artist|20008 125 | 124|34|M|student|60615 126 | 125|30|M|lawyer|22202 127 | 126|28|F|lawyer|20015 128 | 127|33|M|none|73439 129 | 128|24|F|marketing|20009 130 | 129|36|F|marketing|07039 131 | 130|20|M|none|60115 132 | 131|59|F|administrator|15237 133 | 132|24|M|other|94612 134 | 133|53|M|engineer|78602 135 | 134|31|M|programmer|80236 136 | 135|23|M|student|38401 137 | 136|51|M|other|97365 138 | 137|50|M|educator|84408 139 | 138|46|M|doctor|53211 140 | 139|20|M|student|08904 141 | 140|30|F|student|32250 142 | 141|49|M|programmer|36117 143 | 142|13|M|other|48118 144 | 143|42|M|technician|08832 145 | 144|53|M|programmer|20910 146 | 145|31|M|entertainment|V3N4P 147 | 146|45|M|artist|83814 148 | 147|40|F|librarian|02143 149 | 148|33|M|engineer|97006 150 | 149|35|F|marketing|17325 151 | 150|20|F|artist|02139 152 | 151|38|F|administrator|48103 153 | 152|33|F|educator|68767 154 | 153|25|M|student|60641 155 | 154|25|M|student|53703 156 | 155|32|F|other|11217 157 | 156|25|M|educator|08360 158 | 157|57|M|engineer|70808 159 | 158|50|M|educator|27606 160 | 159|23|F|student|55346 161 | 160|27|M|programmer|66215 162 | 161|50|M|lawyer|55104 163 | 162|25|M|artist|15610 164 | 163|49|M|administrator|97212 165 | 164|47|M|healthcare|80123 166 | 165|20|F|other|53715 167 | 166|47|M|educator|55113 168 | 167|37|M|other|L9G2B 169 | 168|48|M|other|80127 170 | 169|52|F|other|53705 171 | 170|53|F|healthcare|30067 172 | 171|48|F|educator|78750 173 | 172|55|M|marketing|22207 174 | 173|56|M|other|22306 175 | 174|30|F|administrator|52302 176 | 175|26|F|scientist|21911 177 | 176|28|M|scientist|07030 178 | 177|20|M|programmer|19104 179 | 178|26|M|other|49512 180 | 179|15|M|entertainment|20755 181 | 180|22|F|administrator|60202 182 | 181|26|M|executive|21218 183 | 182|36|M|programmer|33884 184 | 183|33|M|scientist|27708 185 | 184|37|M|librarian|76013 186 | 185|53|F|librarian|97403 187 | 186|39|F|executive|00000 188 | 187|26|M|educator|16801 189 | 188|42|M|student|29440 190 | 189|32|M|artist|95014 191 | 190|30|M|administrator|95938 192 | 191|33|M|administrator|95161 193 | 192|42|M|educator|90840 194 | 193|29|M|student|49931 195 | 194|38|M|administrator|02154 196 | 195|42|M|scientist|93555 197 | 196|49|M|writer|55105 198 | 197|55|M|technician|75094 199 | 198|21|F|student|55414 200 | 199|30|M|writer|17604 201 | 200|40|M|programmer|93402 202 | 201|27|M|writer|E2A4H 203 | 202|41|F|educator|60201 204 | 203|25|F|student|32301 205 | 204|52|F|librarian|10960 206 | 205|47|M|lawyer|06371 207 | 206|14|F|student|53115 208 | 207|39|M|marketing|92037 209 | 208|43|M|engineer|01720 210 | 209|33|F|educator|85710 211 | 210|39|M|engineer|03060 212 | 211|66|M|salesman|32605 213 | 212|49|F|educator|61401 214 | 213|33|M|executive|55345 215 | 214|26|F|librarian|11231 216 | 215|35|M|programmer|63033 217 | 216|22|M|engineer|02215 218 | 217|22|M|other|11727 219 | 218|37|M|administrator|06513 220 | 219|32|M|programmer|43212 221 | 220|30|M|librarian|78205 222 | 221|19|M|student|20685 223 | 222|29|M|programmer|27502 224 | 223|19|F|student|47906 225 | 224|31|F|educator|43512 226 | 225|51|F|administrator|58202 227 | 226|28|M|student|92103 228 | 227|46|M|executive|60659 229 | 228|21|F|student|22003 230 | 229|29|F|librarian|22903 231 | 230|28|F|student|14476 232 | 231|48|M|librarian|01080 233 | 232|45|M|scientist|99709 234 | 233|38|M|engineer|98682 235 | 234|60|M|retired|94702 236 | 235|37|M|educator|22973 237 | 236|44|F|writer|53214 238 | 237|49|M|administrator|63146 239 | 238|42|F|administrator|44124 240 | 239|39|M|artist|95628 241 | 240|23|F|educator|20784 242 | 241|26|F|student|20001 243 | 242|33|M|educator|31404 244 | 243|33|M|educator|60201 245 | 244|28|M|technician|80525 246 | 245|22|M|student|55109 247 | 246|19|M|student|28734 248 | 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