lec/lec11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 11 #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_list = ['hello', 7, 3.14, True]\n",
    "simple_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_array = make_array(1, 2, 3)\n",
    "\n",
    "crowded_list = [my_array, 'what is going on', -10]\n",
    "crowded_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Table().with_columns('Numbers', [1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = Table(['Drink', 'Cafe', 'Price'])\n",
    "drinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = drinks.with_rows([\n",
    "    ['Milk Tea', 'Asha', 5.5],\n",
    "    ['Espresso', 'Strada',  1.75],\n",
    "    ['Latte',    'Strada',  3.25],\n",
    "    ['Espresso', \"FSM\",   2]\n",
    "])\n",
    "drinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping by one column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = Table.read_table('welcome_survey_v4.csv')\n",
    "survey.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.group('Sleep position').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.group('Sleep position', np.average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "(survey.select('Sleep position', 'Hours of sleep').group('Sleep position', np.average)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-classification: grouping by two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.group(['Handedness','Sleep position']).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.pivot('Sleep position', 'Handedness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.pivot('Sleep position', 'Handedness', 'Hours of sleep', np.average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(survey.select('Handedness', 'Sleep position', 'Hours of sleep')\n",
    "       .group(['Handedness','Sleep position'], np.average)).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, pivot doesn't know how to combine all the hours of sleep\n",
    "# for each subgroup of students\n",
    "survey.pivot('Sleep position', 'Handedness', 'Hours of sleep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge Question ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sky = Table.read_table('skyscrapers_v2.csv')\n",
    "sky = (sky.with_column('age', 2020 - sky.column('completed'))\n",
    "          .drop('completed'))\n",
    "sky.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. For each city, what’s the tallest building for each material?\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. For each city, what’s the height difference between the tallest \n",
    "#    steel building and the tallest concrete building?\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't read ahead until you try the challenge questions yourself first!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sky.select('material', 'city', 'height').group(['city', 'material'], max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sky_p = sky.pivot('material', 'city', 'height', max)\n",
    "sky_p.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sky_p = sky_p.with_column(\n",
    "    'difference', \n",
    "    abs(sky_p.column('steel') - sky_p.column('concrete'))\n",
    ")\n",
    "sky_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sky_p.sort('difference', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take-home question: try it here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a table of the names of the oldest buildings for each \n",
    "# material for each city:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joins ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "discounts = Table().with_columns(\n",
    "    'Coupon % off', make_array(10, 25, 5),\n",
    "    'Location', make_array('Asha', 'Strada', 'Asha')\n",
    ")\n",
    "discounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "combined = drinks.join('Cafe', discounts, 'Location')\n",
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "discounted_frac = 1 - combined.column('Coupon % off') / 100\n",
    "combined.with_column(\n",
    "    'Discounted Price', \n",
    "    combined.column('Price') * discounted_frac\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks.join('Cafe', drinks, 'Cafe')"
   ]
  }
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