{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d2b17da-2328-4961-84ce-aad210042017",
   "metadata": {},
   "source": [
    "# Chapter 6: 6.3 - 6.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "728a799d-fd83-4d56-8653-2794841ab424",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "actors = Table.read_table(\"actors.csv\")\n",
    "actors.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f44203-ebbb-4973-9d42-113e66c2de9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a column that calculates the ratio of the \"#1 Movie\" to the \"Average per Movie\"\n",
    "actors = actors.with_column(\"#1 Movie Impact\", actors.column(\"Gross\") / actors.column(\"Average per Movie\"))\n",
    "actors.set_format(\"#1 Movie Impact\", NumberFormatter(decimals=1))\n",
    "actors.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8cba2c4-f32c-475c-9b87-330a04703bcd",
   "metadata": {},
   "source": [
    "How does one learn about options such as **NumberFormatter**?  Use the [documentation](http://www.data8.org/datascience/index.html)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48d2712e-63cb-48ee-8374-4b3810d44364",
   "metadata": {},
   "outputs": [],
   "source": [
    "partial_actors = actors.select(\"Actor\", \"#1 Movie\", \"#1 Movie Impact\").sort(\"#1 Movie Impact\", descending=True)\n",
    "partial_actors.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9518aae-14a2-49cc-8052-dd144492e823",
   "metadata": {},
   "outputs": [],
   "source": [
    "partial_actors.where(\"#1 Movie Impact\", are.above_or_equal_to(5)).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1da83b1-ad83-4a74-8f9e-6b7e4874e535",
   "metadata": {},
   "outputs": [],
   "source": [
    "partial_actors.num_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb27fd7b-e294-429a-83de-f66919439384",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "numbers = np.arange(1, partial_actors.num_rows + 1)\n",
    "numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9041aea5-753b-4db8-83e0-daf02e4c12c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add an index column to the table\n",
    "partial_actors = partial_actors.with_column(\"Index\", numbers)\n",
    "partial_actors.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3baaade-c820-43f4-a7dd-2005b03fbfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "partial_actors.plot(\"Index\", \"#1 Movie Impact\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0c6f6a-d13c-4978-9b9d-499bc502c506",
   "metadata": {},
   "source": [
    "How can I learn more about plot?  Use the help function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1604405-f1fd-471b-b68d-71316db73924",
   "metadata": {},
   "outputs": [],
   "source": [
    "help(partial_actors.plot)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}