kylemcdonald/fluxus.ipynb

## fluxus.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fluxus = \"\"\"Eric Andersen (born 1940)\n",
    "John Armleder (born 1948)\n",
    "Ay-O (born 1931)\n",
    "Joseph Beuys (1921-1986)\n",
    "Bazon Brock (born 1936)\n",
    "Joseph Byrd (born 1937)\n",
    "John Cage (1912-1992)[137]\n",
    "George Brecht (1926-2008)\n",
    "Giuseppe Chiari (1926-2007)\n",
    "Henning Christiansen (1932-2008)\n",
    "Philip Corner (born 1933)\n",
    "Jean Dupuy (1925-2021)\n",
    "Öyvind Fahlström (1928-1976)\n",
    "Robert Filliou (1926-1987)\n",
    "Simone Forti (born 1935)\n",
    "Henry Flynt (born 1940)\n",
    "Ken Friedman (born 1949)\n",
    "Al Hansen (1927-1995)\n",
    "Geoffrey Hendricks (1931-2018)\n",
    "Bici Hendricks (born 1932)\n",
    "Dick Higgins (1938-1998)\n",
    "Davi Det Hompson (1939-1996)\n",
    "Alice Hutchins (1916-2009)\n",
    "Toshi Ichiyanagi (born 1933)\n",
    "Terry Jennings (1940-1981)\n",
    "Ray Johnson (1927-1995)\n",
    "Joe Jones (1934-1993)\n",
    "Allan Kaprow (1927-2006)\n",
    "Bengt af Klintberg (born 1938)\n",
    "Milan Knížák (born 1940)\n",
    "Alison Knowles (born 1933)\n",
    "Arthur Köpcke(1928-1977)\n",
    "Takehisa Kosugi (1938-2018)\n",
    "Philip Krumm (born 1941)\n",
    "Shigeko Kubota (1937-2015)\n",
    "George Landow (1944-2011)\n",
    "Vytautas Landsbergis (born 1932)\n",
    "John Lennon (1940-1980)\n",
    "Jackson Mac Low (1922-2004)\n",
    "Richard Maxfield (1927-1969)\n",
    "George Maciunas (1931-1978)\n",
    "Jonas Mekas (1922-2019)\n",
    "Gustav Metzger (1926-2017)\n",
    "Larry Miller (born 1944)\n",
    "Kate Millett (1934-2017)\n",
    "Charlotte Moorman (1933-1991)\n",
    "Maurizio Nannucci (born 1939)\n",
    "Louise Odes Neaderland (born 1932)\n",
    "Yoko Ono (born 1933)\n",
    "Robin Page (1932-2015)\n",
    "Nam June Paik (1932-2006)\n",
    "Ben Patterson (1934-2016)\n",
    "Terry Riley (born 1935)\n",
    "Dieter Roth (1930-1998)\n",
    "Takako Saito (born 1929)\n",
    "Wim T. Schippers (born 1942)\n",
    "Tomas Schmit (1943-2006)\n",
    "Carolee Schneemann (1939-2019)\n",
    "Mieko Shiomi (born 1938)\n",
    "Gianni-Emilio Simonetti (born 1940)\n",
    "Daniel Spoerri (born 1930)\n",
    "James Tenney (1934-2006)\n",
    "Yasunao Tone (born 1935)\n",
    "Peter Van Riper (1942-1998)\n",
    "Ben Vautier (born 1935)\n",
    "Wolf Vostell (1932-1998)\n",
    "Yoshi Wada (1943-2021)\n",
    "Robert Watts (1923-1988)\n",
    "Chuck Welch (born 1948)\n",
    "Emmett Williams (1925-2007)\n",
    "La Monte Young (born 1935)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x6000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re \n",
    "from datetime import datetime\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['font.family'] = 'Helvetica'\n",
    "\n",
    "# Parse the data\n",
    "parsed_data = []\n",
    "for person in fluxus.splitlines():\n",
    "    name, years = person.strip().split('(')\n",
    "    years = re.sub(r'\\[.*\\]','', years)\n",
    "    years = years.replace('born', '').replace(')', '').split('-')\n",
    "    years = [year.strip() for year in years]\n",
    "    if len(years) == 1:\n",
    "        years.append(str(datetime.now().year))\n",
    "    parsed_data.append([name.strip()] + years)\n",
    "\n",
    "# Create a data frame\n",
    "df = pd.DataFrame(parsed_data, columns=['Name', 'BirthYear', 'DeathYear'])\n",
    "df['BirthYear'] = pd.to_numeric(df['BirthYear'])\n",
    "df['DeathYear'] = pd.to_numeric(df['DeathYear'])\n",
    "df['is_alive'] = df['DeathYear'] == datetime.now().year\n",
    "df = df.sort_values(by='BirthYear')\n",
    "\n",
    "# Create color list\n",
    "colors = ['green' if is_alive else 'red' for is_alive in df['is_alive']]\n",
    "\n",
    "# Create the visualization\n",
    "fig, ax = plt.subplots(figsize=(10,20))\n",
    "ax.barh(df['Name'], df['DeathYear']-df['BirthYear'], left=df['BirthYear'], align='center', color=colors)\n",
    "# ax.set_xlabel('Years')\n",
    "# ax.set_ylabel('People')\n",
    "ax.set_title('Fluxus')\n",
    "# remove padding on bottom and top\n",
    "plt.gca().set_ylim(-1, len(parsed_data))\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.6)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "datascience",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}