volc_cam_images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many volcano camera images do we have?\n",
    "--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'year': [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018],\n",
    "       'images': [1566, 33560, 66236, 78671, 78510, 83226, 118047, 117891, 131982, 185581, 219884, 239438, 294214, 479994, 531858, 568306, 653613, 668258, 135935]}\n",
    "images = pd.DataFrame(data, index=data['year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "images = images.set_index('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4686770"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.images.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FwE9ayOVnuef3AGNyr1uJsyL9HN4FfA8YAK4AXtdCjHvJvqD2AB4H9kjtuwA/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5862acce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl = images.plot.bar(legend=False)\n",
    "pl.set_ylabel('number of images')\n",
    "plt.savefig('number_images.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}