Basic visualization concepts in Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Draft Contents\n",
    "\n",
    "\n",
    "Making a chart\n",
    "Saving to file\n",
    "\n",
    "\n",
    "\n",
    "Changing the characteristics of the line\n",
    "    - color\n",
    "    - width\n",
    "    - pattern\n",
    "    \n",
    "\n",
    "Creating different chart types\n",
    "\n",
    "    \n",
    "Adjusting the axes\n",
    "\n",
    "- Changing the limits\n",
    "- Labeling the axes\n",
    "- Changing tick frequency\n",
    "\n",
    "\n",
    "Changing the background\n",
    "\n",
    "\n",
    "Annotating the chart points\n",
    "\n",
    "\n",
    "Labeling the data and the chart\n",
    "\n",
    "\n",
    "Changing spacing\n",
    "\n",
    "\n",
    "\n",
    "Bucketing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "# only necessary for notebook mode\n",
    "from csv import DictReader\n",
    "from os import makedirs\n",
    "from os.path import join\n",
    "\n",
    "DATA_FILENAME = join('data', 'climate', 'extracted', 'nasa-ghgases-co2-mean.csv')\n",
    "IMAGES_DIR = join('images', 'climate')\n",
    "makedirs(IMAGES_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# loading the data\n",
    "with open(DATA_FILENAME, 'r') as f:\n",
    "    data = list(DictReader(f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the data looks like; because the CSV text format does not indicate whether `'285.2'` is intended to be a float or a string literal, the data deserializes as a bunch of strings: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'co2_ppm_mean': '285.2', 'year': '1850'},\n",
       " {'co2_ppm_mean': '285.1', 'year': '1851'},\n",
       " {'co2_ppm_mean': '285.0', 'year': '1852'},\n",
       " {'co2_ppm_mean': '285.0', 'year': '1853'},\n",
       " {'co2_ppm_mean': '284.9', 'year': '1854'}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it turns out, MPL's plotting handles the numerical-values-as-strings without a hitch (for *now*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5a9AguPxyJYB8opqAiKTFjBnQqxe8/76Wh84k1QREJOd8+y307w8DBigB5BvVBERkg5SX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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f0a8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = [d['year'] for d in data]\n",
    "co2ppm = [d['co2_ppm_mean'] for d in data]\n",
    "plt.plot(years, co2ppm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Axes limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1860, 1900)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ffrjZqz/nHLjkEnjDG5rHJZfARRe5N6/hZAhIU3j22amnbn70o2ZP/5WvhNe97tBe/MSA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\nrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb4b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(years, co2ppm);\n",
    "plt.xlim(1860, 1900) # bucket it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1900, 2000)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffb7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylim(ymin=0, ymax=max([float(i) for i in co2ppm]))\n",
    "plt.xlim(1900, 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TKTKTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}