More-Pandas-Examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar charts with numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# the following line only needed for this particular ipython notebook\n",
    "%matplotlib inline\n",
    "from os.path import join\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "datafname = join(\"data\", \"climate\", \"nasa-gistemp-annual-mean.csv\")\n",
    "with open(datafname) as f:\n",
    "    annual_gistemps = list(csv.DictReader(f))\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-12-d0f3771a9fa6>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-12-d0f3771a9fa6>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    yrs = [d['year') for d in annual_gistemps]\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "yrs = [d['year') for d in annual_gistemps]\n",
    "means = [d['annual_mean'] for d in annual_gistemps]\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(yrs, means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 136 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0qaZNcLsMeFPx+o3ApasLRMRjI+Kw4vVm4CTgxgn3u9/evbttMqrAfhdJo0xaYzgS+Dzw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd1eeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yrs = [int(d['year']) for d in annual_gistemps]\n",
    "means = [float(d['annual_mean']) for d in annual_gistemps]\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(yrs, means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar charts with categorical values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'apples'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-89664a134fd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'apples'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'oranges'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/dtown/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1810\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1811\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1812\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1813\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1814\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dtown/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, left, height, width, bottom, **kwargs)\u001b[0m\n\u001b[1;32m   2127\u001b[0m                 \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m                 \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2129\u001b[0;31m                 \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_nolegend_'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2130\u001b[0m                 )\n\u001b[1;32m   2131\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dtown/.pyenv/versions/anaconda3-2.5.0/lib/python3.5/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xy, width, height, angle, **kwargs)\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0mPatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_width\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'apples'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqbH/BTlbs8dE2Xm9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc5b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [('apples', 100), ('oranges', 42)]\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar([d[0] for d in data], [d[1] for d in data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 2 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpif8DnGl20L3hMo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11035f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(range(len(data)), [d[1] for d in data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x1111c3588>, <matplotlib.text.Text at 0x1110ed390>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EQZekJgy6JDXx/8nxWkWb5WEuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11108f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "width = 1.0\n",
    "ax.bar(range(len(data)), [d[1] for d in data], align='center')\n",
    "ax.set_xticks([0, 1])\n",
    "ax.set_xticklabels(['apples', 'oranges'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Congressmembers by party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('R', 301), ('D', 236), ('I', 3)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "datafname = join(\"data\", \"congress\", \"legislators.csv\")\n",
    "with open(datafname) as f:\n",
    "    legislators = list(csv.DictReader(f))\n",
    "    legislators = [d for d in legislators if d['in_office'] == '1']\n",
    "\n",
    "byparty = Counter(d['party'] for d in legislators).most_common()\n",
    "print(byparty)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAm/t+3lT1zZPJnT5s8WdKz0AAJJJ/f/s+k3a5NYOVvf6rfzawaTXb/FSVeM/aXIO8Bzw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a07898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(range(len(byparty)), [d[1] for d in byparty])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x11155c0b8>,\n",
       " <matplotlib.text.Text at 0x111342358>,\n",
       " <matplotlib.text.Text at 0x112426be0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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78nH8UFTSVc2Hea2j1/QE1DDca7XeMMd1h34W+K0FX2/v5q6QZIhDDrN2UqyvuwQY9poM\ndeQxHXec1tf1G9+1g/V3/dbXtYPVuX6pqlUoZdFBk9cALwDvAX4GHAU+WFUnV/1kkiRgTHfoVfXr\nJA8Ch5lv63zJMJek8RrLHbok6dXnh6KrLMmvkzyb5HiSx5K8Ya1r0mAWXLvnkzyX5C8y3sa0xiDJ\nr9a6hrVioK++/6mq26rqd4FfAH+21gVpYJeu3e8A7wXuZP6RCa0vG7btYKCP1z8z/0y+1pmq+k/g\nT4EH17oWaVAG+uoLXH7S5z3AobUtRytVVf8ObEry5rWuRRqEgb76XpvkWeYf17weeHKN69Fo7KFr\n3TDQV9/LVXUb879YFfyRfd1K8jbgQlX9x1rXIg3CQF99Aaiq/wU+Bvxl979PavJdvhvv2ix/D/zt\n2pWjFdqwP1WN61f/N7LLn7BX1bEk/wp8EHhk7UrSgH6ja5dtAc4DX62qv1njmjS8DfuUi79YJEmN\nsBUgSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJasT/AQPc2VlRZAjTAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111582630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "parties = [d[0] for d in byparty]\n",
    "pcount = [d[1] for d in byparty]\n",
    "ax.bar(range(len(parties)), pcount, align='center')\n",
    "ax.set_xticks(range(len(parties)))\n",
    "ax.set_xticklabels(parties)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked bar chart\n",
    "\n",
    "http://matplotlib.org/examples/pylab_examples/bar_stacked.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11279c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "y1 = [10, 42, 33]\n",
    "y2 = [32, 19, 7]\n",
    "\n",
    "ax.bar(range(3), y1, color='red')\n",
    "ax.bar(range(3), y2, color='orange', bottom=y1)\n"
   ]
  },
  {
   "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
}