{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(12345)\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A brief matplotlib API primer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures and Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax1 = fig.add_subplot(2, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax3 = fig.add_subplot(2, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "plt.plot(randn(50).cumsum(), 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)\n",
    "ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 3)\n",
    "axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adjusting the spacing around subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None,\n",
    "                wspace=None, hspace=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colors, markers, and line styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(randn(30).cumsum(), 'ko--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = randn(30).cumsum()\n",
    "plt.plot(data, 'k--', label='Default')\n",
    "plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ticks, labels, and legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting the title, axis labels, ticks, and ticklabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(randn(1000).cumsum())\n",
    "\n",
    "ticks = ax.set_xticks([0, 250, 500, 750, 1000])\n",
    "labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'],\n",
    "                            rotation=30, fontsize='small')\n",
    "ax.set_title('My first matplotlib plot')\n",
    "ax.set_xlabel('Stages')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding legends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(randn(1000).cumsum(), 'k', label='one')\n",
    "ax.plot(randn(1000).cumsum(), 'k--', label='two')\n",
    "ax.plot(randn(1000).cumsum(), 'k.', label='three')\n",
    "\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annotations and drawing on a subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "data = pd.read_csv('ch08/spx.csv', index_col=0, parse_dates=True)\n",
    "spx = data['SPX']\n",
    "\n",
    "spx.plot(ax=ax, style='k-')\n",
    "\n",
    "crisis_data = [\n",
    "    (datetime(2007, 10, 11), 'Peak of bull market'),\n",
    "    (datetime(2008, 3, 12), 'Bear Stearns Fails'),\n",
    "    (datetime(2008, 9, 15), 'Lehman Bankruptcy')\n",
    "]\n",
    "\n",
    "for date, label in crisis_data:\n",
    "    ax.annotate(label, xy=(date, spx.asof(date) + 50),\n",
    "                xytext=(date, spx.asof(date) + 200),\n",
    "                arrowprops=dict(facecolor='black'),\n",
    "                horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "# Zoom in on 2007-2010\n",
    "ax.set_xlim(['1/1/2007', '1/1/2011'])\n",
    "ax.set_ylim([600, 1800])\n",
    "\n",
    "ax.set_title('Important dates in 2008-2009 financial crisis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)\n",
    "circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)\n",
    "pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]],\n",
    "                   color='g', alpha=0.5)\n",
    "\n",
    "ax.add_patch(rect)\n",
    "ax.add_patch(circ)\n",
    "ax.add_patch(pgon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving plots to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig.savefig('figpath.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig.savefig('figpath.png', dpi=400, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from io import BytesIO\n",
    "buffer = BytesIO()\n",
    "plt.savefig(buffer)\n",
    "plot_data = buffer.getvalue()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.rc('figure', figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting functions in pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))\n",
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.randn(10, 4).cumsum(0),\n",
    "               columns=['A', 'B', 'C', 'D'],\n",
    "               index=np.arange(0, 100, 10))\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n",
    "data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.rand(6, 4),\n",
    "               index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
    "               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))\n",
    "df\n",
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.plot(kind='barh', stacked=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips = pd.read_csv('ch08/tips.csv')\n",
    "party_counts = pd.crosstab(tips.day, tips.size)\n",
    "party_counts\n",
    "# Not many 1- and 6-person parties\n",
    "party_counts = party_counts.ix[:, 2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Normalize to sum to 1\n",
    "party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0)\n",
    "party_pcts\n",
    "\n",
    "party_pcts.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms and density plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips['tip_pct'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tips['tip_pct'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "comp1 = np.random.normal(0, 1, size=200)  # N(0, 1)\n",
    "comp2 = np.random.normal(10, 2, size=200)  # N(10, 4)\n",
    "values = Series(np.concatenate([comp1, comp2]))\n",
    "values.hist(bins=100, alpha=0.3, color='k', normed=True)\n",
    "values.plot(kind='kde', style='k--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "macro = pd.read_csv('ch08/macrodata.csv')\n",
    "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]\n",
    "trans_data = np.log(data).diff().dropna()\n",
    "trans_data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.scatter(trans_data['m1'], trans_data['unemp'])\n",
    "plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Maps: Visualizing Haiti Earthquake Crisis data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('ch08/Haiti.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[['INCIDENT DATE', 'LATITUDE', 'LONGITUDE']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['CATEGORY'][:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data[(data.LATITUDE > 18) & (data.LATITUDE < 20) &\n",
    "            (data.LONGITUDE > -75) & (data.LONGITUDE < -70)\n",
    "            & data.CATEGORY.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def to_cat_list(catstr):\n",
    "    stripped = (x.strip() for x in catstr.split(','))\n",
    "    return [x for x in stripped if x]\n",
    "\n",
    "def get_all_categories(cat_series):\n",
    "    cat_sets = (set(to_cat_list(x)) for x in cat_series)\n",
    "    return sorted(set.union(*cat_sets))\n",
    "\n",
    "def get_english(cat):\n",
    "    code, names = cat.split('.')\n",
    "    if '|' in names:\n",
    "        names = names.split(' | ')[1]\n",
    "    return code, names.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "get_english('2. Urgences logistiques | Vital Lines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_cats = get_all_categories(data.CATEGORY)\n",
    "# Generator expression\n",
    "english_mapping = dict(get_english(x) for x in all_cats)\n",
    "english_mapping['2a']\n",
    "english_mapping['6c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_code(seq):\n",
    "    return [x.split('.')[0] for x in seq if x]\n",
    "\n",
    "all_codes = get_code(all_cats)\n",
    "code_index = pd.Index(np.unique(all_codes))\n",
    "dummy_frame = DataFrame(np.zeros((len(data), len(code_index))),\n",
    "                        index=data.index, columns=code_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dummy_frame.ix[:, :6].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for row, cat in zip(data.index, data.CATEGORY):\n",
    "    codes = get_code(to_cat_list(cat))\n",
    "    dummy_frame.ix[row, codes] = 1\n",
    "\n",
    "data = data.join(dummy_frame.add_prefix('category_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.ix[:, 10:15].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def basic_haiti_map(ax=None, lllat=17.25, urlat=20.25,\n",
    "                    lllon=-75, urlon=-71):\n",
    "    # create polar stereographic Basemap instance.\n",
    "    m = Basemap(ax=ax, projection='stere',\n",
    "                lon_0=(urlon + lllon) / 2,\n",
    "                lat_0=(urlat + lllat) / 2,\n",
    "                llcrnrlat=lllat, urcrnrlat=urlat,\n",
    "                llcrnrlon=lllon, urcrnrlon=urlon,\n",
    "                resolution='f')\n",
    "    # draw coastlines, state and country boundaries, edge of map.\n",
    "    m.drawcoastlines()\n",
    "    m.drawstates()\n",
    "    m.drawcountries()\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
    "fig.subplots_adjust(hspace=0.05, wspace=0.05)\n",
    "\n",
    "to_plot = ['2a', '1', '3c', '7a']\n",
    "\n",
    "lllat=17.25; urlat=20.25; lllon=-75; urlon=-71\n",
    "\n",
    "for code, ax in zip(to_plot, axes.flat):\n",
    "    m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,\n",
    "                        lllon=lllon, urlon=urlon)\n",
    "\n",
    "    cat_data = data[data['category_%s' % code] == 1]\n",
    "\n",
    "    # compute map proj coordinates.\n",
    "    x, y = m(cat_data.LONGITUDE.values, cat_data.LATITUDE.values)\n",
    "\n",
    "    m.plot(x, y, 'k.', alpha=0.5)\n",
    "    ax.set_title('%s: %s' % (code, english_mapping[code]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
    "fig.subplots_adjust(hspace=0.05, wspace=0.05)\n",
    "\n",
    "to_plot = ['2a', '1', '3c', '7a']\n",
    "\n",
    "lllat=17.25; urlat=20.25; lllon=-75; urlon=-71\n",
    "\n",
    "def make_plot():\n",
    "\n",
    "    for i, code in enumerate(to_plot):\n",
    "        cat_data = data[data['category_%s' % code] == 1]\n",
    "        lons, lats = cat_data.LONGITUDE, cat_data.LATITUDE\n",
    "\n",
    "        ax = axes.flat[i]\n",
    "        m = basic_haiti_map(ax, lllat=lllat, urlat=urlat,\n",
    "                            lllon=lllon, urlon=urlon)\n",
    "\n",
    "        # compute map proj coordinates.\n",
    "        x, y = m(lons.values, lats.values)\n",
    "\n",
    "        m.plot(x, y, 'k.', alpha=0.5)\n",
    "        ax.set_title('%s: %s' % (code, english_mapping[code]))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "make_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "shapefile_path = 'ch08/PortAuPrince_Roads/PortAuPrince_Roads'\n",
    "m.readshapefile(shapefile_path, 'roads')"
   ]
  }
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