quanteconomics/pandas_and_matplotlib.ipynb

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    "# Visualizing Data with Pandas and Matplotlib\n",
    "\n",
    "### David Backus\n",
    "\n",
    "We illustrate three approaches to graphing data with Python's Matplotlib package:    \n",
    "\n",
    "* Approach #1:  Apply a `plot()` method to a dataframe\n",
    "* Approach #2:  Use the `plot(x,y)` function \n",
    "* Approach #3:  Create a figure object and apply methods to it\n",
    "\n",
    "The last one is the least intuitive but also the most useful.  We work up to it gradually.  This [book chapter](https://davebackus.gitbooks.io/test/content/graphs1.html) covers the same material with more words and fewer pictures.  \n",
    "\n",
    "This IPython notebook was created by Dave Backus for the NYU Stern course [Data Bootcamp](http://databootcamp.nyuecon.com/).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries \n",
    "\n",
    "### Jupyter \n",
    "\n",
    "Look around, what do you see?  Check out the **menubar** at the top:  File, Edit, etc.  Also the **toolbar** below it.  Click on Help -> User Interface Tour for a tour of the landscape.  \n",
    "\n",
    "The **cells** below come in two forms.  Those labeled Code (see the menu in the toolbar) are Python code.  Those labeled Markdown are text.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markdown\n",
    "\n",
    "Markdown is a user-friendly language for text formatting.  You can see how it works by clicking on any of the Markdown cells and looking at the raw text that underlies it. In addition to just plain text, we'll use three things a lot:\n",
    "\n",
    "* Bold and italics.  The raw text `**bold**` displays as **bold**.  The raw text `*italics*` displays as *italics*.  \n",
    "* Bullet lists.  If we want a list of items marked by bullets, we start with a blank line and mark each item with an asterisk on a new line.  Double click on this cell for an example.   \n",
    "* Headings.  We create section headings by putting a hash in front of the text.  `# Heading` gives us a large heading.  Two hashes a smaller heading, three hashes smaller still, up to four hashes.  In this cell there's a two-hash heading at the top.  \n",
    "\n",
    "**Exercise.** Click on the blank cell below.  Note that it's labeled Markdown in the menubar.  Add a heading and some text.  Execute the cell by either (i) clicking on the \"run cell\" button in the toolbar or (ii) clicking on \"Cell\" in the menubar and choosing Run. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.5.1 |Anaconda 2.4.1 (64-bit)| (default, Dec  7 2015, 11:16:01) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "Pandas version:  0.17.1\n",
      "Matplotlib version:  1.5.0\n",
      "Today:  2016-01-13\n"
     ]
    }
   ],
   "source": [
    "import sys                             # system module \n",
    "import pandas as pd                    # data package\n",
    "import matplotlib as mpl               # graphics package\n",
    "import datetime as dt                  # date and time module\n",
    "\n",
    "# check versions (overkill, but why not?)\n",
    "print('Python version:', sys.version)\n",
    "print('Pandas version: ', pd.__version__)\n",
    "print('Matplotlib version: ', mpl.__version__)\n",
    "print('Today: ', dt.date.today())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.**  When you run the code cell above, its output appears below it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is an IPython command.  It puts plots here in the notebook, rather than  a separate window.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dataframes to play with \n",
    "\n",
    "* US GDP and consumption \n",
    "* World Bank GDP per capita for several countries \n",
    "* Fama-French equity returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          gdp     pce\n",
      "2003  13271.1  8867.6\n",
      "2004  13773.5  9208.2\n",
      "2005  14234.2  9531.8\n"
     ]
    }
   ],
   "source": [
    "# US GDP and consumption \n",
    "gdp  = [13271.1, 13773.5, 14234.2, 14613.8, 14873.7, 14830.4, 14418.7,\n",
    "        14783.8, 15020.6, 15369.2, 15710.3]\n",
    "pce  = [8867.6, 9208.2, 9531.8, 9821.7, 10041.6, 10007.2, 9847.0, 10036.3,\n",
    "        10263.5, 10449.7, 10699.7]\n",
    "year = list(range(2003,2014))        # use range for years 2003-2013 \n",
    "\n",
    "# create dataframe from dictionary \n",
    "us = pd.DataFrame({'gdp': gdp, 'pce': pce}, index=year) \n",
    "print(us.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>gdppc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "      <td>53.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRA</th>\n",
       "      <td>France</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPN</th>\n",
       "      <td>Japan</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHN</th>\n",
       "      <td>China</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>India</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRA</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEX</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  gdppc\n",
       "USA  United States   53.1\n",
       "FRA         France   36.9\n",
       "JPN          Japan   36.3\n",
       "CHN          China   11.9\n",
       "IND          India    5.4\n",
       "BRA         Brazil   15.0\n",
       "MEX         Mexico   16.5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GDP per capita (World Bank data, 2013, thousands of USD) \n",
    "code    = ['USA', 'FRA', 'JPN', 'CHN', 'IND', 'BRA', 'MEX']\n",
    "country = ['United States', 'France', 'Japan', 'China', 'India',\n",
    "             'Brazil', 'Mexico']\n",
    "gdppc   = [53.1, 36.9, 36.3, 11.9, 5.4, 15.0, 16.5]\n",
    "\n",
    "wbdf = pd.DataFrame({'gdppc': gdppc, 'country': country}, index=code)\n",
    "wbdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.**  In the previous cell, we used the `print()` function to produce output.  Here we just put the name of the dataframe.  The latter displays the dataframe -- and formats it nicely -- if it's the last line in the cell.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rm</th>\n",
       "      <th>rf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>17.49</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>16.34</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>35.21</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>11.72</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rm    rf\n",
       "Date             \n",
       "2010  17.49  0.12\n",
       "2011   0.48  0.04\n",
       "2012  16.34  0.06\n",
       "2013  35.21  0.02\n",
       "2014  11.72  0.02"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fama-French \n",
    "import pandas_datareader.data as web\n",
    "\n",
    "# read annual data from website and rename variables \n",
    "ff = web.DataReader('F-F_Research_Data_factors', 'famafrench')[1]\n",
    "ff.columns = ['xsm', 'smb', 'hml', 'rf']\n",
    "ff['rm'] = ff['xsm'] + ff['rf']\n",
    "ff = ff[['rm', 'rf']]     # extract rm and rf (return on market, riskfree rate, percent)\n",
    "ff.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.**  The warning in pink tells us that the Pandas DataReader will be spun off into a separate package in the near future.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** What kind of object is `wb`?  How would you access its column and row labels?  What are they?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is an IPython command:  it puts plots here in the notebook, rather than a separate window.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Digression:   Graphing in Excel\n",
    "\n",
    "Remind yourself that we need to choose:  \n",
    "    \n",
    "* Data.  Typically a block of cells in a spreadsheet.  \n",
    "* Chart type.  Lines, bars, scatter, or something else.  \n",
    "* x and y variables.  What is the x axis?  What is y?  \n",
    "\n",
    "We'll see the same in Matplotlib. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach #1:  Apply `plot()` method to dataframe\n",
    "\n",
    "Good simple approach, we use it a lot.  It comes with some useful defaults:\n",
    "\n",
    "* Data.  The whole dataframe.  \n",
    "* Chart type.  We have options for lines, bars, or other things.  \n",
    "* `x` and `y` variables.  By default, the `x` variable is the dataframe's index and the `y` variables are all the columns of the dataframe.  \n",
    "\n",
    "All of these things can be changed, but this is the starting point.  \n",
    "\n",
    "Let's do some examples, see how they work. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US GDP and consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd1a27470>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i1aTgRAGbD2xm7Z61rN2zlnV71rF2b9g+Xnici7tefFIrv7SB2+qmICAiUkV5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1b726d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try this with US GDP\n",
    "us.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd25355f8>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xh8FgWhxeRGSHVIwYHg9MBYa4+1ZgK4C7zzezj4F2wCpgn4Rz2kRlJOz73Mwa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\n7P8DaRYVkdoCuPQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1bfcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do GDP alone\n",
    "us['gdp'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd225f208>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6EXEYeEnSiCL7ZWZmx6GvDgxHH70PgHpuYtaFGjxwV7X8WdeEYi8Wa5c0KiLaJY0G9ibx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\nYGaWYf8fB7UvGjNa0q8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1ce9e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart \n",
    "us.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Show that we get the output from `us.plot.bar()`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.tools.plotting.FramePlotMethods object at 0x7fdcd1c72c50>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd1cc8fd0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd24cb3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot \n",
    "# we need to be explicit about the x and y variables: x = 'gdp', y = 'pce'\n",
    "us.plot.scatter('gdp', 'pce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Comment.**  We can get help in IPython by adding a question mark after a function or method.  \n",
    "\n",
    "**Exercise.** How can you get help for `us.plot()`?  Try it and see.  \n",
    "\n",
    "**Exercise.** Add each of these arguments/parameters to `us.plot()`in the code cell below and describe what they do:  \n",
    "\n",
    "* `kind='area'`\n",
    "* `subplots=True`\n",
    "* `sharey=True`\n",
    "* `figsize=(3,6)`\n",
    "* `xlim=(0,16000)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fama-French asset returns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd18ac4a8>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1860240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now try a few things with the Fama-French data\n",
    "ff.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** We can dress up the plots using the arguments of the `plot()` function.  Try adding, one at a time, the arguments `title='Fama-French returns'`, `grid=True`, and `legend=False`.  What does the documentation say about them?  What do they do?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd188d278>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1845c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ff.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** What do each of the arguments do in the code below?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fdcd180c630>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcd172c080>], dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd17d42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ff.plot(kind='hist', bins=20, subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** What do you see here?  How do the returns differ?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fdcd16aa400>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcd1599be0>], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bbLNNSWJorkwmk0hrYGMzqwE2At5NOB4RaeEKTQKl0qVLl8TeO5PdXO7+LvA7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\n+/+T8YFIMAb4kAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd16ed1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ff.plot(kind='kde', subplots=True, sharex=True)    # smoothed histogram (\"kernel density estimate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### World Bank data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Use the World Bank dataframe `wbdf` to create a bar chart of GDP per capita.  *Bonus points:* Create a horizontal bar chart.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach #2:  the `plot(x,y)` function \n",
    "\n",
    "Here we plot variable `y` against variable `x`.  This comes closest to what we would do in Excel:  identify a dataset, a plot type, and the `x` and `y` variables, then press play.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import pyplot module of Matplotlib \n",
    "import matplotlib.pyplot as plt      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdcd150d2b0>]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m+Pvf4V/+Bc47D848M+9ozMzqV8qWhpNGM5x5ZtqQcPjwtC7DzKwcee+pMvC7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1518588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(us.index, us['gdp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** What is the `x` variable here?  The `y` variable?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdcd1476550>]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k9KdP2z5c2PjCdDdLagBd3Ce1RpEX8d4n77Fw40Le2f4OS/OW0ur8ViEcoqGm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\nLAx68pBSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1498358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can do two lines together\n",
    "plt.plot(us.index, us['gdp'])\n",
    "plt.plot(us.index, us['pce'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 11 artists>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd18e8ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# or a bar chart \n",
    "plt.bar(us.index, us['gdp'], align='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Experiment with \n",
    "```python\n",
    "plt.bar(us.index, us['gdp'], \n",
    "        align='center', \n",
    "        alpha=0.65, \n",
    "        color='red', \n",
    "        edgecolor='green')\n",
    "```\n",
    "Play with the arguments one by one to see what they do.  Or use `plt.bar?` to look them up.  Add comments to remind yourself. *Bonus points:*  Can you make this graph even uglier?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdcd13d6f60>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YF5YxxpRfwM8DEZEGInK+qmb6Jw8R6RnsII0xxkSX0h5pOxrYBLwjIutFpK/f\n4VfKdHeR6YjsQ2SNX9lURDYishqRdxBp4HdsEiLp3vHL/cp7I7IGkS2IPONXHoPILO+aZbinJRpj\njKkEpbVA7gP6qGov4JfA6yIyyjtWbHOmGDOAK4qUfQB0w903HZjk7ihdgdG4548MB15ApOB7pgG3\n4mZ+JSNScM9bgUOoJgHPAFPLGJcxxpgKKi2B1FDVPQCqugK4FPijiNwBlG0wQfUzis7YUv0I1Xzv\n0xe4tSUAVwGzUM1FdRsuuaQg0hKIQ3Wld95ruMWN4LZZedV7/zYwtExxGWOMqbDSEsgxETm/4IOX\nTFJxP7S7Ben7xwEF+2olcmbTRnCr3hO91y6/8l2cWRl/5hrVPCALkSZBis0YY0wpStuNdzxFuqpU\n9ZiI/ATX1VQxIvcDOai+VeF7+d21tINTpkzxvU9NTSU1NTWIX22MMdEvLS2NtLS0Mp0b+s0URdoB\n76La069sLPDfwBBUs72yewFF9XHv8yJgMrAdWIJqF698DDAY1fG+c1SXI1ID2INq8+LDsGm8xhhT\nXgFP4w3W9+PfMnAtmHuAq3zJw5kHjPFmVnUAOgErUN0LHEEkxRtUvxm3oWPBNbd4768FFoe0JsYY\nY3xK68KqOJGZuHGTeER24FoU9wExwIe4SVZfoDoB1Q2IzAY2ADnABM40GW7DTR2uAyxEteCBVtOB\n1xFJBzKxFfLGGFNpSttM8WNVHSoij6vqxEqOK+isC8sYY8ovoOeBAK1EZABwlYjM4uwB9a+DGKMx\nxpgoU1oL5BrcQr1LgC+LHFZVHRLi2ILKWiDGGFN+FXqkrYg8oKqPhCSySmQJxBhjyq/Cz0QXkauA\nH3sf01R1fhDjqxSWQIwxpvwq2gJ5DEgB3vSKrgdWqup9QY0yxCyBGGNM+VU0gawBeqm3f5W4BXur\n1H9hYBSwBGKMMeUXjIWEjfzeN6x4SMYYY6JdWRYSPgasEpEluKm8PwbuDWlUxhhjIl5ZB9FbAQUP\nlFqhbnuRqGJdWMYYU34VnoVVFVgCMcaY8gv3ZorGGGOqIEsgxhhjAnLOBCIi54tIbe99qojcISKN\nznWdMcaYqq0sLZB3gDwR6QT8DWgDzAxpVMYYYyJeWRJIvqrmAqOA/1XVe4BWoQ3LGGNMpCtLAskR\nketxT/4r2AOrVuhCMsYYEw3KkkB+CfQH/kdVt4p73OzroQ3LGGNMpLN1IMYYY0oU6BMJCy4eCEwB\n2nnnC+5ou+6qAAAVnUlEQVSBUh2DGaQxxpjoUpYurOnA07gnE/YFLubMtialE5mOyD7cjr4FZdcg\nsg6RPER6Fzl/EiLpiGxE5HK/8t6IrEFkCyLP+JXHIDLLu2YZIm3LFJcxxpgKK0sCOaKq76nqflXN\nLHiV8f4zgCuKlK3Fzej6pFCpSBdgNNAFGA68gEhBs2kacCuqyUAyIgX3vBU4hGoS8AwwtYxxGWOM\nqaCyJJAlIvKEiPQXkd4FrzLdXfUz4HCRss2opuO6wvxdDcxCNRfVbUA6kIJISyAO1ZXeea8BI/2u\nedV7/zYwtExxGWOMqbCybOf+I+/Pi/3KFBgS5FgSgWV+nzO8slxgl1/5Lq+84JqdLiLNQyQLkSao\nHgpybMYYY4o4ZwJR1UsrI5AgKXamQIEpU6b43qemppKamhricIwxJrqkpaWRlpZWpnPLMgurITAZ\n9yApcGMXD6vqkUADLEEGbpuUAq29spLK/a/ZjXvUboPSWh/+CcQYY8zZiv5y/dBDD5V4blnGQP4B\nHMMNcI8GjuIGx8tKKLll4F8+DxjjzazqAHQCVuAeXnUEkRRvUP1mYK7fNbd4768FFpcjLmOMMRVw\nzoWEIrJaVXudq6yEi2cCqUA8sA/XkjkM/C/QFMgCVqM63Dt/Em5mVQ5wJ6ofeOV9gFeAOsBCVO/0\nymvjVsVfBGQCY7wB+OLqYQsJjTGmnCr0REIRWQbco25GVcHCwidVtX/QIw0hSyDGGFN+FVqJDowH\nXvXGQgQ4BIwNXnjGGGOiUZn3whKRBgCqejSkEYWItUCMMab8AmqBiMiNqvqGiPyu6M0AVPXpoEZp\njDEm6H7I/YGYGjGcJ8F/gnlpXVj1vT/jgv6txhhjQmLf8X18vvNzPt/5OUt3LuWbfd+w+terSYpP\nCvp32XbuxhgTpfI1n40HNrJ051KW7lzK5zs/58CJA/Rv058BrQcwsO1AUhJTiI2JDfg7ApqFJSLP\nlXZTVb0j4IjCwBKIMSbanTh9ghUZK3zJYtmuZTSp24SBbQYysM1ABrQZQLfm3YLaXRVoArml2AMe\nVX21tOORxhKIMSba7Dq6y3VF7XAtjI0HN9KzRU9f62JAmwG0jG0Z0hgqtA6kqrAEYoyJZLn5uazd\nt9bXuli6cyknTp9gQJsBvtbFxQkXU7dW3UqNK9AWyLu4XXeLpapXBSe8ymEJxBgTSY78cITlGct9\nrYsVGStIbJDoa10MbDOQ5Phkv8cihUegCWRwaTdV1U9KOx5pLIEYY8JFVdmWta1Q6+LbQ9/Sp1Uf\nX+uif5v+NK3XNNyhnsW6sLAEYoypHAdOHGDd/nWsP7CedfvX+V51a9X1dUcNbDOQi1pdREyNmHCH\ne06BtkBmq+poEVlLMV1ZqtozuGGGliUQY0wwZf2Qxfr96wslivUH1pOdm0335t3p3rw73Zp1c382\n70bz+s3DHXJAAk0grVR1j4i0K+64qm4PYowhZwnEGBOIE6dPsPHgxkJJYt3+dRw+dZiuzbrSrXk3\nujfr7ksaCXEJYR+3CKagdWGJSFMgMxp/ElsCMcaUJjs3m82Zm12S2L+edQdcwth9bDed4zsXShTd\nmnejfaP2IdkeJNIE2gLpB/wZt/vuI7jnbjTFPYTqZlVdFJpwQ8MSiDEG3HTZbw9965LE/nW+RLEt\naxvtG7V3LQm/RNGpSSdqnleWjcurpkATyJfAfUBD4G/AcFX9QkQuAN5S1YtCFXAoWAIxpvrJzc9l\n/f71rNy9kpUZK1m5eyUbD24kIS7Blyi6NXfjFJ3jO1O7Zu1whxxxAk0gvqcOishGVe3id2yVJRBj\nTCRRVb47/B0rMlb4ksXqvatp3aA1fRP7kpKQQt/EvvRo3oP6MfXPfUMDBP5AqXy/96eKHLOfxMaY\nsNp9bLcvUazIWMGXu78kNiaWvol96ZvQl4cvfZg+rfrQsE7DcIdaZZXWAskDTuCeQlgXOFlwCKij\nqrUqJcIgsRaIMdEr64csvtz9pWtdeN1Rp3JPkZKYQt8ElzD6JvYN+b5Q1VH4FhKKTAdGAPsoWDci\n0hj4J9AO2AaMRvWId2wSMA7IBe5E9QOvvDfwClAHWIjqXV55DPAa0Ac4CFyH6o7iQ7EEYkw0OJVz\nilV7VxVqXew5voeLWl5E34S+Lmkk9qVDow5VarpspApnArkEOA685pdAHgcyUZ2KyESgMar3ItIV\neBPoC7QGPgKSUFVElgO/RXUlIguBZ1F9H5HxQA9UJyByHTAK1THFh2IJxJhI4z/IXdC62HxwM12b\ndfW1KlISU+jStAs1zqsR7nCrpfBuZeIWIr7rl0A2AYNR3YdISyAN1QsQuRdQVB/3znsPmAJsBxaj\n2tUrH+NdPx6RRcBkVJcjUgPYi2qz4sOwBGIKy8vPY3PmZr7a/RVf7v6SAycPUKdmnbNedWvWLba8\npFfdWmfOr87TP4s6mXOSDQc2sG7/Or7Z+41vkLtNwzaFuqIubHkhdWrWCXe4xhPoIHqoNEd1HwCq\nexEpWN+fCCzzOy/DK8sFdvmV7/LKC67Z6d0rD5EsRJqgeih04ZtolK/5bMnc4ksWX+75ktV7V9Oi\nfgsuTriYPq36kJKYQnZeNj/k/lDodeDkAd/7U7mnzjpe2ktVz5lwYmNi6dCoA8nxySTFJ5Ecn0zr\nBq2jdpFabn4u6ZnprN2/lnX71/n+zDiaQXJ8Mt2bd6dni548cukj9EnoQ4PaDcIdsglQJPx6FMxm\nQakdolOmTPG9T01NJTU1NYhfbSJFvubz7aFvfcniqz1f8fWer2lar6kvWUwZPIXerXrTuG7jkMaS\nm597ziRzNPso3x/+nlV7VzF7w2y2ZG7h8KnDnN/kfJdUmiQV+rN5/eYR0fevquw4sqNQkli3fx1b\nMreQ2CCRHs170L15d27ofgPdm3cnKT7JWmRRIC0tjbS0tDKdG44urI1Aql8X1hJUuxTTheW6p1wX\nljvHlZfWhbUH1WJ3LLMurKpJVfn+8PeuVeGXLBrVaeRLFhcnXEzvVr2Jrxcf7nDL7Pjp43x76Fu2\nZG4hPTOdLYe8PzO3kJOfc1ZSKWi9NKrTKCTxHDx5kLX7Crco1h9YT2xMLN2bd/clix7Ne9ClWRfq\n1aoXkjhM5Qv3GEh7XALp4X1+HDiE6uMlDKL/CNc19SFnBtG/AO4AVgILgOdQXYTIBKC7N4g+Bhhp\ng+hVV8EzFfyTxVd7viIuJq5QsuiT0Ccin6sQLIdOHfIlk/RDhf+sW7OurxvMP7l0atKpTD/Uj58+\nzoYDG3zJYt2Bdazdt5Yfcn+gR4sevi0+erToQbdm3aIqKZvAhHMW1kwgFYgH9uFaFHOAfwFtcK2L\n0ahmeedPAm4Fcig8jbcPhafx3umV18bt0XURkAmMQXVb8aFYAokmBd0jRZNF3Zp1z0oW0bpNdrCp\nKnuP7z2TVLyWy5bMLXx/+Hua1mtaqOWSFJ/EyZyTLll4+0HtObaHC5peULhV0aIHiXGJEdFtZiqf\nPVAKSyCRLjs3m2W7lrF462LfquKYGjFnJQtbKBaYvPw8dh7deVbLpV6teoWSxflNzrdxClOIJRAs\ngUSafM1nzb41fPT9R3z0/Ud8vvNzujbrypAOQ+jfuj99EvqQEJcQ7jCNqfYsgWAJJBJsy9rmSxgf\nb/2Y+LrxDOs4jGEdh5HaPjVkA8DGmMBZAsESSDhknsxkybYlvqRx/PRxX8IY2mEobRq2CXeIxphz\nsASCJZDKcCrnFEt3LvUljPRD6QxqO8iXNLo162YDscZEGUsgWAIJhbz8PFbtXeVLGMszltOzRU8u\n63gZwzoOIyUxhZgaMeEO0xhTAZZAsAQSDAUP7ClIGEu2LaFVbCuGdhjKsI7DGNx+sG1LYUwVYwkE\nSyCB2n9iP4u3LvYljZz8HNcl1WEYQzsOtZlSxlRxlkCwBFJWp/NO8+n2T1n07SI+/P5DtmVtY3D7\nwQzr4MYxLmh6gY1jGFONWALBEkhp9p/Yz3vp7zE/fT4ffvchXZp1YXin4VzW8TL6Jva1hWXGVGOW\nQLAE4k9V+WbfN8zfMp/5W+az6eAmLjv/MkYkjWB40nDbGsQY42MJBEsgJ3NOsnjrYuZvmc+C9AXU\nrlGbK5OvZETyCAa1G2SzpYwxxbIEQvVMIDuP7GRB+gLmb5nPf7b/hz4JfRiRNIIRySNIjk+2sQxj\nzDlZAqF6JJC8/DxWZKzwtTJ2Hd3F8KThjEgawRWdrrCtQowx5WYJhKqbQI78cIQPvvuA+enzeS/9\nPVrGtuRnST9jRPII+rXuR43zaoQ7RGNMFLMEQtVKIOmZ6W4APH0+KzNWcknbSxiRPIKfJf2Mdo3a\nhTs8Y0wVYgmE6E4gOXk5fLbjM1/SOJZ9jBHJbixjaIeh1I+pH+4QjTFVlCUQoi+BHDp1iAVbFjA/\nfT4ffPcBSU2SfEnjopYX2QC4MaZSWAIhOhLIjiM7mLtpLnM2z2FlxkqGdBjCVZ2v4qdJP7Un8Rlj\nwsISCJGZQFSVdfvXMWfTHOZsnsP2rO1c2flKRnYeyWXnX0a9WvXCHaIxppqLzAQicifwX96nl1F9\nDpHGwD+BdsA2YDSqR7zzJwHjgFzgTlQ/8Mp7A68AdYCFqN5V/NdFRgLJy89j6c6lLmlsmoOijOw8\nkpEXjGRg24G2bYgxJqJEXgIR6Qa8BfTFJYT3gPHAr4BMVKciMhFojOq9iHQF3vTObw18BCShqogs\nB36L6kpEFgLPovr+2V8ZvgRyKucUH37/IXM2zeHdLe/SukFrX9Lo2aKnjWcYYyJWaQkkXL/udgGW\no5oNgMh/gJ8DVwGp3jmvAmnAvV75LFRzgW2IpAMpiGwH4lBd6V3zGjASOCuBVLbMk5ksSF/AnE1z\n+Hjrx/Rp1YeRF4zkwcEP0r5R+3CHZ4wxFRauBLIOeNTrssoGfgp8CbRAdR8AqnsRKdjVLxFY5nd9\nhleWC+zyK9/llYfF9qztzN08lzmb5vDVnq8Y2mEoIy8YyctXvkx8vfhwhWWMMSERngSiugmRx4EP\ngePAKiCvuDOD+bVTpkzxvU9NTSU1NbVC91NV1uxb4xsE33V0F1cmX8ld/e5iWMdhNghujIk6aWlp\npKWllencyJiFJfI/wE7gTiAV1X2ItASWoNoFkXsBRfVx7/xFwGRgu+8cVz4GGIzq+LO/IjhjILn5\nuSzdsdSXNARh1AWjGHnBSAa0GWBbhxhjqpRIHAMBkWaoHkCkLTAK6Ad0AMYCjwO3AHO9s+cBbyLy\nF1wXVSdghTeIfgSRFGAlcDPwXLBDPZlzkg+/+5A5m+cwf8t82jZsy8jOI5k3Zh7dm3e3QXBjTLUU\nzmm8/wGaADnA3aimIdIEmA20wbUuRqOa5Z0/CbjVO99/Gm8fCk/jvbP4rwusBbJm3xoGzRjExQkX\nM7LzSK6+4GraNmxb7vsYY0w0irxpvGEQaALJzc/laPZRmtRtEoKojDEmslkCIXIWEhpjTDQpLYGc\nV9nBGGOMqRosgRhjjAmIJRBjjDEBsQRijDEmIJZAjDHGBMQSiDHGmIBYAjHGGBMQSyDGGGMCYgnE\nGGNMQCyBGGOMCYglEGOMMQGxBGKMMSYglkCMMcYExBKIMcaYgFgCMcYYExBLIMYYYwJiCcQYY0xA\nwpdARO5GZB0iaxB5E5EYRBoj8gEimxF5H5GGfudPQiQdkY2IXO5X3tu7xxZEnglHVYwxpjoKTwIR\nSQBuB3qj2hOoCVwP3At8hGpnYDEwyTu/KzAa6AIMB15ApOARi9OAW1FNBpIRuaIyqxJqaWlp4Q6h\n0lW3Ole3+oLVuaoIZxdWDaA+IjWBukAGcDXwqnf8VWCk9/4qYBaquahuA9KBFERaAnGorvTOe83v\nmiqhKv5Hdy7Vrc7Vrb5gda4qwpNAVHcDTwE7cInjCKofAS1Q3eedsxdo7l2RCOz0u0OGV5YI7PIr\n3+WVGWOMCbFwdWE1wrU22gEJuJbILwAtcmbRz8YYYyKEqIbhZ7TINcAVqP639/kmoB8wBEhFdZ/X\nPbUE1S6I3Asoqo975y8CJgPbfee48jHAYFTHn/2VYsnIGGMCoKpSXHnNyg7EswPoh0gdIBsYCqwE\njgNjgceBW4C53vnzgDcR+Quui6oTsAJVReQIIine9TcDzxX3hSX9BRhjjAlMeBKI6gpE3gZWATne\nn38D4oDZiIzDtS5Ge+dvQGQ2sME7fwJnmk63Aa8AdYCFqC6qxJoYY0y1FZ4uLGOMMVHPVqJXNpHW\niCxGZD0iaxG5wysv/yLKM8fnIbKm0upQXsGss0gtRF7yrtmAyKhKr8+5BLe+13sLZVcjshCRJpVe\nn7Iob51FmnjnH0PkuSL3io7FwcGqs0hdROZ7//ZrEflTWOoTCFW1V2W+oKVCL+99rMJmhQsUHlf4\ng1c+UeHP3vuuCqsUaiq0V/hWvZajd3yUwhsKa8Jet8qoM0xReNjv3k3CXr9Q1RdqKOxTaOyd97jC\ng2GvX3DqXE9hgMKvFJ4rcq/lCn299wsVrgh7/UJZZ6irMNh7X1PhPxFb5yIva4FUNtW9qK723h8H\nNgKtKe8iSgCR+sDdwKOVFH1ggllnGAc85nfvQyGOvvyCV9+CiR9x3s4LDYDdlVGFcitvnVVPovo5\nbhLNGdG0ODhYdVY9heon3vtc4GvvPhHPEkg4ibQHegFfUP5FlACPAE8Cp0IfbJBUpM5nunweReQr\nRP6JSLPKCDtgFamv+2EyAViLWyTbBZheGWFXSNnqXJLoXBxcsTr736cRcCXwcbBDDAVLIOEiEgu8\nDdzp/fZSvkWUIhcC56M6D/ebauRPU65ond2swdbAZ6j2wf3P+lTQ4wyWiv8b1wTGAxeimohLJPeF\nINLgqfi/cfQJVp1FagAzgWe8lmjEswQSDu4Hw9vA66gWrHXZh0gL73hLYL9XngG08bu6tVfWH+iD\nyPfAp7iNJBdXQvSBCUadVTOBE6j+P6/8X8BFoQ49IMH5N+6FW0C7zSufjft3j0zlq3NJSvq7iEzB\nqXOBvwGbUf3foMcZIpZAwuMfwAZUn/Urm4dbRAlnL6Icg9vuvgNnFlG+iGprVDsCl+D+wxtSOeEH\npOJ1dt5F5FLv/TDc2qBIFIz6ZgBdEYn3zrsM188eqcpTZ39nWs+uy8ctDnbjPjeXcE2kqHidAUQe\nBRqgencIYgydcI/iV7sXDFTIU1jtzbz5WuEnCk0UPvJmcnyg0MjvmknezJyNCpcXc892ET4LK3h1\nhrYKn3j3+lChddjrF9r6/kphg3evub4ZWZH2CqzOWxUOKhxV2KFwgVfeR2GtQrrCs2GvW6jrDIkK\n+Qrr/e4zLuz1K8PLFhIaY4wJiHVhGWOMCYglEGOMMQGxBGKMMSYglkCMMcYExBKIMcaYgFgCMcYY\nExBLIMYYYwJiCcQYY0xA/j9ww3Q+gvNuIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd14be710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can also add things to plots \n",
    "plt.plot(us.index, us['gdp']) \n",
    "plt.plot(us.index, us['pce']) \n",
    "\n",
    "plt.title('US GDP', fontsize=14, loc='left') # add title\n",
    "plt.ylabel('Billions of 2009 USD')           # y axis label \n",
    "plt.xlim(2002.5, 2013.5)                     # shrink x axis limits\n",
    "plt.tick_params(labelcolor='red')            # change tick labels to red\n",
    "plt.legend(['GDP', 'Consumption'])           # more descriptive variable names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** All of these statements must be in the same cell for this to work.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** This is overkill -- it looks horrible -- but it makes the point that we control everything in the plot.  We recommend you do very little of this until you're more comfortable with the basics.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Add a `plt.ylim()` statement to make the `y` axis start at zero, as it did in the bar charts.  *Bonus points:*  Change the color to magenta and the linewidth to 2.  *Hint:*  Use `plt.ylim?` and `plt.plot?` to get the documentation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise.** Create a line plot for the Fama-French dataframe `ff` that includes both returns.  *Bonus points:* Add a title and label the y axis.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach #3:  Create figure objects and apply methods\n",
    "\n",
    "This approach is the most foreign to beginners, but now that we’re used to it we like it a lot. We either use it on its own, or adapt its functionality to the dataframe plot methods we saw in Approach #1. The idea is to generate an object – two objects, in fact – and apply methods to them to produce the various elements of a graph: the data, their axes, their labels, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd14098d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create fig and ax objects\n",
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** What do we have here?  What `type` are `fig` and `ax`?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We say `fig` is a **figure object** and `ax` is an **axis object**.  This means:\n",
    "    \n",
    "* `fig` is a blank canvas for creating a figure.\n",
    "* `ax` is everything in it:  axes, labels, lines or bars, and so on.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Use tab completion to see what methods are available for `fig` and `ax`.  What do you see?  Do you feel like screaming?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Atsym2+3lN1Q33iEDiKR1gb3JG0SAW4GzIuKhEbz4eDJIbFnsj4uI+4vtzwCviYi9Jb0S\n+Bk5RnQDcsjP5sWdytXAwcAs4HfAiRExU9IU4FURMaUILLtFxF6D5KOrA4hZJVwO7E+O8YBs6/gW\nTe1lVXV1zYVV9Iq6lBx+cz0Z218DHC7prRFx2zBeeAbQA6wraR55R/EWSRPJ2su55J8DEXGLpHOA\nW8jvFVNqPvEPBKYDqwIX9vXcAqYBZ0qaAzwEDBg8zKzkHgY+T/7HQ/bV/BE51sPaZtA7EEnnAudE\nxDn90t8H7B0R72tB/hqm2+9Auv02upu57IYQ5JTrnwYeIHtVfRH4ArBKG/NVo9vLr96R6Fv2Dx4A\nEfFL4FWNypyZ2YDuBP6LrER/ANgeuAH4Ch0TPKpuqDuQ6yJi0kiPdapuvwMx6xqLyWHBRwBPAmOB\n44CP4MmX2qDe9UBeLGmghR4FvKghOTMzq3Ut2TB+fbH/QXJcx/pty5ENYah4/hNgzQEeawCnND9r\nNhK1fdFtFP5B1q9/hfwrv4jse/hE817SZUf+fj9D9sG8HtiYXPRpBh0fPKpcfoPegUTEka3MiFlb\nBXAq2Vg7WLB4IbAR8NJ+P/u2/wN4ftNz2n1+S/aznEd+pT0EOBLPYVUCQ7WBfAzojYg5xaSG08iZ\nZu4C9o2I6wd8YodyG4gNagHwMXKtbIDdyW4i84rH/OKxaAXXeR7wEgYOLn0/16Mtg9060n3Ap4Bf\nFPvbktOSlKp1tfvVNZBQ0s3ANhHxjKS9ye8F7wC2IUd/l6oHtgOIDeg8Mnj8E1gb+AHZ66f/v0sU\n5/QFlHn9tueTH4gr+hNblecGlwnA+6lOz6KlZAX5F4BHyfmrjgYOwmukdqB6A8jsiJhYbM8AromI\nE4p998LqMN3eF73hHiPr3E8t9t9KDlV96Siu+TRwLwMHl76fC5/7tF566dmpB35N91eB3UI2kv+p\n2N+JDNobty1Ho9bt/3v19sJaKukl5HTsOwJfrzm2WgPzZ9ZaVwD7kPMgrEJOhXEwo+8iOgYYXzwG\n8xjLq8T6As33yAl6/odcQacbu6r+G/gG+bvugPmrrDGGugN5DzlZwErkXFYfK9J3AD4fETu1LJcN\n0O13IDYMi8jeVceSVU3bAD8FXtnOTAHXkHdATwL/S+avm/SSExbdXux7/qpSGc1kiiuTizk9UpO2\nevG8JnZsbDwHkIq7Efhw8fN5wGFkMGnBgkPDchHwHnIQ3bFkICm7INs5+gKi568qpbqmMpH0XmAX\ncvLD90raXdKbgeeVLXhUQZX7og9pCfkB9hoyeLwMuJJstO2Q4NHb25vLsE4vEj5HrrtZZkvJrrnH\nku06R5LjO7oweFT5f2+oNpCdB0hbB9hK0n4RcWmT8mTWGHPJto4riv39ySkx1mhXhlbgv4EHybEo\nHwXWJRuZy2YJ+bueRrYx/Yqc08q6zogXlCoWgTonIl7bnCw1h6uwKiSA08mG8cfJBttplOfD+HBy\nsaTVgEuAN7Q3OyOymAx+Z5L5Pw94e1tzZKNU72y8A4qIu+j+zoZWVv8E3ktOvPd4sX0z5QkekP0d\n9wOeIttF/l97szNszwAfIoPH6uRUJA4eXW3EAUTSy1nxmFxrsSrXwy5zATmC/DfkrG2nA+eSo787\n2HPKTsAPgV3JTvTvJLv7drKnyeXcfk7+7i8il5KrgCr/7w21IuEFPHdc7TrkZA0famamzEbkceCz\nLJ/is4dskC7x4DRWBs4ig8cV5BwQV9KZwXAROZ7jAnI0/0XkpIjW9YYaB7JDv6Qgl42dExFPNztj\njeY2kC71J7J77p1kr6pvko3Q3TIYbyG5kNJNwHbAH+isTgBPkXOHXUR+vfw9nsuqy9Q9DqSbOIB0\nmafJBYeOIbuMbk0OCuzGtTLvIxvS55J3JOfTGV2Q/0V29L+UXCHoEmCrtubImqChjejWmSpVD3sz\nWUXyrWL/MOAvlDZ4rLDsXgJcTH5IX0R2EFja7FytwOPAu8ngMY4cbV7R4FGp/71+HECsPJYCx5PT\nfs8GNgX+SM6x1AnfyJtpc+D/yOqrGWSbT7tuqB9ledvMBsDltH86GGuLodpA/hARO0r6dkR8ocX5\najhXYZXcXcBk8psu5MSDx5M9fqrkEnJQ3jNk4Dysxa//MBk8riWno7+UHN1vXave6dxvIf9NpzHA\nCgkRcV2D89lUDiAldibwSXIm2xeTva0GmiehKs4hu8wG+bvYr0Wv+yA5rqPv7u9Syt3TzYal3jaQ\nrwBfBjYkv+t9p+ZxXKMzaaPTtfWwR5DTkTwG7Ea2f3RZ8Bhx2e0BfL/Y/jg52rvZFpDdo2eTC2Bd\njoNHoWv/94ZhqDXRzwXOlfTliPhaC/Nklo4qHs8DTiZXDvTaEWkK+aF+FHk3cjHNm6jwXnJFoNvI\nto5LyIZ9q7xhdeOVtAvZGx1ynfTfNjVXTeAqrJL5BvBFMnj8FPhge7PTkQI4gJwifW2yQ0Gje0LN\nJ9cquQPYkgweL27wa1hHG9U4EEnfJIcw/axI+iAwKyIOb2gum8wBpES+DRxK3m2cgec9GMoSYE/g\nl+RdwZ+ATRp07TvJ4DGXXHzr9+QMwVYpox0HshPw9og4NSJOBd5FTvFmHaRr6mGPY3nwmE4lgseo\nym4l8g7tLeSAw3cADzQgU3cAO5DBo28EvIPHgLrmf68Owx0HMrZme+3hXlzSNEkLJN04wLFDJC2V\ntE6xv7GkJyVdVzxOqjl3kqQbJd0uaWpN+hhJZ0uaI+kqSRsNN2/Wgb5LLqYksu/fPu3NTmmsSk4g\nuQ35wf9f5EC/et1GVljPJ0fA/x4vP2sDGk4V1gfJMb+Xkf/a2wOHRsTPV3hx6U3AE8AZEbFVTfqG\nZAfElwPbRsTDxTojF9SeV3P+NcAnI2KWpAuBEyLiIkkHAFtGxBRJewK7R8Reg+TFVVid7ARyDito\nbdfUbrIAeCPwd7LR+3fkgk4jcTPwtuJaOwC/pbPm3rKWG1UVVkScBbyOXFfsl8DrhxM8iudeSU5I\n3V/fd83n5PU5CdI4cl32WUXSGWSHTsgJr08vts8l/22sbL7P8uDxIxw86rU+2RtrfbLKaR+yjWS4\nbiCrwhaQQeRCHDxsSMOqwoqI+yLi/OJx/2hesOjRNT8ibhrg8Pii+uqy4u4FcrKEu2vOubtI6zs2\nv8jjEmBhX5VY1ZS2HvZk4KBi+wfkuIaKaWjZbQrMBNYiBxx+iuFNeXItGTweJOe4ugB4QeOy1c1K\n+7/XAEOtid5wklYjF+ysXaes767jXmCjiHhE0iTgN5JGOsPOkKMEJk+ezPjx4wEYO3YsEydOpKen\nB1j+R1DW/dmzZ3dUfoa1fwH0HF/sH9wLr4QeOih/Zd2fCL1H9sLnoecHPbA+9L55iPOvht4de+FJ\n6Nm1B34OvVd10Pvxfkv3e3t7mT59OsCyz8vBNH0699q2DUmvInuSP0l+2G8I3ANsFxEP9HveZcAh\nZGC5LCK2KNL3AnaIiAMkzQSOiIhrJK0E3BcRA/ZSdxtIhzmFHBgIMJX8pmyN9Wvg/eQklCcDnxjg\nnCvIRvcninNn4AWr7VlG1QYi6WWSVim2eyQdLGnsip5Xe4niQUTcHBHjImLTiNiErI7aJiIekLSe\npOcVr7MpsBnwj6LK7FFJ20kSWbPbN3nD+cC+xfYHyNl5rNOdxvKqqu/g4NEsu5NL40KOXP9lv+OX\nkp3ynyBnuzsLBw8bkeG0gfwSWCJpM+DHwEvJ7ykrJGkG8GdggqR5kj7S75RgebXT9sCNkq4ja2/3\nj4iFxbEDyY6dt5MrIs4s0qcB60maQzbDHjqcfHWjvlvQjnc62Uge5GJQn21vdjpBU8vuY8DXyN/3\n3mRfSsh1RXYi6wImk11TWlqh3T1K87/XBMP5k1kaEYsl7Q58LyK+J+n64Vw8IvZewfFNa7Z/Rfb0\nGui8v5ITKfRPX0ROLWdl8FNyMaQgl54dqB+eNd4XyZ5V3yf7LX6lSHuavBM8Ga8MZHUZzjiQa8ha\n6i8CO0fEnZJujohSrf/mNpA2O4scVb4UOJr8a7LWWQr8N3B2TdongRPxBJU2pNFOZfIR4PXA14vg\nsQm5QoPZ8JzD8uBxJA4e7fA8svrwHcX+ITh42KgNZyDhLRFxcDGgkIi4MyK+3fys2Uh0bD3suWTd\n+1Ky6uQr7c1OJ2pZ2Y0hBwfOIeccc/BoiI7932uBFbaBSHoj8FVy+ZiVyT+7qG2/MBvQr8m5m5eQ\ndx1fbWtuDHLyxc3anQnrFsNpA7kN+AzwV2omRoiIh5qbtcZyG0iLnUeOK1hM9o37Bv7Ga1ZCQ7WB\nDKcX1qMR8X8NzpN1swvIUTmLgf/FwcOsSw2nEf0yScdKen0xrfqkYqoR6yAdUw97IXnn8Qx533oM\nDh4r0DFlZ3WpcvkN5w7ktcXPV9ekBblWmdlyM8nRz0+To8u/g4OHWRdr+lxYncJtIE12MbALsAiP\nLzDrIqOdC2ttScdLurZ4fEfSsFcltAq4hBzhvAg4AAcPs4oYThvIqeQCmXsUj8fI6fCsg7StHvZS\n8s7j3+S0GN/HwWOEqlyH3g2qXH7DaQN5WUS8r2b/SEmzm5UhK5HLgfcATwH/g+dUMquY4YwDuQr4\nXLE8bd/AwuMi4vUtyF/DuA2kwa4gV677FznZzSk4eJh1odGOAzkAOL1o9xDwMDkBtFXVn8hFiP5F\nrs7yExw8zCpoOHNhzY6IrYGtgC0jYpuIuKH5WbORaFk97E3knccT5Oyup5LTY1jdqlyH3g2qXH6D\n3oFI+lBE/FTSZ/ulAxARxzc5b9ZpHiQbzPu6VEzHwcOswgZtA5G0f0T8SNIRAx2PiCObmrMGcxvI\nKD0DvJNc0e41ZAP6am3NkZm1wFBtIB5IaMNzMPA9YBxwLbBBe7NjZq1RVyO6pBOHumhEHDzajFnj\n9Pb20tPT05yLTyODxxhy0WEHj4ZqatlZ01W5/IbqhfXXluXCOtefyX54AD8k16Y0M8NVWDaU+WR7\nxwKyCuuE9mbHzFqvrjYQSReQs+4OKCJ2aUz2WsMBZISeAt5M3oe+lZxp9/ltzZGZtUG9AwmPa1J+\nrAkaWg8bwMfI4LEJcA4OHk1U5Tr0blDl8hs0gETE5a3MiHWQ44CfAauTS9Ou297smFlnGqoK65yI\n2EPSTQxQlRURWzU7c43kKqxhmklOUxJkj6vd25sdM2uvettAXhIR90naeKDjEXFXA/PYdA4gw3A7\nsB3wKPBVYMAhpGZWJXUtKBUR9xU/7+p7kNPnzStb8KiCUc/H8yg5Tcmj5F3Hl0edJRumKs+l1A2q\nXH6DBhBJr5PUK+lXkraRdDNwM7BA0rtal0VruiXkxIh/A14FnIFn1zWzFRqqCuta4HBgbeDHwLsj\n4mpJrwDOiohtVnhxaRq55NCC/m0mkg4BjgXWi4iHi7TDgI8Ci4FPRcTFRfokcuq+VYELI+LTRfoY\n8uNuW3Kqvz0jYt4geXEV1mAOA74FrAPMAjZtb3bMrHPUuyb6yhFxcUT8Arg/Iq4GiIjbRvDap5FT\n8PXP0IbA24G7atK2IOd43YKcMPwk9U39m2vd7RcRE4AJkvquuR/wcERsDkwFjhlB3gzgLDJ4rAT8\nAgcPMxu2oQLI0prtp/odG9ZX+WIVw0cGOPRd4HP90nYFzo6IxRExF5gDbCdpHLBmRMwqzjsD2K3m\nOacX2+cCOw4nX92ornrY68gQDFkib21cfmz4qlyH3g2qXH5DDSTcWtJj5CqEqxXbFPur1vuCknYB\n5kfETctvMICcou+qmv17irTFwN016XezfDq/DcgJN4iIJZIWSlqnr0rMhrCADMNPkZWGn2xvdsys\nfIYaSNjwpYIkrUa2q7y90dfue4mhDk6ePJnx48cDMHbsWCZOnLhsBGnft4iy7velDev8p6H3bb0w\nH3pe3wMnQe/lnfV+qrTf09PTUfnxfrXLr7e3l+nTpwMs+7wcTNMnUyzGkVwQEVtJehVwCfAk+WG/\nIXmnsR35PZiI+FbxvJnkSIS7gMsiYosifS9gh4g4oO+ciLhG0krAfRHx4kHy4Ub0Pp8AfkTev80C\nXtLe7JhZ56q3Eb1hr188iIibI2JcRGwaEZuQ1VHbRMQDwPnAnpLGSNoE2Az4S0TcDzwqabuiUX0f\ncoINiuf1oN9mAAALlElEQVTsW2x/ALi0Be+nI/V9g1ihk8ngsQrwaxw8OsCwy846UpXLr6kBRNIM\nckWJCZLmSfpIv1OC5cHlFnLavluAC4EpNbcMB5LLGt0OzImImUX6NGA9SXOATwOHNvP9lN7l5LTs\nAKeQU7WbmdXJ64FUxVwyYDwI/C85AsfMbAW8JjoVDyD/At4I3ECOyvkdOe7DzGwF2t0GYi0waD1s\nAB8hg8fm5MBBB4+OUuU69G5Q5fJzAOl23yBHmK9Jdj14YXuzY2bdw1VY3ex8cqy+iu33tDc7ZlY+\nrsKqoluADxXbX8fBw8wazgGkSzyrHvZhcm2Px4E9cefmDlflOvRuUOXycwDpNouBvYC/A9sAp7KC\nCV7MzOrjNpBucwhwPPAi4Fpgo/Zmx8zKzW0gVXEGGTxWBn6Jg4eZNZUDSJfoPbkXPl7sfB94cxsz\nYyNS5Tr0blDl8nMA6Qb3AV8GFpEz7e7f3uyYWTW4DaTsngZ2AK4m7zouAca0NUdm1kXcBtLNPk8G\nj43IRX0dPMysRRxAyuw3wAnAytD7hV4YcCkt63RVrkPvBlUuPweQsppLTpII8G3gle3LiplVk9tA\nyuhpYHvgGmBncpJEDxY0syZwG0i3OZwMHi8FpuPgYWZt4QBSNhcA3yHX9DgbWCeTq1wPW3Yuu3Kr\ncvk5gJTJPGDfYvsbwBvamBczqzy3gZTFM+R4j6uAdwO/xeHfzJrObSDd4Etk8NiAnPPKJWdmbeaP\noTK4EDiG5e0e6z33lCrXw5ady67cqlx+DiCd7m5gn2L7a8Cb2pgXM7MabgPpZIuBtwBXAu8k70Qc\n8s2shdwGUlZHkMHjJbjdw8w6jj+SOtXFwDfJEjqLFc5zVeV62LJz2ZVblctv5XZnwAZwL/AhIIAj\nye67ZmYdxm0gnWYJ8DagF9gRuIjsfWVm1gZtawORNE3SAkk31qQdJekGSddLmilpXJG+saQnJV1X\nPE6qec4kSTdKul3S1Jr0MZLOljRH0lWSyr8K+FFk8Fgf+BkOHmbWsZrdBnIa2X+o1jERsXVEbAP8\njmwq7nNHREwqHlNq0k8G9ouICcAESX3X3A94OCI2B6aSoyXK6xKyq67I4LH+8J9a5XrYsnPZlVuV\ny6+pASQirgQe6Zf2RM3u6sDSmv3n3CYVdyhrRsSsIukMYLdie1fg9GL7XLLSp5zuZ3m7x5cp8zsx\ns4poSy8sSUdLmgfsDXyl5tD4ovrqMkl9Q+Y2IIfT9bm7SOs7Nh8gIpYACyWt09zcN8ES4L+BBUAP\nz/6NDFNPT08jc2Qt5LIrtyqXX1sCSER8KSI2IitqDiqS7wM2iohJwCHADElrjPDS5VwZ4+vApcCL\ncLuHmZVGu7vxziDHV381Ip4m19ojIq6T9HdgAnAPuXRSnw2LNGqO3StpJWCtiHh4sBebPHky48eP\nB2Ds2LFMnDhx2beHvnrMlu/TA0dCL73wOej5j/quN3Xq1M54P94f8X5tHXon5Mf71S6/3t5epk+f\nDrDs83IwTe/GK2k8cEFEbFnsbxYRdxTbBwFvjog9JK1HNogvlbQpcDmwZUQslHQ1cDAwi2x4PzEi\nZkqaArwqIqZI2gvYLSL2GiQfndeN9wFgInnvdTh5J1Kn3t7eZX8MVi4uu3Lr9vIbqhtvUwOIpBlk\nrf66ZA3/EcBOwMvJmv+7gE9ExH2S3kt2Yn2abFj/SkRcWFxnW3Lx1lWBCyPiU0X6KsCZwDbAQ8Be\nETF3kLx0VgBZSq7rcTHwZrIKq933g2Zm/bQtgHSSjgsg3wC+SE7Nfj1ZMWdm1mE8mWKnuYLsqgt5\n/9SA4FFbD2vl4rIrtyqXnwNIq/0T+CBZhfUF4F3tzY6ZWb1chdVKS8kWoJnAG8gpS57fzgyZmQ3N\nVVid4lgyeKxDLk3r4GFmJeYA0ip/IhvNISdfeekQ59ahyvWwZeeyK7cql58DSCs8BOxFdlw+BHhP\ne7NjZtYIbgNptgB2AX4LvJbsgeWqKzMrCbeBtNPxZPAYi9s9zKyrOIA009XAocX2acD45r1Uleth\ny85lV25VLj8HkGZ5hGz3WAx8iuUrmJiZdQm3gTRDALsD5wGvJntgjWnNS5uZNZLbQFrtRDJ4rAX8\nHAcPM+tKDiCNdhPwuWL7VGDT1rxslethy85lV25VLj9PIN5oWwD/CzwBvK/NeTEzayK3gTRLUNYF\nds3MlnEbSDs4eJhZl3MA6RJVroctO5dduVW5/BxAzMysLm4DMTOzQbkNxMzMGs4BpEtUuR627Fx2\n5Vbl8nMAMTOzurgNxMzMBuU2EDMzazgHkC5R5XrYsnPZlVuVy88BxMzM6uI2EDMzG5TbQMzMrOGa\nGkAkTZO0QNKNNWlHSbpB0vWSZkoaV3PsMElzJN0q6R016ZMk3SjpdklTa9LHSDq7eM5VkjZq5vvp\nZFWuhy07l125Vbn8mn0Hchrwzn5px0TE1hGxDfA74AgASa8E9iBX1Hg3cJKkvtumk4H9ImICMEFS\n3zX3Ax6OiM2BqcAxTX03HWz27NntzoLVyWVXblUuv6YGkIi4EnikX9oTNburA0uL7V2AsyNicUTM\nBeYA2xV3KGtGxKzivDOA3YrtXYHTi+1zgR0b/iZKYuHChe3OgtXJZVduVS6/tqxIKOloYB9gIfCW\nInkD4Kqa0+4p0hYDd9ek312k9z1nPkBELJG0UNI6EfFwE7NvZma0qRE9Ir4UERsBPwMOauClK7uM\n09y5c9udBauTy67cKl1+EdHUB7AxcOMgx17adww4FPhCzbGZwGuBccCtNel7ASfXnlNsrwQ8MEQ+\nwg8//PDDj5E/BvtcbUUVlqi5M5C0WUTcUezuBtxWbJ8P/EzSd8mqqc2Av0RESHpU0nbALLLq68Sa\n5+wLXAN8ALh0sEwM1o/ZzMzq09QAImkG0AOsK2ke2eNqJ0kvB5YAdwGfAIiIWySdA9wCPANMqRn5\ndyAwHVgVuDAiZhbp04AzJc0BHiLvTszMrAUqMxLdzMwayyPRO5SkDSVdKun/SbpJ0sFF+gslXSzp\nb5IukrR2zXMGHIhZc/z82kGd1hyNLDtJHywG0c6WdKGkddrxnqpkpOUnaZ3i/MclnVhzndUk/bYo\n05skfaNd76lZHEA612LgsxHxn8DrgQMlvYLsbHBJRLycbPM5DFY4EBNJuwOPtfYtVFZDyk7SSuQA\n2R0iYiJwE/DJlr+b6hlR+QH/Br4EHDLAtY6NiC2AbYA31QyC7goOIB0qIu6PiNnF9hPArcCGPHvw\n5OksH1Q54EBMAEmrA58Bjm7ZG6iwBpZd3xeANYsvA2sB97bkTVTYSMsvIp6MiD8Di/pd56mIuLzY\nXgxcV1ynaziAlICk8cBE4Gpg/YhYAPmHDry4OG3ZoMpC30BMgK8BxwFPtSC7VmM0ZVd86Ewh7zzu\nJu9QprUk4wYMu/yGc52xwM7AHxqfy/ZxAOlwktYgp2n5VPFtqH+vhyF7QUjaGnhZRJxPvy7V1lwN\nKLuVgQOArSNiAzKQHN6MvNpzjbb8aq6zEjADmFrcYXYNB5AOVnyAnAucGRHnFckLJK1fHB8HPFCk\n30MOzOyzYZH2emBbSf8AriAnoxx0vIw1RoPKbiI5iGtukX4OWZ7WZCMsvxX5MfC3iPhe43PaXg4g\nne1U4JaIOKEm7XxgcrG9L3BeTfpexRT3m7B8IOYPI2LDiNgUeBP5h/zW1mS/0kZddmQQeaWkdYvz\n3k7Wx1vzjaT8aj3rDr+Y92+tiPhMMzLZbh4H0qEkvRH4I1lt0TelwOHkB8s55DfWu4A9ImJh8ZzD\nyCnunyFvuy/ud82NgQsiYqtWvY8qamTZSfo48Gng6eI5kyPiWTNcW2PVWX53AmsCY8hJYt8BPE62\nbd1Kll8A34+IU1v5fprJAcTMzOriKiwzM6uLA4iZmdXFAcTMzOriAGJmZnVxADEzs7o4gJiZWV0c\nQMzMrC4OIGZmVpf/D20jWU5pRMkBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd136f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's try that again, this time with content  \n",
    "# create objects \n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# add things by applying methods to ax \n",
    "ax.plot(us.index, us['gdp'], linewidth=2, color='magenta')\n",
    "ax.set_title('US GDP', fontsize=14, loc='left')\n",
    "ax.set_ylabel('Billions of USD')\n",
    "ax.set_xticks([2004, 2008, 2012])\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** All of these statements must be in the same cell.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# a figure method: save figure as a pdf \n",
    "fig.savefig('us_gdp.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise.** Use figure and axis objects to create a bar chart of variable `rm` in the `ff` dataframe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Multiple subplots \n",
    "\n",
    "Same idea, but we create a multidimensional `ax` and apply methods to each component. Here we redo the plots of US GDP and consumption.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object ax has dimension 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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cPPj4YuClqvrDFGeciqp6BHhllSVj5eYkQt43Ty3rshcrfQp4aKITzc6ae5HkMuDjVfVV\noOV/idXl5+Jq4NIkx5KcTHLL1Kabri57cRh4b5JzwJPAZ6c027wZKze7/BNKTUGSj7D0r5J2zXqW\nGfoSsPKabMtBv5a3AB8EbgDeDjya5NGqOjvbsWZiN/B4Vd2Q5ErgP5JcW1X/N+vB/hxMIuQXge0r\nji8ffG54zbvXWNOCLntBkmuBI8Ceqlrtf9f+nHXZi78FvpUkLF17vSnJ+ao6OqUZp6XLXvwWeLGq\nfg/8Psl/Ae9n6fp1S7rsxQHgHoCqejbJr4AdwE+nMuH8GCs3J3G55s03TyXZzNKbp4b/kh4FPgFv\nvqN25JunGrDmXiTZDjwI3FJVz85gxmlZcy+q6m8Gr79m6br8PzcY8NDt78j3gF1JNiV5G0s32k5P\nec5p6LIXvwb+HmBwDfpq4JdTnXJ6woX/D3as3Oz9N/nyzVNv6rIXwOeAS4GvDH6DPV9V189u6sno\nuBd/9C1TH3JKOv4dOZPkYeAU8DpwpKp+McOxJ6Ljz8Xnga+v+KeFt1fVyzMaeWKSfBNYAN6Z5DfA\nQWAzG8xN3wwlSQ3zKZSS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhv0/qpo+tswT\nyTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1385828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this creates a 2-dimensional ax \n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)  \n",
    "print('Object ax has dimension', len(ax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdcd11854a8>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XYSX2eefBPvsku3YZRUFDRNLTsmXFgeLTT8Pq64suCkNPGbYKO5UoaIhI+li6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\nPzPECFDaqEonAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd12262b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now add some content \n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)\n",
    "\n",
    "ax[0].plot(us.index, us['gdp'], color='green')   # first plot \n",
    "ax[1].plot(us.index, us['pce'], color='red')     # second plot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approach #1 revisited \n",
    "\n",
    "In Approach #1, we applied `plot()` and related methods to a dataframe.  We also used arguments to fix up the graph, but that got complicated pretty quickly.  \n",
    "\n",
    "Here we combine Approaches 1 and 3.  If we check the documentation of `df.plot()` we see that it \"returns\" an axis object.  We can assign it to a variable and then apply methods to make the figure more compelling.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i1aTgRAGbD2xm7Z61rN2zlnV71rF2b9g+Xnici7tefFIrv7SB2+qmICAiUkV5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd2464a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grab the axis\n",
    "ax = us.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdcd11166d8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G9I+BnwDrgXeAp1R1mYjcA1zqNaInAbdaI7ox9VNREaSklA4UGzZAy5auVBEoXQwbBp07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\nY0JiAcQYY0xILIAYY4wJiQUQY4wxIfn/sKKB9/fFPsMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1116208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grab it and apply methods \n",
    "ax = us.plot()  \n",
    "ax.set_title('US GDP and Consumption', fontsize=14, loc='left')\n",
    "ax.set_ylabel('Billions of 2013 USD')\n",
    "ax.legend(loc='center right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** If we want the figure object for this plot, we apply a method to the axis object `ax`:\n",
    "\n",
    "```python\n",
    "fig = ax.get_figure()\n",
    "```\n",
    "That's not something we'll do often, but it completes the connection between Approaches #1 and #3.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick review of the bidding\n",
    "\n",
    "Take a deep breath.  We've covered a lot of ground, let's take stock.  \n",
    "\n",
    "We looked at three ways to use Matplotlib:\n",
    "\n",
    "* Approach #1:  apply plot method to dataframe\n",
    "* Approach #2:  use `plot(x,y)` function \n",
    "* Approach #3:  create `fig, ax` objects, apply plot methods to them\n",
    "\n",
    "Same result, different syntax.  This is what each of them looks like applied to US GDP: \n",
    "\n",
    "```python\n",
    "us['gdp'].plot()                   # Approach #1\n",
    "\n",
    "plt.plot(us.index, us['gdp'])      # Approach #2\n",
    "\n",
    "fig, ax = plt.subplots()           # Approach #3 \n",
    "ax.plot(us.index, us['gdp'])       \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples\n",
    "\n",
    "We conclude with examples that take the data from the previous chapter and make better graphs with it.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Student test scores (PISA) \n",
    "\n",
    "The international test scores often used to compare quality of education across countries.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data input \n",
    "import pandas as pd\n",
    "url = 'http://dx.doi.org/10.1787/888932937035'\n",
    "pisa = pd.read_excel(url, \n",
    "                     skiprows=18,     # skip the first 18 rows \n",
    "                     skipfooter=7,    # skip the last 7 \n",
    "                     parse_cols=[0,1,9,13], # select columns \n",
    "                     index_col=0,     # set index = first column\n",
    "                     header=[0,1]     # set variable names \n",
    "                     )\n",
    "pisa = pisa.dropna()                          # drop blank lines \n",
    "pisa.columns = ['Math', 'Reading', 'Science'] # simplify variable names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd103c4e0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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067Du8Newy3l61fEblvVDgQ5RlQ9OkmRShg17LYeZJ21Gew//jsQi+V0iYgw2UG+UfTsD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\nQEqsMJi/Ni8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd108bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simple plot \n",
    "pisa['Math'].plot(kind='barh') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** Yikes!  That's horrible!  What can we do about it?  \n",
    "\n",
    "Let's make the figure taller.  The `figsize` argument has the form `(width, height)`.  The default is `(6, 4)`.  We want a tall figure, so we need to increase the height setting.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd0e1b160>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GzshS68WA3wHTSYKRCyUdSzL4Fc/AsD1V0nnAvXmMs2xXcGNASLIDCEn2YCQE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+JCj/cBt5vZ69F+FXNmmq6UjVtUkFhb7aD8No9HrZQBu5zfmAdX+ruk6cBQ4AXgKEm7R59VYvwV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\nkrQZOTEnSZK0GTkxJ0mStBn/D6VmXRBY9lM+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0fa6438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the plot taller \n",
    "ax = pisa['Math'].plot(kind='barh', figsize=(4,13))  # note figsize \n",
    "ax.set_title('PISA Math Score', loc='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** What if we wanted to make the US bar red?  This is ridiculously complicated, but we used our Google fu and found [a solution](http://stackoverflow.com/questions/18973404/setting-different-bar-color-in-matplotlib-python).  Remember: The solution to many problems is Google fu + patience.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7UmTsZ+lsvE/I17OBu4HlS83Khq2xCyRs9uJAyLNbj5Bk1xFJGwFn2t6qCeYSkuwgaAAh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cBtZvZqtF/J3JmmK2XjFhUk1lY7KL/18qiVMmCX82vz4Ep/lzQTGA48Bxwpabfos0qMv0KuwvGF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\nOUmSpM3IiTlJkqTN+H96kbMGkEKNggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0d09c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pisa['Math'].plot(kind='barh', figsize=(4,13))\n",
    "ax.set_title('PISA Math Score', loc='left')\n",
    "ax.get_children()[38].set_color('r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Create the same graph for the Reading score.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### World Bank data\n",
    "\n",
    "We'll use World Bank data for GDP, GDP per capita, and life expectancy to produce a few graphs and illsutrate some methods we haven't seen yet.   \n",
    "\n",
    "* Bar charts of GDP and GDP per capita \n",
    "* Scatter plot (bubble plot) of life expectancy v GDP per capita "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdppc</th>\n",
       "      <th>gdp</th>\n",
       "      <th>life</th>\n",
       "      <th>pop</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>16.140664</td>\n",
       "      <td>1.997247</td>\n",
       "      <td>76.532659</td>\n",
       "      <td>1.237401e+08</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>15.222320</td>\n",
       "      <td>3.109302</td>\n",
       "      <td>74.122439</td>\n",
       "      <td>2.042594e+08</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>5.131826</td>\n",
       "      <td>6.566166</td>\n",
       "      <td>67.660415</td>\n",
       "      <td>1.279499e+09</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>11.805087</td>\n",
       "      <td>16.023988</td>\n",
       "      <td>75.353024</td>\n",
       "      <td>1.357380e+09</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>35.614310</td>\n",
       "      <td>4.535077</td>\n",
       "      <td>83.331951</td>\n",
       "      <td>1.273386e+08</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>37.306283</td>\n",
       "      <td>2.459435</td>\n",
       "      <td>81.968293</td>\n",
       "      <td>6.592550e+07</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>51.281583</td>\n",
       "      <td>16.230494</td>\n",
       "      <td>78.841463</td>\n",
       "      <td>3.164975e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   gdppc        gdp       life           pop  order\n",
       "country                                                            \n",
       "Mexico         16.140664   1.997247  76.532659  1.237401e+08      6\n",
       "Brazil         15.222320   3.109302  74.122439  2.042594e+08      5\n",
       "India           5.131826   6.566166  67.660415  1.279499e+09      4\n",
       "China          11.805087  16.023988  75.353024  1.357380e+09      3\n",
       "Japan          35.614310   4.535077  83.331951  1.273386e+08      2\n",
       "France         37.306283   2.459435  81.968293  6.592550e+07      1\n",
       "United States  51.281583  16.230494  78.841463  3.164975e+08      0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load packages (redundancy is ok)\n",
    "import pandas as pd                   # data management tools\n",
    "from pandas_datareader import data, wb # World Bank api\n",
    "import matplotlib.pyplot as plt       # plotting tools\n",
    "\n",
    "# variable list (GDP, GDP per capita, life expectancy)\n",
    "var = ['NY.GDP.PCAP.PP.KD', 'NY.GDP.MKTP.PP.KD', 'SP.DYN.LE00.IN']  \n",
    "# country list (ISO codes)\n",
    "iso = ['USA', 'FRA', 'JPN', 'CHN', 'IND', 'BRA', 'MEX']\n",
    "year = 2013\n",
    "\n",
    "# get data from World Bank \n",
    "df = wb.download(indicator=var, country=iso, start=year, end=year)\n",
    "\n",
    "# massage data\n",
    "df = df.reset_index(level='year', drop=True)\n",
    "df.columns = ['gdppc', 'gdp', 'life'] # rename variables\n",
    "df['pop']  = df['gdp']/df['gdppc']    # population \n",
    "df['gdp'] = df['gdp']/10**12          # convert to trillions\n",
    "df['gdppc'] = df['gdppc']/10**3       # convert to thousands\n",
    "df['order'] = [5, 3, 1, 4, 2, 6, 0]   # reorder countries\n",
    "df = df.sort_values(by='order', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd132d208>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmVn2HFZmZpY9h5WZmWXPYWVmZtlzWJmZWfYcVmZmlj2HlZmZZc9hZWZm2XNYmZlZ9hxW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd10157b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GDP bar chart\n",
    "ax = df['gdp'].plot(kind='barh', alpha=0.5)\n",
    "ax.set_title('GDP', loc='left', fontsize=14)\n",
    "ax.set_xlabel('Trillions of US Dollars')\n",
    "ax.set_ylabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd0c35128>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd1328c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ditto for GDP per capita (per person)\n",
    "ax = df['gdppc'].plot(kind='barh', color='m', alpha=0.5)\n",
    "ax.set_title('GDP Per Capita', loc='left', fontsize=14)\n",
    "ax.set_xlabel('Thousands of US Dollars')\n",
    "ax.set_ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And just because it's fun, here's an example of Tufte-like axes from [Matplotlib examples](http://matplotlib.org/examples/ticks_and_spines/spines_demo_dropped.html).  If you want to do this yourself, copy the last six line and prepare yourself to sink some time into it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OVH5Fuvv63NId2JG0UNL3U907Ja2fylvS+9mSvleqv5GkW1P7c9JdUpYdmLRn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\nZpaN/w+KsssRAAAABElEQVTmYfpNthxs7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0c398d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ditto for GDP per capita (per person)\n",
    "ax = df['gdppc'].plot(kind='barh', color='b', alpha=0.5)\n",
    "ax.set_title('GDP Per Capita', loc='left', fontsize=14)\n",
    "ax.set_xlabel('Thousands of US Dollars')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "# Tufte-like axes \n",
    "ax.spines['left'].set_position(('outward', 10))\n",
    "ax.spines['bottom'].set_position(('outward', 10))\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd0c098d0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MjIkA3bt3p1Wr3ezalXPYsoMHCxFZxJAhGSGIzDQUlgyMiQBRUVFMmzaRAwfeZvPm7ygu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\nmLPcZiUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0c09908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot of life expectancy vs gdp per capita\n",
    "plt.scatter(df['gdppc'], df['life'],    # x,y variables \n",
    "            s=df['pop']/10**6,          # size of bubbles \n",
    "            alpha=0.5)   \n",
    "plt.title('Life expectancy vs. GDP per capita', loc='left', fontsize=14)\n",
    "plt.xlabel('GDP Per Capita')\n",
    "plt.ylabel('Life Expectancy')\n",
    "plt.text(58, 66, 'Bubble size represents population', horizontalalignment='right',)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Styles (optional)\n",
    "\n",
    "Graph settings you might like.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd0bbdb00>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmVn2HFZmZpY9h5WZmWXPYWVmZtlzWJmZWfYcVmZmlj2HlZmZZc9hZWZm2XNYmZlZ9hxW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0c0dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['gdp'].plot(kind='barh', alpha=0.5)\n",
    "ax.set_title('GDP', loc='left', fontsize=14)\n",
    "ax.set_xlabel('Trillions of US Dollars')\n",
    "ax.set_ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.**  Create the same graph with this statement at the top:\n",
    "```python\n",
    "plt.style.use('fivethirtyeight')\n",
    "```\n",
    "(Once we execute this statement, it stays executed.) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** We can get a list of files from `plt.style.available`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-dark-palette',\n",
       " 'classic',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn-deep',\n",
       " 'fivethirtyeight',\n",
       " 'grayscale',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-white',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-ticks',\n",
       " 'bmh',\n",
       " 'seaborn-colorblind',\n",
       " 'ggplot',\n",
       " 'dark_background']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise.** Try another one by editing the code beloe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd0ae84a8>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSvntPBERCV6lmFo/dOgQbGxslLe9vLwQHx8PAKhduzamTZumsn1ISIjyZzs7O0ydOhVD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\nIgFjkRMREQnY/wPWhiVPmxnWOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0ac4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "ax = df['gdp'].plot(kind='barh', alpha=0.5)\n",
    "ax.set_title('GDP', loc='left', fontsize=14)\n",
    "ax.set_xlabel('Trillions of US Dollars')\n",
    "ax.set_ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** For aficionados, the always tasteful [xkcd style](http://xkcd.com/1235/).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdcd0a449e8>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matthewmckay/anaconda/lib/python3.5/site-packages/matplotlib/font_manager.py:1287: UserWarning: findfont: Font family ['Humor Sans', 'Comic Sans MS'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z7zR68XgciqL0++nFRCKRd8q+eDyupvlMJZlMgnOe9dzXX389br31ViQSCdx2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\nQRAEQQQwJOQEQRAEEcCQkBMEQRBEAPP/HY3s3T/dU00AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdcd0a3bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xkcd()\n",
    "ax = df['gdp'].plot(kind='barh', alpha=0.5)\n",
    "ax.set_title('GDP', loc='left', fontsize=14)\n",
    "ax.set_xlabel('Trillions of US Dollars')\n",
    "ax.set_ylabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comment.** We reset the style with these two lines: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where does that leave us?\n",
    "\n",
    "* We now have several ways to produce graphs.  \n",
    "* Next up:  think about what we want to graph and why. The tools serve that higher purpose.  "
   ]
  },
  {
   "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
}