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 * [Python 101](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Python_101.ipynb)
 * [Pandas for Excel Developers](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Pandas%20for%20Excel%20Developers.ipynb)
 * [Pandas for SQL Developers](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Pandas%20for%20SQL%20Developers.ipynb)
-* [Dates](https://squareup.com/market/david-rojas-llc/data-analysis-dates)
-* [Plotting in Pandas](https://squareup.com/market/david-rojas-llc/data-analysis-plotting-in-pandas)  
+* [Dates](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Data%20Analysis%20-%20Dates.ipynb)
+* [Plotting in Pandas](http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/Data%20Analysis%20-%20Plotting%20in%20Pandas.ipynb) 
 
 Donations
 -------  

lessons/Data Analysis - Dates.ipynb

+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import dateutil.relativedelta as du\n",
+      "import datetime as dt\n",
+      "import pandas as pd"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For more examples please see the official documentation for [DateUtil](http://labix.org/python-dateutil)  "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Get today's date with timestamp"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Get today's date with timestamp\n",
+      "TodayTimeStamp = dt.datetime.now()\n",
+      "TodayTimeStamp"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "datetime.datetime(2013, 11, 4, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Today's date with ***NO*** timestamp"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Today's date with no timestamp\n",
+      "TodayTimeStamp.date()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "datetime.date(2013, 11, 4)"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Get the timestamp"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Get the timestamp of a date\n",
+      "TodayTimeStamp.time()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "datetime.time(10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Get the day"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Get the day of a date\n",
+      "TodayTimeStamp.day"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "4"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Get the month"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Get the month of a date\n",
+      "TodayTimeStamp.month"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "11"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Get the year"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Get the year of a date\n",
+      "TodayTimeStamp.year"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "2013"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Yesterday"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Yesterday\n",
+      "TodayTimeStamp + du.relativedelta(days=-1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "datetime.datetime(2013, 11, 3, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Last month"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Last month\n",
+      "TodayTimeStamp + du.relativedelta(months=-1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "datetime.datetime(2013, 10, 4, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### First day of last month"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# First day of last month\n",
+      "TodayTimeStamp + du.relativedelta(months=-1, day=1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "datetime.datetime(2013, 10, 1, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Last day of last month (31 always gives you the last day)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Last day of last month (31 always gives you the last day)\n",
+      "TodayTimeStamp + du.relativedelta(months=-1, day=31)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "datetime.datetime(2013, 10, 31, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Monday of Last week"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Monday of Last week\n",
+      "TodayTimeStamp + du.relativedelta(weekday=du.MO, weeks=-1)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "datetime.datetime(2013, 10, 28, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Sunday of Last week (default start of the week is Monday)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sunday of Last week (default start of the week is Monday)\n",
+      "TodayTimeStamp + du.relativedelta(weekday=du.SU, weeks=-2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "datetime.datetime(2013, 10, 27, 10, 54, 49, 616000)"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Date Math"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a = pd.to_datetime('1/1/2013')\n",
+      "b = pd.to_datetime('2/3/2014')\n",
+      "\n",
+      "diff = a - b\n",
+      "diff"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 14,
+       "text": [
+        "datetime.timedelta(-398)"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "diff.days"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 15,
+       "text": [
+        "-398"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# alternative\n",
+      "diff = du.relativedelta(a,b)\n",
+      "diff"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": [
+        "relativedelta(years=-1, months=-1, days=-2)"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "---  \n",
+      "\n",
+      "**Author:** [David Rojas LLC](http://hdrojas.pythonanywhere.com/)  "
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

lessons/Data Analysis - Plotting in Pandas.ipynb

+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "%matplotlib inline\n",
+      "import matplotlib.pyplot as plt # only needed for advanced plotting\n",
+      "import matplotlib as mpl # only needed to get version"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'pandas version: ' + pd.__version__\n",
+      "print 'matplotlib version: ' + mpl.__version__"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "pandas version: 0.13.0\n",
+        "matplotlib version: 1.3.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Cheat Sheet\n",
+      "\n",
+      "**Simple plotting**  \n",
+      "`df.plot()`  \n",
+      "\n",
+      "**Advanced plotting**  \n",
+      "`fig, axes = plt.subplots(nrows=n, ncols=n, figsize=(n, n))`  \n",
+      "\n",
+      "**Plot type**  \n",
+      "`df.plot(kind='line', 'bar', 'barh', 'kde', 'density')`  \n",
+      "\n",
+      "**Set title**  \n",
+      "`set_title('type title here')`  \n",
+      "\n",
+      "**Set y label**  \n",
+      "`set_ylabel('type y axis label')`  \n",
+      "\n",
+      "**Set x label**  \n",
+      "`set_xlabel('type x axis label')`  \n",
+      "\n",
+      "**Show legend**  \n",
+      "`legend()`  \n",
+      "\n",
+      "**Set legend labels**  \n",
+      "`df.plot(label='label name')`  \n",
+      "`axes.legend([\"label name\"], loc='best')`  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sample dataframe\n",
+      "d = {'date': [pd.to_datetime('1/1/2013'), pd.to_datetime('1/1/2014'), pd.to_datetime('1/1/2015')],\n",
+      "     'number': [1,2,3],\n",
+      "     'letter': ['A','B','C']}\n",
+      "df = pd.DataFrame(d)\n",
+      "df"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>date</th>\n",
+        "      <th>letter</th>\n",
+        "      <th>number</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>2013-01-01 00:00:00</td>\n",
+        "      <td> A</td>\n",
+        "      <td> 1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>2014-01-01 00:00:00</td>\n",
+        "      <td> B</td>\n",
+        "      <td> 2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>2015-01-01 00:00:00</td>\n",
+        "      <td> C</td>\n",
+        "      <td> 3</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>3 rows \u00d7 3 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "                 date letter  number\n",
+        "0 2013-01-01 00:00:00      A       1\n",
+        "1 2014-01-01 00:00:00      B       2\n",
+        "2 2015-01-01 00:00:00      C       3\n",
+        "\n",
+        "[3 rows x 3 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# simple plotting\n",
+      "df.plot();"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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7kJ8yzEJ89qif6lkI1NN+NHY4FlM6kB0ydjgOWTrwMTJ2uB5TOpA7GrsLeTHDLESWnogX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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x91e7860>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# pretty plotting\n",
+      "p = df['number'].plot(label='label name')\n",
+      "p.set_title('This is the title')\n",
+      "p.set_ylabel('the y axis')\n",
+      "p.set_xlabel('the x axis')\n",
+      "p.legend(loc='best');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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gAnf0DuzhnK28GE95MZ7qY1JwM1YHROTJ2FNwE/YOiMjd2FPwUKwOiMhbMCko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\nKRARkYRJgYiIJEwKREQk+X/cGa4yLiSAIAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x92b77f0>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Two simple plots\n",
+      "df.plot()\n",
+      "df.plot();"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x9f74550>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x92c2e80>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Advanced Plotting\n",
+      "####################\n",
+      "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(13, 10))\n",
+      "fig.subplots_adjust(hspace=2.0) ## Create space between plots\n",
+      "\n",
+      "# Chart 1\n",
+      "df.plot(ax=axes)\n",
+      "\n",
+      "# add a little sugar\n",
+      "axes.set_title('This is the title')\n",
+      "axes.set_ylabel('the y axis')\n",
+      "axes.set_xlabel('the x axis')\n",
+      "axes.legend([\"label goes here\"], loc='best');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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9a/PixW5tlvrCGQZJkhQqHR0weTKMGgVz5sCQIUFHJOUGZxgkSVLBW7QITj4Z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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x92e8da0>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Advanced Plotting\n",
+      "####################\n",
+      "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(13, 10))\n",
+      "fig.subplots_adjust(hspace=2.0) ## Create space between plots\n",
+      "\n",
+      "# Chart 1\n",
+      "df.plot(ax=axes[0])\n",
+      "\n",
+      "# Chart 2\n",
+      "df.set_index('date')['number'].plot(ax=axes[1])\n",
+      "\n",
+      "# add a little sugar\n",
+      "axes[0].set_title('This is the title')\n",
+      "axes[0].set_ylabel('the y axis')\n",
+      "axes[0].set_xlabel('the x axis')\n",
+      "axes[0].legend([\"label chart 1\"], loc='best')\n",
+      "axes[1].legend([\"label chart 2\"], loc='best');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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K2U4TOfRIgzoAqAzHkW66SerbV3rwQdtpIotzIgDgRw4elKZOlTZujK4BBACg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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x957dcc0>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Advanced Plotting\n",
+      "####################\n",
+      "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(13, 10))\n",
+      "fig.subplots_adjust(hspace=0.25) ## Create space between plots\n",
+      "\n",
+      "# Chart 1\n",
+      "df.plot(ax=axes[0,0])\n",
+      "\n",
+      "# Chart 2\n",
+      "df.set_index('date')['number'].plot(ax=axes[0,1])\n",
+      "\n",
+      "# Chart 3\n",
+      "df.plot(ax=axes[1,0], color='r')\n",
+      "\n",
+      "# Chart 4\n",
+      "df.plot(ax=axes[1,1], color='g', style='--')\n",
+      "\n",
+      "# add a little sugar\n",
+      "axes[0,1].set_title('This is the title')\n",
+      "axes[0,1].set_ylabel('the y axis')\n",
+      "axes[0,1].set_xlabel('the x axis')\n",
+      "axes[0,0].legend([\"label chart 1\"], loc='best')\n",
+      "axes[0,1].legend([\"label chart 2\"], loc='best')\n",
+      "axes[1,0].legend([\"label chart 3\"], loc='best')\n",
+      "axes[1,1].legend([\"label chart 4\"], loc='best');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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0449mV6AWLaTNm6WgIKcjgjexRgDwMz/8YJL6LbeYRcEkdd9AjjQYB8A7LEua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+       "text": [
+        "<matplotlib.figure.Figure at 0x92db1d0>"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Author:** [David Rojas LLC](http://hdrojas.pythonanywhere.com/)  "
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}