diff --git a/consumer_complaints.ipynb b/consumer_complaints.ipynb
new file mode 100644
index 0000000..5f9352e
--- /dev/null
+++ b/consumer_complaints.ipynb
@@ -0,0 +1,1195 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:fa90b858e97578897a3c10d4c65cbd7bb24cc9c5053411ce0a53a58a8a714c05"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Normal Mode"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as ply\n",
+      "import seaborn as sbn\n",
+      "import numpy as np\n",
+      "%matplotlib inline\n",
+      "\n",
+      "cc_file = pd.read_csv(\"complaints_dec_2014.csv\")\n",
+      "\n",
+      "cc_file.index = pd.to_datetime(cc_file.pop(\"Date received\"), format(\"%Y%m%d\"))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Number of complaints by product"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "product_compl = cc_file.Product.value_counts()[::-1]\n",
+      "product_compl"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "Other financial service      14\n",
+        "Prepaid card                 70\n",
+        "Money transfers             107\n",
+        "Payday loan                 141\n",
+        "Student loan                340\n",
+        "Consumer loan               578\n",
+        "Credit card                1100\n",
+        "Bank account or service    1136\n",
+        "Credit reporting           2113\n",
+        "Debt collection            2942\n",
+        "Mortgage                   3002\n",
+        "dtype: int64"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mean_product_compl = product_compl.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "product_compl.plot(kind='barh', figsize=(8,8), color = sbn.color_palette())\n",
+      "ply.axvline(mean_product_compl, linewidth=2, color = 'r', linestyle='dashed')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<matplotlib.lines.Line2D at 0x10f5c0240>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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2x4EzI+Ja4O7M/BXwJWCHiLgS+Dmwf2Y+1We8nmMesoxj9nz/IeA7EXEJsCXwLyRJ0qBq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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10f5c02e8>"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The dotted red line shows the mean complaints across all products. You can see that 3 products are responsible for the large majority of complaints: 'Mortgage' and 'Debt collection'. "
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Number of complaints by company (top 10 companies only)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "top_complaints_comp = cc_file['Company'].value_counts()[:10]\n",
+      "top_complaints_comp"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "Bank of America        766\n",
+        "Equifax                737\n",
+        "Experian               675\n",
+        "TransUnion             604\n",
+        "Wells Fargo            598\n",
+        "JPMorgan Chase         545\n",
+        "Ocwen                  408\n",
+        "Citibank               403\n",
+        "Nationstar Mortgage    357\n",
+        "Capital One            252\n",
+        "dtype: int64"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mean_complaints_comp = cc_file['Company'].value_counts().mean()\n",
+      "mean_complaints_comp"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "11.542999999999999"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "top_complaints_comp[::-1].plot(kind='barh', figsize =(8,8), color = sbn.color_palette())\n",
+      "ply.axvline(mean_complaints_comp, linewidth=2, color = 'r', linestyle='dashed')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "<matplotlib.lines.Line2D at 0x10e7e1630>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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n3W53uwatxCte8t72Jhtv3u0ypCnv3vvu5nVvPZA5c7brdiljotXq63jfulLY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\nFy7BAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10e71c5c0>"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "BOA is responsible for the most complaints and Capital One is responsible for the least amount of complaints (out of the top 10). All of the top 10 produce far more than the mean complaints per company as defined by the dotted red line. In reference to the rest of the listed companies, these top 10 have FAR more complaints on average."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Number of complaints by company response"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "num_complaints_resp = cc_file['Company response'].value_counts()[::-1]\n",
+      "num_complaints_resp"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "Untimely response                   167\n",
+        "Closed                              239\n",
+        "Closed with monetary relief         643\n",
+        "In progress                        1056\n",
+        "Closed with non-monetary relief    1253\n",
+        "Closed with explanation            8185\n",
+        "dtype: int64"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "mean_num_complaints_resp = num_complaints_resp.mean()\n",
+      "mean_num_complaints_resp"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "1923.8333333333333"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "num_complaints_resp.plot(kind='barh', figsize=(7,7), color = sbn.color_palette())\n",
+      "ply.axvline(mean_num_complaints_resp, linewidth=2, color = 'r', linestyle='dashed')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "<matplotlib.lines.Line2D at 0x10f5c0320>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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aNcq5nNrDuZwkaSgLjSRJKp6FRpIkFc9CI0mSimehkSRJxbPQSJKk4jmXkxrl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\nkTuTAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ea08a58>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A very large percentage of the complaints were 'closed with explanation', which is good to hear. The mean was just a hair under 2,000 and all responses were far below that except the expected outcome."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Mean number of complaints by day of week"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "temp = pd.DatetimeIndex(cc_file.index)\n",
+      "cc_file['weekday'] = temp.weekday"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "weekday_count = cc_file.weekday.value_counts()\n",
+      "weekday_count.index = ['tuesday', 'monday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n",
+      "day_count = [5,5,5,4,4,4,4]\n",
+      "weekday_count = weekday_count / day_count\n",
+      "weekday_count"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "tuesday      516.00\n",
+        "monday       508.80\n",
+        "wednesday    457.80\n",
+        "thursday     408.50\n",
+        "friday       361.50\n",
+        "saturday     135.75\n",
+        "sunday       126.75\n",
+        "dtype: float64"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "weekday_count.plot(kind='barh', figsize =(8,8), color = sbn.color_palette())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10ea290f0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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hW3jsO7Zw3yeATzzJCJeWTSpJkkq0+1DFlIuIpwLX09rb8JO655EkaTqZduGQ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\nYoaDJEkqZjhIkqRi/wmvYh/LdfGa/wAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10eb84e48>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The weekends have a very low amount of complaints as compared to the rest of the week. Tuesday is the highest and Friday is the lowest for during the week complaints."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Any other insights you find interesting"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "timely_resp = cc_file['Timely response?'].value_counts()[::-1]\n",
+      "timely_resp.plot(kind='pie', figsize=(7,7), colors = ['red','green'], autopct='%.2f')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 14,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10e86c438>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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DUsNLzrlG3zGCEqlCAaCKF3C+Q4iInIADqnjed4wgRa9QKvgfanTklogkuWo6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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ebbec88>"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Percentage of responses that were timely was 96.94%. ~3.06% of all cases are not responded to quick enough. They need to get that lower, although I have no point of reference, so this could be really good..."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Hard Mode"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Combine the complaints data with US population by state data (find this yourself) and then chart the frequency of complaints by state per capita."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "abbr_file = pd.read_csv('states.csv')\n",
+      "abbr_file.index = abbr_file['state']"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "state_complaints = cc_file['State'].value_counts()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "combined_state = pd.concat([state_complaints, abbr_file], axis=1)\n",
+      "combined_state = combined_state[pd.notnull(combined_state['state'])]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "complaints_per_ten_thousand = combined_state[0] / combined_state['pop'] * 10000\n",
+      "mean_comp_per_ten_thousand = complaints_per_ten_thousand.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "complaints_per_ten_thousand.plot(kind='barh', figsize =(9,9), color = sbn.color_palette())\n",
+      "ply.axvline(mean_comp_per_ten_thousand, linewidth=2, color = 'r', linestyle='dashed')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 19,
+       "text": [
+        "<matplotlib.lines.Line2D at 0x10eec53c8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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sttTXtm7d2TPZCOY0lGU9y7Ke5cx6smpm/kNE/F9gXUTspLb193XAbwMX13fU7AHuBZ4/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\nUsanz8GnW5cAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10eec52e8>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Find statistically significant outliers of complaints by ZIP code. Look these up to see if there's a possible reason (military bases are often surrounded by predatory lending companies, for example.)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "zip_codes = cc_file['ZIP code'].value_counts()[:10]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 41
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "zip_codes_mean = zip_codes.mean()\n",
+      "zip_codes_mean"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "3.1617995264404106"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "zip_codes.plot(kind ='box', figsize =(9,9))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 34,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x10f6290b8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAIZCAYAAAD6EXYGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10f632a58>"
+       ]
+      }
+     ],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Nightmare"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 6,
+     "metadata": {},
+     "source": [
+      "Take the frequency of complaints by state per capita and then make a chart of the US, with frequency of complaints matched to a scale of lighter color (low frequency) to darker color (high frequency), with a legend."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "unempl_file = pd.read_csv(\"unemployed.csv\")\n",
+      "unempl_file.index = unempl_file['Zip']\n",
+      "unempl_file.head()"
+     ],
+     "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>Zip</th>\n",
+        "      <th>Unemp. Rate</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Zip</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1074</th>\n",
+        "      <td> 1074</td>\n",
+        "      <td> 0%</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1086</th>\n",
+        "      <td> 1086</td>\n",
+        "      <td> 0%</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1094</th>\n",
+        "      <td> 1094</td>\n",
+        "      <td> 0%</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1222</th>\n",
+        "      <td> 1222</td>\n",
+        "      <td> 0%</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1242</th>\n",
+        "      <td> 1242</td>\n",
+        "      <td> 0%</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 57,
+       "text": [
+        "       Zip Unemp. Rate\n",
+        "Zip                   \n",
+        "1074  1074          0%\n",
+        "1086  1086          0%\n",
+        "1094  1094          0%\n",
+        "1222  1222          0%\n",
+        "1242  1242          0%"
+       ]
+      }
+     ],
+     "prompt_number": 57
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "combined_zip_unempl = pd.concat([unempl_file, zip_codes], axis=1)\n",
+      "combined_zip_unempl = combined_zip_unempl[pd.notnull(combined_zip_unempl['Unemp. Rate'])]\n",
+      "combined_zip_unempl = combined_zip_unempl[pd.notnull(combined_zip_unempl[0])]\n",
+      "sorted_unemp = combined_zip_unempl.sort(columns =[0], ascending=False)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 102
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sorted_unemp"
+     ],
+     "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>Zip</th>\n",
+        "      <th>Unemp. Rate</th>\n",
+        "      <th>0</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>60445</th>\n",
+        "      <td> 60445</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td> 42</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>94030</th>\n",
+        "      <td> 94030</td>\n",
+        "      <td>  4%</td>\n",
+        "      <td> 27</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>77065</th>\n",
+        "      <td> 77065</td>\n",
+        "      <td>  3%</td>\n",
+        "      <td> 25</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>78232</th>\n",
+        "      <td> 78232</td>\n",
+        "      <td>  5%</td>\n",
+        "      <td> 23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>76028</th>\n",
+        "      <td> 76028</td>\n",
+        "      <td>  4%</td>\n",
+        "      <td> 22</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>53072</th>\n",
+        "      <td> 53072</td>\n",
+        "      <td>  4%</td>\n",
+        "      <td> 21</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>35040</th>\n",
+        "      <td> 35040</td>\n",
+        "      <td> 11%</td>\n",
+        "      <td> 19</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>89113</th>\n",
+        "      <td> 89113</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td> 18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>85251</th>\n",
+        "      <td> 85251</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td> 18</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>76116</th>\n",
+        "      <td> 76116</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td> 17</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>79424</th>\n",
+        "      <td> 79424</td>\n",
+        "      <td>  2%</td>\n",
+        "      <td> 17</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>78204</th>\n",
+        "      <td> 78204</td>\n",
+        "      <td> 10%</td>\n",
+        "      <td> 16</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>21117</th>\n",
+        "      <td> 21117</td>\n",
+        "      <td>  5%</td>\n",
+        "      <td> 16</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>20744</th>\n",
+        "      <td> 20744</td>\n",
+        "      <td> 10%</td>\n",
+        "      <td> 15</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>33173</th>\n",
+        "      <td> 33173</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td> 15</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>11219</th>\n",
+        "      <td> 11219</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td> 14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>80134</th>\n",
+        "      <td> 80134</td>\n",
+        "      <td>  3%</td>\n",
+        "      <td> 14</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>33462</th>\n",
+        "      <td> 33462</td>\n",
+        "      <td> 12%</td>\n",
+        "      <td> 13</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>96727</th>\n",
+        "      <td> 96727</td>\n",
+        "      <td>  9%</td>\n",
+        "      <td> 13</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>77083</th>\n",
+        "      <td> 77083</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td> 13</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>20017</th>\n",
+        "      <td> 20017</td>\n",
+        "      <td> 14%</td>\n",
+        "      <td> 12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>90046</th>\n",
+        "      <td> 90046</td>\n",
+        "      <td>  9%</td>\n",
+        "      <td> 12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>32904</th>\n",
+        "      <td> 32904</td>\n",
+        "      <td>  9%</td>\n",
+        "      <td> 12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>75211</th>\n",
+        "      <td> 75211</td>\n",
+        "      <td>  7%</td>\n",
+        "      <td> 12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>75232</th>\n",
+        "      <td> 75232</td>\n",
+        "      <td> 11%</td>\n",
+        "      <td> 12</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>30324</th>\n",
+        "      <td> 30324</td>\n",
+        "      <td>  7%</td>\n",
+        "      <td> 11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>33009</th>\n",
+        "      <td> 33009</td>\n",
+        "      <td> 13%</td>\n",
+        "      <td> 11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>92101</th>\n",
+        "      <td> 92101</td>\n",
+        "      <td>  7%</td>\n",
+        "      <td> 11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>30038</th>\n",
+        "      <td> 30038</td>\n",
+        "      <td> 16%</td>\n",
+        "      <td> 11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>23831</th>\n",
+        "      <td> 23831</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td> 11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>...</th>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "      <td>...</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8742 </th>\n",
+        "      <td>  8742</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>60030</th>\n",
+        "      <td> 60030</td>\n",
+        "      <td>  4%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>60026</th>\n",
+        "      <td> 60026</td>\n",
+        "      <td>  3%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>60022</th>\n",
+        "      <td> 60022</td>\n",
+        "      <td>  5%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>55082</th>\n",
+        "      <td> 55082</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>44230</th>\n",
+        "      <td> 44230</td>\n",
+        "      <td>  7%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>43615</th>\n",
+        "      <td> 43615</td>\n",
+        "      <td> 12%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>32779</th>\n",
+        "      <td> 32779</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>82007</th>\n",
+        "      <td> 82007</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>75965</th>\n",
+        "      <td> 75965</td>\n",
+        "      <td>  5%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2532 </th>\n",
+        "      <td>  2532</td>\n",
+        "      <td>  9%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2540 </th>\n",
+        "      <td>  2540</td>\n",
+        "      <td>  8%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2657 </th>\n",
+        "      <td>  2657</td>\n",
+        "      <td> 13%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>34736</th>\n",
+        "      <td> 34736</td>\n",
+        "      <td> 15%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>74403</th>\n",
+        "      <td> 74403</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>32092</th>\n",
+        "      <td> 32092</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5677 </th>\n",
+        "      <td>  5677</td>\n",
+        "      <td>  2%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>82001</th>\n",
+        "      <td> 82001</td>\n",
+        "      <td>  3%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>82501</th>\n",
+        "      <td> 82501</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>43614</th>\n",
+        "      <td> 43614</td>\n",
+        "      <td> 10%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>70452</th>\n",
+        "      <td> 70452</td>\n",
+        "      <td>  9%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14062</th>\n",
+        "      <td> 14062</td>\n",
+        "      <td> 10%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>41042</th>\n",
+        "      <td> 41042</td>\n",
+        "      <td>  7%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>66205</th>\n",
+        "      <td> 66205</td>\n",
+        "      <td>  2%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>66112</th>\n",
+        "      <td> 66112</td>\n",
+        "      <td> 13%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7016 </th>\n",
+        "      <td>  7016</td>\n",
+        "      <td>  6%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>30306</th>\n",
+        "      <td> 30306</td>\n",
+        "      <td>  5%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>94534</th>\n",
+        "      <td> 94534</td>\n",
+        "      <td>  3%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>43542</th>\n",
+        "      <td> 43542</td>\n",
+        "      <td>  5%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>43506</th>\n",
+        "      <td> 43506</td>\n",
+        "      <td> 10%</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>2474 rows \u00d7 3 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 103,
+       "text": [
+        "         Zip Unemp. Rate   0\n",
+        "60445  60445          8%  42\n",
+        "94030  94030          4%  27\n",
+        "77065  77065          3%  25\n",
+        "78232  78232          5%  23\n",
+        "76028  76028          4%  22\n",
+        "53072  53072          4%  21\n",
+        "35040  35040         11%  19\n",
+        "89113  89113          6%  18\n",
+        "85251  85251          6%  18\n",
+        "76116  76116          8%  17\n",
+        "79424  79424          2%  17\n",
+        "78204  78204         10%  16\n",
+        "21117  21117          5%  16\n",
+        "20744  20744         10%  15\n",
+        "33173  33173          8%  15\n",
+        "11219  11219          8%  14\n",
+        "80134  80134          3%  14\n",
+        "33462  33462         12%  13\n",
+        "96727  96727          9%  13\n",
+        "77083  77083          6%  13\n",
+        "20017  20017         14%  12\n",
+        "90046  90046          9%  12\n",
+        "32904  32904          9%  12\n",
+        "75211  75211          7%  12\n",
+        "75232  75232         11%  12\n",
+        "30324  30324          7%  11\n",
+        "33009  33009         13%  11\n",
+        "92101  92101          7%  11\n",
+        "30038  30038         16%  11\n",
+        "23831  23831          6%  11\n",
+        "...      ...         ...  ..\n",
+        "8742    8742          8%   1\n",
+        "60030  60030          4%   1\n",
+        "60026  60026          3%   1\n",
+        "60022  60022          5%   1\n",
+        "55082  55082          6%   1\n",
+        "44230  44230          7%   1\n",
+        "43615  43615         12%   1\n",
+        "32779  32779          8%   1\n",
+        "82007  82007          6%   1\n",
+        "75965  75965          5%   1\n",
+        "2532    2532          9%   1\n",
+        "2540    2540          8%   1\n",
+        "2657    2657         13%   1\n",
+        "34736  34736         15%   1\n",
+        "74403  74403          6%   1\n",
+        "32092  32092          6%   1\n",
+        "5677    5677          2%   1\n",
+        "82001  82001          3%   1\n",
+        "82501  82501          6%   1\n",
+        "43614  43614         10%   1\n",
+        "70452  70452          9%   1\n",
+        "14062  14062         10%   1\n",
+        "41042  41042          7%   1\n",
+        "66205  66205          2%   1\n",
+        "66112  66112         13%   1\n",
+        "7016    7016          6%   1\n",
+        "30306  30306          5%   1\n",
+        "94534  94534          3%   1\n",
+        "43542  43542          5%   1\n",
+        "43506  43506         10%   1\n",
+        "\n",
+        "[2474 rows x 3 columns]"
+       ]
+      }
+     ],
+     "prompt_number": 103
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sorted_unemp.plot(kind='barh', figsize =(9,9))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 107,
+       "text": [
+        "<matplotlib.axes._subplots.AxesSubplot at 0x11626fa90>"
+       ]
+      }
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..5fb6e16
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+matplotlib
+ipython[all]
+seaborn
+pandas
diff --git a/states.csv b/states.csv
new file mode 100644
index 0000000..6ed87d6
--- /dev/null
+++ b/states.csv
@@ -0,0 +1 @@
+state,pop
AL,4849377
AK,736732
AZ,6731484
AR,2966369
CA,38802500
CO,5355866
CT,3596677
DE,935614
FL,19893297
GA,10097343
HI,1419561
ID,1634464
IL,12880580
IN,6596855
IA,3107126
KS,2904021
KY,4413457
LA,4649676
ME,1330089
MD,5976407
MA,6745408
MI,9909877
MN,5457173
MS,2994079
MO,6063589
MT,1023579
NE,1881503
NV,2839099
NH,1326813
NJ,8938175
NM,2085572
NY,19746227
NC,9943964
ND,739482
OH,11594163
OK,3878051
OR,3970239
PA,12787209
RI,1055173
SC,4832482
SD,853175
TN,6549352
TX,26956958
UT,2942902
VT,626562
VA,8326289
WA,7061530
WV,1850326
WI,5757564
WY,584153
\ No newline at end of file