{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many newspaper titles are available for each year in the Chronicling America collection?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To answer this question, we'll use the Python library pandas, which is intended for data analysis. The ultimate goal is to create a CSV file with a column for each year and a column with the count of titles available for that year.\n",
    "\n",
    "I'm still wrapping my brain around pandas and I'm sure there are other ways to achieve similar results more efficiently. Let me know!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial steps of this workbook are from the \"Read a Plain Text File of Newspapers into a DataFrame\" notebook posted by the Library of Congress at https://github.com/LibraryOfCongress/data-exploration/blob/master/Newspapers.txt.ipynb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The newspapers.txt file on Chronicling America is delimited by the pipe | character, not a comma, with spaces around it. You can read it into a pandas DataFrame with the following command. Note we have to specify the python engine because we have to use a regex (regular expression) to define the separator. Also, we're not bringing in the final column from the original file, which is a \"more info\" link. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "papers = pd.read_csv('http://chroniclingamerica.loc.gov/newspapers.txt', sep=' \\| ', skipinitialspace=True, usecols=[0,1,2,3,4,5,6,7,8], engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(papers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the first few lines of the initial data looks like: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "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>Persistent Link</th>\n",
       "      <th>State</th>\n",
       "      <th>Title</th>\n",
       "      <th>LCCN</th>\n",
       "      <th>OCLC</th>\n",
       "      <th>ISSN</th>\n",
       "      <th>No. of Issues</th>\n",
       "      <th>First Issue Date</th>\n",
       "      <th>Last Issue Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn85025...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chattanooga daily rebel. (Selma, Ala.) 1865-1865</td>\n",
       "      <td>sn85025905</td>\n",
       "      <td>2638820</td>\n",
       "      <td>2328-5869</td>\n",
       "      <td>3</td>\n",
       "      <td>April 19, 1865</td>\n",
       "      <td>April 27, 1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn82015...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>The Chattanooga Daily Rebel. (Chattanooga, Ten...</td>\n",
       "      <td>sn82015209</td>\n",
       "      <td>8807897</td>\n",
       "      <td>2328-5826</td>\n",
       "      <td>228</td>\n",
       "      <td>Aug. 9, 1862</td>\n",
       "      <td>April 22, 1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn82014...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>The daily Chattanooga rebel. (Griffin, Ga.) 18...</td>\n",
       "      <td>sn82014371</td>\n",
       "      <td>8793427</td>\n",
       "      <td>2328-5761</td>\n",
       "      <td>41</td>\n",
       "      <td>June 9, 1864</td>\n",
       "      <td>April 15, 1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn83045...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Memphis daily appeal. (Memphis, Tenn.) 1847-1886</td>\n",
       "      <td>sn83045160</td>\n",
       "      <td>9355541</td>\n",
       "      <td>2166-1898</td>\n",
       "      <td>8088</td>\n",
       "      <td>Jan. 1, 1857</td>\n",
       "      <td>Jan. 27, 1886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn94051...</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>The argus. (Holbrook, Ariz.) 1895-1900</td>\n",
       "      <td>sn94051341</td>\n",
       "      <td>25084608</td>\n",
       "      <td>2375-169X</td>\n",
       "      <td>220</td>\n",
       "      <td>Dec. 12, 1895</td>\n",
       "      <td>May 5, 1900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Persistent Link    State  \\\n",
       "0  http://chroniclingamerica.loc.gov/lccn/sn85025...  Alabama   \n",
       "1  http://chroniclingamerica.loc.gov/lccn/sn82015...  Alabama   \n",
       "2  http://chroniclingamerica.loc.gov/lccn/sn82014...  Alabama   \n",
       "3  http://chroniclingamerica.loc.gov/lccn/sn83045...  Alabama   \n",
       "4  http://chroniclingamerica.loc.gov/lccn/sn94051...  Arizona   \n",
       "\n",
       "                                               Title        LCCN      OCLC  \\\n",
       "0   Chattanooga daily rebel. (Selma, Ala.) 1865-1865  sn85025905   2638820   \n",
       "1  The Chattanooga Daily Rebel. (Chattanooga, Ten...  sn82015209   8807897   \n",
       "2  The daily Chattanooga rebel. (Griffin, Ga.) 18...  sn82014371   8793427   \n",
       "3   Memphis daily appeal. (Memphis, Tenn.) 1847-1886  sn83045160   9355541   \n",
       "4             The argus. (Holbrook, Ariz.) 1895-1900  sn94051341  25084608   \n",
       "\n",
       "        ISSN  No. of Issues First Issue Date Last Issue Date  \n",
       "0  2328-5869              3   April 19, 1865  April 27, 1865  \n",
       "1  2328-5826            228     Aug. 9, 1862  April 22, 1864  \n",
       "2  2328-5761             41     June 9, 1864  April 15, 1865  \n",
       "3  2166-1898           8088     Jan. 1, 1857   Jan. 27, 1886  \n",
       "4  2375-169X            220    Dec. 12, 1895     May 5, 1900  "
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many papers are listed in the set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2240"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(papers.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of these fields in the Last Issue Date have trailing delimiters. Let's use a regular expression to remove a space followed by a | in those cells. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "papers[\"Last Issue Date\"] = papers[\"Last Issue Date\"].str.replace('(\\s+\\|)', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now convert each of those dates to a datetime format. That will make it easier to pull out just the year later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "papers[\"First Issue Date\"] = pd.to_datetime(papers[\"First Issue Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "papers[\"Last Issue Date\"] = pd.to_datetime(papers[\"Last Issue Date\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What data type is the Issue date column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers[\"Last Issue Date\"].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That will work. So what does the data look like with the dates converted to date formats?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "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>Persistent Link</th>\n",
       "      <th>State</th>\n",
       "      <th>Title</th>\n",
       "      <th>LCCN</th>\n",
       "      <th>OCLC</th>\n",
       "      <th>ISSN</th>\n",
       "      <th>No. of Issues</th>\n",
       "      <th>First Issue Date</th>\n",
       "      <th>Last Issue Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn85025...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chattanooga daily rebel. (Selma, Ala.) 1865-1865</td>\n",
       "      <td>sn85025905</td>\n",
       "      <td>2638820</td>\n",
       "      <td>2328-5869</td>\n",
       "      <td>3</td>\n",
       "      <td>1865-04-19</td>\n",
       "      <td>1865-04-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn82015...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>The Chattanooga Daily Rebel. (Chattanooga, Ten...</td>\n",
       "      <td>sn82015209</td>\n",
       "      <td>8807897</td>\n",
       "      <td>2328-5826</td>\n",
       "      <td>228</td>\n",
       "      <td>1862-08-09</td>\n",
       "      <td>1864-04-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn82014...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>The daily Chattanooga rebel. (Griffin, Ga.) 18...</td>\n",
       "      <td>sn82014371</td>\n",
       "      <td>8793427</td>\n",
       "      <td>2328-5761</td>\n",
       "      <td>41</td>\n",
       "      <td>1864-06-09</td>\n",
       "      <td>1865-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn83045...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Memphis daily appeal. (Memphis, Tenn.) 1847-1886</td>\n",
       "      <td>sn83045160</td>\n",
       "      <td>9355541</td>\n",
       "      <td>2166-1898</td>\n",
       "      <td>8088</td>\n",
       "      <td>1857-01-01</td>\n",
       "      <td>1886-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn94051...</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>The argus. (Holbrook, Ariz.) 1895-1900</td>\n",
       "      <td>sn94051341</td>\n",
       "      <td>25084608</td>\n",
       "      <td>2375-169X</td>\n",
       "      <td>220</td>\n",
       "      <td>1895-12-12</td>\n",
       "      <td>1900-05-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Persistent Link    State  \\\n",
       "0  http://chroniclingamerica.loc.gov/lccn/sn85025...  Alabama   \n",
       "1  http://chroniclingamerica.loc.gov/lccn/sn82015...  Alabama   \n",
       "2  http://chroniclingamerica.loc.gov/lccn/sn82014...  Alabama   \n",
       "3  http://chroniclingamerica.loc.gov/lccn/sn83045...  Alabama   \n",
       "4  http://chroniclingamerica.loc.gov/lccn/sn94051...  Arizona   \n",
       "\n",
       "                                               Title        LCCN      OCLC  \\\n",
       "0   Chattanooga daily rebel. (Selma, Ala.) 1865-1865  sn85025905   2638820   \n",
       "1  The Chattanooga Daily Rebel. (Chattanooga, Ten...  sn82015209   8807897   \n",
       "2  The daily Chattanooga rebel. (Griffin, Ga.) 18...  sn82014371   8793427   \n",
       "3   Memphis daily appeal. (Memphis, Tenn.) 1847-1886  sn83045160   9355541   \n",
       "4             The argus. (Holbrook, Ariz.) 1895-1900  sn94051341  25084608   \n",
       "\n",
       "        ISSN  No. of Issues First Issue Date Last Issue Date  \n",
       "0  2328-5869              3       1865-04-19      1865-04-27  \n",
       "1  2328-5826            228       1862-08-09      1864-04-22  \n",
       "2  2328-5761             41       1864-06-09      1865-04-15  \n",
       "3  2166-1898           8088       1857-01-01      1886-01-27  \n",
       "4  2375-169X            220       1895-12-12      1900-05-05  "
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's pull out the years and put them into their own new columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "papers[\"Start\"] = papers[\"First Issue Date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "papers[\"Last\"] = papers[\"Last Issue Date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "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>Persistent Link</th>\n",
       "      <th>State</th>\n",
       "      <th>Title</th>\n",
       "      <th>LCCN</th>\n",
       "      <th>OCLC</th>\n",
       "      <th>ISSN</th>\n",
       "      <th>No. of Issues</th>\n",
       "      <th>First Issue Date</th>\n",
       "      <th>Last Issue Date</th>\n",
       "      <th>Start</th>\n",
       "      <th>Last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn85025...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chattanooga daily rebel. (Selma, Ala.) 1865-1865</td>\n",
       "      <td>sn85025905</td>\n",
       "      <td>2638820</td>\n",
       "      <td>2328-5869</td>\n",
       "      <td>3</td>\n",
       "      <td>1865-04-19</td>\n",
       "      <td>1865-04-27</td>\n",
       "      <td>1865</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn82015...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>The Chattanooga Daily Rebel. (Chattanooga, Ten...</td>\n",
       "      <td>sn82015209</td>\n",
       "      <td>8807897</td>\n",
       "      <td>2328-5826</td>\n",
       "      <td>228</td>\n",
       "      <td>1862-08-09</td>\n",
       "      <td>1864-04-22</td>\n",
       "      <td>1862</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn82014...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>The daily Chattanooga rebel. (Griffin, Ga.) 18...</td>\n",
       "      <td>sn82014371</td>\n",
       "      <td>8793427</td>\n",
       "      <td>2328-5761</td>\n",
       "      <td>41</td>\n",
       "      <td>1864-06-09</td>\n",
       "      <td>1865-04-15</td>\n",
       "      <td>1864</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn83045...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Memphis daily appeal. (Memphis, Tenn.) 1847-1886</td>\n",
       "      <td>sn83045160</td>\n",
       "      <td>9355541</td>\n",
       "      <td>2166-1898</td>\n",
       "      <td>8088</td>\n",
       "      <td>1857-01-01</td>\n",
       "      <td>1886-01-27</td>\n",
       "      <td>1857</td>\n",
       "      <td>1886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://chroniclingamerica.loc.gov/lccn/sn94051...</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>The argus. (Holbrook, Ariz.) 1895-1900</td>\n",
       "      <td>sn94051341</td>\n",
       "      <td>25084608</td>\n",
       "      <td>2375-169X</td>\n",
       "      <td>220</td>\n",
       "      <td>1895-12-12</td>\n",
       "      <td>1900-05-05</td>\n",
       "      <td>1895</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Persistent Link    State  \\\n",
       "0  http://chroniclingamerica.loc.gov/lccn/sn85025...  Alabama   \n",
       "1  http://chroniclingamerica.loc.gov/lccn/sn82015...  Alabama   \n",
       "2  http://chroniclingamerica.loc.gov/lccn/sn82014...  Alabama   \n",
       "3  http://chroniclingamerica.loc.gov/lccn/sn83045...  Alabama   \n",
       "4  http://chroniclingamerica.loc.gov/lccn/sn94051...  Arizona   \n",
       "\n",
       "                                               Title        LCCN      OCLC  \\\n",
       "0   Chattanooga daily rebel. (Selma, Ala.) 1865-1865  sn85025905   2638820   \n",
       "1  The Chattanooga Daily Rebel. (Chattanooga, Ten...  sn82015209   8807897   \n",
       "2  The daily Chattanooga rebel. (Griffin, Ga.) 18...  sn82014371   8793427   \n",
       "3   Memphis daily appeal. (Memphis, Tenn.) 1847-1886  sn83045160   9355541   \n",
       "4             The argus. (Holbrook, Ariz.) 1895-1900  sn94051341  25084608   \n",
       "\n",
       "        ISSN  No. of Issues First Issue Date Last Issue Date  Start  Last  \n",
       "0  2328-5869              3       1865-04-19      1865-04-27   1865  1865  \n",
       "1  2328-5826            228       1862-08-09      1864-04-22   1862  1864  \n",
       "2  2328-5761             41       1864-06-09      1865-04-15   1864  1865  \n",
       "3  2166-1898           8088       1857-01-01      1886-01-27   1857  1886  \n",
       "4  2375-169X            220       1895-12-12      1900-05-05   1895  1900  "
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for kicks, let's create a new dataframe with a row for each year of a title's coverage. This might be useful for other purposes later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "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>State</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [State, Title, Year]\n",
       "Index: []"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_df = pd.DataFrame(columns=(\"State\",\"Title\",\"Year\"))\n",
    "years_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# For each title in the original dataframe, \n",
    "# create a range for the years from Start to Last. \n",
    "# Then add a new row for each year of coverage. \n",
    "for i in range(len(papers.index)):\n",
    "    for year in range(papers.loc[i][\"Start\"], (papers.loc[i][\"Last\"] + 1)):\n",
    "        s = pd.Series({\"State\": papers.loc[i][\"State\"], \"Title\": papers.loc[i][\"Title\"], \"Year\": year})\n",
    "        years_df.loc[len(years_df)]= s.astype(str)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many rows are in this new dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24146"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(years_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "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>State</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chattanooga daily rebel. (Selma, Ala.) 1865-1865</td>\n",
       "      <td>1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>The Chattanooga Daily Rebel. (Chattanooga, Ten...</td>\n",
       "      <td>1862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>The Chattanooga Daily Rebel. (Chattanooga, Ten...</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>The Chattanooga Daily Rebel. (Chattanooga, Ten...</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>The daily Chattanooga rebel. (Griffin, Ga.) 18...</td>\n",
       "      <td>1864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State                                              Title  Year\n",
       "0  Alabama   Chattanooga daily rebel. (Selma, Ala.) 1865-1865  1865\n",
       "1  Alabama  The Chattanooga Daily Rebel. (Chattanooga, Ten...  1862\n",
       "2  Alabama  The Chattanooga Daily Rebel. (Chattanooga, Ten...  1863\n",
       "3  Alabama  The Chattanooga Daily Rebel. (Chattanooga, Ten...  1864\n",
       "4  Alabama  The daily Chattanooga rebel. (Griffin, Ga.) 18...  1864"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a useful table. Let's save it as CSV, without the index column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years_df.to_csv(path_or_buf=\"years-list.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now roll up this list by year, with a show the count for each year, and put in a new DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "years_count_df = pd.DataFrame(years_df.groupby([\"Year\"]).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "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>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "Year   \n",
       "1789  2\n",
       "1790  2\n",
       "1791  3\n",
       "1792  3\n",
       "1793  3"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_count_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(years_count_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's not enough years between 1789 and 1924, so some years must be missing. We'll need to find and insert those years, with a count of 0. Then we need to sort the index so that those new index values fall into place in the timeline, rather than being added at the end. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1789',\n",
       " '1790',\n",
       " '1791',\n",
       " '1792',\n",
       " '1793',\n",
       " '1794',\n",
       " '1795',\n",
       " '1796',\n",
       " '1797',\n",
       " '1798',\n",
       " '1799',\n",
       " '1800',\n",
       " '1801',\n",
       " '1802',\n",
       " '1803',\n",
       " '1804',\n",
       " '1805',\n",
       " '1806',\n",
       " '1807',\n",
       " '1808',\n",
       " '1809',\n",
       " '1828',\n",
       " '1829',\n",
       " '1830',\n",
       " '1831',\n",
       " '1832',\n",
       " '1833',\n",
       " '1834',\n",
       " '1835',\n",
       " '1836',\n",
       " '1837',\n",
       " '1838',\n",
       " '1839',\n",
       " '1840',\n",
       " '1841',\n",
       " '1842',\n",
       " '1843',\n",
       " '1844',\n",
       " '1845',\n",
       " '1846',\n",
       " '1847',\n",
       " '1848',\n",
       " '1849',\n",
       " '1850',\n",
       " '1851',\n",
       " '1852',\n",
       " '1853',\n",
       " '1854',\n",
       " '1855',\n",
       " '1856',\n",
       " '1857',\n",
       " '1858',\n",
       " '1859',\n",
       " '1860',\n",
       " '1861',\n",
       " '1862',\n",
       " '1863',\n",
       " '1864',\n",
       " '1865',\n",
       " '1866',\n",
       " '1867',\n",
       " '1868',\n",
       " '1869',\n",
       " '1870',\n",
       " '1871',\n",
       " '1872',\n",
       " '1873',\n",
       " '1874',\n",
       " '1875',\n",
       " '1876',\n",
       " '1877',\n",
       " '1878',\n",
       " '1879',\n",
       " '1880',\n",
       " '1881',\n",
       " '1882',\n",
       " '1883',\n",
       " '1884',\n",
       " '1885',\n",
       " '1886',\n",
       " '1887',\n",
       " '1888',\n",
       " '1889',\n",
       " '1890',\n",
       " '1891',\n",
       " '1892',\n",
       " '1893',\n",
       " '1894',\n",
       " '1895',\n",
       " '1896',\n",
       " '1897',\n",
       " '1898',\n",
       " '1899',\n",
       " '1900',\n",
       " '1901',\n",
       " '1902',\n",
       " '1903',\n",
       " '1904',\n",
       " '1905',\n",
       " '1906',\n",
       " '1907',\n",
       " '1908',\n",
       " '1909',\n",
       " '1910',\n",
       " '1911',\n",
       " '1912',\n",
       " '1913',\n",
       " '1914',\n",
       " '1915',\n",
       " '1916',\n",
       " '1917',\n",
       " '1918',\n",
       " '1919',\n",
       " '1920',\n",
       " '1921',\n",
       " '1922',\n",
       " '1923',\n",
       " '1924',\n",
       " '1810',\n",
       " '1811',\n",
       " '1812',\n",
       " '1813',\n",
       " '1814',\n",
       " '1815',\n",
       " '1816',\n",
       " '1817',\n",
       " '1818',\n",
       " '1819',\n",
       " '1820',\n",
       " '1821',\n",
       " '1822',\n",
       " '1823',\n",
       " '1824',\n",
       " '1825',\n",
       " '1826',\n",
       " '1827']"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#need to identify missing years (e.g. 1810s) and add a count of 0\n",
    "for y in range(1789,1925):\n",
    "    x = str(y)\n",
    "    if x not in years_count_df.index.values:\n",
    "        years_count_df.loc[x]=[0]\n",
    "\n",
    "years_count_df.index.tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oops, those new rows got added at the end, which won't make sense for time-ordered data. Let's sort the index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years_count_df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(years_count_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to create a chart showing the numbers of papers each year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WDU4AAAAAXB6CEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIIT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\nSZL/LcnPdveNSf6zqvrZGXsGAAAA4CLNFjh193NJvr22nGTLNN6a5My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q80Y3MJcTr76e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Xen4B3v+UC/57rPrnQbACfl+R1bV7oFAACAZVXV49296XifO96v1P37JN9J\n8o4kPxiXhQMAAABwHG92h9PVSe5I8sUkF3f3qzPrCgAAAIAz1pvtcPpUFi4Id3cTAAAAACfsB5Z7\n0N0fPpWwqaruraoXq+qpRbWNVfVYVe2vqn1VtXmor6uq7w71/VV111D/wUW1/VX1UlW5mAkAAADg\nNHa8O5xOxX1JPpfk84tqO5Pc1t2/U1UfHd5fPjx7rrs3Ll6gu7+d5D/UqurxJA9P2DMAAAAAp2jZ\nHU6nqrv3JnllaTnJmmE8n+TQia5XVeuTvDvJl0ZpEAAAAIBJTLnD6VhuSrKnqm7PQth16aJn76+q\n/UkOJ/l0dy8Nlj6e5De7u5dbvKq2JdmWJKvWrB21cQAAAABOzGQ7nJZxY5Lt3X1+ku1J7hnq30zy\nvuFI3SeS3F9Va5bM/XiSB95s8e6+u7s3dfemVavnR24dAAAAgBMx68Dpunz/DqYHk2xOku7+Xne/\nPIwfT/JckvVHJ1XVh5LMDc8AAAAAOI3NOnA6lGTLML4iybNJUlVrq2rVML4gyQeSfH3RvGtynN1N\nAAAAAJweJrvDqaoeyMIv0J1bVQeT3Jrk+iR3VtVcktcy3LeU5CNJPlNVR5K8keSG7l584fjPJvno\nVL0CAAAAMJ7JAqfuvmaZR5cc47MPJXnoTda6YKy+AAAAAJjWrI/UAQAAAHCWEzgBAAAAMKrJjtSt\ntA3nzWffjq0r3QYAAADAW44dTgAAAACMSuAEAAAAwKgETgAAAACMSuAEAAAAwKgETgAAAACMSuAE\nAAAAwKgETgAAAACMSuAEAAAAwKgETgAAAACMSuAEAAAAwKgETgAAAACMSuAEAAAAwKgETgAAAACM\nSuAEAAAAwKjmVrqBqRx44XDW3bx7pdsAOC09v2PrSrcAAACcxexwAgAAAGBUAicAAAAARiVwAgAA\nAGBUkwVOVXVvVb1YVU8tqm2sqseqan9V7auqzUN9XVV9d6jvr6q7Fs25pqoOVNWTVfW7VXXuVD0D\nAAAAcOqm3OF0X5Krl9R2JrmtuzcmuWV4f9Rz3b1xeN2QJFU1l+TOJD/Z3RcleTLJL0zYMwAAAACn\naLLAqbv3JnllaTnJmmE8n+TQcZap4fXOqqph7vHmAAAAALCC5mb8/25Ksqeqbs9C2HXpomfvr6r9\nSQ4n+XR3f6m7j1TVjUkOJPlOkmeT/KPlFq+qbUm2JcmqNWsn+goAAAAAvJlZXxp+Y5Lt3X1+ku1J\n7hnq30zyvuGo3SeS3F9Va6rqbcOcH0/yI1k4UvfJ5Rbv7ru7e1N3b1q1en7K7wEAAADAMmYdOF2X\n5OFh/GCSzUnS3d/r7peH8eNJnkuyPsnGofZcd3eSf5n/eFcUAAAAAKeZWQdOh5JsGcZXZOGIXKpq\nbVWtGsYXJPlAkq8neSHJj1bV0fNx/32S/2emHQMAAABwUia7w6mqHkhyeZJzq+pgkluTXJ/kzuHX\n517LcN9Sko8k+UxVHUnyRpIbuvuVYZ3bkuwdnv1pkv9lqp4BAAAAOHWTBU7dfc0yjy45xmcfSvLQ\nMuvcleSuEVsDAAAAYEKzPlIHAAAAwFlO4AQAAADAqCY7UrfSNpw3n307tq50GwAAAABvOXY4AQAA\nADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAoxI4\nAQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAo5pb6QamcuCFw1l38+6V\nbgPgLen5HVtXugUAAGAF2eEEAAAAwKgETgAAAACMSuAEAAAAwKgmC5yq6t6qerGqnlpU21hVj1XV\n/qraV1Wbh/q6qvruUN9fVXctmvP3q+rJqnq6qv7JVP0CAAAAMI4pdzjdl+TqJbWdSW7r7o1Jbhne\nH/Vcd28cXjckSVX99SS/kuTK7r4wyX9RVVdO2DMAAAAAp2iywKm79yZ5ZWk5yZphPJ/k0HGWuSDJ\ns93958P730vyM6M1CQAAAMDo5mb8/25Ksqeqbs9C2HXpomfvr6r9SQ4n+XR3fynJnyT5r6pqXZKD\nSX46yduXW7yqtiXZliSr1qydon8AAAAAjmPWl4bfmGR7d5+fZHuSe4b6N5O8bzhq94kk91fVmu7+\n1jDnN5N8KcnzSV5fbvHuvru7N3X3plWr5yf8GgAAAAAsZ9aB03VJHh7GDybZnCTd/b3ufnkYP57k\nuSTrh/e/3d3/dXf/RJI/TvK1GfcMAAAAwEmYdeB0KMmWYXxFkmeTpKrWVtWqYXxBkg8k+frw/t3D\n37+W5OeT/NqMewYAAADgJEx2h1NVPZDk8iTnVtXBJLcmuT7JnVU1l+S1DPctJflIks9U1ZEkbyS5\nobuPXjh+Z1V9aBh/prvtcAIAAAA4jU0WOHX3Ncs8uuQYn30oyUMnuQ4AAAAAp6FZH6kDAAAA4Cwn\ncAIAAABgVJMdqVtpG86bz74dW1e6DQAAAIC3HDucAAAAABiVwAkAAACAUQmcAAAAABiVwAkAAACA\nUQmcAAAAABiVwAkAAACAUQmcAAAAABiVwAkAAACAUQmcAAAAABiVwAkAAACAUQmcAAAAABiVwAkA\nAACAUQmcAAAAABiVwAkAAACAUc2tdANTOfDC4ay7efdKtwHAxJ7fsXWlWwAAAJawwwkAAACAUQmc\nAAAAABiVwAkAAACAUU0WOFXVvVX1YlU9tai2saoeq6r9VbWvqjYvenZRVT1aVU9X1YGqOmeoXzK8\n/5Oq+mdVVVP1DAAAAMCpm3KH031Jrl5S25nktu7emOSW4X2qai7JriQ3dPeFSS5PcmSY86tJrk/y\ngeG1dE0AAAAATiOTBU7dvTfJK0vLSdYM4/kkh4bxVUme7O4nhrkvd/frVfWeJGu6+7Hu7iSfT/LT\nU/UMAAAAwKmbm/H/uynJnqq6PQth16VDfX2Srqo9SdYm+UJ370xyXpKDi+YfHGrHVFXbkmxLklVr\n1o7fPQAAAADHNetLw29Msr27z0+yPck9Q30uyWVJrh3+fqyqrjzZxbv77u7e1N2bVq2eH6tnAAAA\nAE7CrAOn65I8PIwfTHL00vCDSfZ290vd/WqSR5JcnOSFJO9dNP+9Qw0AAACA09SsA6dDSbYM4yuS\nPDuM9yTZUFWrhwvEtyR5pru/meQvquq/GX6d7n9O8lsz7hkAAACAkzDZHU5V9UAWfm3u3Ko6mOTW\nLPza3J1DqPRahvuWuvtbVXVHki9n4WLxR7p797DUz2fhF+/+syS/M7wAAAAAOE1NFjh19zXLPLpk\nmc/vSrLrGPV9SX5sxNYAAAAAmNCsj9QBAAAAcJYTOAEAAAAwqsmO1K20DefNZ9+OrSvdBgAAAMBb\njh1OAAAAAIxK4AQAAADAqAROAAAAAIxK4AQAAADAqAROAAAAAIxK4AQAAADAqAROAAAAAIxK4AQA\nAADAqAROAAAAAIxK4AQAAADAqAROAAAAAIxK4AQAAADAqAROAAAAAIxK4AQAAADAqOZWuoGpHHjh\ncNbdvHul2wDgDPP8jq0r3QIAAJzx7HACAAAAYFQCJwAAAABGJXACAAAAYFSTBU5VdW9VvVhVTy2q\nbayqx6pqf1Xtq6rNi55dVFWPVtXTVXWgqs4Z6v9nVX2jqv5yql4BAAAAGM+UO5zuS3L1ktrOJLd1\n98YktwzvU1VzSXYluaG7L0xyeZIjw5zfTrI5AAAAAJwRJvuVuu7eW1XrlpaTrBnG80kODeOrkjzZ\n3U8Mc19etM5jSVJVU7UKAAAAwIgmC5yWcVOSPVV1exZ2V1061Ncn6arak2Rtki90986TXbyqtiXZ\nliSr1qwdp2MAAAAATsqsLw2/Mcn27j4/yfYk9wz1uSSXJbl2+PuxqrryZBfv7ru7e1N3b1q1en6s\nngEAAAA4CbMOnK5L8vAwfjDfv5vpYJK93f1Sd7+a5JEkF8+4NwAAAABGMOvA6VCSLcP4iiTPDuM9\nSTZU1erhAvEtSZ6ZcW8AAAAAjGCyO5yq6oEs/NrcuVV1MMmtSa5PcucQKr2W4b6l7v5WVd2R5MtZ\nuFj8ke7ePayzM8n/mGT1sM6vdff/MVXfAAAAAJyaKX+l7pplHl2yzOd3Jdl1jPovJvnFEVsDAAAA\nYEKzPlIHAAAAwFlO4AQAAADAqCY7UrfSNpw3n307tq50GwAAAABvOXY4AQAAADAqgRMAAAAAoxI4\nAQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAA\noxI4AQAAADAqgRMAAAAAoxI4AQAAADAqgRMAAAAAo5pb6QamcuCFw1l38+6VbgOAM8zzO7audAsA\nAHDGs8MJAAAAgFEJnAAAAAAYlcAJAAAAgFFNFjhV1b1V9WJVPbWotrGqHquq/VW1r6o2L3p2UVU9\nWlVPV9WBqjqnqlZX1e6q+upQ3zFVvwAAAACMY8odTvcluXpJbWeS27p7Y5Jbhvepqrkku5Lc0N0X\nJrk8yZFhzu3d/cEkP57kv62qvzVhzwAAAACcoskCp+7em+SVpeUka4bxfJJDw/iqJE929xPD3Je7\n+/XufrW7f3+o/fskX0ny3ql6BgAAAODUzc34/92UZE9V3Z6FsOvSob4+SVfVniRrk3yhu3cunlhV\nP5Tkf0hy53KLV9W2JNuSZNWateN3DwAAAMBxzfrS8BuTbO/u85NsT3LPUJ9LclmSa4e/H6uqK49O\nGo7cPZDkn3X315dbvLvv7u5N3b1p1er5qb4DAAAAAG9i1oHTdUkeHsYPJjl6afjBJHu7+6XufjXJ\nI0kuXjTv7iTPdvdnZ9YpAAAAA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MSa+SeSXDO7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\nd3K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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b2895908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years_count_df.plot(kind='barh', figsize=(20,75)).invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save this data to CSV! This file will be useful in other applications for examining the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "years_count_df.to_csv(path_or_buf=\"final-counts.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change of pace: want to hear a sonification of this graph? I posted [a sound file on Soundcloud](https://soundcloud.com/laurasaurus5/chronicling-america-titles-over-time). Be sure to read the description to get the details on what you're hearing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what are the min and max counts? How many years altogether in this file? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>177.544118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>179.249252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>320.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>526.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "count  136.000000\n",
       "mean   177.544118\n",
       "std    179.249252\n",
       "min      0.000000\n",
       "25%      1.000000\n",
       "50%    130.000000\n",
       "75%    320.500000\n",
       "max    526.000000"
      ]
     },
     "execution_count": 570,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_count_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One other way to look at the data from one of the earlier dataframes is to group by year and then group within that by state. (We're going to ignore the years with no titles.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  State               \n",
       "1789  New York                 1\n",
       "      Pennsylvania             1\n",
       "1790  New York                 1\n",
       "      Pennsylvania             1\n",
       "1791  New York                 1\n",
       "      Pennsylvania             2\n",
       "1792  New York                 1\n",
       "      Pennsylvania             2\n",
       "1793  New York                 1\n",
       "      Pennsylvania             2\n",
       "1794  Pennsylvania             2\n",
       "1795  Pennsylvania             2\n",
       "1796  Pennsylvania             2\n",
       "1797  Pennsylvania             1\n",
       "1798  Pennsylvania             1\n",
       "1799  Pennsylvania             1\n",
       "1800  District of Columbia     1\n",
       "      Pennsylvania             2\n",
       "1801  District of Columbia     1\n",
       "1802  District of Columbia     1\n",
       "1803  District of Columbia     1\n",
       "1804  District of Columbia     1\n",
       "1805  District of Columbia     1\n",
       "1806  District of Columbia     1\n",
       "1807  District of Columbia     1\n",
       "1808  District of Columbia     1\n",
       "1809  District of Columbia     1\n",
       "1828  Georgia                  1\n",
       "1829  Georgia                  2\n",
       "1830  Georgia                  1\n",
       "                              ..\n",
       "1922  Kansas                   4\n",
       "      Kentucky                17\n",
       "      Louisiana               18\n",
       "      Maryland                 5\n",
       "      Michigan                 7\n",
       "      Minnesota               14\n",
       "      Mississippi              9\n",
       "      Missouri                17\n",
       "      Montana                  4\n",
       "      Nebraska                 5\n",
       "      Nevada                   3\n",
       "      New Mexico               7\n",
       "      New York                 3\n",
       "      North Carolina           9\n",
       "      North Dakota            10\n",
       "      Ohio                     2\n",
       "      Oklahoma                 7\n",
       "      Oregon                  11\n",
       "      Pennsylvania             2\n",
       "      South Carolina          20\n",
       "      Tennessee               12\n",
       "      Texas                    4\n",
       "      Utah                     5\n",
       "      Vermont                 11\n",
       "      Virginia                 6\n",
       "      Washington               7\n",
       "      West Virginia            3\n",
       "      Wisconsin                1\n",
       "1923  Michigan                 1\n",
       "1924  Michigan                 1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_df.groupby([\"Year\",\"State\"]).size()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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