EbooksPackageAnalysis.ipynb

{
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    "# Understanding and Measuring eBook Packages: Purchasing Patterns, Usage, and an Analysis Framework\n",
    "## 2018 Charleston Conference\n",
    "#### John Vickery \n",
    "#### This notebook covers the \"Analysis Framework\" portion of the presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## About me...\n",
    "\n",
    "I am Analytics Coordinator and Collections & Research Librarian for Social Sciences at NC State Libraries.  \n",
    "My work primarily involves data analysis for internal, operational decision making.  \n",
    "I do most of my work in Python, SAS and R.\n",
    "\n",
    "You can find this notebook and sample data to run it on GitHub  \n",
    "https://github.com/jnvickery/EbookPackageAnalysis"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Analysis framework for ebook packages\n",
    "\n",
    "We'll use Elsevier packages purchased by NC State between 2012 and 2016 as an example.  \n",
    "The aim is to analyze usage at the package level to inform future decisions to update the collection. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "## What you need for the framework:\n",
    "1. List of packages purchased with date (or year) purchased\n",
    "2. Publisher title lists for each package\n",
    "3. Yearly COUNTER usage reports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## About the data files:\n",
    "\n",
    "### List of packages purchased by year\n",
    "- This is a homegrown file that will vary depending on your situation\n",
    "- It will require a link to the package title lists. In this case it's Elsevier's \"Package Code\"\n",
    "- *The important part is to determine the first year that usage would be relevant based on the purchase year*\n",
    "\n",
    "### Title lists\n",
    "- These are the lists of titles in Elsevier ebook packages\n",
    "- Downloaded from: https://www.elsevier.com/solutions/sciencedirect/content/book-title-lists\n",
    "- Each title list file has the following columns (Thanks Elsevier!). We'll just use the ones in **bold**\n",
    "    1. Source\n",
    "    2. **Package name**\n",
    "    3. **Package ID**\n",
    "    4. **ISBN**\n",
    "    5. **Unformatted ISBN**\n",
    "    6. **Book Title**\n",
    "    7. **Year**\n",
    "    8. ISSN\n",
    "    9. Series Title\n",
    "    10. Volume\n",
    "    11. Imprint\n",
    "    12. Available Online\n",
    "    13. Status\n",
    "    14. Remarks\n",
    "    15. Shortcut URL\n",
    "\n",
    "### COUNTER reports by year\n",
    "- These are the regular BR2 reports\n",
    "\n",
    "- These are saved to the same folder and named \"YYYY_Elsevier.xlsx\" (e.g. 2016_Elsevier.xlsx) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## So... down in the weeds a bit with some python code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The first thing we need to do is import some python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "# import the python packages that we need\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Next, use the \"read_excel\" method to create a dataframe of package codes that have been purchased\n",
    "1. This file inlcudes the Elsevier \"Package Code\" identifying each collection\n",
    "2. Date purchased\n",
    "3. The first year that usage would be relevant\n",
    "    - For example, if the \"Agricultural and Biological Sciences pre-2007\" collection was purchased at the end of 2011 then usage from 2012 would be the first relevant usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
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     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EBCABS</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EBCABS06</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EBCABS95S</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EBCABS14</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EBCABS15</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PackageCode  FirstUsageDate\n",
       "0      EBCABS            2012\n",
       "1    EBCABS06            2012\n",
       "2   EBCABS95S            2012\n",
       "3    EBCABS14            2014\n",
       "4    EBCABS15            2015"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get list of elsevier ebook package codes into a dataframe\n",
    "pkgcodes = pd.read_excel('H:\\presentations and conferences\\Charleston\\Charleston 2018\\presentation\\packageNames.xlsx',\n",
    "                         usecols='C,E',\n",
    "                         converters={'PackageCode':str,'FirstUsageDate':int})\n",
    "\n",
    "# and look at the first few rows\n",
    "pkgcodes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The next few cells get the ebook title lists from whatever folder you saved them to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# build out base directory for files\n",
    "# change to match your folder structure\n",
    "mydir = os.path.join('H:\\presentations and conferences',\n",
    "                     'Charleston',\n",
    "                     'Charleston 2018',\n",
    "                     'presentation',\n",
    "                     'title lists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# initialize a dataframe for the title lists\n",
    "titles = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Since there are several title list files, use a \"for loop\" to read each one and save them to the \"titles\" dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# loop through the Elsevier title lists\n",
    "# we're also replacing spaces in the column names with an underscore\n",
    "for yr in range(2007,2018):\n",
    "    fpath = os.path.join(mydir,'ebook'+str(yr)+'.xlsx')\n",
    "    df = pd.read_excel(fpath, converters={'ISBN':str})\n",
    "    df.columns = [c.replace(' ', '_') for c in df.columns]\n",
    "    titles = titles.append(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "** Check out the columns from the title lists that were imported **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9071 entries, 0 to 1238\n",
      "Data columns (total 16 columns):\n",
      "Available_Online       9071 non-null object\n",
      "Book_Title             9071 non-null object\n",
      "Editor(s)/Author(s)    1972 non-null object\n",
      "ISBN                   9054 non-null object\n",
      "ISSN                   665 non-null object\n",
      "Imprint                9054 non-null object\n",
      "Package_ID             9063 non-null object\n",
      "Package_name           9071 non-null object\n",
      "Remarks                43 non-null object\n",
      "Series_Title           1997 non-null object\n",
      "Shortcut_URL           9054 non-null object\n",
      "Source                 9071 non-null object\n",
      "Status                 80 non-null object\n",
      "Unformatted_ISBN       7099 non-null float64\n",
      "Volume                 588 non-null object\n",
      "Year                   9071 non-null int64\n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# check out the dataframe\n",
    "titles.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Now we need to do a little clean-up of the title lists  \n",
    "1. keep selected columns\n",
    "2. subset them to only include purchased packages\n",
    "3. reformat ISBN to remove dashes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# just keep the columns we want\n",
    "titles = titles[['Book_Title', 'ISBN', 'Package_ID', 'Package_name', 'Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# subset title lists where code only in list of purchased codes\n",
    "titles = titles[titles['Package_ID'].isin(pkgcodes['PackageCode'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# reformat ISBN to remove dashes\n",
    "titles['ISBN'].replace(regex=True,inplace=True,to_replace=r'\\D',value=r'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Book_Title</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Package_ID</th>\n",
       "      <th>Package_name</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Anaesthesia of Exotic Pets</td>\n",
       "      <td>9780702028885</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Animal Abuse and Unlawful Killing</td>\n",
       "      <td>9780702028786</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Color Atlas of Diseases and Disorders of the Foal</td>\n",
       "      <td>9780702028106</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Equine Emergencies (Third Edition)</td>\n",
       "      <td>9781416036098</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>Equine Exercise Physiology</td>\n",
       "      <td>9780702028571</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Book_Title           ISBN  \\\n",
       "636                         Anaesthesia of Exotic Pets  9780702028885   \n",
       "637                  Animal Abuse and Unlawful Killing  9780702028786   \n",
       "638  Color Atlas of Diseases and Disorders of the Foal  9780702028106   \n",
       "639                 Equine Emergencies (Third Edition)  9781416036098   \n",
       "640                         Equine Exercise Physiology  9780702028571   \n",
       "\n",
       "    Package_ID                      Package_name  Year  \n",
       "636     EBVM08  eBook - Veterinary Medicine 2008  2008  \n",
       "637     EBVM08  eBook - Veterinary Medicine 2008  2008  \n",
       "638     EBVM08  eBook - Veterinary Medicine 2008  2008  \n",
       "639     EBVM08  eBook - Veterinary Medicine 2008  2008  \n",
       "640     EBVM08  eBook - Veterinary Medicine 2008  2008  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see how it looks\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Now we'll merge the \"FirstUsageDate\" column from \"pkgcodes\" dataframe to \"titles\" dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Book_Title</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Package_ID</th>\n",
       "      <th>Package_name</th>\n",
       "      <th>Year</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anaesthesia of Exotic Pets</td>\n",
       "      <td>9780702028885</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Animal Abuse and Unlawful Killing</td>\n",
       "      <td>9780702028786</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color Atlas of Diseases and Disorders of the Foal</td>\n",
       "      <td>9780702028106</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Equine Emergencies (Third Edition)</td>\n",
       "      <td>9781416036098</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Equine Exercise Physiology</td>\n",
       "      <td>9780702028571</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>eBook - Veterinary Medicine 2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>EBVM08</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Book_Title           ISBN  \\\n",
       "0                         Anaesthesia of Exotic Pets  9780702028885   \n",
       "1                  Animal Abuse and Unlawful Killing  9780702028786   \n",
       "2  Color Atlas of Diseases and Disorders of the Foal  9780702028106   \n",
       "3                 Equine Emergencies (Third Edition)  9781416036098   \n",
       "4                         Equine Exercise Physiology  9780702028571   \n",
       "\n",
       "  Package_ID                      Package_name  Year PackageCode  \\\n",
       "0     EBVM08  eBook - Veterinary Medicine 2008  2008      EBVM08   \n",
       "1     EBVM08  eBook - Veterinary Medicine 2008  2008      EBVM08   \n",
       "2     EBVM08  eBook - Veterinary Medicine 2008  2008      EBVM08   \n",
       "3     EBVM08  eBook - Veterinary Medicine 2008  2008      EBVM08   \n",
       "4     EBVM08  eBook - Veterinary Medicine 2008  2008      EBVM08   \n",
       "\n",
       "   FirstUsageDate  \n",
       "0            2012  \n",
       "1            2012  \n",
       "2            2012  \n",
       "3            2012  \n",
       "4            2012  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add FirstUsageDate from the pkgcodes dataframe\n",
    "titles = pd.merge(titles,pkgcodes, how='left', left_on='Package_ID', right_on='PackageCode')\n",
    "titles.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### This next section gets the Elsevier usage data\n",
    "1. Re-assign the \"mydir\" variable to point to the usage reports folder\n",
    "2. Get the minimum and maximum values for \"FirstUsage\" in the titles dataframe\n",
    "3. Use the min and max year as parameters in a loop to get usage reports\n",
    "4. Get the total usage per title per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# re-assign mydir variable for base directory for usage reports\n",
    "mydir = os.path.join('H:\\presentations and conferences',\n",
    "                     'Charleston',\n",
    "                     'Charleston 2018',\n",
    "                     'presentation',\n",
    "                     'usage reports')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012 2017\n"
     ]
    }
   ],
   "source": [
    "# minimum and maximum FirstUsage from the titles dataframe\n",
    "minyr = titles.FirstUsageDate.min()\n",
    "maxyr = titles.FirstUsageDate.max()\n",
    "\n",
    "# print out the two values\n",
    "print(minyr, maxyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Loop through the Elsevier COUNTER usage reports and merge them to \"titles\" dataframe.  \n",
    "** A few things happen in the loop... **\n",
    "1. Just use the ISBN and TOTAL columns from each COUNTER report\n",
    "2. Save each year's usage report to a temporary dataframe\n",
    "3. Merge that to the \"titles\" dataframe so we have a column for each year's usage\n",
    "4. Get rid of dashes in the ISBN\n",
    "5. And set usage to zero if it falls before the \"FirstUsage\" date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# loop through years to get Elsevier usage\n",
    "# usage must be from the year greater than or equal to the first usage year of the package\n",
    "for yr in range(minyr,maxyr+1):\n",
    "    year = yr\n",
    "    fpath = os.path.join(mydir,str(yr)+'_Elsevier.xlsx')\n",
    "    use = pd.read_excel(fpath, sheet_name='Sheet1', usecols=\"F,H\", converters={'ISBN':str})\n",
    "    use = use.rename(columns={'TOTAL':'use'+str(yr)})\n",
    "    use['ISBN'].replace(regex=True,inplace=True,to_replace=r'\\D',value=r'')\n",
    "    use['UsageYear'] = year\n",
    "    use['use'+str(yr)].fillna(0, inplace=True)\n",
    "    titles = pd.merge(titles, use, how='left', on='ISBN')    \n",
    "    titles.loc[(titles['UsageYear'] < titles['FirstUsageDate']), 'use'+str(yr)] = 0\n",
    "    titles['use'+str(yr)].fillna(0, inplace=True)\n",
    "    titles.drop('UsageYear', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Book_Title</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Package_ID</th>\n",
       "      <th>Package_name</th>\n",
       "      <th>Year</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>use2012</th>\n",
       "      <th>use2013</th>\n",
       "      <th>use2014</th>\n",
       "      <th>use2015</th>\n",
       "      <th>use2016</th>\n",
       "      <th>use2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>Modeling, Characterization and Production of N...</td>\n",
       "      <td>9781782422280</td>\n",
       "      <td>EBCE15</td>\n",
       "      <td>eBook - Engineering 2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>EBCE15</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>Intelligent Vehicles</td>\n",
       "      <td>9780128128008</td>\n",
       "      <td>EBCE17</td>\n",
       "      <td>eBook - Engineering 2017</td>\n",
       "      <td>2018</td>\n",
       "      <td>EBCE17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3472</th>\n",
       "      <td>Modeling of Microscale Transport in Biological...</td>\n",
       "      <td>9780128045954</td>\n",
       "      <td>EBCBGMB17</td>\n",
       "      <td>eBook - Biochemistry, Genetics and Molecular B...</td>\n",
       "      <td>2017</td>\n",
       "      <td>EBCBGMB17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3488</th>\n",
       "      <td>Penetration Tester's Open Source Toolkit (Four...</td>\n",
       "      <td>9780128021491</td>\n",
       "      <td>EBCCS17</td>\n",
       "      <td>eBook - Computer Science 2017</td>\n",
       "      <td>2016</td>\n",
       "      <td>EBCCS17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>Altmetrics for Information Professionals</td>\n",
       "      <td>9780081002735</td>\n",
       "      <td>EBCSS15</td>\n",
       "      <td>eBook - Social Sciences 2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>EBCSS15</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Book_Title           ISBN  \\\n",
       "933   Modeling, Characterization and Production of N...  9781782422280   \n",
       "3144                               Intelligent Vehicles  9780128128008   \n",
       "3472  Modeling of Microscale Transport in Biological...  9780128045954   \n",
       "3488  Penetration Tester's Open Source Toolkit (Four...  9780128021491   \n",
       "781            Altmetrics for Information Professionals  9780081002735   \n",
       "\n",
       "     Package_ID                                       Package_name  Year  \\\n",
       "933      EBCE15                           eBook - Engineering 2015  2015   \n",
       "3144     EBCE17                           eBook - Engineering 2017  2018   \n",
       "3472  EBCBGMB17  eBook - Biochemistry, Genetics and Molecular B...  2017   \n",
       "3488    EBCCS17                      eBook - Computer Science 2017  2016   \n",
       "781     EBCSS15                       eBook - Social Sciences 2015  2016   \n",
       "\n",
       "     PackageCode  FirstUsageDate  use2012  use2013  use2014  use2015  use2016  \\\n",
       "933       EBCE15            2016      0.0      0.0      0.0      0.0      0.0   \n",
       "3144      EBCE17            2017      0.0      0.0      0.0      0.0      0.0   \n",
       "3472   EBCBGMB17            2017      0.0      0.0      0.0      0.0      0.0   \n",
       "3488     EBCCS17            2017      0.0      0.0      0.0      0.0      0.0   \n",
       "781      EBCSS15            2017      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "      use2017  \n",
       "933       0.0  \n",
       "3144      0.0  \n",
       "3472      0.0  \n",
       "3488     24.0  \n",
       "781       0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check out a few random rows\n",
    "titles.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sum across \"USE\" columns to create a \"TotalUse\" column for each title and save to a \"TotalUse\" dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalUse</th>\n",
       "      <th>Book_Title</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>Package_ID</th>\n",
       "      <th>Package_name</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>21046</td>\n",
       "      <td>Small Animal Critical Care Medicine (Second Ed...</td>\n",
       "      <td>2014</td>\n",
       "      <td>9781455703067</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>eBook - Veterinary Medicine 2014</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>19071</td>\n",
       "      <td>Saunders Handbook of Veterinary Drugs (Fourth ...</td>\n",
       "      <td>2015</td>\n",
       "      <td>9780323244855</td>\n",
       "      <td>EBCVM15</td>\n",
       "      <td>EBCVM15</td>\n",
       "      <td>eBook - Veterinary Medicine 2015</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6252</td>\n",
       "      <td>Small Animal Critical Care Medicine</td>\n",
       "      <td>2012</td>\n",
       "      <td>9781416025917</td>\n",
       "      <td>EBCVM09</td>\n",
       "      <td>EBCVM09</td>\n",
       "      <td>eBook - Veterinary Medicine 2009</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>5413</td>\n",
       "      <td>Fowler's Zoo and Wild Animal Medicine, Volume 8</td>\n",
       "      <td>2014</td>\n",
       "      <td>9781455773978</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>eBook - Veterinary Medicine 2014</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>5008</td>\n",
       "      <td>Robinson's Current Therapy in Equine Medicine ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>9781455745555</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>eBook - Veterinary Medicine 2014</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TotalUse                                         Book_Title  \\\n",
       "226     21046  Small Animal Critical Care Medicine (Second Ed...   \n",
       "1365    19071  Saunders Handbook of Veterinary Drugs (Fourth ...   \n",
       "26       6252                Small Animal Critical Care Medicine   \n",
       "228      5413    Fowler's Zoo and Wild Animal Medicine, Volume 8   \n",
       "227      5008  Robinson's Current Therapy in Equine Medicine ...   \n",
       "\n",
       "     FirstUsageDate           ISBN PackageCode Package_ID  \\\n",
       "226            2014  9781455703067     EBCVM14    EBCVM14   \n",
       "1365           2015  9780323244855     EBCVM15    EBCVM15   \n",
       "26             2012  9781416025917     EBCVM09    EBCVM09   \n",
       "228            2014  9781455773978     EBCVM14    EBCVM14   \n",
       "227            2014  9781455745555     EBCVM14    EBCVM14   \n",
       "\n",
       "                          Package_name  Year  \n",
       "226   eBook - Veterinary Medicine 2014  2015  \n",
       "1365  eBook - Veterinary Medicine 2015  2016  \n",
       "26    eBook - Veterinary Medicine 2009  2009  \n",
       "228   eBook - Veterinary Medicine 2014  2015  \n",
       "227   eBook - Veterinary Medicine 2014  2015  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum across \"USE\" columns to create a \"TotalUse\" column for each title\n",
    "# transpose then sum then transpose back\n",
    "TotalUse = titles.T.groupby([s.split('use')[0] for s in titles.T.index.values]).sum().T.rename(columns={'':'TotalUse'})\n",
    "TotalUse.sort_values(by='TotalUse', ascending=False, inplace=True)\n",
    "TotalUse.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The next section defines and calls a function to group TotalUse into categories\n",
    "\n",
    "1. I've grouped usage into categories of 0, 1, 2-4, 5-50 and > 50\n",
    "2. That may or may not be the best groupings\n",
    "3. Edit the function to create different categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a function to group\n",
    "def usegroup(TotalUse):\n",
    "    global UseCategory\n",
    "    if TotalUse > 50:\n",
    "        return '> 50'\n",
    "    elif 5 <= TotalUse <= 50:\n",
    "        return '5-50'\n",
    "    elif 2 <= TotalUse <= 4:\n",
    "        return '2-4'        \n",
    "    elif TotalUse == 1:\n",
    "        return '1'        \n",
    "    elif TotalUse == 0:\n",
    "        return '0'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalUse</th>\n",
       "      <th>Book_Title</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>Package_ID</th>\n",
       "      <th>Package_name</th>\n",
       "      <th>Year</th>\n",
       "      <th>UseCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>0</td>\n",
       "      <td>Gas and Oil Reliability Engineering (Second Ed...</td>\n",
       "      <td>2016</td>\n",
       "      <td>9780128054277</td>\n",
       "      <td>EBCE16</td>\n",
       "      <td>EBCE16</td>\n",
       "      <td>eBook - Engineering 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2640</th>\n",
       "      <td>0</td>\n",
       "      <td>Chemical Contaminants and Residues in Food (Se...</td>\n",
       "      <td>2017</td>\n",
       "      <td>9780081006740</td>\n",
       "      <td>EBCABS17</td>\n",
       "      <td>EBCABS17</td>\n",
       "      <td>eBook - Agricultural, Biological, and Food Sci...</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>1</td>\n",
       "      <td>The Teeth of Non-Mammalian Vertebrates</td>\n",
       "      <td>2017</td>\n",
       "      <td>9780128028506</td>\n",
       "      <td>EBCABS17</td>\n",
       "      <td>EBCABS17</td>\n",
       "      <td>eBook - Agricultural, Biological, and Food Sci...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>0</td>\n",
       "      <td>Seismic Safety of High Arch Dams</td>\n",
       "      <td>2016</td>\n",
       "      <td>9780128036280</td>\n",
       "      <td>EBCE16</td>\n",
       "      <td>EBCE16</td>\n",
       "      <td>eBook - Engineering 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3033</th>\n",
       "      <td>20</td>\n",
       "      <td>Biomechanics of Tendons and Ligaments</td>\n",
       "      <td>2017</td>\n",
       "      <td>9780081004890</td>\n",
       "      <td>EBCMS17</td>\n",
       "      <td>EBCMS17</td>\n",
       "      <td>eBook - Materials Science 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>5-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TotalUse                                         Book_Title  \\\n",
       "2190        0  Gas and Oil Reliability Engineering (Second Ed...   \n",
       "2640        0  Chemical Contaminants and Residues in Food (Se...   \n",
       "3211        1             The Teeth of Non-Mammalian Vertebrates   \n",
       "2446        0                   Seismic Safety of High Arch Dams   \n",
       "3033       20              Biomechanics of Tendons and Ligaments   \n",
       "\n",
       "     FirstUsageDate           ISBN PackageCode Package_ID  \\\n",
       "2190           2016  9780128054277      EBCE16     EBCE16   \n",
       "2640           2017  9780081006740    EBCABS17   EBCABS17   \n",
       "3211           2017  9780128028506    EBCABS17   EBCABS17   \n",
       "2446           2016  9780128036280      EBCE16     EBCE16   \n",
       "3033           2017  9780081004890     EBCMS17    EBCMS17   \n",
       "\n",
       "                                           Package_name  Year UseCategory  \n",
       "2190                           eBook - Engineering 2016  2016           0  \n",
       "2640  eBook - Agricultural, Biological, and Food Sci...  2017           0  \n",
       "3211  eBook - Agricultural, Biological, and Food Sci...  2017           1  \n",
       "2446                           eBook - Engineering 2016  2016           0  \n",
       "3033                     eBook - Materials Science 2017  2017        5-50  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply the function\n",
    "TotalUse['UseCategory'] = TotalUse.TotalUse.apply(usegroup)\n",
    "\n",
    "# check a few random rows\n",
    "TotalUse.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reformat the package names so that they can be put into broad subject groups\n",
    "1. Clean up names so something like \"eBook - Computer Science 2015\" is just \"Computer Science\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# strip out \"eBook - \" and the date from PackageName\n",
    "TotalUse['Package'] = TotalUse['Package_name'].replace(regex=True,inplace=False,to_replace=([r'eBook - ',r'\\d*']),value=r'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalUse</th>\n",
       "      <th>Book_Title</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>Package_ID</th>\n",
       "      <th>Package_name</th>\n",
       "      <th>Year</th>\n",
       "      <th>UseCategory</th>\n",
       "      <th>Package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>21046</td>\n",
       "      <td>Small Animal Critical Care Medicine (Second Ed...</td>\n",
       "      <td>2014</td>\n",
       "      <td>9781455703067</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>eBook - Veterinary Medicine 2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>&gt; 50</td>\n",
       "      <td>Veterinary Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>19071</td>\n",
       "      <td>Saunders Handbook of Veterinary Drugs (Fourth ...</td>\n",
       "      <td>2015</td>\n",
       "      <td>9780323244855</td>\n",
       "      <td>EBCVM15</td>\n",
       "      <td>EBCVM15</td>\n",
       "      <td>eBook - Veterinary Medicine 2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>&gt; 50</td>\n",
       "      <td>Veterinary Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6252</td>\n",
       "      <td>Small Animal Critical Care Medicine</td>\n",
       "      <td>2012</td>\n",
       "      <td>9781416025917</td>\n",
       "      <td>EBCVM09</td>\n",
       "      <td>EBCVM09</td>\n",
       "      <td>eBook - Veterinary Medicine 2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>&gt; 50</td>\n",
       "      <td>Veterinary Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>5413</td>\n",
       "      <td>Fowler's Zoo and Wild Animal Medicine, Volume 8</td>\n",
       "      <td>2014</td>\n",
       "      <td>9781455773978</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>eBook - Veterinary Medicine 2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>&gt; 50</td>\n",
       "      <td>Veterinary Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>5008</td>\n",
       "      <td>Robinson's Current Therapy in Equine Medicine ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>9781455745555</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>EBCVM14</td>\n",
       "      <td>eBook - Veterinary Medicine 2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>&gt; 50</td>\n",
       "      <td>Veterinary Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TotalUse                                         Book_Title  \\\n",
       "226     21046  Small Animal Critical Care Medicine (Second Ed...   \n",
       "1365    19071  Saunders Handbook of Veterinary Drugs (Fourth ...   \n",
       "26       6252                Small Animal Critical Care Medicine   \n",
       "228      5413    Fowler's Zoo and Wild Animal Medicine, Volume 8   \n",
       "227      5008  Robinson's Current Therapy in Equine Medicine ...   \n",
       "\n",
       "     FirstUsageDate           ISBN PackageCode Package_ID  \\\n",
       "226            2014  9781455703067     EBCVM14    EBCVM14   \n",
       "1365           2015  9780323244855     EBCVM15    EBCVM15   \n",
       "26             2012  9781416025917     EBCVM09    EBCVM09   \n",
       "228            2014  9781455773978     EBCVM14    EBCVM14   \n",
       "227            2014  9781455745555     EBCVM14    EBCVM14   \n",
       "\n",
       "                          Package_name  Year UseCategory               Package  \n",
       "226   eBook - Veterinary Medicine 2014  2015        > 50  Veterinary Medicine   \n",
       "1365  eBook - Veterinary Medicine 2015  2016        > 50  Veterinary Medicine   \n",
       "26    eBook - Veterinary Medicine 2009  2009        > 50  Veterinary Medicine   \n",
       "228   eBook - Veterinary Medicine 2014  2015        > 50  Veterinary Medicine   \n",
       "227   eBook - Veterinary Medicine 2014  2015        > 50  Veterinary Medicine   "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check out a few rows\n",
    "TotalUse.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The next section gets counts of UseCategory by Package and save to a \"Groups\" dataframe\n",
    "1. get the the count of titles in each UseCategory for each package\n",
    "2. add percentages to counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>Total</th>\n",
       "      <th>Package</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2-4</th>\n",
       "      <th>5-50</th>\n",
       "      <th>&gt; 50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EBCABS14</td>\n",
       "      <td>58</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EBCABS15</td>\n",
       "      <td>72</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EBCABS16</td>\n",
       "      <td>108</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2016</td>\n",
       "      <td>52</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EBCABS17</td>\n",
       "      <td>114</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2017</td>\n",
       "      <td>73</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EBCBGMB16</td>\n",
       "      <td>53</td>\n",
       "      <td>Biochemistry, Genetics and Molecular Biology</td>\n",
       "      <td>2016</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PackageCode  Total                                        Package  \\\n",
       "0    EBCABS14     58   Agricultural, Biological, and Food Sciences    \n",
       "1    EBCABS15     72   Agricultural, Biological, and Food Sciences    \n",
       "2    EBCABS16    108   Agricultural, Biological, and Food Sciences    \n",
       "3    EBCABS17    114   Agricultural, Biological, and Food Sciences    \n",
       "4   EBCBGMB16     53  Biochemistry, Genetics and Molecular Biology    \n",
       "\n",
       "   FirstUsageDate   0   1  2-4  5-50  > 50  \n",
       "0            2014   4   3    5    34    12  \n",
       "1            2015  12   9    9    31    11  \n",
       "2            2016  52  15   15    23     3  \n",
       "3            2017  73  14    9    15     3  \n",
       "4            2016  30   2    5    13     3  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = TotalUse.groupby('PackageCode').size().to_frame('Total').reset_index()\n",
    "y = TotalUse.groupby(['Package', 'FirstUsageDate', 'PackageCode', 'UseCategory']).size().unstack(fill_value=0).reset_index()\n",
    "groups = pd.merge(x,y, on='PackageCode')\n",
    "groups.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>Total</th>\n",
       "      <th>Package</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2-4</th>\n",
       "      <th>5-50</th>\n",
       "      <th>&gt; 50</th>\n",
       "      <th>Pct 0</th>\n",
       "      <th>Pct 1</th>\n",
       "      <th>Pct 2-4</th>\n",
       "      <th>Pct 5-50</th>\n",
       "      <th>Pct &gt; 50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EBCABS14</td>\n",
       "      <td>58</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EBCABS15</td>\n",
       "      <td>72</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EBCABS16</td>\n",
       "      <td>108</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2016</td>\n",
       "      <td>52</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EBCABS17</td>\n",
       "      <td>114</td>\n",
       "      <td>Agricultural, Biological, and Food Sciences</td>\n",
       "      <td>2017</td>\n",
       "      <td>73</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EBCBGMB16</td>\n",
       "      <td>53</td>\n",
       "      <td>Biochemistry, Genetics and Molecular Biology</td>\n",
       "      <td>2016</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PackageCode  Total                                        Package  \\\n",
       "0    EBCABS14     58   Agricultural, Biological, and Food Sciences    \n",
       "1    EBCABS15     72   Agricultural, Biological, and Food Sciences    \n",
       "2    EBCABS16    108   Agricultural, Biological, and Food Sciences    \n",
       "3    EBCABS17    114   Agricultural, Biological, and Food Sciences    \n",
       "4   EBCBGMB16     53  Biochemistry, Genetics and Molecular Biology    \n",
       "\n",
       "   FirstUsageDate   0   1  2-4  5-50  > 50  Pct 0  Pct 1  Pct 2-4  Pct 5-50  \\\n",
       "0            2014   4   3    5    34    12   0.07   0.05     0.09      0.59   \n",
       "1            2015  12   9    9    31    11   0.17   0.12     0.12      0.43   \n",
       "2            2016  52  15   15    23     3   0.48   0.14     0.14      0.21   \n",
       "3            2017  73  14    9    15     3   0.64   0.12     0.08      0.13   \n",
       "4            2016  30   2    5    13     3   0.57   0.04     0.09      0.25   \n",
       "\n",
       "   Pct > 50  \n",
       "0      0.21  \n",
       "1      0.15  \n",
       "2      0.03  \n",
       "3      0.03  \n",
       "4      0.06  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add percentages\n",
    "cols = ['0','1','2-4','5-50','> 50']\n",
    "for c in cols:\n",
    "    groups['Pct '+c] = groups[c]/groups['Total']\n",
    "\n",
    "# check out a few rows\n",
    "groups.round(2).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we can start to get some answers...\n",
    "\n",
    "### For example, we can see which packages have the highest percentage of zero use titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Package</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>Pct 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Physics and Astronomy</td>\n",
       "      <td>EBCPA15</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Social Sciences</td>\n",
       "      <td>EBCSS17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Immunology and Microbiology</td>\n",
       "      <td>EBCIM17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Social Sciences</td>\n",
       "      <td>EBCSS15</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Pharmacology, Toxicology and Pharmaceutical Sc...</td>\n",
       "      <td>EBCPTPS17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Specialty Medicine</td>\n",
       "      <td>EBCSM17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Biomedical Science and Medicine</td>\n",
       "      <td>EBCBSM17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Health Professions</td>\n",
       "      <td>EBCHP15</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Earth and Planetary Sciences</td>\n",
       "      <td>EBCEPS16</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Psychology</td>\n",
       "      <td>EBCP17</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Package PackageCode  \\\n",
       "67                             Physics and Astronomy      EBCPA15   \n",
       "78                                   Social Sciences      EBCSS17   \n",
       "47                       Immunology and Microbiology      EBCIM17   \n",
       "76                                   Social Sciences      EBCSS15   \n",
       "72  Pharmacology, Toxicology and Pharmaceutical Sc...   EBCPTPS17   \n",
       "75                                Specialty Medicine      EBCSM17   \n",
       "7                    Biomedical Science and Medicine     EBCBSM17   \n",
       "42                                Health Professions      EBCHP15   \n",
       "28                      Earth and Planetary Sciences     EBCEPS16   \n",
       "66                                        Psychology       EBCP17   \n",
       "\n",
       "    FirstUsageDate  Pct 0  \n",
       "67            2017   0.89  \n",
       "78            2017   0.87  \n",
       "47            2017   0.87  \n",
       "76            2017   0.87  \n",
       "72            2017   0.86  \n",
       "75            2017   0.85  \n",
       "7             2017   0.85  \n",
       "42            2016   0.82  \n",
       "28            2016   0.79  \n",
       "66            2017   0.79  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups[['Package', 'PackageCode', 'FirstUsageDate', 'Pct 0']].sort_values(by='Pct 0', ascending=False).round(2).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the list above, it's clear that more recent purchases have more zero use titles\n",
    "\n",
    "1. Limit to packages with a first usage year of 2012-2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Package</th>\n",
       "      <th>PackageCode</th>\n",
       "      <th>FirstUsageDate</th>\n",
       "      <th>Pct 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Forensics, Security, and Criminal Justice</td>\n",
       "      <td>EBCFOR14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Finance</td>\n",
       "      <td>EBCF14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Environmental Science</td>\n",
       "      <td>EBCES12</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Immunology and Microbiology</td>\n",
       "      <td>EBCIM14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Finance</td>\n",
       "      <td>EBCF12</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Energy</td>\n",
       "      <td>EBCEN14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chemistry</td>\n",
       "      <td>EBCC14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Pharmacology, Toxicology and Pharmaceutical Sc...</td>\n",
       "      <td>EBCPTPS14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Neuroscience</td>\n",
       "      <td>EBCN14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Psychology</td>\n",
       "      <td>EBCP14</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Package PackageCode  \\\n",
       "39         Forensics, Security, and Criminal Justice     EBCFOR14   \n",
       "35                                           Finance       EBCF14   \n",
       "30                             Environmental Science      EBCES12   \n",
       "44                       Immunology and Microbiology      EBCIM14   \n",
       "34                                           Finance       EBCF12   \n",
       "23                                            Energy      EBCEN14   \n",
       "8                                          Chemistry       EBCC14   \n",
       "69  Pharmacology, Toxicology and Pharmaceutical Sc...   EBCPTPS14   \n",
       "58                                      Neuroscience       EBCN14   \n",
       "63                                        Psychology       EBCP14   \n",
       "\n",
       "    FirstUsageDate  Pct 0  \n",
       "39            2014   0.31  \n",
       "35            2014   0.31  \n",
       "30            2014   0.27  \n",
       "44            2014   0.21  \n",
       "34            2012   0.17  \n",
       "23            2014   0.16  \n",
       "8             2014   0.16  \n",
       "69            2014   0.15  \n",
       "58            2014   0.14  \n",
       "63            2014   0.13  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# limit to 2012-2014\n",
    "groups[['Package', 'PackageCode', 'FirstUsageDate', 'Pct 0']].loc[groups['FirstUsageDate'].isin([2012,2013,2014])].sort_values(by='Pct 0', ascending=False).round(2).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize use categories for each package across purchase years  \n",
    "1. Create some basic stacked bar charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 87 entries, EBCABS14 to EBVM08\n",
      "Data columns (total 6 columns):\n",
      "Package     87 non-null object\n",
      "Pct 0       87 non-null float64\n",
      "Pct 1       87 non-null float64\n",
      "Pct 2-4     87 non-null float64\n",
      "Pct 5-50    87 non-null float64\n",
      "Pct > 50    87 non-null float64\n",
      "dtypes: float64(5), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# subset for bar charts\n",
    "cols = ['Package', 'PackageCode', 'Pct 0', 'Pct 1', 'Pct 2-4', 'Pct 5-50', 'Pct > 50']\n",
    "charts = groups.filter(cols).set_index(['PackageCode'])\n",
    "charts.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** We kept the percentage columns (i.e. Pct 0, Pct 1) to make the stacked bar charts total 100% **  \n",
    "** We'll rename them so they make more sense in the legend **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "charts.columns = [c.replace('Pct ', '') for c in charts.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89792128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89887b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89774978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea899d16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89dfb5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89ea56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89c67160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89ab1b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89481198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea88fc5668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea8971f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea8986f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4KUaf+O+VWTfhf6G3KSPNMZUsR2WEmiVUWGvn3/D9rP/1MkkXyGsu9q1zrry26FUVWh9fljH+Zwdhnsu1d5ucGC2Bv5+W+zCpcjzmv59qZjFt16rcz1IH1cS+UJ7QPlLWMStV4riN4XwY83EaZdortTeYr8y5KXQuHF3ZeQLYF4H8ofGSvJNlE0U8SVGS/OD+96G05dwQW56Z/vtoMzupvIRmVi03bJlZUgVJQs0hymtSFO7v/vsYM+tVDWUIzd+09x6CA2LeEy8raip0ftjnr8I+vyAvyE9W+T0QRdtGoXVzipmdFpk+Gv/GyZX+1zOjzKOJvG5LD5bwG4BjEWpe80t59xpI+/67UZ1y/feekSP8NtO3VvcM/fsqXvS/jjOzaMfFr1X19sDT5F0sxEl6rqwncIb4x9ijVZxXXXLI94UKfOO/tzazvpEj/ZvoT4iWsYrnw9BxmqaqCfU69Vsza11uyr1m+u/9zOzi8hJW1+8VUFcRyB8CzrlV2ltbdpeZXRX6ETezLvIeRnKEvG7n7qjibJ6QV2sbJ+k18x73vuemN/Me232eeY/9vqGK84h0upl9bGZXmv/Yb39eKWZ2pbxaVcm7kSwWT8gLfOtJmu0/ujvFn2Z9MzvWzB6PeHT3t2b2F785RpKf1szsWEkP+Wk+d87lhM/I9j5uPLuSy3yfpGXmPSL9GL+3ndCjxjuY2Z2SHvTTfiWvRxtJknNui7wHnUjSpWb2vJnt6QrPzJLNbKCZTZHXTV+4N/2XSXrRzK43szQ/X5KZ9TSz+80sssek5/3328xspHk9HMnM+kt6R9V0gVOGpfL6zG9sZhXWvPndNn4lL6DoLu/fjKfLy3MA2/E//vskM/tpqGbar8meLalpJacXqzvl3XR5pKRXzKyDP9/6ZjZO3gN7csvJXyb/wu0ceTW6HSXNM7ObQ/Pw55PkL+90ef8Udok+tcNKTe0LUfm/F6GuMmeaWU+/PIlm9nN5nQvklJG9KufDUFe6A82scxWKfLe8G7SbSvrAP8+E5t3Qr/x4zsz2/MPgd6EbuoF9upndbmZ7mpSZWbqZnWlm/1T0bkwBhLha0Jl9kF6q+pNdU7T3cfROXpAS/gjrXYr+QJzBquChOmFpm2vvUzBDD9HZqn0fHb/fg1rClim7nGm/56cZEzZsVMR0d2r/x4G/rv2fpLhSUR6E4o9rq71PxnSK/ojxwWHpt0Wk3eKv29CwTYrycKmw8WUucxnr4eOIZS7xy7c7YvhCRXmUuT+N2yKWJ9+fRknYsBVR8qWFbYfweYfnGxORJ13S9xH7WeiR7Kvk3aQcdf8qbztVYns+GTbvbX7alZLOKWNa14Wl/0cM26Oq27Gjv2+E8heErZedkk4JG5cVkXdmRfNUlOMlbNwv/X01NP0ceRc8oRr7JyuafgXL1k3eBVH4/rhL+x9HeZKurc5trtgeZlTuPMqbRjnbJFsVPNSorO12gPtChfMtoyxZYdP8aZTxx/nzDt9WoQdavSWvwme/+aoK50N5vfiEHkhYKu8BbSv9V5uwdFHXgT+up6TVYWkif9+irbsG2vt01/BzRG7EsBlVOQ548TpcXtTIHyLOawM8TF5Thg/knaRT5AVTf5PU0zn3zwOcx0Z57dEvkNen70Z5DyAyeU+SfEJeO8a/HMh8wvxX3sNAnpQXfO+U90juLfJqey+RNMI5V1zmFPZfhtXy2gj/Wt5FSZ689fSDvB+wK7W3tkrymozcKa8Ge5285d0t7/Hyd0nq7iIeLhVe86Py+wKP5iR5FzAPyfsHZKu8ZS6R90P2mqTLJf3EldHTgnPuDnk9izwmr9ba5P2orZdX6/4reT/kkfm2ybuX4hJ563erv7zr5XWDOU7SqxF5cuT1yPGYvPUTJ2/7PCTv+Qbh3Z4eDNfI2z6L5f3T0t5/NSwjfXg3k+Xe5Hog29E5t1zesxeelnecxMsLIp6RdIxz7u3KTK+S835OXtOI1/15Jsm78Bsn78ZLd4DTXyTpaHn3yzwt70KuSN4+tkFeDfSN8p6UO/VA5lUX1NC+EP5P2H49MjnnPpU0UN7DqLbJ65p3ibwmmKcr7Eb2CJU+Hzrv/qSh8prIrJV38R86TmPqoto59428f9Fuk3e/VoG8JoTL5f2DcJ4izjXOa7t/lqQz5B33a+V1y5sk78Li/+T9w3RtLGUADlfm3AH9ZgCBY2a/lHdj5TznXFVvLMRBYGYXyAuo1kpq78q5X4TtiKDy27mHmt118i8mAKDSqJHH4ein/vvtNVoKRBPqqnR6eUG8j+2IoAo99Xm7pBU1WRAAwUaNPA47ZrZAUqFzrk9NlwV7mdnl8pqZFUrq6Jwr7+mnbEcEjn+z+any/nVKk3fBennNlgpAkBHIA6gxfk8Wc+TdZxDqZelPzrkJZecCgsfMXpI0XHvbx6+T1wa/3AtWAChPTDeyAMBBkiDvprpSeU0MHpfXnR1Q14QuVFfIu6n9zwTxAA5Ura+Rb9q0qcvKyqrpYgAAAFRo3rx5m51zzWq6HDg81Poa+aysLM2dO7emiwEAAFAhM1tV02XA4YNeawAAAIAAIpAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIAI5AEAAIAAIpAHAAAAAiimQN7M2pjZQ2b2sZntNDNnZlkx5o0zs1vMbKWZ7TKz+WY2+kAKDQAAABzuYq2RP0LSuZJyJH1QyXn8SVK2pIclDZP0iaQXzOz0Sk4HAAAAgC8hxnT/c861kCQzu0LSKbFkMrPmkn4n6S7n3H3+4HfN7AhJd0l6o5LlBQAAAKAYa+Sdc6VVnP6pkpIkPR0x/GlJPc2sQxWnCwAAABzWDvbNrt0lFUpaFjF8gf9+1EGePwAAAFAnxdq0pqoyJG1zzrmI4VvDxu/HzK6SdJUktWvX7uCVDnVK6Y9daroIAOqguMwlNV0EAIjqYNfIm6TIID40vEzOucecc/2cc/2aNWt2cEoGAAAABNjBDuS3Sko3s8jAPT1sPAAAAIBKOtiB/AJJ9SR1ihgeahu/8CDPHwAAAKiTDnYg/29JuyVdEDH8QknfOudWHOT5AwAAAHVSzDe7mtk5/se+/vswM9skaZNz7n0/TbGkJ51zl0uSc26jmf1V0i1mlifpC0m/kDRE0pnVtAwAAADAYacyvda8EPF9qv/+vqTB/ud4/xXuVkk7JN0gKVPSYknnOuf+VamSAgAAANgj5kDeOVduTzNlpXHOlUi6w38BAAAAqAYHu408AAAAgIOAQB4AAAAIIAJ5AAAAIIAI5AEAAIAAIpAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIASaroAQHW59IdBNV0EAHXQk5k1XQIAiI4aeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIAI5AEAAIAAIpAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIAI5AEAAIAAIpAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIAI5AEAAIAAIpAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIAI5AEAAIAAIpAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAKIQB4AAAAIIAJ5AAAAIIAI5AEAAIAAiimQN7O2ZvYPM8s1s+1m9pKZtYsxbzsze9LMfjCznWa2xMzuMLMGB1Z0AAAA4PCVUFECM0uR9F9JhZIukeQk3SHpXTPr5ZzLLydvA0nvSEqU9AdJP0g6RtLtkjpL+sWBLgAAAABwOKowkJd0paSOkro655ZJkpl9LWmppKslTSon7wnyAvZTnXNv+8PeNbMMSb8zsxTn3M4qlx4AAAA4TMXStGakpE9CQbwkOedWSPpQ0pkV5E3y37dHDN/mz9tiLCcAAACAMLEE8t0lfRtl+AJJR1WQ9x15Nfd3m9lRZtbQzIZIukHStPKa5QAAAAAoWyyBfIaknCjDt0pKLy+jc26XpIH+fBZIypM0W9JrksaWlc/MrjKzuWY2d9OmTTEUEQAAADi8xNr9pIsyrMJmMWaWLGmWpOaSLpL0U0m/l3eT65QyZ+bcY865fs65fs2aNYuxiAAAAMDhI5abXXPk1cpHSlf0mvpwl0saLOkI59z3/rD/mVmupMfMbJpzbn6shQUAAADgiaVGfoG8dvKRjpK0sIK8PSXlhAXxIZ/570fGMH8AAAAAEWIJ5F+V1N/MOoYGmFmWvK4lX60g74+S0s3siIjhx/nva2MrJgAAAIBwsQTyj0taKemfZnammY2U9E9JqyU9GkpkZu3NrNjMJoTlnSnvBtc3zOwSMzvJzH4v6T5J8+R1YQkAAACgkioM5P0uIodIWiLpKUnPSFohaYhzbkdYUpMUHz5N59xKSf0lfSXvabBvyHvA1GOSTnbOlVbLUgAAAACHmVhudpVz7gdJoytIs1JRerJxzi2UdG5VCgcAAAAguli7nwQAAABQixDIAwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABlFDTBQAAADiczJs3b1hiYuJNzrksSVbT5UGt5MxsZVFR0T19+/Z9s6xEBPIAAACHyLx58zrXq1fvgaysrOIGDRpsMSOOx/6cc8rPz2+xcuXKB+bNm7esb9++S6Olo2kNAADAIZKQkPD/MjMzrWHDhjsJ4lEWM1PDhg13ZmZmxiUkJNxSVjoCeQAAgEPEzHo3atQov6bLgWBo1KjRDjPrXdZ4AnkAAIBDxDmXlpiYWFzT5UAwJCYmFjvn0ssaTyAPAABw6MTRpAax8veVMuN1AnkAAAAggAjkAQAAgAAikAcAAKgFRo8endWiRYte0ca99tprqWbW95VXXkk91OWqTu+8806D008/vWPz5s17JSYm9klLS/vJgAEDOj/00ENNiosrd+vAU089lZadnd3iIBU1EAjkAQAAcNBNnDix+SmnnNJt27ZtCX/84x/XvPrqq0sefvjhlZ06dSq86aab2j333HNplZneK6+8kvbII48c1oE8D4QCAADAQfXmm282zM7ObnvxxRdvnDlz5urwcRdeeOG28ePHb9ixY0edq2AuKCiw+vXru4M1/Tq3wgAAAA4H06ZNyzjyyCOPSklJOTo1NfUnXbp0Oeree+9tGhr//vvvp5x22mkdW7Ro0Ss5OblPVlZWj7Fjx7besWPHPt3mFBcX69e//nWrZs2a9apfv/7R/fv37/Lll18mm1nfG2+8sVV42o8//rj+kCFDjmjUqNFPkpOT+/Tp06fbv//974YVlfWuu+7KbNy4cfHUqVPXRBvfvXv3wuOOO65AktatW5dw/vnnt8/KyupRv379ozMzM3uNGDGiw4oVKxJD6UePHp310ksvNdm4cWOimfU1s76tW7fuGRq/fv36hAsuuKBd8+bNeyUlJfXp0KFD9/vuu69p5HxfeeWV1COPPPKoevXq9WnXrl2PSZMmNR09enRW+LQkadWqVYlnnXWJXyAOAAAgAElEQVRWVnp6eu+kpKQ+Xbp0OWrq1KkZ4WkefPDBJmbW980332w4bNiwjqmpqT85+uijj5wwYUKLpKSkPuvWrdunAr20tFRt2rTpOWLEiA4Vrb+yUCMPAAAQMG+99VbDa6+9tsOYMWM23nnnnatLS0tt4cKFydu2bdsT261YsSKpV69eBZdccsmWRo0alXzzzTf177vvvlYrV66s99prry0PpbvxxhtbPfzwwy2vvvrqH0855ZS8uXPnppx11llHRM5zzpw5KSeffHLXo446aucDDzywKiUlpfTRRx9tNnLkyC6zZ89eNGjQoJ3RylpcXKzPPvss9Wc/+9m2lJSUCmunN23aFF+vXr3S7OzsNS1atChevXp14oMPPpg5cODAbkuXLv02JSXFTZw4cf2WLVsSvv766wYvvPDCMklKTk4ulaStW7fGHX/88d0KCwvt5ptvXtepU6fCN998s/HNN9/cvrCwMO7WW2/dKEnz5s1LPvfcczv37Nkz/29/+9vywsJCu+eee1rm5eXFx8Xtrevevn173E9/+tOuubm58bfeeuvadu3a7X766aebXHfddR127twZ97vf/W5zePkvvfTSjqNGjdp69dVXf19cXGyDBg3Kv+eee1pPnTq1yR133LEhlO7ll19utHbt2qRp06btk78yCOQBAAAC5sMPP2yQmppaMn369D3NVM4+++zt4WnGjBmzTdI2yav9PeWUU3Y0atSoZOzYsR1+/PHH+MzMzJJNmzbF/+1vf2tx/vnnb3rkkUfWStJZZ521PTEx0d1+++1twqf3+9//vk3Lli13f/jhh0uSk5OdJI0ePTq3S5cu3W+//faW77zzzvfRyrp+/fqEXbt2xbVr1253LMvWu3fvwhkzZuxZruLiYg0ZMmRH586de/3jH/9ofPHFF2/r3r17YZMmTYoTExPd0KFD93lS7p133tli/fr1SXPnzl3Qs2fPQkkaNWpUXm5ubvx9993X8qabbtqYmJio7Ozslg0aNCh57733lqamppZK0sknn7yjS5cuPZs2bVoUmt7DDz/cZNWqVfX+9a9/LTnjjDPyJOncc8/dPmDAgMS//OUvrceNG7c5IWFvSD18+PCcadOm7fPPw/Dhw7f+/e9/bzZx4sQNoYuERx99tFlWVtau0DSrgqY1AAAAAXPcccflb9++Pf7MM8/s8OyzzzbevHlzfGSarVu3xv3qV79q3bZt2x716tXrk5SU1Pe6667r4JzTggULkiVp7ty59QsKCuJ+8Ytf5ITnveCCC/b5vmPHDvv8889TR44cmRMfH++KiopUVFQk55wGDRq0/fPPP6/W3nTuvvvuZl27dj0qJSXl6MTExL6dO3fuJUmLFi1Krijv7NmzG/fq1Su/W7duhaFyFhUV6dRTT92+bdu2hC+++KK+JH355ZcNTzrppNxQEC9J7du3Lzr66KN3hE9vzpw5qc2bNy+KDLh/+ctfbsnJydkzvZBzzjlnn3UnSWPHjt24evXqeq+++mqq5DXV+e9//9t4zJgxmyqzXiJRIw8AAFALJCQkuNLS0qjjSkpKQmkkScOHD98xffr05VOnTm1+0UUXdZKkY489dsdf//rX1aG25uedd16Hjz76KPWmm25a16dPn52pqamlH330UYNbbrmlXUFBQZwkrVmzJlGSWrZsWRQ+vzZt2uzzfdOmTQklJSV64IEHWj7wwAMtyypjfPx+1xPKzMwsTk5OLv3hhx+SYlkPf/7zn5vfdtttba+44ooNw4YN296kSZPikpISGzp0aLddu3ZVWAm9ZcuWhB9++KFeUlJS32jjN27cmCBJmzdvTmzWrNl+fV42a9asaPXq1fVC37dt25bQrFmzosh0rVq1KpK8pkDhwyPXnSSddNJJO7t3775z2rRpzUaNGpX38MMPN01ISNA111yzpaLlKQ+BPAAAQC3QrFmz4pycnIRdu3ZZqOlKSCjgDgWPknTppZfmXHrppTm5ublxb7zxRuof/vCHNiNGjOi8fv36rwsLC2327NlpN95447o//OEPG0N5vvzyy31qj0NB5/r16xMl7YqcX0iTJk1K4uLidNFFF2287LLLogaf0YJ4SUpMTNSxxx6bN2fOnEax9OLy4osvZhx//PF5jz/++J7mKYsWLYrpIkCS0tLSips0aVI8efLkH6KN79mz5y5Jatq0adGmTZv2i4U3bdq0z7KnpaUVL1++fL9/AtatW5coSc2bN9/nYsDMIpNKkq644oqNv/vd79qvWLEi8Zlnnmk6bNiwrS1atCiJdbmioWkNAABALTB06NC8kpISe/bZZ/frT/3ll19Ob9asWVHv3r13RY5r3Lhx6XnnnZc7ZsyYTZs2bUrcsGFDQkFBQVxJSYkSExP3CZqffvrpfXpu6devX0H9+vVLZ82alR6Rbp/vjRo1Ku3bt++OBQsWpJxwwgk7TzzxxP1e5S3b+PHjf8zNzU341a9+1Sba+EWLFiV9+umn9SWpoKAgLiEhYZ9yT5s2bb8eZ+rVq+cKCwv3i2WHDh26ffny5cmdOnXaHa2c6enppZJ09NFH73j33Xcb5+Xl7ZnGqlWrEr/44ot9euEZNGhQ3oYNGxLffvvtBuHDZ82alZGRkVF89NFH77dNorniiiu2NmjQoPTcc8/tuH79+qRrr732gJrVSNTIAwAA1Apnnnnm9gEDBmy/7rrrsr777rvk448/Pn/79u1xs2bNypg9e3baAw88sDJU6z1u3LhWGzduTBw8ePD2tm3bFq1atSrpsccea96tW7eCVq1aFUtS79698x955JEWLVu2LGrWrFnxjBkzmmzYsGGf2uZmzZqVXHHFFRsefvjhlqmpqSWhXmueeeaZppIUFxe3J6CeNGnS6lNOOaXroEGDOo8ZM2Zz69atizZt2pQwb968BiUlJZo6deraspZt2LBhO7Kzs1dnZ2e3XbJkSfJFF120pUOHDru3bNkS/8477zR67rnnmj7++OMrjjvuuIIhQ4bkPvLII5njx4/P7N+/f/4777zT6LXXXkuPnOaRRx5Z8Oyzzza9++67m/Xv3z+/fv367thjjy249dZbN7zyyivpJ5xwQrdrr712w5FHHrlrx44dcd99913yhx9+2HD27NnfS1J2dvb6/v37pw8ePLjzb37zmw27du2ye+65p2WTJk2KwmvVr7vuui2PPvpoi/POO++I8F5rPvroo0b33nvvqvAbXcvTsGFD9/Of/3zzE0880aJz584FJ598cn7FucpHIA8AAFALxMXF6T//+c+y8ePHt3z++eebTJ48uWViYqLr1q3bzqeeeur7Cy+8cFsobf/+/fMffvjh5rfeemvb3NzchIyMjOITTzwx95577lkXSjNr1qzlV155Zfubb765Xb169UrPOOOMnDFjxqw+77zz9ulactKkSeucc/bss882nTFjRotevXrlP/744ytOOeWUbo0bN97T9GPgwIE758yZ892ECRNajR8/vt2OHTvi09PTi7t3777z6quvrrB2ecKECRuPP/74/EmTJrWYMGFCm5ycnIQGDRqU9ujRI//+++9fdd55522TpLvvvnvdtm3b4h999NEWDz30UNyxxx6b9+9//3vJkUceuU/f7jfccMPmzz77rMGf//zn1nl5efGtWrXavXbt2m+aNGlS8umnny4aP358qwceeCBz48aNiampqSUdOnTYdeaZZ+65EbVv3767nn/++WW33HJLm8suu6xj8+bNi2644Yb1b7/9duM1a9bsaSPfqFGj0vfff3/xDTfc0OZPf/pT6/z8/PisrKxdU6ZMWXHttddurcw2/uUvf5nzxBNPtLj00ksPuDZeksy5g/awqWrRr18/N3fu3JouBgLgks8ur+kiAKiDnjz2iZouAgLEzOY55/qVNX7+/Pkre/fuXeV+ww+V6dOnp19++eUd33zzzcWnnXbajopz1A25ublxnTp16jlkyJBtzz///Krqnv7111/f+oknnmi+Zs2a+RkZGdHvbI4wf/78pr17986KNo4aeQAAgMPYf//73wavvvpqY795Sunnn3+e8tBDD7Xs3bt3/imnnFKng/hLLrmk7YABA/LbtGmze82aNUlTp05tvn379vjf/va3GyvOHbsPP/yw/oIFC5KfeOKJ5ueff/6mWIP4ihDIAwAAHMYaNWpU8tFHH6XOnDmz+Y4dO+IyMjKKhw8fvvWBBx5YG/6E07qosLAwLjs7u/WWLVsSExMTXa9evfJfffXVJaEuPKvLueeee8SWLVsSBw4cmHvvvfeuqzhHbAjkAQAADmP9+vXb9dlnny2u6XLUhOeee67am89Es3bt2m8OxnTr9mUWAAAAUEcRyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEEP3IAwAA1FEnx/28b03O/z+lL8yrat5ly5Yljh07tu2HH37YyDlnAwYM2D5lypTVnTt33l2dZQwyauQBAABQq+Tl5cUNHTq06/fff19/6tSpK6dNm7Zi5cqV9YYMGdJl+/btxK8+auQBAABQq0yePLnpmjVr6s2fP//bHj16FEpSv379dvbo0aPnpEmTmmVnZ2+o6TLWBlzRAAAAoFZ544030nr37p0fCuIlqVu3bruPPvroHa+//npaTZatNiGQBwAAQK2ydOnS+t26dSuIHN61a9eCZcuWJddEmWqjmAJ5M2trZv8ws1wz225mL5lZu1hnYmZHmtkLZrbZzArMbLGZ3VD1YgMAAKCuys3NjU9LSyuOHJ6RkVGcl5dH03BfhSvCzFIk/VdSoaRLJDlJd0h618x6OefyK8jfz8//nqQrJOVK6iyp4QGVHAAAAHWWme03zDm3/8DDWCxXNFdK6iipq3NumSSZ2deSlkq6WtKksjKaWZykJyXNds6dFTbq3SqXGAAAAHVao0aNSnJycvaLU3NycuJTU1P3q6k/XMXStGakpE9CQbwkOedWSPpQ0pkV5B0s6SiVE+wDAAAA4Tp37lywePHi/drCL1mypP4RRxyxqybKVBvFEsh3l/RtlOEL5AXp5Rnovyeb2SdmVmRmG83sQTOrX5mCAgAA4PBw+umnb5s/f37DhQsXJoWGLV68OOmLL75ocPrpp2+rybLVJrEE8hmScqIM3yopvYK8rfz3WZLelnSypHvktZX/v7IymdlVZjbXzOZu2rQphiICAACgrhg3btzmVq1aFY4aNeqIp59+Ou2ZZ55pPHLkyCMyMzOLbrzxRoJDX6x3/boow2K52SB0ofC0c26C//k9M4uXdJeZHeWcW7jfzJx7TNJjktSvX79o8wYAAEAF/lP6wryaLkNVNGrUqHT27NlLxo4d2/aaa67p4JzT8ccfv33KlCmrGzduXFrT5astYgnkc+TVykdKV/Sa+nBb/Pf/RAx/W9Jdkn4iab9AHgAAAIe3zp07737rrbe+r+ly1GaxNK1ZIK+dfKSjVHEQvsB/j6xVD9Xmc0UFAAAAVEEsgfyrkvqbWcfQADPLknSCP648b8rrf/60iOGn+u9zYyolAAAAgH3EEsg/LmmlpH+a2ZlmNlLSPyWtlvRoKJGZtTezYjMLtYWXc26LpDslXWNmfzGzn5nZeEkTJD0Z3qUlAAAAgNhV2EbeOZdvZkMk/VXSU/KaxcyWNM45tyMsqUmK1/4XBxMl5Um6VtLvJK2XdK+kPx1w6QEAAIDDVEy91jjnfpA0uoI0KxWlJxvnnJP3QCgeCgUAAABUk1ia1gAAAACoZQjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCAYuq1BgAAAMEzcPR9fWty/nNe/N28qub9/vvvEydOnJg5f/78BosXL66/a9euuEWLFn3TtWvX3dVZxiCjRh4AAAC1znfffZf82muvZTRu3Li4b9++OyrOcfghkAcAAECtM2zYsLwtW7bMf//995edffbZOTVdntqIQB4AAAC1Tnx8fE0XodYjkAcAAAACiJtdUWd88MlRNV0EAHXRsTVdAACIjhp5AAAAIIAI5AEAAIAAIpAHAAAAAohAHgAAAAggbnYFAABArTRjxox0SZo3b16KJL3yyiuNmzdvXty8efOi4cOHH/YPiSKQBwAAqKPmvPi7eTVdhgNx2WWXdQz/Pn78+HaSdMwxx+wYPnz44popVe1BIA8AAIBayTkX6AuRg4028gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAGUUNMFAAAAwMHR8cH7+9bk/Jf/+rfzqpJvxowZ6c8991zGN998k7J169bEzMzM3cOHD8+544471qenp5fGOp3CwkLr2bPnkUuXLq1///33r7rxxhs3V6U8tRWBPAAAAGqVyZMnt2jduvXuP/zhD2vbtWu3e968eSn33ntvqzlz5qR+8cUXi+Lj42OaTnZ2doucnJw6G+/W2QUDAABAML355pvLWrVqVRz6Pnz48B0ZGRkl119/fdbrr7+eOnLkyLyKprFw4cKkyZMnt5w0adKqa6+9tsPBLXHNoI08AAAAapXwID5kwIAB+ZK0evXqxFimcdVVV7UfMWLE1iFDhuyo7vLVFtTIAwAAoNZ75513UiWpZ8+euypK+8gjj2R8++23DV544YUV27dvr7MV13V2wQAAAFA3rFixIvGuu+5qdfzxx28/8cQTd5aXdtOmTfG33XZb2wkTJqxp2bLlfjX7dQk18gAAAKi1cnNz40aMGHFEQkKCe+qpp1aGhhcVFe2TLjHRa3EzduzYNu3atSscN25cneqhJhoCeQAAANRKO3futFNPPfWI1atX1/vPf/6zuFOnTkWStHjx4qRu3br1DE+7aNGib9auXZv44osvNv3nP/+5eOvWrfGStG3btnhJKigoiNu8eXN8RkZGSVxc3WiUQiAPAACAWqewsNBOP/30Tl9//XWDf/3rX0uOPfbYgtC49u3bF73//vvfhadv37590VtvvZVaUlKiM844o2vk9G677ba2t912W9tNmzZ91bRp05JDsQwHG4E8AAAAapWSkhKdddZZHT7++ONGzz///NKhQ4fmh49PTk520drKjxo1Krdjx45LwoetW7cu4eqrr+541VVXbRgxYkRu48aN60QQLxHIAwAAoJa5+OKL27355pvp119//fqGDRuWzp49u0FoXFZW1u5QE5tI7dq1K27Xrt0+fcwvXrw4SZK6du2664wzzqiw//kgIZAHAACoo5b/+rfzaroMVfHee+81lqSHHnqo5UMPPdQyfNxvfvOb9ZMmTVpXMyWrXQjkAQAAUKusXbv2m+qaVteuXXc75wJ5QVORunHLLgAAAHCYIZAHAAAAAohAHgAAAAggAnkAAAAggAjkAQAAgAAikAcAAAACiEAeAAAACCACeQAAACCACOQBAACAACKQBwAAAAIooaYLAAAAgIPjks8u71uT83/y2CfmVSXfa6+9ljpixIgukcMbNmxYkpeX91V5eR988MEmN9xwQ1bk8K5duxYsWrRoYfiwZcuWJY4dO7bthx9+2Mg5ZwMGDNg+ZcqU1Z07d95dlXIfagTyAAAAqJXuuOOO1f37988PfU9MTHSx5p0+ffrydu3a7QnIU1NTS8PH5+XlxQ0dOrRrUlKSmzp16koz08SJE1sNGTKky4IFCxY2atSodP+p1i4E8gAAAKiVunfvXjB06ND8ilPu75hjjtnZo0ePwrLGT548uemaNWvqzZ8//9tQun79+u3s0aNHz0mTJjXLzs7eUNVyHyq0kQcAAMBh54033kjr3bt3fniw361bt91HH330jtdffz2tJssWKwJ5AAAA1EqXX355x/j4+L5paWk/GTFiRIelS5cmxZp38ODB3eLj4/s2a9as1/nnn99uw4YN8eHjly5dWr9bt24Fkfm6du1asGzZsuTqKP/BRtMaAAAA1Crp6enFV1555YbBgwfnNW7cuGTevHkpkydPbnnCCSekfvnllwtbt25dXFbe1q1bF40bN259//7981NSUko/+OCDhlOmTMmcO3duw6+++uq7lJQUJ0m5ubnxaWlp+00nIyOjOC8vLxAxciAKCQAAgMPHCSecUHDCCSesCX0fPnz4jiFDhuwYPHjwkXfffXfzBx98cF1RUdE+eRITEyVJo0eP3j569OjtoeEjRozI69WrV8FFF13Uadq0aU1uvPHGzaFxZrbfvJ1z+w+spWhaAwAAgFpv4MCBO9u3b7/riy++aLB48eKkpKSkvuGvxYsXl9ns5vzzz99Wv3790rlz5zYIDWvUqFFJTk7OfpXaOTk58ampqWXW+Ncm1MgDAAAgEJxzMjO1b9++6P333/8ufFz79u2LysoXPonQh86dOxcsXrx4v7bwS5YsqX/EEUfsqobiHnTUyAMAAKDW+9///peyatWq5H79+uUnJye7E088cWf4Kzk5ucw+5p9++um0goKCuOOOO25PV5ann376tvnz5zdcuHDhnpr8xYsXJ33xxRcNTj/99G0He3mqAzXyAAAAqFVGjhzZISsrq7Bv374709PTS+bNm5fy4IMPZjZv3nz3TTfdtLG8vAMGDOh84okn5vXs2bMgJSXFffDBBw2nTZvWomvXrgVXXXXV1lC6cePGbf7b3/7WfNSoUUdMmDBhnZm5iRMnts7MzCy68cYbNx38pTxwBPIAAAB11JPHPjGvpstQFd27dy948cUXM2bMmNG8oKAgrmnTpsWnnXbatrvuumtdy5Yty22/3q1bt13PP/98k4ceeihp9+7dlpmZufvCCy/cdOedd66vX7/+nlr7Ro0alc6ePXvJ2LFj215zzTUdnHM6/vjjt0+ZMmV148aNa/1TXSUCeQAAANQyd95554933nnnj1XJO3369NWxpu3cufPut9566/uqzKc2oI08AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQDEF8mbW1sz+YWa5ZrbdzF4ys3aVnZmZ3WJmzszmVL6oAAAAAEIqDOTNLEXSfyV1k3SJpIskdZb0rpk1iHVGZtZR0q2Syu3EHwAAAEDFYulH/kpJHSV1dc4tkyQz+1rSUklXS5oU47wekfSMpK4xzhcAAABAGWJpWjNS0iehIF6SnHMrJH0o6cxYZmJm50vqI+mWqhQSAAAAwL5iCeS7S/o2yvAFko6qKLOZpUv6q6SbnHNbK1c8AAAAANHE0sQlQ1JOlOFbJaXHkP9eSUskzYy9WAAAADhQpT926VuT84/LXDKvJucvSaNHj8566aWXmkQOv/TSSzdOnz59dfiwt956q+H48ePbfPfddykNGzYsGTVq1JbJkyevbdiwoTt0JY5drG3VoxXeKspkZoMkXSypj3Mu5hVgZldJukqS2rWrdOc4AAAAwB7p6enFL7zwwrLwYW3bti0K//7pp5/WP/PMM7sMHDgwd9asWUu///77etnZ2W3Wr1+f9Prrry8/tCWOTSyBfI68WvlI6YpeUx/uUUlPSFpjZmlh84z3vxc45wojMznnHpP0mCT169evVl4BAQAA4ODYsGFDvHNOmZmZJdUxvcTERDd06ND88tLcdtttrVq0aLH79ddfX16vXj0nKS8pKcldf/31WXPmzEkZOHDgzuooS3WKpY38Annt5CMdJWlhBXmPlHSNvIA/9DpBUn//869iLikAAAAOC59//nlK27Zte//sZz/rNHPmzLSCgoIKW4IciMLCQvvggw8ajxw5MscP4iVJl1122dbExET34osvppWXv6bEEsi/Kqm/3w+8JMnMsuQF5K9WkPekKK/58m6ePUnSPypdYgAAANRpQ4YM2TF58uSVu3btirviiis6ZWZm9j7//PPbvf322zE/wyjc1q1bE9LT03snJCT0zcrK6nHrrbdmFhcX7xm/cOHCeoWFhdajR4+C8HwpKSmubdu2hYsWLUo+wEU6KGJpWvO4pLGS/mlmt8lrL/8nSavlNZ2RJJlZe0nfS5ronJsoSc659yInZmbbJCVEGwcAAACkpKS46667but11123dfXq1QkzZszIeP7555uceuqpzdq0aVN4zjnnbL388su39OjRY78m2pF69+69s0+fPjt79uxZUFBQEPfyyy+n3Xnnna2XLVtWb9asWaskafPmzfGS1KRJk+LI/GlpacXbtm2rlc9AqrBQzrl8MxsirwvJp+Td5Dpb0jjn3I6wpCYpXrHV8gMAAAAVatu2bfGECRM2TpgwYeP8+fPrTZ8+vckLL7zQZPLkyS1//vOfb37++edXlZd/woQJG8O//+IXv8ht0KBB6fTp05vfdtttP/bs2bMw1CeL2f4teJxzpugdv9S4mIJu59wPzrnRzrlGzrlU59wo59zKiDQrnXPmnMuuYFqDnXMDq15kAAAAHI62bt0an5ubG19QUBAXHx+vlJSU0qpM58ILL9wiSR999FEDSWratGmJJG3evHm/Su7c3Nz4tLS0/Wrqa4Na+TcBAAAAIEnffvttvRkzZmS8+OKLTVatWlWvW7duBddff/2Pl1122ZZ27dpVKcD2a9llZk6SjjrqqMKkpCS3YMGCfdrC79y509asWVNv5MiRFfXUWCMI5AEAAFCr5Obmxk2ZMqXJrFmzmnz99dcNmjdvXnT22Wdvueyyy7Ycc8wxuw50+k899VSGmemEE07YKUnJyclu0KBBua+++mrG/fffvy4xMVGSNHPmzPTdu3fb2Wefve1A53kwEMgDAACgVvnggw8a/PnPf25z6qmn5tx+++1rR4wYkRcfH1/p6SxZsiTp/PPP73DOOeds7dKlS+GuXbvs5ZdfTn/xxRebnHfeeZu6d+++52bZiRMnrhsyZMiRw4cP73TddddtXL58eb3s7Ow2p512Ws6gQYNqXR/yEoE8AABAnRWXuWReTZehKgYMGJD/448/zk9NTa1SG/iQxg1TlqMAABAaSURBVI0bl6SlpRU/+OCDmVu2bEk0M3Xs2LHgjjvu+OHmm2/eFDHPgpdffnnJLbfc0ubcc8/t3LBhw5LRo0dveeCBB9Ye2NIcPATyAAAAqFUyMjIOKIAPadGiRck777zzfazphw0btmPYsGGLqmPehwKBPOqMVu/Xyp6hAATdr2u6AAAQHX2+AwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAIdOaWlpqdV0IRAM/r5S5o2/BPIAAACHiJn9WFBQkFxxSkAqKChINrMfyxpPIA8AAHCIFBcX375y5cqk/Pz8+tTMoyylpaWWn59ff+XKlUnFxcW3l5WO7icBAAAOkT59+rz1xRdfjP3+++//6JzLFJWqiK7UzH4sLi6+vU+fPm+VlYhAHgAA4BDyA7MygzMgVlwFAgAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEUEJNFwCoLvVf/rSmiwAAAHDIUCMPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAAAAQAARyAMAAAABRCAPAAAABBCBPAAAABBABPIAAABAABHIAwAAAAFEIA8AAAAEEIE8AAAAEEAE8gAAAEAAEcgDAAAAAUQgDwAAAAQQgTwAAAAQQATyAID/397dxmhalXcA/1/sIm+2Lio0EZYFIoWCpYQsDYWUWowFE4UPDS0QKWLColGxsTGhxZKG0mpLgtL2g9JaQ9HUEooBS0JRINgQbUpJS0CrIq9N0G55Z7Ug4fTD80wyDLM7z7w+e+75/ZLJzHPu68xz7Zdn/3Ny7nMD0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRooiBfVZur6oaqeraqnquqG6vqkAnmba2qa6rqv6rqx1X1WFV9qaoOW37rAACwfi0Y5Ktq3yR3JDkqyflJzktyRJI7q2q/BaafneSYJH+R5F1JLklyfJJ7qmrzMvoGAIB1beMENRcmOTzJka21B5Okqu5L8v0kFyW5ahdz/6y1tn32QFXdneTh8e+9bClNAwDAejfJ1pozknxrJsQnSWvt4SR3JzlzVxPnhvjx2KNJtic5aHGtAgAAMyYJ8sckuX+e8QeSHL3YN6yqX0hyYJLvLHYuAAAwMkmQf2OSp+cZfyrJ/ot5s6ramOSzGa3If34Xdduq6p6qumf79tcs6gMAwLo36fGTbZ6xWsL7/VWSk5K8t7U23x8Hozdr7ZrW2tbW2tYDDjhgCW8DAADDNsnNrk9ntCo/1/6Zf6V+XlX1ySTbkpzfWrtt0nkAAMBrTRLkH8hon/xcRyf59iRvUlWXZnT05MWttesmbw8AAJjPJFtrbk5yYlUdPjNQVYcmOXl8bZeq6uIkVyS5tLX2l0trEwAAmG2SIP/XSR5JclNVnVlVZyS5KcnjST43U1RVW6rq5aq6bNbY2Uk+k+TWJHdU1YmzvhZ94g0AADCy4Naa1tqOqjo1yaeTXJfRTa63J/nd1toLs0oryYa8+o+D08fjp4+/ZrsryduX3DkAAKxjk+yRT2vtsSS/uUDNI5lzkk1r7X1J3re01gAAgJ2Z9PhJAABgNyLIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRIkAcAgA4J8gAA0CFBHgAAOiTIAwBAhwR5AADokCAPAAAdEuQBAKBDgjwAAHRooiBfVZur6oaqeraqnquqG6vqkAnn7l1VV1bVE1X1k6r6ZlWdsry2AQBgfVswyFfVvknuSHJUkvOTnJfkiCR3VtV+E7zH55NcmOSyJO9O8kSSf66q45baNAAArHcbJ6i5MMnhSY5srT2YJFV1X5LvJ7koyVU7m1hVv5Tk3CTvb619YTx2V5IHklye5IxldQ8AAOvUJFtrzkjyrZkQnySttYeT3J3kzAnm/jTJP8ya+3KSLyc5rar2WnTHAADAREH+mCT3zzP+QJKjJ5j7cGvtx/PMfV2St07w/gAAwByTbK15Y5Kn5xl/Ksn+y5g7c/01qmpbkm3jly9U1Xcn6BNgMd6c5H+n3QS7v6qadgv0Zcu0G2D9mCTIJ0mbZ2yST7ZaytzW2jVJrpng9wMsSVXd01rbOu0+AGCpJtla83TmXznfP/Ovts/21C7mzlwHAAAWaZIg/0BGe93nOjrJtyeYe9j4CMu5c19K8uBrpwAAAAuZJMjfnOTEqjp8ZqCqDk1y8vjaQnP3THLWrLkbk/x2kttaay8usl+AlWL7HgBdq9bm28I+q2D00Kf/TPKTJJ/IaM/7Hyf5mSTHttZeGNdtSfKDJJe31i6fNf/LSU5L8vEkDyf5YEYPhjqptXbvSv+DAABgPVhwRb61tiPJqUm+l+S6JF/KKJCfOhPixyrJhnl+5wVJvpDkiiS3JNmc5HQhHgAAlm7BFXkAAGD3M8keeQAAYDcjyAMAQIcEeQAA6JAgDwAAHRLkgXWpqvauqqtmPyMDAHoiyAPr1V5JPprkoGk3AgBL4fhJYLCq6rFdXc4oxG9P8mKS1lrbsiaNAcAK2DjtBgBW0cFJnkhy2zzXXpfknCT3JvnhWjYFACvBijwwWFV1TpKrkvxHkg+11h6adW1TkqeSvL219o0ptQgAS2aPPDBYrbW/T3JUkseT3FdVl1XV62YuT68zAFg+QR4YtNbas621bUlOS/JbSe6vqndOuS0AWDZBHlgXWmt3Jzkuyd8luSnJF2NVHoCOCfLAutFae7m1dkWSY5NsSPJYkv+bblcAsDRudgUAgA5ZkQcAgA4J8sC6V1WnV9VDC1cCwO5DkAdI9kviqa4AdMWTXYHBqqpTJiw9ZlUbAYBV4GZXYLCq6pVMdsRkJWmttQ2r3BIArBgr8sCQPZ/ktiSfXaDulCSfWP12AGDlCPLAkN2b5A2ttdt3VVRVm9aoHwBYMW52BYbs35McP0HdjoweDgUA3bBHHhisqnp9kje11h6ddi8AsNIEeQAA6JCtNcC6V1V7VdVHp90HACyGIA+sC1X15qqqOWP7VNXvJXkkyVVTaQwAlkiQBwZrvNJ+dVW9kORHSZ6sqg+Or703yUNJrszoRtfTp9cpACye4yeBIbssyUeSfD2joygPS3J1VR2d5ENJvpdkW2vtq9NrEQCWxs2uwGBV1YNJbm2tfXjW2PuT/E2SryV5T2vtpWn1BwDLYWsNMGSbk3xlztiN4+9XCfEA9EyQB4ZszyTPzxmbeb19jXsBgBVljzwwdAdV1eGzXm+YNf7M7MLW2kNr1xYALI898sBgVdUrSeb7kKv5xltrG+apBYDdkhV5YMgumHYDALBarMgDAECHrMgD68b4ya5vGr98slnJAKBjTq0BBq+qTqmqW5I8l9ETXn+U5Pmq+qeq+rXpdgcAS2NrDTBoVXVJkj9J8nRGD4F6JKObXbckeWeS/ZP8YWvtT6fVIwAshSAPDFZV/UaSW5N8Jsnvt9ZenHN9rySfSnJxkne11m5b+y4BYGkEeWCwqurWJM+31s5aoO4fk+zXWjt9bToDgOWzRx4YshOSXDdB3bXjWgDohiAPDNm+SZ5ZsGpUs88q9wIAK0qQB4bs0STHT1C3Ncljq9wLAKwoQR4Ysq8kuaSqDtlZQVUdmuTj41oA6IabXYHBqqpNSe5J8oYkn0zy1YyOn0ySQ5OckeSSJM8m2dpam2QbDgDsFgR5YNCq6uAkX0xySpK5H3iV5F+SnNdas7UGgK4I8sC6UFW/nOQdSTZnFOAfT3J7a+1fp9oYACyRIA8AAB1ysyswWFV1eVW9Zc6Yzz0ABsF/aMCQXZrk4JkXVbUhyU+rapIjKQFgtybIA0NWE44BQHcEeQAA6JAgDwAAHdo47QYAVtl7qupt45/3yOgs+TOq6ri5ha21v13TzgBgGRw/CQxWVb2yiPLWWtuwas0AwAqzIg8M2WHTbgAAVosVeQAA6JCbXYHBqqq9quojVfUru6g5aVyz51r2BgDLZWsNMGTbkvxRkp/fRc13k9yc5KUkn1uDngBgRViRB4bsrCTXtNae3FnB+No1Sc5ds64AYAUI8sCQHZvkrgnqvjGuBYBuCPLAkO2dZMcEdTuS7LPKvQDAihLkgSH7YZIjJqg7YlwLAN0Q5IEhuzPJB6pqp59142sfSHLHmnUFACtAkAeG7M+T/GKS66vqwLkXx2PXj2uuXOPeAGBZPBAKGLSqOivJtUk2JPm3JI+OL21JsjXJK0l+p7V2w3Q6BIClEeSBwauqtyb5WJJ3JNk8Hn48ydeTfLq19uC0egOApRLkAQCgQ/bIAwBAhwR5YNCqaktVXVRVH66qLeOxX62qb1bVjqp6pKo+Nu0+AWCxbK0BBquqTkjytSQ/Ox7akeSsJDckeSjJd5K8LclRSc5prV0/jT4BYCkEeWCwquqWJAcmOTvJ80muTnJakltba+eOayqjIyjf0lo7eVq9AsBi2VoDDNnxST7VWvtBa+1/kvxBkk0ZHUeZJGmj1Yxrkxw5nRYBYGkEeWDIfi7Jf896/fj4+/Y5dduT7L8mHQHAChHkgaFrO/kZALq2cdoNAKyyrVX1+vHPe2QU5k+oqk2zao5e+7YAYHnc7AoMVlW9klFwr12UzVxvrbUNa9IYAKwAK/LAkP36tBsAgNViRR5Y96pqjySbWmtPTbsXAJiUm12Bwaqqp6rq+Fmvq6purqrD55SekNeeZAMAuzVBHhiyTXn1FsI9krx7PA4AXRPkAQCgQ4I8AAB0SJAHAIAOOX4SGLqDZt3cumHW2DOzag5e454AYNkcPwkM1qwHQr1qeGdjHggFQE+syANDdsG0GwCA1WJFHgAAOuRmVwAA6JAgDwAAHRLkAQCgQ4I8AAB06P8BcYEZ09fHa4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea895375c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89933cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89dab8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea8995e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89684240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea8956be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89be2b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89778320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea89813fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea8960a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea899de358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea8a0625f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hide a \"max open warning\" since we'll be looping over quite a few packages\n",
    "plt.rcParams.update({'figure.max_open_warning': 0})\n",
    "\n",
    "# Loop over each Package and create a stacked bar chart\n",
    "for title, pkg in charts.groupby(['Package']):\n",
    "    ax = pkg.plot(kind='bar', title=title, stacked=True, rot=90, colormap='viridis', figsize=(10,8), fontsize=16)\n",
    "    ax.set_title(title, fontsize=24)\n",
    "    ax.set_xlabel('')\n",
    "    leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=16)\n",
    "    leg.set_title('Usage Category',prop={'size':16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}