crunchbase-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bddac626-3cef-487c-afac-7ae484586416",
   "metadata": {},
   "source": [
    "# Crunchbase Company Profiles: An Analysis\n",
    "### 1. Introduction\n",
    "\n",
    "[Crunchbase](https://www.crunchbase.com/home) is the leading destination for company insights from early-stage startups to the Fortune 1000.\n",
    "\n",
    "The goal of this project is to analyze the organization profiles available on its platform via its daily CSV export, mainly the geographic data.\n",
    "\n",
    "The `organizations.csv` dataset explored in this project is from `Wed, Mar 15, 2023`. The latest dataset file can be downloaded Crunchbase's [Daily CSV Export](https://data.crunchbase.com/docs/daily-csv-export) page documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65442320-bf8a-45f9-a659-18b345761099",
   "metadata": {},
   "source": [
    "### 2. Download the organizations dataset\n",
    "\n",
    "After generating and obtaining an API key, downloading the dataset is as easy as just visiting the below URL in a browser:\n",
    "\n",
    "`https://api.crunchbase.com/odm/v4/odm.tar.gz?user_key=user_key`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9687c008-a198-4366-ace1-c8af866f7087",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "\n",
    "cb_file = 'data/crunchbase.tar.gz'\n",
    "\n",
    "if not os.path.exists(cb_file):\n",
    "    url = 'https://api.crunchbase.com/odm/v4/odm.tar.gz?user_key=' + os.environ.get('cb_api_key')\n",
    "    response = requests.get(url)\n",
    "    open(cb_file, 'wb').write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d789f1c-4cfe-4e21-a571-0713ef602608",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crunchbase.tar.gz\n"
     ]
    }
   ],
   "source": [
    "!ls -1 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69f2541f-3a64-469d-a421-8c5734f6047f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxr-xr-x 1 jovyan users 437M Mar 16 13:15 data/crunchbase.tar.gz\n"
     ]
    }
   ],
   "source": [
    "# check the file size\n",
    "!ls -lh data/crunchbase.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd8a1c17-da20-4138-b209-c89bc976c413",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "people.csv\n",
      "organizations.csv\n",
      "checksum.csv\n"
     ]
    }
   ],
   "source": [
    "# untar the file to the 'data' directory\n",
    "!tar -xvf data/crunchbase.tar.gz --directory data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31d21355-3dea-432a-b04d-2489f478fd84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxr-xr-x 1 jovyan users  210 Mar 15 20:19 data/checksum.csv\n",
      "-rwxr-xr-x 1 jovyan users 1.3G Mar 15 20:19 data/organizations.csv\n",
      "-rwxr-xr-x 1 jovyan users 394M Mar 15 20:15 data/people.csv\n"
     ]
    }
   ],
   "source": [
    "# check the file sizes\n",
    "!ls -lh data/*.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32353a80-9b87-4836-bde2-dfb479931d41",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filename,previous_check_sum,current_check_sum\n",
      "organizations.csv,28a9f3b94e26176acdbc224d815b3d34,b0b84b53c5882a70ced6a70366a07f03\n",
      "people.csv,1647b38fe8dc3febe53fd861bab1c26e,99aeb4099447e30fd813c0c69a587422\n"
     ]
    }
   ],
   "source": [
    "# view the checksum\n",
    "!cat data/checksum.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42b9c1a4-65fb-4edd-b648-91a350421923",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b0b84b53c5882a70ced6a70366a07f03  data/organizations.csv\n"
     ]
    }
   ],
   "source": [
    "# verify the checksum matches\n",
    "!md5sum data/organizations.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64879dbc-05cc-443c-bbc7-473e6ed9706e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data:\n",
      "crunchbase.tar.gz\n",
      "organizations.csv\n"
     ]
    }
   ],
   "source": [
    "# delete un-needed files\n",
    "!rm data/checksum.csv data/people.csv\n",
    "!ls -R1 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa36fc3b-e9b1-4231-9391-a14912278a87",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2502933 data/organizations.csv\n"
     ]
    }
   ],
   "source": [
    "# check to see approximately how many rows\n",
    "!wc -l data/organizations.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e6a965-3a85-4601-a3b1-07b5ba02afa7",
   "metadata": {},
   "source": [
    "### 3. Preload the dataset\n",
    "\n",
    "Let's see a preview of the first 3 rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb49bc45-f1d8-497f-91ba-b903b05b49d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>uuid</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>cb_url</th>\n",
       "      <th>domain</th>\n",
       "      <th>homepage_url</th>\n",
       "      <th>logo_url</th>\n",
       "      <th>facebook_url</th>\n",
       "      <th>twitter_url</th>\n",
       "      <th>linkedin_url</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>short_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e1393508-30ea-8a36-3f96-dd3226033abd</td>\n",
       "      <td>Wetpaint</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>https://www.crunchbase.com/organization/wetpai...</td>\n",
       "      <td>wetpaint.com</td>\n",
       "      <td>http://www.wetpaint.com/</td>\n",
       "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
       "      <td>https://www.facebook.com/Wetpaint</td>\n",
       "      <td>https://twitter.com/wetpainttv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>Wetpaint offers an online social publishing pl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bf4d7b0e-b34d-2fd8-d292-6049c4f7efc7</td>\n",
       "      <td>Zoho</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>https://www.crunchbase.com/organization/zoho?u...</td>\n",
       "      <td>zoho.com</td>\n",
       "      <td>https://www.zoho.com</td>\n",
       "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
       "      <td>http://www.facebook.com/zoho</td>\n",
       "      <td>http://twitter.com/zoho</td>\n",
       "      <td>https://www.linkedin.com/company/zoho</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>Zoho offers a suite of business, collaboration...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5f2b40b8-d1b3-d323-d81a-b7a8e89553d0</td>\n",
       "      <td>Digg</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>https://www.crunchbase.com/organization/digg?u...</td>\n",
       "      <td>digg.com</td>\n",
       "      <td>http://www.digg.com</td>\n",
       "      <td>https://res.cloudinary.com/crunchbase-producti...</td>\n",
       "      <td>http://www.facebook.com/digg</td>\n",
       "      <td>http://twitter.com/digg</td>\n",
       "      <td>http://www.linkedin.com/company/digg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>Digg Inc. operates a website that enables its ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   uuid      name          type primary_role  \\\n",
       "0  e1393508-30ea-8a36-3f96-dd3226033abd  Wetpaint  organization      company   \n",
       "1  bf4d7b0e-b34d-2fd8-d292-6049c4f7efc7      Zoho  organization      company   \n",
       "2  5f2b40b8-d1b3-d323-d81a-b7a8e89553d0      Digg  organization      company   \n",
       "\n",
       "                                              cb_url        domain  \\\n",
       "0  https://www.crunchbase.com/organization/wetpai...  wetpaint.com   \n",
       "1  https://www.crunchbase.com/organization/zoho?u...      zoho.com   \n",
       "2  https://www.crunchbase.com/organization/digg?u...      digg.com   \n",
       "\n",
       "               homepage_url  \\\n",
       "0  http://www.wetpaint.com/   \n",
       "1      https://www.zoho.com   \n",
       "2       http://www.digg.com   \n",
       "\n",
       "                                            logo_url  \\\n",
       "0  https://res.cloudinary.com/crunchbase-producti...   \n",
       "1  https://res.cloudinary.com/crunchbase-producti...   \n",
       "2  https://res.cloudinary.com/crunchbase-producti...   \n",
       "\n",
       "                        facebook_url                     twitter_url  \\\n",
       "0  https://www.facebook.com/Wetpaint  https://twitter.com/wetpainttv   \n",
       "1       http://www.facebook.com/zoho         http://twitter.com/zoho   \n",
       "2       http://www.facebook.com/digg         http://twitter.com/digg   \n",
       "\n",
       "                            linkedin_url  combined_stock_symbols        city  \\\n",
       "0                                    NaN                     NaN    New York   \n",
       "1  https://www.linkedin.com/company/zoho                     NaN  Pleasanton   \n",
       "2   http://www.linkedin.com/company/digg                     NaN    New York   \n",
       "\n",
       "       region country_code                                  short_description  \n",
       "0    New York          USA  Wetpaint offers an online social publishing pl...  \n",
       "1  California          USA  Zoho offers a suite of business, collaboration...  \n",
       "2    New York          USA  Digg Inc. operates a website that enables its ...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "first_3_rows = pd.read_csv('data/organizations.csv', nrows=3)\n",
    "\n",
    "first_3_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882609f2-4b58-4715-b9c4-4c189266a482",
   "metadata": {},
   "source": [
    "### 4. Select which columns to drop.\n",
    "\n",
    "For our analysis purposes, we can drop these 9 columns:\n",
    "\n",
    "```\n",
    "uuid\n",
    "cb_url\n",
    "domain\n",
    "homepage_url\n",
    "logo_url\n",
    "facebook_url\n",
    "twitter_url\n",
    "linkedin_url\n",
    "short_description\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dce6db7e-0129-4858-bbff-b746b9ae9ca6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name',\n",
       " 'type',\n",
       " 'primary_role',\n",
       " 'combined_stock_symbols',\n",
       " 'city',\n",
       " 'region',\n",
       " 'country_code']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_drop = ['uuid', 'cb_url', 'domain',\n",
    "           'homepage_url', 'logo_url', 'facebook_url', \n",
    "           'twitter_url', 'linkedin_url', 'short_description']\n",
    "\n",
    "to_keep = first_3_rows.columns.drop(to_drop).tolist()\n",
    "\n",
    "to_keep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65beb62f-995f-4425-89b7-fdec47bafd4a",
   "metadata": {},
   "source": [
    "### 5. Load the dataset\n",
    "\n",
    "The next step is to load the dataset into a dataframe with the columns above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5a7e8de-8e56-4749-8add-809435b355e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of entire dataset in MB is: 1011.74\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/organizations.csv', usecols=to_keep)\n",
    "\n",
    "def get_mem_usage(dataframe):\n",
    "    print(f'Memory usage of entire dataset in MB is: {round(dataframe.memory_usage(deep=True).sum() / 2 ** 20, 2)}')\n",
    "    \n",
    "get_mem_usage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0371239-e90f-4750-be3b-a357fda3d858",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2502929 entries, 0 to 2502928\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Dtype \n",
      "---  ------                  ----- \n",
      " 0   name                    object\n",
      " 1   type                    object\n",
      " 2   primary_role            object\n",
      " 3   combined_stock_symbols  object\n",
      " 4   city                    object\n",
      " 5   region                  object\n",
      " 6   country_code            object\n",
      "dtypes: object(7)\n",
      "memory usage: 133.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "554e328c-5f1d-4947-bca5-5ff3005c3337",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2502929, 7)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8b0708e-95c3-4d6d-9569-4d4bbf0ec2f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'type', 'primary_role', 'combined_stock_symbols', 'city',\n",
       "       'region', 'country_code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c815ed81-2017-4419-9521-54a2da973c37",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974942</th>\n",
       "      <td>CRYPTO</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Lorraine</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334352</th>\n",
       "      <td>CASES</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795271</th>\n",
       "      <td>Eugenia L Foster CPA</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cumming</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357012</th>\n",
       "      <td>Express Dental Care</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278318</th>\n",
       "      <td>Hartford Technologies</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rocky Hill</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name          type primary_role  \\\n",
       "1974942                 CRYPTO  organization      company   \n",
       "1334352                  CASES  organization      company   \n",
       "795271    Eugenia L Foster CPA  organization      company   \n",
       "1357012    Express Dental Care  organization      company   \n",
       "1278318  Hartford Technologies  organization      company   \n",
       "\n",
       "        combined_stock_symbols        city       region country_code  \n",
       "1974942                    NaN       Nancy     Lorraine          FRA  \n",
       "1334352                    NaN    New York     New York          USA  \n",
       "795271                     NaN     Cumming      Georgia          USA  \n",
       "1357012                    NaN       Tampa      Florida          USA  \n",
       "1278318                    NaN  Rocky Hill  Connecticut          USA  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fe8546-56a8-4d61-b135-35b8332464b9",
   "metadata": {},
   "source": [
    "### 6. Data Wrangling\n",
    "Let's start by checking for any NaN values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8e235d0b-6743-4e73-851f-2a416f4677ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    2502927\n",
      "True           2\n",
      "Name: name, dtype: int64 \n",
      "\n",
      "False    2502929\n",
      "Name: type, dtype: int64 \n",
      "\n",
      "False    2502929\n",
      "Name: primary_role, dtype: int64 \n",
      "\n",
      "True     2456652\n",
      "False      46277\n",
      "Name: combined_stock_symbols, dtype: int64 \n",
      "\n",
      "False    2304691\n",
      "True      198238\n",
      "Name: city, dtype: int64 \n",
      "\n",
      "False    2304691\n",
      "True      198238\n",
      "Name: region, dtype: int64 \n",
      "\n",
      "False    2304691\n",
      "True      198238\n",
      "Name: country_code, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(df[col].isna().value_counts(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9b8b27-f1d0-43d3-b07e-379ea758cdda",
   "metadata": {
    "tags": []
   },
   "source": [
    "The `type`, and `primary_role` columns appear to be okay.\n",
    "\n",
    "For `name`, there are 2 `NaN` values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90f86b07-3e28-4b1e-957a-15b3d04b1ad4",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668362</th>\n",
       "      <td>NaN</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127588</th>\n",
       "      <td>NaN</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name          type primary_role combined_stock_symbols      city  \\\n",
       "668362   NaN  organization      company                    NaN       NaN   \n",
       "1127588  NaN  organization      company                    NaN  New York   \n",
       "\n",
       "           region country_code  \n",
       "668362        NaN          NaN  \n",
       "1127588  New York          USA  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['name'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6642e9-d494-43ad-9088-1332a2fcd46c",
   "metadata": {},
   "source": [
    "Their names were not entered in properly. Let's just remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30ee5134-783d-4e18-9ea2-90343c9d4ea2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,502,929 rows\n",
      "2,502,927 rows\n"
     ]
    }
   ],
   "source": [
    "print('{:,.0f}'.format(df.shape[0]), 'rows')\n",
    "df = df[df['name'].notna()]\n",
    "print('{:,.0f}'.format(df.shape[0]), 'rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe63437b-b9aa-4107-9825-af51ca6d409a",
   "metadata": {},
   "source": [
    "Next, for `combined_stock_symbols` there are 46,277 `NaN` values. Let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc8b5017-5d48-46a9-9e0f-38616f74fcec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wetpaint</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zoho</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Digg</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Omidyar Network</td>\n",
       "      <td>organization</td>\n",
       "      <td>investor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Trinity Ventures</td>\n",
       "      <td>organization</td>\n",
       "      <td>investor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name          type primary_role combined_stock_symbols  \\\n",
       "0          Wetpaint  organization      company                    NaN   \n",
       "1              Zoho  organization      company                    NaN   \n",
       "2              Digg  organization      company                    NaN   \n",
       "3   Omidyar Network  organization     investor                    NaN   \n",
       "5  Trinity Ventures  organization     investor                    NaN   \n",
       "\n",
       "           city      region country_code  \n",
       "0      New York    New York          USA  \n",
       "1    Pleasanton  California          USA  \n",
       "2      New York    New York          USA  \n",
       "3  Redwood City  California          USA  \n",
       "5    Menlo Park  California          USA  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['combined_stock_symbols'].isna()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ace170c-7e5d-4aac-a67f-c4d11032bb2a",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Meta</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nasdaq:META</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Twitter</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nyse:TWTR</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>eBay</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nasdaq:EBAY</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Hutchison Whampoa</td>\n",
       "      <td>organization</td>\n",
       "      <td>investor</td>\n",
       "      <td>hkg:0013</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>Hong Kong Island</td>\n",
       "      <td>HKG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>VIACOM PARAMOUNT on contract at CBS PARAMOUNT+</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nasdaq:VIAB</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name          type primary_role  \\\n",
       "4                                             Meta  organization      company   \n",
       "12                                         Twitter  organization      company   \n",
       "24                                            eBay  organization      company   \n",
       "30                               Hutchison Whampoa  organization     investor   \n",
       "34  VIACOM PARAMOUNT on contract at CBS PARAMOUNT+  organization      company   \n",
       "\n",
       "   combined_stock_symbols           city            region country_code  \n",
       "4             nasdaq:META     Menlo Park        California          USA  \n",
       "12              nyse:TWTR  San Francisco        California          USA  \n",
       "24            nasdaq:EBAY       San Jose        California          USA  \n",
       "30               hkg:0013      Hong Kong  Hong Kong Island          HKG  \n",
       "34            nasdaq:VIAB       New York          New York          USA  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['combined_stock_symbols'].notna()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b7596a-8fd8-4689-abd3-2f3fb86fe341",
   "metadata": {},
   "source": [
    "This makes sense, as the `combined_stock_symbols` value indicates whether the organization is listed in a stock exchange, i.e. public vs. private (`NaN` = private).\n",
    "\n",
    "We may want to keep note of this to serve as another data point, at least for organizations in the U.S.\n",
    "\n",
    "For `city`, `region`, and `country_code`, about **7.9%** of the values for each are `NaN` (198,238 / 2,502,927). Let's check it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4087790f-30fc-496d-a9cc-bcfc4b945d1d",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>623779</th>\n",
       "      <td>Foot and Skin Traction Kit Manufacturers Suppl...</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647689</th>\n",
       "      <td>Bin mk</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685247</th>\n",
       "      <td>Studio WTF</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392278</th>\n",
       "      <td>Cloud Perspectives</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182437</th>\n",
       "      <td>Nederland Jewelers</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      name          type  \\\n",
       "623779   Foot and Skin Traction Kit Manufacturers Suppl...  organization   \n",
       "647689                                              Bin mk  organization   \n",
       "685247                                          Studio WTF  organization   \n",
       "1392278                                 Cloud Perspectives  organization   \n",
       "1182437                                 Nederland Jewelers  organization   \n",
       "\n",
       "        primary_role combined_stock_symbols city region country_code  \n",
       "623779       company                    NaN  NaN    NaN          NaN  \n",
       "647689       company                    NaN  NaN    NaN          NaN  \n",
       "685247       company                    NaN  NaN    NaN          NaN  \n",
       "1392278      company                    NaN  NaN    NaN          NaN  \n",
       "1182437      company                    NaN  NaN    NaN          NaN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['country_code'].isna()].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1fd8001-629e-40b6-9fb9-a4745332370c",
   "metadata": {},
   "source": [
    "There is no geo data for those 198,238 rows. Let's just drop them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e775e8d7-acdc-4f78-a5d9-74856a22077b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,502,927 rows\n",
      "2,304,690 rows\n"
     ]
    }
   ],
   "source": [
    "print('{:,.0f}'.format(df.shape[0]), 'rows')\n",
    "df = df[df['country_code'].notna()]\n",
    "print('{:,.0f}'.format(df.shape[0]), 'rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "758f9432-52c1-4834-a184-ea515f5a71bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['country_code'].isna()].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042f0a22-a87d-42a6-8c8f-d94b62cf8ef6",
   "metadata": {},
   "source": [
    "`organization` appears to be the only value for the `type` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7bfac2e0-b0b6-475c-9d4b-4528aa1dfa4b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "organization    2304690\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bf35fc-be1f-4abf-aa0f-936dee9b8f06",
   "metadata": {},
   "source": [
    "For `primary_role`, let's only look at `company` and drop `investor` and `school`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa48f39d-c8ae-4ae7-bd03-ace8f0cb4f27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company     2279966\n",
       "investor      23658\n",
       "school         1066\n",
       "Name: primary_role, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.primary_role.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7f35c97-ac54-47b4-b5a8-b7860d6048fd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,279,966 rows\n"
     ]
    }
   ],
   "source": [
    "df.query('primary_role == \"company\"', inplace=True)\n",
    "print('{:,.0f}'.format(df.shape[0]), 'rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063bf343-1805-421e-826d-f4ed5ac1f425",
   "metadata": {},
   "source": [
    "Let's convert the 'dtype' for all columns except 'name' and 'combined_stock_symbols' to `category` to bring down the memory usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "223c8d6a-736b-4a60-9d7b-1cca7e4ed405",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for col in ['type', 'primary_role', 'city', 'region', 'country_code']:\n",
    "    df[col] = df[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d82cb7a-595f-44e9-ad92-38ca410debc2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of entire dataset in MB is: 275.42\n"
     ]
    }
   ],
   "source": [
    "get_mem_usage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "27bfb790-478a-43aa-bf98-86401e43f9ba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2279966 entries, 0 to 2502928\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Dtype   \n",
      "---  ------                  -----   \n",
      " 0   name                    object  \n",
      " 1   type                    category\n",
      " 2   primary_role            category\n",
      " 3   combined_stock_symbols  object  \n",
      " 4   city                    category\n",
      " 5   region                  category\n",
      " 6   country_code            category\n",
      "dtypes: category(5), object(2)\n",
      "memory usage: 76.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6681f989-859e-4676-b9e8-697cce64ef44",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1292146</th>\n",
       "      <td>Sayle Sandifer &amp; Associates</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greenville</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803067</th>\n",
       "      <td>Rapid Learning Institute</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537981</th>\n",
       "      <td>Beacontron</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oakville</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439022</th>\n",
       "      <td>Battery Electric</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38804</th>\n",
       "      <td>Infinite Designs</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name          type primary_role  \\\n",
       "1292146  Sayle Sandifer & Associates  organization      company   \n",
       "803067      Rapid Learning Institute  organization      company   \n",
       "537981                    Beacontron  organization      company   \n",
       "1439022             Battery Electric  organization      company   \n",
       "38804               Infinite Designs  organization      company   \n",
       "\n",
       "        combined_stock_symbols        city        region country_code  \n",
       "1292146                    NaN  Greenville   Mississippi          USA  \n",
       "803067                     NaN       Wayne  Pennsylvania          USA  \n",
       "537981                     NaN    Oakville       Ontario          CAN  \n",
       "1439022                    NaN    Edmonton       Alberta          CAN  \n",
       "38804                      NaN     Atlanta       Georgia          USA  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add5c582-6e29-4da1-9ad2-a802e11099e4",
   "metadata": {},
   "source": [
    "### 7. Analyze the dataset\n",
    "Now that we have the dataframe initialized, let's explore it.\n",
    "\n",
    "#### 7.1 What is the total number of countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8c71ec52-5d13-4a77-9124-bed7f556b892",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.country_code.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1f8ac5-8e8d-4d61-93c9-30746be48499",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 7.2 What is the total number of regions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0355200a-3917-4e22-bf18-16e6367a1a8f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2311"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.region.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241b8eaf-9712-42b9-ab70-c4fb3063d3d2",
   "metadata": {},
   "source": [
    "#### 7.3 What is the total number of cities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed47b9b8-a284-426d-b798-ea5aeea22e04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61079"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.city.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15f7c87-8f9d-4ac0-88a4-29e889616e8d",
   "metadata": {},
   "source": [
    "This sounds misleading, as some city names may be repeated, i.e. the same city name may be in multiple regions. Let's try and find some examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "44ad7b82-7b5a-431c-ad19-d898d221e74a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clinton       24\n",
       "Madison       23\n",
       "Franklin      21\n",
       "Washington    21\n",
       "Jackson       20\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# USA\n",
    "df[['city', 'region', 'country_code']] \\\n",
    "    .query('country_code==\"USA\"') \\\n",
    "    .value_counts() \\\n",
    "    .to_frame(name='count') \\\n",
    "    .reset_index()['city'] \\\n",
    "    .value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f14f2b-e2ea-4bf7-9d88-101099efd60a",
   "metadata": {},
   "source": [
    "Let's take `Clinton` as an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b1a0dbe5-5548-43ea-849a-a90d146a09a9",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>USA</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>USA</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>USA</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>USA</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>USA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>USA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Maine</td>\n",
       "      <td>USA</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>USA</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>USA</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>USA</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>USA</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>USA</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Montana</td>\n",
       "      <td>USA</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>USA</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>USA</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>USA</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>CAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>USA</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>USA</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>USA</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Clinton</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>USA</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city          region country_code  count\n",
       "20  Clinton        Arkansas          USA      5\n",
       "7   Clinton     Connecticut          USA     22\n",
       "12  Clinton        Illinois          USA     12\n",
       "16  Clinton         Indiana          USA      8\n",
       "3   Clinton            Iowa          USA     39\n",
       "23  Clinton        Kentucky          USA      2\n",
       "22  Clinton       Louisiana          USA      3\n",
       "24  Clinton           Maine          USA      1\n",
       "0   Clinton        Maryland          USA     72\n",
       "10  Clinton   Massachusetts          USA     15\n",
       "4   Clinton        Michigan          USA     31\n",
       "2   Clinton     Mississippi          USA     45\n",
       "9   Clinton        Missouri          USA     21\n",
       "18  Clinton         Montana          USA      6\n",
       "1   Clinton      New Jersey          USA     48\n",
       "8   Clinton        New York          USA     22\n",
       "5   Clinton  North Carolina          USA     28\n",
       "19  Clinton            Ohio          USA      5\n",
       "11  Clinton        Oklahoma          USA     13\n",
       "21  Clinton         Ontario          CAN      4\n",
       "15  Clinton    Pennsylvania          USA      9\n",
       "13  Clinton  South Carolina          USA     10\n",
       "6   Clinton       Tennessee          USA     23\n",
       "14  Clinton      Washington          USA     10\n",
       "17  Clinton       Wisconsin          USA      6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('(city in [\"Clinton\"])')[['city', 'region', 'country_code']] \\\n",
    "    .value_counts() \\\n",
    "    .to_frame(name='count') \\\n",
    "    .reset_index() \\\n",
    "    .sort_values(['city', 'region']) \\\n",
    "    .head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c0ac85-6e90-4fcf-8aff-c54cada9fcf5",
   "metadata": {
    "tags": []
   },
   "source": [
    "The city `Clinton` is a city name in 25 different regions. The total number of cities should be more than the `61,079` we calculated previously. Let's try calculating it again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1a47a3a4-5583-41f7-ab40-5e622081180b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61079"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# incorrect\n",
    "len(df.city.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ba6bd7fc-9709-49ee-861e-da1b6b42b175",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                 region                country_code\n",
       "London               England               GBR             53904\n",
       "New York             New York              USA             49347\n",
       "San Francisco        California            USA             26597\n",
       "Los Angeles          California            USA             20634\n",
       "Tokyo                Tokyo                 JPN             19412\n",
       "                                                           ...  \n",
       "Rex                  Georgia               USA                 1\n",
       "La Cure              Geneve                CHE                 1\n",
       "La Dehesa            Region Metropolitana  CHL                 1\n",
       "Revúca               Banska Bystrica       SVK                 1\n",
       "Nowogródek Pomorski  Zachodniopomorskie    POL                 1\n",
       "Length: 72876, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['city', 'region', 'country_code']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f0cf49-d0ad-4a65-b8be-01553b4f2b57",
   "metadata": {},
   "source": [
    "The total number of cities is **72,876**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28e77d1-1c2a-4396-816f-ce5becc95d15",
   "metadata": {},
   "source": [
    "#### 7.4 What percentage of the total companies are in the U.S.?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "09b56b78-912a-44ed-bd56-224e2d73c550",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_code\n",
       "USA             1130976\n",
       "GBR              173206\n",
       "IND               86203\n",
       "DEU               80353\n",
       "CAN               75397\n",
       "                 ...   \n",
       "GNB                   2\n",
       "TUV                   1\n",
       "FLK                   1\n",
       "FSM                   1\n",
       "IOT                   1\n",
       "Length: 223, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['country_code']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "487ad9c6-1544-4f70-9f94-4f290292a80b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49.60%\n"
     ]
    }
   ],
   "source": [
    "print( '{0:.2f}%'.format( df.query('country_code==\"USA\"').shape[0] / df[['country_code']].value_counts().sum() * 100 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3f267c-c473-4d58-8cac-9cdc9d8d25c7",
   "metadata": {},
   "source": [
    "#### 7.5 What is the breakdown percentages by country?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7ef6d44b-576f-462b-b1c8-6bbf4759d274",
   "metadata": {
    "tags": []
   },
   "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>country_code</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>1130976</td>\n",
       "      <td>49.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBR</td>\n",
       "      <td>173206</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IND</td>\n",
       "      <td>86203</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEU</td>\n",
       "      <td>80353</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAN</td>\n",
       "      <td>75397</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FRA</td>\n",
       "      <td>58166</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AUS</td>\n",
       "      <td>53043</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>JPN</td>\n",
       "      <td>50465</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NLD</td>\n",
       "      <td>50395</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ESP</td>\n",
       "      <td>42973</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code  num_of_companies      %\n",
       "0          USA           1130976  49.60\n",
       "1          GBR            173206   7.60\n",
       "2          IND             86203   3.78\n",
       "3          DEU             80353   3.52\n",
       "4          CAN             75397   3.31\n",
       "5          FRA             58166   2.55\n",
       "6          AUS             53043   2.33\n",
       "7          JPN             50465   2.21\n",
       "8          NLD             50395   2.21\n",
       "9          ESP             42973   1.88"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_country_code_count = df[['country_code']].value_counts().to_frame(name='num_of_companies').reset_index()\n",
    "\n",
    "df_country_code_count['%'] = round( df_country_code_count['num_of_companies'] / df_country_code_count['num_of_companies'].sum() * 100, 2 )\n",
    "\n",
    "df_country_code_count.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09b4ec2-9b5d-499c-8d04-c5b961d1987a",
   "metadata": {},
   "source": [
    "#### 7.6 What is the breakdown percentages by continent?\n",
    "\n",
    "To see this, we need to have a `country code <-> continent` table and map it to our dataframe.\n",
    "\n",
    "First, it'd be nice to have the country names too, so let's find a `country code <-> country name` table. This one by [opendatasoft](https://www.opendatasoft.com/) looks perfect:\n",
    "\n",
    "https://public.opendatasoft.com/explore/dataset/countries-codes/export/?rows=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9d852c3f-79f8-4991-96a7-ea32f3c1d088",
   "metadata": {
    "tags": []
   },
   "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>ISO3 CODE</th>\n",
       "      <th>LABEL EN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>SWE</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>WSM</td>\n",
       "      <td>Samoa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>GGY</td>\n",
       "      <td>Guernsey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>TUR</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ISO3 CODE  LABEL EN\n",
       "238       SWE    Sweden\n",
       "159       WSM     Samoa\n",
       "194       GGY  Guernsey\n",
       "110       TUR    Turkey\n",
       "48        AUT   Austria"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'https://public.opendatasoft.com/api/explore/v2.1/catalog/datasets/countries-codes/exports/csv?lang=en&timezone=America%2FChicago&use_labels=true&delimiter=%3B'\n",
    "\n",
    "df_country_codes = pd.read_csv(file, sep=';', usecols=['ISO3 CODE', 'LABEL EN'])\n",
    "\n",
    "df_country_codes.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "466e066d-e12c-4a8a-9596-4a00f8233995",
   "metadata": {
    "tags": []
   },
   "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>iso3_code</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>RWA</td>\n",
       "      <td>Rwanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>TUR</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>MNG</td>\n",
       "      <td>Mongolia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>SSD</td>\n",
       "      <td>South Sudan, The Republic of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>JEY</td>\n",
       "      <td>Jersey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iso3_code                  country_name\n",
       "78        RWA                        Rwanda\n",
       "110       TUR                        Turkey\n",
       "121       MNG                      Mongolia\n",
       "109       SSD  South Sudan, The Republic of\n",
       "119       JEY                        Jersey"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "df_country_codes.rename(columns={'ISO3 CODE': 'iso3_code', 'LABEL EN': 'country_name'}, inplace=True)\n",
    "df_country_codes.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b113c10-7bfa-4b71-adcd-e1db950ead25",
   "metadata": {},
   "source": [
    "Now let's procure a `country code <-> continent` table. This one on GitHub by [stevewithington](https://gist.github.com/stevewithington) looks to fit the bill:\n",
    "\n",
    "https://gist.github.com/stevewithington/20a69c0b6d2ff846ea5d35e5fc47f26c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "164d37a2-4c41-4bcb-ae97-b3192ce6ca3f",
   "metadata": {
    "tags": []
   },
   "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>Continent_Name</th>\n",
       "      <th>Three_Letter_Country_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antarctica</td>\n",
       "      <td>ATA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>ASM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Continent_Name Three_Letter_Country_Code\n",
       "0           Asia                       AFG\n",
       "1         Europe                       ALB\n",
       "2     Antarctica                       ATA\n",
       "3         Africa                       DZA\n",
       "4        Oceania                       ASM"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continent_codes = pd.read_csv(\n",
    "    'https://gist.githubusercontent.com/stevewithington/20a69c0b6d2ff846ea5d35e5fc47f26c/raw/13716ceb2f22b5643ce5e7039643c86a0e0c6da6/country-and-continent-codes-list-csv.csv',\n",
    "    usecols=['Continent_Name', 'Three_Letter_Country_Code']\n",
    ")\n",
    "\n",
    "df_continent_codes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ae2ec185-12fe-45ed-bcef-df191ffe301b",
   "metadata": {
    "tags": []
   },
   "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>continent_name</th>\n",
       "      <th>country_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antarctica</td>\n",
       "      <td>ATA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>ASM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent_name country_code\n",
       "0           Asia          AFG\n",
       "1         Europe          ALB\n",
       "2     Antarctica          ATA\n",
       "3         Africa          DZA\n",
       "4        Oceania          ASM"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "df_continent_codes.rename(\n",
    "    columns={'Continent_Name': 'continent_name', 'Three_Letter_Country_Code': 'country_code'},\n",
    "    inplace=True\n",
    ")\n",
    "\n",
    "df_continent_codes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864140f4-bb08-4389-8d72-163a11602afd",
   "metadata": {},
   "source": [
    "Now that we have both `country code <-> country name` and `country code <-> continent` tables, let's merge them in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "514a506d-8a13-4196-bf28-066ae1b44dd8",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>iso3_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wetpaint</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zoho</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Digg</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Meta</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nasdaq:META</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Omnidrive</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name          type primary_role combined_stock_symbols        city  \\\n",
       "0   Wetpaint  organization      company                    NaN    New York   \n",
       "1       Zoho  organization      company                    NaN  Pleasanton   \n",
       "2       Digg  organization      company                    NaN    New York   \n",
       "3       Meta  organization      company            nasdaq:META  Menlo Park   \n",
       "4  Omnidrive  organization      company                    NaN   Palo Alto   \n",
       "\n",
       "       region country_code iso3_code   country_name continent_name  \n",
       "0    New York          USA       USA  United States  North America  \n",
       "1  California          USA       USA  United States  North America  \n",
       "2    New York          USA       USA  United States  North America  \n",
       "3  California          USA       USA  United States  North America  \n",
       "4  California          USA       USA  United States  North America  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continents = df.merge(\n",
    "    df_country_codes,\n",
    "    left_on=['country_code'],\n",
    "    right_on=['iso3_code'],\n",
    "    how='left').merge(\n",
    "    df_continent_codes,\n",
    "    left_on=['country_code'],\n",
    "    right_on=['country_code'],\n",
    "    how='left'\n",
    ")\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "87c1c097-874e-48bb-bb1e-20410ef0a3c2",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wetpaint</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zoho</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Digg</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Meta</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nasdaq:META</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Omnidrive</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name          type primary_role combined_stock_symbols        city  \\\n",
       "0   Wetpaint  organization      company                    NaN    New York   \n",
       "1       Zoho  organization      company                    NaN  Pleasanton   \n",
       "2       Digg  organization      company                    NaN    New York   \n",
       "3       Meta  organization      company            nasdaq:META  Menlo Park   \n",
       "4  Omnidrive  organization      company                    NaN   Palo Alto   \n",
       "\n",
       "       region country_code   country_name continent_name  \n",
       "0    New York          USA  United States  North America  \n",
       "1  California          USA  United States  North America  \n",
       "2    New York          USA  United States  North America  \n",
       "3  California          USA  United States  North America  \n",
       "4  California          USA  United States  North America  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop unneeded column\n",
    "df_continents.drop(columns=['iso3_code'], inplace=True)\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d9fb8fe5-1ae7-4bc8-a7c4-61927dd3ee84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2301808 entries, 0 to 2301807\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Dtype   \n",
      "---  ------                  -----   \n",
      " 0   name                    object  \n",
      " 1   type                    category\n",
      " 2   primary_role            category\n",
      " 3   combined_stock_symbols  object  \n",
      " 4   city                    category\n",
      " 5   region                  category\n",
      " 6   country_code            object  \n",
      " 7   country_name            object  \n",
      " 8   continent_name          object  \n",
      "dtypes: category(4), object(5)\n",
      "memory usage: 125.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_continents.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e46289-2ce3-4ec1-ba4b-24345445c237",
   "metadata": {},
   "source": [
    "We can convert the 'dtype' of both 'country_name' and 'continent_name' columns to `category`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "29494bc6-a2e8-4b4a-8323-6e0cb37856f1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for col in ['country_name', 'continent_name']:\n",
    "    df_continents[col] = df_continents[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b203e3d5-531c-4bd5-80a6-57b0830f2ef6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of entire dataset in MB is: 411.88\n"
     ]
    }
   ],
   "source": [
    "get_mem_usage(df_continents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "25221597-b749-45a1-adce-6dbcf2aa1b76",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2301808 entries, 0 to 2301807\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Dtype   \n",
      "---  ------                  -----   \n",
      " 0   name                    object  \n",
      " 1   type                    category\n",
      " 2   primary_role            category\n",
      " 3   combined_stock_symbols  object  \n",
      " 4   city                    category\n",
      " 5   region                  category\n",
      " 6   country_code            object  \n",
      " 7   country_name            category\n",
      " 8   continent_name          category\n",
      "dtypes: category(6), object(3)\n",
      "memory usage: 97.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_continents.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "685592a1-1342-40be-bde7-35f944fda16b",
   "metadata": {},
   "source": [
    "#### 7.6 What is the breakdown percentages by continent?\n",
    "\n",
    "Now we can answer the question:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1d38dd4e-5e0b-4558-9fec-ccdbe587b95a",
   "metadata": {
    "tags": []
   },
   "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>continent_name</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North America</td>\n",
       "      <td>1222576</td>\n",
       "      <td>53.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>624250</td>\n",
       "      <td>27.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>310936</td>\n",
       "      <td>13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>61045</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South America</td>\n",
       "      <td>46784</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Africa</td>\n",
       "      <td>28213</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antarctica</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent_name  num_of_companies      %\n",
       "0  North America           1222576  53.30\n",
       "1         Europe            624250  27.21\n",
       "2           Asia            310936  13.56\n",
       "3        Oceania             61045   2.66\n",
       "4  South America             46784   2.04\n",
       "5         Africa             28213   1.23\n",
       "6     Antarctica                 2   0.00"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continent_count = df_continents[['continent_name']].value_counts().to_frame(name='num_of_companies').reset_index()\n",
    "\n",
    "df_continent_count['%'] = round( df_continent_count['num_of_companies'] / df_continent_count['num_of_companies'].sum() * 100, 2 )\n",
    "\n",
    "df_continent_count.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dfce528d-88c9-4b56-b72d-eb47acc892e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HtsR2OUqTWJPNqhyHS+puuWRaRFjpKUBsm2nZ2NoKpO1tl6NB8mCClNqUlAARcSAZBvsm60oyXCLCExMg0ldqVERcJpYpFGEiR9ZEHMuRQ3lIg3xXbE0yWiJO7AWIiEVZMyIK5GEKUmtjexiACDOJVcIMiIhyyZaIAJGjXlpiKscKJcMl48n8cqRObgBI3YpkfCSTKN8tEdNypIsChP9XIwESIAESIAFzEzCFALEd+ZHNlWxuEt6Rlg29bBZtNQqyIZbiaVsBq4TIVnwrRdBy59ZRASIZBdnM257KZTunL1kCORsvdRayqZPsha2JaJAMzMiRI1W9htaNlH0GRO5GyyZMrpenB9k3KdaWImWpe7EVg9vukEs/m5CQwmQp6k9KgEjGxn4DLBtBuVtuf+RH6kkkUySZJtmkJlV8bptDMiTydCTpJ091kiaF/HKkS3hJfY74JRtW+xoQ2RSLPcJAajyeZLMvWRwb/8RqQJLLWkjBuRxpkzUn2RVbkydjSfxtYjilNiUlQIStZAVs61yEoRRqSx2UHKtLSoAIH1n3ckzJvkncRZTKeLYHGIhvtvehyOeSZZJsm2SxpNnXgMiaEw7itxxDlOvkZoDUpNjqmWzziTgWoSkCRObTElO5VgSvCC8Z1/ZwBZsgluOIkhWRrJdkRLR+b8z9n2ZaTwIkQAIkQALWJWAKASL4befxZYMtT6eSu7dSeyAbSykWtr9rL8JANmOSbZAjIHIERTbA0lc20XK31VEBIndtpVZENoTy1CIRQbJBkjvL8pJEmVs2z7JBCwgIUOJIjiDJY3vlOJJs6OU6ubP7JEewhIVsIuXOsIgdOZYi48gdarlTLE+jkoJsEQbyuFU5xiIbOylGFhtkcyr1IYllkmwbPtmsyvl72ZTK44qlRkU2flLUL0/DsjWxW/yR7EdSxee2vvJ0LzleJEepJFMimZrJkyerzIwIASmIlrvnEmexWfySueVIldTfyNzCUetmXzIG4qMc35HHBcuGPuFTsJITIGK7rB8RdlL/IqJSbJJMjLCVQng5fpRSmxLLqggjWVtypEyO/kmTY25yRE2Eh6z7pASI7SlY8rhaWWMiFERQiOCVGEpMbWK+a9eu6uihPG5Zsg0iVGVdyIs95Slusum3fwyviBSpe5GbAOK/rCfb0UJhLJkgEScyhjyVzfYYXi0xFR+lJkWO8sm18v21FcgLD7l5IOxlvUijALHu/5DoGQmQAAmQgGsQMI0AkXDIU51koyKCQ+5AyxEY22ZbRIV9kzvtskmX94DI8RPpJ3eQbU/xcVSAyCNQ5QlXsqmXTalsDGWTJ5syabJBlQyFbLCl/kPu7MujUeXRsbJpl3P4khFJiQCR8eU4yoIFC9QxF9nMFy1aVD19Szau0mTDLxtXOfIkvspmVDZx0keEU2LNlm2QTIUwlkehykZTfJXjbFLfYt9sR8psj79N7isjG3kRajK2xESOqUmdju1Yjxy9kSNC8m4IKdwXv0TciHCwHRfSutkXMSb+y/E8ObYk6yUlAkREo2y4ZdMv2QURDXJXX0SULZOTUpsS1roIP8l0yKZb/JUNuayt/Pnzq7Viq91JSoDI9XJcSjJSkoUQxhIzqVORH1sTUSxH6ISJrAkRfPJULBHt8pAEydKJoEz4HhARbLKGbbUvUjMivsufsr7k4QGyDiV7ImJcRImWmIpd8oACqXORY2Ii7GxNxhJRLd8bycJQgCT3LePnJEACJEACJGB8AqYSIEbBaZS3Vac1D8kKSD2NbBgTFsSntW2cnwRIgARIgARIgARIwJgEKEBSEBdXFiBSDyDZCTkSJZkB24sdU4CRl5AACZAACZAACZAACbggAQqQFATdlQWIvENCjtpInYAUIUsxPBsJkAAJkAAJkAAJkAAJaCVAAaKVFPuRAAmQAAmQAAmQAAmQAAk4TIACxGGEHIAESIAESIAESIAESIAESEArAQoQraTYjwRIgARIgARIgARIgARIwGECFCAOI+QAJEACJEACJEACJEACJEACWglQgGglxX4kQAIkQAIkQAIkQAIkQAIOE6AAcRghByABEiABEiABEiABEiABEtBKgAJEKyn2IwESIAESIAESIAESIAEScJgABYjDCDkACZAACZAACZAACZAACZCAVgIUIFpJsR8JkAAJkAAJkAAJkAAJkIDDBChAHEbIAUiABEiABEiABEiABEiABLQSoADRSor9SIAESIAESIAESIAESIAEHCZAAeIwQg5AAiRAAiRAAiRAAiRAAiSglQAFiFZS7EcCJEACJEACJEACJEACJOAwAQoQhxFyABIgARIgARIgARIgARIgAa0EKEC0kmI/EiABEiABEiABEiABEiABhwlQgDiMkAOQAAmQAAmQAAmQAAmQAAloJUABopUU+5EACZAACZAACZAACZAACThMgALEYYQcgARIgARIgARIgARIgARIQCsBChCtpNiPBEiABEiABEiABEiABEjAYQIUIA4j5AAkQAIkQAIkQAIkQAIkQAJaCVCAaCXFfiRAAiRAAiRAAiRAAiRAAg4ToABxGCEHIAESIAESIAESIAESIAES0EqAAkQrKfYjARIgARIgARIgARIgARJwmAAFiMMIOQAJkAAJkAAJkAAJkAAJkIBWAhQgWkmxHwmQAAmQAAmQAAmQAAmQgMMEKEAcRsgBSIAESIAESIAESIAESIAEtBKgANFKiv1IgARIgARIgARIgARIgAQcJkAB4jBCDkACJEACJEACJEACJEACJKCVAAWIVlLsRwIkQAIkQAIkQAIkQAIk4DABChCHEXIAEiABEiABEiABEiABEiABrQQoQLSSYj8SIAESIAESIAESIAESIAGHCVCAOIyQA5AACZAACZAACZAACZAACWglQAGilRT7kQAJkAAJkAAJkAAJkAAJOEyAAsRhhByABEiABEiABEiABEiABEhAKwEKEK2k2I8ESIAESIAESIAESIAESMBhAhQgDiPkACRAAiRAAiRAAiRAAiRAAloJUIBoJcV+JEACJEACJEACJEACJEACDhOgAHEYIQcgARIgARIgARIgARIgARLQSoACRCsp9iMBEiABEiABEiABEiABEnCYAAWIwwg5AAmQAAmQAAmQAAmQAAmQgFYCFCBaSbEfCZAACZAACZAACZAACZCAwwQoQBxGyAFIgARIgARIgARIgARIgAS0EqAA0UqK/UiABEiABEiABEiABEiABBwmQAHiMEIOQAIkQAIkQAIkQAIkQAIkoJUABYhWUuxHAiRAAiRAAiRAAiRAAiTgMAEKEIcRcgASIAESIAESIAESIAESIAGtBChAtJJiPxIgARIgARIgARIgARIgAYcJUIA4jJADkAAJkMATEHhwB7h75d+f8Mtxfw//53f3rwOR94CoB//+RNv9PfIe8kd8g4y+nvDz9kQ6bw/4eXsgvY/82wP+3p7wt/sznY8H/Lw8IX/afp/e1xPBWfwRmMnvCQxnVxIgARIgARJwDgEKEOdw5CgkQAIkAMREA7fPA7f++ufnHHDz3L9/l98/DHeYlAgQZzQRLyE5MyAkR3oUlj/VT3oKE2fA5RgkQAIkQAJJEqAA4eIgARIggZQQiI0BrhwFLuwFzv/zc+kPIDoyJaM90TXOEiBJTSrCpFCODCicM06YFMoZ93dmTJ4oTOxMAiRAAiSQBAEKEC4NEiABEtBC4MaZR8XG3/uBh3e1XOn0PqktQJIy2M9LMibpEZIjA0oEBqBSgSwoniej0/3jgCRAAiRAAtYmQAFi7fjSOxIggZQSuHEaOLsdOLMt7k/5t0FaWgmQxNyXWpSKBbLgmQJZUfmprEqQeLi7GYQUzSABEiABEjAiAQoQI0aFNpEACehPQI5Tnd0KnNkOnN0G3Plbfxs0zmgkAZLQZCl0r5AvC6oWyooahXPg6dwZNHrFbiRAAiRAAq5CgALEVSJNP0mABB4lILUaIjhC1wBH18QVipukGVmAJESYK6MvahXNoX6qFcqmnsTFRgIkQAIk4NoEKEBcO/70ngRci8CD28DxdXGi4/h6QP5twmYmAWKP18vDDZUKZEWtInGCpGD2dCakT5NJgARIgAQcJUAB4ihBXk8CJGBsAtEP48TGwcXAsbVx79YweTOrAEmIvVD29GhWLhDNygYhTyZfk0eF5pMACThKYNPRK9gXdkPzMM8VyYFywZk092dH4xCgADFOLGgJCZCAswjExgJh2+NEx6HvgYibzhrZEONYRYDYw5QnajUvG4RGpXOrlyqykQAJuB6BYSsPY9Y27Q/8GNyoGN6qVsD1QFnAYwoQCwSRLpAACfxD4PJh4MC3wJ9LTVXT8aTxs6IAsTHw9nDH80/nQPNyQeqolhzbYiMBEnANAnoKkPHjx2P27NlYv349cuXKFQ+4cePG+PTTT1GsWDHN0KOiorB69Wo0adIEx44dQ4cOHbB161bN17/88su4d+8eVq1apfmalHScOXMmTp06hREjRqTkcqdeQwHiVJwcjARIQHcCcqTqz++A32bEvafDBZqVBYh9+LKk88YrFfPi9cr5EJTZzwUiSxdJwLUJ6C1Avv/+e5QvXx4TJkxwSIAcPnwYY8eOxaxZs55YgBw/fhyjRo2Ch4cHunbtirJly6baIrh//z6io6ORPn36VJtD68AUIFpJsR8JkICxCMhTq36fCeydC9y7bizbUtkaVxEgNoxubkDNIjnQpkp+1CycHfJvNhIgAesR0FuAeHl5YdmyZUo8iBCRZp8BWbt2LT7//HPExMQgW7ZsGDZsGAoWLKgyJ5LpOHToEKpXr44VK1bg9u3bKF26NAYNGoS3334bLVq0wHfffYfY2FgMHz5c9UusifgoVKgQfHx88Pvvv6s5pImo6devH6pVq4aDBw/i+vXrGDJkCBYsWICzZ8+iYsWK+Oijj1TfjRs3Yty4cRCBERQUhM8++wxZsmR5xM46derA398/PgNy8+ZN9O/fH/v27UPmzJnx4YcfKhuvXr2Kvn37qjlErLRv3x5t27Z1+mKjAHE6Ug5IAiSQagSktuP0prhsx9G1QGxMqk1l5IFdTYDYx0IyIZIRebVSMDL7exk5TLSNBEjgCQnoLUB8fX1RoEABfPHFF1i+fDnc3NziBUimTJnQsGFDJVCkj2z8JWOydOlSzJ8/H5MnT8aSJUsQHBwMESryd1sGpFmzZkp0yJ+LFi1SYyxevPgxGrLBf+GFF5SAkQzIiy++iA0bNsDb21sJnEaNGqnry5Urh169euHIkSP44YcflKipVKmS6itjSD+xr2jRovjqq6+we/duTJs27TE77Y9gDRw4EH5+fkowicBp164ddu7cqY6fRUREqGNaYWFhqF+/vhI4OXPmfMJo/nd3ChCn4uRgJEACqUJAisj3zgd+/8pQbyRPFV81DOrKAsSGx8/LA68/E4y3axREjgw+GqixCwmQgNEJ6C1AJOvw7rvv4vXXX0fTpk3RqlWreAEi2Q3Z4Is4kfbgwQOULFkSe/fuVWJFNuWSCZGWUIBITcf+/fvVZyIaOnfujM2bNz+Gf9OmTWqsiRMnqs/69OmD2rVro169ekqAiF2SFZEmx8QkazF06FD1b+k3adIkJR5+/PFHzJkzR/3+7t27qFChgvq9iBd7O+0FSNWqVTFjxoz4Wpdbt24hICBACRr5EREkTTIno0ePViLImY0CxJk0ORYJkIBzCTy8C+z+Ctg+CQi/7NyxTTwaBci/wZMi9Zbl8+KdmgWRN4u/iaNK00mABNJKgIhI6NixI3766Se0bt1aZQG2bNmC06dPq/oMW5MjVpKtEDEhR5ekkD0xAWJfhP5fRendu3eHiBDJfkiTjX+VKlWU6JHrOnXqhF9//VV9JkfBRFwMGDBA/btu3brq2NWOHTtUNkaOUdnanTt3lCgRf+zttBcgJUqUUH3kyJZ9ExYidi5fvgxPT0+EhoYqcSOixpmNAsSZNDkWCZCAcwhE3AJ++0+7Q6cAACAASURBVALYMRW4r/2Z8M6Z3PijUIA8HiN3N6BRqTx47/kQhORM+wJL468iWkgCxiOQVgJESMhRJCnOlg39yJEjVQ2GZECmT5+uQCXMgEiGQwRASgWI1IzI8SsROrZsgzxNS2o+1qxZo2oxtAgQeaqVZGBsdtpHVY6K2dtpL0Ck3mPKlCkoVaqUukTGETEimSARUC1btlS/r1WrFsaMGUMBYryvCy0iARJwGgERHpLt2PWFad9S7jQW/zEQBch/U25YKjf6vlgU+bIyI6LHeuQcJOAsAmkpQKTIW+odRAxIBkKKuBs0aKCOSOXLl08dt1q3bh0WLlyoaivsN/a2o1pS5yFPtdKSAfn666/x22+/xR+/sjGUY1iSnZBMiBYBkjt3blWrIsetpFZFjl6JzVKg/l8CRD5/+PChqlWRLIcUmm/btk0JIPFVbFi5cqUqfJcjYjVq1HBWmNU4zIA4FScHIwESSBEBeVu5iI7NYy330sAU8UjmIgqQ5Kl6uLuhdaVgvF8nBNnTs0YkeWLsQQJpTyAtBYh4L0Xkkv2QQm95D4gIDtl8R0ZGQjb6n3zyiSo6T7ixv3LlinrqlfSbO3euJgEiGQYp/JYCcvsm7yWRzITUXWgRIFKX8ssvv6gneUnxeIYMGVQ2R45M/ZcAkWNaInakYF3E1uDBg9VTsEQYSTZFjnTJe01u3LihCuylkD4wMNBpi4QCxGkoORAJkMATE5CnWslLA3/+BLh59okvd9ULKEC0R16K1eVNyV1qFuQb1rVjY08SSBMCegqQNHGQk8YToADhYiABEkgbAqd+ATZ8DFyIe1IIm3YCFCDaWdl6ZvL3QrdaIWjzbD7I29bZSIAEjEdg09Er2Bemve7vuSI5UC44k/EcoUXJEqAASRYRO5AACTiVwOXDwI8DgFObnDqsKw1GAZLyaMt7RP7XrCSeK5w95YPwShIgARIgAYcIUIA4hI8XkwAJaCYQeQ/YNArYMRmIidZ8GTs+ToACxPFVUa94LgxtUhw5M/o6PhhHIAESIAESeCICFCBPhIudSYAEUkTg2I/Aql7A7fMpupwXPUqAAsQ5K0LqQ6RIvWP1p+Apz/FlIwESIAES0IUABYgumDkJCbgogfBLwIr3gGM/uSiA1HGbAsS5XJ/Klg4jW5RE5QJZnTswRyMBEiABEkiUAAUIFwYJkIDzCcTGAL/PAH4ezvd5OJ8uKEBSASqAJmUCMajh08iegY/tTR3CHJUESIAE4ghQgHAlkAAJOJfA3/uBFd2Bvw84d1yOFk+AAiT1FkN6H0+MbVUK9UrkTr1JODIJkAAJuDgBChAXXwB0nwScRiA6Etg4HNj+OSAZELZUI0ABkmpo4weWbMjwpiWQwdcz9SfjDCRAAnEETqwHzu3WTiOkDhBUUXt/9jQMAQoQw4SChpCAiQlcPQ4saQdc+tPETpjHdAoQfWKVM6MPPm9dlrUh+uDmLCQA/Ngf2DlNO4l6I4Fn3tHenz0NQ4ACxDChoCEkYEICkunYORX4eRgQ9cCEDpjTZAoQ/eLm5gZ0qv4U+rxYFF4efFKWfuQ5k0sS0FGAjB8/HtOmTYO7+6MvJq1UqRLmz5/vkvj1dJoCRE/anIsErETg5llgSXvg/B4reWUKXyhA9A/T07kzYOrr5VEgWzr9J+eMJOAqBHQWIFevXsWIESNcha6h/KQAMVQ4aAwJmITA7lnAukHAw7smMdhaZlKApE085b0h414ujQYlWaCeNhHgrJYnYCAB0qFDBzRv3hyNGjVS2G3/fuqppzBgwACEhITgypUrmDt3LtauXYvPP/8cMTExyJYtG4YNG4aCBQtixowZOHz4MB48eIDr16/D09MTknnJkSMHwsPD8fHHH2P//v2Ijo5G165d8fLLL1s+xDYHKUBcJtR0lAScQCDyPrC6F7D/GycMxiFSSoACJKXknHNd22fz48OGT8Pb49GjG84ZnaOQgAsTMIEAKVy4MFq2bKkyJyJOLly4gIYNG2LZsmUoUKAAFixYgO+//x5Lly7F7NmzMXHiRPz888/ImjUrRo8ejdu3b2P48OHq58aNG/G/a9asGaZOnYpixYq5xAKgAHGJMNNJEnACgVvngK9fBi4fdsJgHMIRAhQgjtBzzrXF8mTErLYVkSvA1zkDchQSIAFdi9AlE/HFF1+orIR9k+xFixYt4jMeCTMgIkAkM3Lw4EFVP7JkyRJs2LBBjSVNsh0lS5bE3r171We7du3C9OnT1Wc7duxQ2RHJmDz77LOYPHkyypUrpz4bNWoU/Pz88P7777vESqAAcYkw00kScJDAma3At68D9286OBAvdwYBChBnUHR8jAA/L0x5rRyqhWRzfDCOQAIkoLsA+a8akKSOYIkAad++PbZt26YiJsLj9OnTSkDYWunSpbFixQps3LgRR44cUVkOaX/88Qc6d+6M7du3o0SJEsiUKRM8PDzUZw8fPkSDBg0wePBgl1gJFCAuEWY6SQIpJBAbC2yfCGwYynd7pBBhalxGAZIaVFM+ZvfaIehRpzDc+ZCslEPklSQgBAx0BKtjx4546aWX1I+0Vq1aoW3bthABIuJk69at6vdy1EoyILYsR8IMiIgNqQWRtmXLFnV0SzIgtWvXxqRJk1zmyFXCBU4Bwq88CZBA4gQe3Il7t8eJDSRkMAIUIAYLCIAaIdnwxZsV4OcddzeTjQRIIAUEDCRABg4ciFy5cqF79+4ICwtT2QnJciQUIBcvXlSfLV++HPny5VN1H+vWrcPChQvV36U4feXKlQgKClLF6z4+Pqr4XITI/fv31ZGsqKgojBkzRtWUlCpVKgXgzHcJBYj5YkaLSSD1Cdw+D8xvDlwJTf25OMMTE6AAeWJkulxQNFcGzH+rMrJn8NFlPk5CApYjoLMASew9IFITcujQIRw9ehQ9e/ZUxeMiHm7evKmERpEiRR7JgEgMRHBIsXlkZCRy586NTz75BMHBwUqA7Nu3T9WFhIaGIjAwUAkSeVKWPAVr6NCh2LMn7lH2NWrUwKBBgx6rSbFcjP9xiALEqpGlXySQUgKXjwALmgO3L6R0BF6XygQoQFIZsAPDy9vTF7xVGSE5MzgwCi8lARcloKMA0YOwCJDjx4/zXSOJwKYA0WMFcg4SMAuB4+uBpR2AB7fNYrFL2kkBYuyw+3t74Ku2FVClIIvTjR0pWmc4AifWA+d2azcrpA4QVFF7f517UoAkDZwCROfFyOlIwLAE9s0HVnRnsblhA/SvYRQgxg+SFKT/r1lJtK4UbHxjaSEJkECqEKAAoQBJlYXFQUnAMgR+GQH8+qll3LG6IxQg5olwzxcK4/3aIeYxmJaSAAmQgA4EmAHRATKnIAHDEoiJBlZ2B/YtMKyJNOxxAhQg5loVHarmx5DGxc1lNK0lARIggVQkQAGSinA5NAkYmkBMFLC4LRC6ytBm0jgKECusgVYVgjC6RSm4ufFlIVaIJ30gARJwjAAFiGP8eDUJmJNA1IO4YnOKD1PGjxkQU4YNDUrmwuevloWnh7s5HaDVJEACJOAkAhQgTgLJYUjANAREfHzzMnBqk2lMpqGPEqAAMe+KeK5wdnzZpjx8PPnCQvNGkZaTAAk4SoACxFGCvJ4EzESA4sNM0UrSVgoQc4exYv7MmNuhEvy9Pc3tCK0nAScT2Hp+Kw5eOah51KqBVVE6e2nN/dnROAQoQIwTC1pCAqlL4GE4sOh1Zj5Sl7Iuo1OA6II5VScpmzcTFr39DHy8mAlJVdAc3FQEPv39Uyw4rP2hKH0r9sWbxd40lY80No4ABQhXAgm4AgERH/ObAed+cwVvLe8jBYg1Qly1UFbMblcJ3p6sCbFGROmFowTSQoBs3LgRvXv3xtChQ/HSSy8l60KnTp3QqFEjNGnSJNm+7JA0AQoQrg4SsDoBedrVvCbAma1W99Rl/KMAsU6oaxbJjpltKrAw3TohpScOEEgLAdKtWzdUqVIFGzZsgLw4MLl2+/Zt+Pj4qB+2lBOgAEk5O15JAsYnEBsDfNcR+PM749tKCzUToADRjMoUHesWy4npb5SHu7w+nY0EXJiA3gLk5s2baNWqFdatW4f69etj7ty5yJkzp4rAli1bMHLkSDx48ADe3t7o168fatasCfsMyOLFi/Hll18iKioKgYGBGDt2LHLnzu3CEdTuOgWIdlbsSQLmI7C2H7BruvnspsX/SYACxHoLpFnZQIx/uTTfE2K90NKjJyCgtwBZsGABLl++jF69emHy5MkqqyECQ5oIkk8++QQVKlRAaGgo5s2bhxEjRsQLkOrVq0N+fvrpJwQFBWHgwIHw8PBQ17AlT4ACJHlG7EEC5iSwdTywYag5bafVFCAuuAbeeCYfhjct4YKe02USiCOgtwBp3rw5xowZg4IFC+LcuXPo0qULVq9erWxp06YNChQogLfeegvBwcHxIbLPgERERMDX11d9tmLFCixbtgxz5sxhODUQoADRAIldSMB0BP5YCnz3lunMpsHaCDADoo2TGXv1rlsY7z0fYkbTaTMJOExATwFy4sQJNGzYEP7+/vF237t3T4mI4sWL48qVK5gyZQrWr1+PjBkzon///njuuefiMyBSsC6fSxG7m5sbbt26hTx58qhMCVvyBChAkmfEHiRgLgLHfgQWvQbERJvLblqrmQAFiGZUpuw47fVyqF+S58hNGTwa7RABPQXI6NGjERAQgM6dO8fbLEXo58+fx6BBgx7xQwrU+/Tpg927d6ssiTwFS45rTZo0CYsWLUKGDBmwfPly9UMBom0JUIBo48ReJGAOAhcPAjNfAKIizGEvrUwRAQqQFGEzzUXeHu5Y2rUKSgVlMo3NNJQEnEFALwESHR2NGjVqKLEgx69sTY5htWzZUj0Rq3379qouJFeuXAgLC1OP3d2zZ48SLCJA5GlYUqguRejy9+7duyM8PBxLly51BgrLj0EBYvkQ00GXIXD3KvBFDeD2eZdx2VUdpQCxfuQz+3th5XvVEJT53+Mh1veaHro6Ab0EyObNmzF8+HD19KuErXHjxujZsyeuX7+O6dOnQ8SK1HnI7+rWrftIEfrbb7+txIc8+apHjx7o2rUrmjZtqo5rsf03AQoQrhASsAKB6EhgTkPg3C4reEMfkiFAAeIaSyR/Vn8lQjL4ermGw/TS5QnoJUBcHrQBAFCAGCAINIEEHCaw4j1gLwvfHOZokgEoQEwSKCeYWblAFnzT6Rl48B0hTqDJIYxOYOv5rTh45aBmM6sGVkXp7KU192dH4xCgADFOLGgJCaSMAB+3mzJuJr6KAsTEwUuB6W2fzY+hLxVPwZW8hARIgASMSYACxJhxoVUkoI3AiZ+Br1sAsbHa+rOXJQhQgFgijE/kxNTXy6EBn4z1RMzYmQRIwLgEKECMGxtaRgL/TeDaCeDLmsCDOyTlYgQoQFws4AB8vdzx4/s1kD9bOtdznh6TAAlYjgAFiOVCSodcgkDkPeDLWsCVUJdwl04+SoACxDVXRMHs6bHqvWrw8/ZwTQD0mgRIwDIEKEAsE0o64lIEVvYA9sx2KZfp7L8EKEBcdzXUL5EL094o77oA6DkJkIAlCFCAWCKMdMKlCBxZCXz7hku5TGeZAeEa+JfAsCYl0KZKPiIhARIgAdMSoAAxbehouEsSuHkWmF4diLjlku7T6TgCzIC49krwdHfD8neqomRQgGuDoPeWI3B3yxbc239As1/pq1eDX5kymvuzo3EIUIAYJxa0hAT+m0D0Q2DWi8D5vSTl4gQoQFx8AQAIzOSH9b1qwN/bkzBIwDIELo0YievztL/TKueA/sjStq1l/HclRyhAXCna9NXcBNZ/BGz7zNw+0HqnEKAAcQpG0w/yWuVgjGhW0vR+0AESsBHQW4CcOHEC48aNw65du/DgwQMULlwYnTp1QoMGDXQNysyZM3Hq1CmMGDFC13nTcjIKkLSkz7lJQCuBsJ3A7Hp834dWXhbvRwFi8QA/gXtz2ldEzSI5nuAKdiUB4xLQU4CEhYWhadOmePXVV9G6dWtkyJABmzdvxtChQ9VPo0aNdAN1//59REdHI3369LrNmdYTUYCkdQQ4PwkkR+DhXWDas8CNM8n15OcuQoACxEUCrcHNrOm8salPTWTw9dLQm11IwNgE9BQgH374Ie7cuYPPP//8ESg//PCDyops2rQJbm5uGDVqFFasWAEvLy+8+eabKkMibdq0aVi2bBkePnyIWrVqYfDgwfDw8MDVq1fRt29fnD17VomK9u3bo23btjh8+DD69euHmjVr4o8//sCFCxfUNdWrV4d9BiSp640duSe3jgLkyZnxChLQl8DafsCu6frOydkMTYACxNDh0d24xqXzYFLrsrrPywlJwNkE9BQgderUQe/evVG/fv1H3BBBUaJECaxatQpHjhzBvHnz8PXXXyMiIkJlRSZNmqREhoiUxYsXI126dOjWrRsqV66Mdu3aYdiwYaqvHKeSLIuMv3HjRty6dUtdP2vWLFSrVg1r165Vf1+yZMkjAiSp63PmzOls3Gk6HgVImuLn5CSQDAEeveISSYQABQiXRUICX75ZHnWL5yIYEjA1AT0FiAiGKVOmoEKFCo8xk89EaCxatAglS5ZUWQxp4eHh8PPzwwcffKDqRbp27ap+/8svv2DGjBn45ptvVNZDfry9vdVnInRGjx6tjle98sor2Ldvn/q9iJvOnTurY1/2GZCkri9XrpypY5vQeAoQS4WTzliKgDz1anJFHr2yVFCd4wwFiHM4WmmUAD8v/Nz7OWRL72Mlt+iLixHQU4BI/cfbb7/9WMF5ZGSkyoCsXr0aI0eOVJ+3aNHikUi89dZbOHjwIPz9/dXvY2JikCVLFsjxLREWEyZMwOXLl+Hp6YnQ0FDMmTMHGTNmRIcOHbB161Z1zbFjx+L/bS9Akro+MaFk5uVBAWLm6NF2axPgU6+sHV8HvKMAcQCehS9tUDI3pr5urbukFg4XXUuEgJ4CRI5InTt3TtVy2LeVK1fis88+w4YNG9CnTx8UKVIkvu7j4sWL8PX1VRmNkJCQ+MyI/fUiWERotGzZUv1a6kPGjBmjWYAkdT0FCL8yJEACqU+AR69Sn7GJZ6AAMXHwUtn0bzs/g8oFsqbyLByeBFKHgJ4C5Pr166hXr54SCm3atEHWrFnVUSopTpfMhxydWrNmDaZOnYqFCxeqLEezZs1U7YdcK0e0pDZEakDkqJYUoLdq1QoVK1bE7NmzVRZFxMyQIUMwceJE5MqVS1MGJKnra9SokTrQ02hUZkDSCDynJYEkCfDoFRdHMgQoQLhEkiJQKEd6/NSjBjzc3QiJBExHQE8BInDk3RuSzZD3gEjxuWQ1pK7jxRdfVOxiY2Pji819fHyUULF/CtZ3332n+uTLl0+JFikUF1Eyffp0ZM6cGU2aNMGNGzdUofnYsWMxYMCAZI9gJXW9PHErMDDQdDFNymAKEMuEko5YhsDW8cCGoZZxh444nwAFiPOZWmnEjxoXR/uq+a3kEn1xEQJ6CxAXwWpINylADBkWGuWyBG79BUypDDwMd1kEdDx5AhQgyTNy5R7pvD2wuW8tZGVBuisvA1P6fnfLFtzbf0Cz7emrV4NfmTKa+7OjcQhQgBgnFrSEBIAl7YFDy0iCBP6TAAUIF0hyBF6pkBeftiyVXDd+TgIkQAJpQoACJE2wc1ISSITA+b3AjFpEQwLJEqAASRYROwBY070aiuUJIAsSIAESMBwBChDDhYQGuSSBmGhgZm3gQtwLithI4L8IUIBwfWghUDIwACvfq6alK/uQAAmQgK4EKEB0xc3JSCAJAru/Alb1Ih4S0ESAAkQTJnYCMP2N8qhXgm9I52IgARIwFgEKEGPFg9a4IoEHd4CJpYB7113Re/qcAgIUICmA5qKXBGX2w699avGxvC4af7pNAkYlQAFi1MjQLtch8MtI4NdRruMvPXWYAAWIwwhdaoDhTUvgjWfyuZTPdJYESMDYBChAjB0fWmd1AuGXgEkVgAe3re4p/XMiAQoQJ8J0gaGypfdWj+X19/Z0AW/popkJhB26hountP//MLh4VuR6KqOZXXZZ2ylAXDb0dNwQBNb2A3ZNN4QpNMI8BChAzBMro1jao05h9KgTYhRzaAcJJEpg6+LjOLDxnGY61VqFoHTtvJr7s6NxCFCAGCcWtMTVCEj247OSQNQDV/Oc/jpIgALEQYAueLmflwd2DHgemfy9XdB7umwWAnoLkC+++AKLFy/G7du34ePjg9q1a2PgwIHq7ylpUVFRWL16NZo0aaIur1ixIpYuXYp8+bQdgRw5cqTqv2rVKuTOnTslJmi6ZubMmTh16hRGjBihqX9qdKIASQ2qHJMEtBCwcPZj782M6HeoKNwR+xiJn6r+roUO+/wHAQoQLo+UEOhYvQAGNSyWkkt5DQnoQkBPAbJy5UqIAJHNeK5cuXD9+nW89957KFu2LD744IMU+Xv48GGMHTsWs2bNemIBEh0djfr166N58+bq2i5duqTIBi0X3b9/HzJf+vTptXRPlT4UIKmClYOSQDIEbpwGplS2bPZDBMhnJ/JhXoU/uBRSgQAFSCpAdYEhfTzd8dvAOgjw93IBb+miGQnoKUDGjRuHmzdv4pNPPolHde3aNbi5uSFLlizqsyFDhkBEhaenJxo3box3330X+/fvR9++fbFu3Tp1ne3fy5YtUwJCsimlS5fGvHnzVAbk/fffx9dff40bN26gbdu26Nq1a6Kh+eWXX7BmzRr07t0b7du3x9q1a+P7iSiScTZv3qwyFwMGDMCvv/6q/i72fvXVV/D398fJkyfx0Ucf4eLFi/Dy8sKoUaOULbNnz8axY8dw6NAh1KlTR/W1ZUDEz/79+2Pfvn3InDkzPvzwQ1SvXh1Xr15Vfp49e1aJFbFJ7HdWowBxFkmOQwJPQmB5F+DAwie5wlR9kxMgi/7KjYsR3uhR6Kzyy/7fTXaWQ+vAC1h6ITcWVDiAvyN88NnJ/LgV6Qlv9xi0D/4LVbLewtE76TD2RAGUDriNM3f9EB7lgXefCkPJgHA15jfncmPd5WyIjHHDM1luqs/c3UyFMUljKUCsEce08KLb84XwQd0iaTE15ySBZAnoKUBkwy0b6jfeeAN169ZFiRIllNCwtcGDB6uN9//+9z+Eh4ejadOmanOfMWPGRAWICBIRDUuWLHkkA9KwYUMlZM6cOYNGjRphz5498PPze4yFZF9effVVVK1aFW+++Sb69OmDUqVKqX4iZDp27IjOnTvj22+/xbBhw5RYkaNd0leua9CggTr61bp1a/Vz8OBB1V9Ey6JFizB58mRlW3BwsMr62ASIHDkTewYNGqSuadeuHXbu3IlPP/0UERER6phWWFiYElcbN25Ezpw5k42jlg4UIFoosQ8JOJOAZD8+LwfExjhzVEON5YgAab6zLOrluoqO+cKUT532lcKrgefxQs7rOHXXDz0OPq2EyZWHPuiyvwT+9/RRVMpyC79dD8DU08GYU/4PbLuWCbPO5sXEUofh7xGNoaGFUDrjbTQPvGwoTik1hgIkpeR4XTpvD+z6sA7S+/CJWFwNxiOgpwAR7yUjINmJLVu24M6dO6hXrx769eunMgGSBZg+fTqKFy+uQEnG5MGDB2qjn1gGJCkBMmfOnPgxypUrhx9++AF58z5aOH/r1i0lcH7++We4u7urOhCxTQSPTYCInYULF8b27duVAPnxxx/VZyIgQkJCULNmTZWlOXDgADw8PNRnzZo1U/4cP35ciQfJhEizFyAieGbMmIFixeKOZ4otAQEBSnzJj7d3XN2YZE5Gjx4N8cEZjQLEGRQ5Bgk8CYGVPYA9cf8RsGpTNSB/FoGX+6M1IM9nv4YPQk4/kvEQBvYZEBEgw4sdQ7GMd3Hhvg/eOVAcyyvvUWlmae8ffBqvBF5ATt9I9P6jCL5/Zp/6fXQsUG9bRSyptBdTT+dDfv/7eC3v3+qzXdcD8O353BhfMtQSyClALBHGNHPigxeLoFutQmk2PycmgaQI6C1A7O2Q40tjxozBw4cPVQZDhMf69euRJ08e1U2OOclxLMk4PIkAsS9CT6ooXcSFZBpsm32ZT45Qbdu2Tf0p1y1fvhxBQUHYtWsXpFj9+++/V3ZJpkYyIdLn5ZdfVvUstia1HkOHDlXHqSTjM378ePWRvQCRzI+IGRnbvh05cgQTJkzA5cuXVWYoNDQUIqYqVKjglAVMAeIUjByEBDQSuH8DGFcUiIrQeIE5uzmaAZlU+jAC/R7gyJ10GHG0IOZXOBgPYtDhEDyb5QaKZLiHIYcL4euK/37WeEd5TCvzJ6aeyofQO+ng5xGtrouBGzJ5RWJamcPmBJrAagoQS4QxzZzIks4b2/s/D1+vuLukbCRgFAJ6ChDJWEh9hP2RIqnnkOJvOYJUo0YNlQGxZQakuFzEiRyj6tWrFzZs2KCwSfZE6kiSyoBoESAtWrRQQqJMmTLxoRA75PcvvPCCJgEidkl2Zu/evY+Fc/78+apWRbI4CQWIZHqmTJkSf9xLjmaJGJGMTIcOHdCyZUt1Ta1atZRAowAxyreFdpDAkxDYOh7YMPRJrjBl3+QEyOK/cuF8hC96Fjqj/PvqTCDuRHmqmhDJgNgEyMUIOWb1aAak+4Gn0TroAnL4RqLHwaJY8UxcduRhjBsa7qiIpZX2YOaZvCiQ7j6a57lkSn7JGU0Bkhwhfp4cgcGNiuGtagWS68bPSUBXAnoKEKmxkALs4cOHKxEiR4/k71L3MGnSJHz88ceIjIxUNSDST44zSZaiQIECqmZERIoUc4twkKyECBARJbZH+8r/lxJmPBLLgJw4cQJt2rRR2Q5bpl+gi3CRY1NTp07VJECkRkRqQORPOYolT/USYSQ/kj1JSoDIMS8RVuK7ZDmkLkZsqVatmjqyJRkSeWKY1LFMnDhRCTNnNGZAnEGRY5CAFgKR94HPywJ34o4FWbklJ0A2SLP9hwAAIABJREFUXM6CtZdyYFzJUCUc3jtQDE9nCH9MgMTGxqLz/pJ4JfBv1M5xDcfu+KPfoSKYV/4gLj/0wTv7i2Fg4ZN4LvsNrL+UBYvO58FX5f7E9msBWHAuUI3v5xGD1Rezw8MtFvVyXrUEdgoQS4QxTZ3IkcEHW/s9D29P9zS1g5OTgD0BPQWIHE+SrIYIh7t376pH0srmWp5CJTUg8jQr2ZxLLYbUZbRq1QpvvfWWMldEidRiyLs6nn32WSxcuFAd17py5YrKWohw2bFjhyYBIlkFmcv+aVwyhwgIsUcyLCJ4kjuCJcLj9OnTShD9/fffqg5EnlwlBen/lQGR2hcRY7t371ZP/5LrJSsix8IkAyQsRNjIU7ykiF2e9hUYGOjwwqUAcRghByABjQR2fwWs6qWxs7m7/dd7QMaVOIxC6e9j8JEQPIjxQGavSAT63sfdaA/0TJABEQpn7/nisxP5cSvKC95u0eiU/y+Uz3wbJ+/6Y1hoIXUca8f1TJAKkd6FTqFEwF0FL+4pWNkRExuLQL8IfBByBlm9I80N9h/rKUAsEcY0d2Lcy6XRotyj577T3Cga4NIE9BQgLg3aAM5TgBggCDTBBQjExgKTKwDXTriAs/q4KAJk6JGCLvmuEQoQfdaY1WcpkzcTvn+3qtXdpH8mIhB26Bounrqt2eLg4lmR66mMmvuzo3EIUIAYJxa0xMoETqwHFsQVcrE5hwAFiHM4chTXJrCuZw0UzpnBtSHQexIgAd0JUIDojpwTuiSBha2Bo2tc0vXUcpoCJLXIclxXIvBKhbz4tGXcy87YSIAESEAvAhQgepHmPK5L4N41YGxhICbKdRnQc6cS4BEsp+J06cG8PdyxZ3AdZPD1cmkOdJ4ESEBfAhQg+vLmbK5IwEUeveuKoU0rnylA0oq8NeflI3mtGVd6RQJGJkABYuTo0DbzE5Di889KArfOmd8XemAYAhQghgmFJQzJl9Ufv/apZQlf6AQJkIA5CFCAmCNOtNKsBM5sAeY0Mqv1tNugBChADBoYE5u1oGNlVCuUzcQe0HQSIAEzEaAAMVO0aKv5CCzvAhxYaD67nWixvBRw5tlgXH/ohYLp7qFnodMI8nuAsccLYOOVrPEzxQII8ovAjLJ/Pjb7/LA86mWCD2PcUSnLLfQoeAa+HjGYdjpYvUHdHTHqPR+2tvBcbni5x6BlIN+E7sRQcigLE3ipdB583rqshT2ka2YgcHr/Hvx9PFSzqQXKlEfukKKa+7OjcQhQgBgnFrTEagSk+HxCCSDyntU80+zPlQde6LSvJIY/fRTFMoZjblgQDt1Oj7Eljz42xuyzgfB0i8WbwRce+Wzz1cyYdTZIvdU8nUc0hhwphJIZw1Ez2zXMOBuMYU8fx9jj+dEw52U8nfEeLkd4YdjREEwsdRge8nZCCzZmQCwY1DR2ycfTHQc+qgtfL480toTTuzKBX+bOwN41P2hGULNNJ5Rv2ERzf3Y0DgEKEOPEgpZYjcDOqcCPA6zm1RP5IwLk8O30eC77DXXdiXB/DDocgkWVDjwyzt8RPhhwqDC+KPMnfDwkF/JvO3onHSJj3VAiY7j65Xfnc+JYeDo8l+0a9t0KwLtPhWFBWB7k9H2AF3Jcw7DQgmia+zJKBdx5IlvN1JkCxEzRMo+tE18tgyZlAs1jMC21HIG0ECAbN25E7969MXToULz00kvxTI8ePYqOHTsiT548+Pbbbx9hPXPmTJw6dQojRoywXAz0cogCRC/SnMf1CMx4Hji/x/X8/g+Pv/0rF46LCCl66pFeY44XwNPpw9Eo95VkeX14uDCeyXwDOXweYteNAHQvGIY5YYEI9otAgGck1l3OhgqZbmLTtWwI9ruPzgWs9wAACpBklwk7pIBAraI5MLtdxRRcyUtIwDkE0kKAdOvWDVWqVMGGDRswe/bseEfmzZuHffv2YcKECY85d//+fURHRyN9+vTOcdwFR6EAccGg02UdCIRfBsaG6DCReabYfSMAE0/mw/iSR5DdJzLecMmSdD9YDHPLH4S3+6PZj4TezQ8LxMHb6TGq+FHcjPTCsNBC+KzkYQw4XARdC4Rh9LECGFDkFD4JLYSpZQ5h4sn8qJXtGspmslY2hALEPOveTJa6uwH7BtdFgD/fCWKmuFnJVr0FyM2bN9GqVSusW7cO9evXx9y5c5EzZ05s3boVffr0wcOHD1GmTBlUq1YNx44dw6FDh1CnTh34+/vHZ0BkjP79+yuxkjlzZnz44YeoXr06rl69ir59++Ls2bNKrLRv3x5t27a1Urgc8oUCxCF8vJgEkiCw+ytgVS/i+YeAFJtLIfnwYscQ6PfgES5LzueCHMHqXvBskrxiY2Mx9XR+/HXfBx8VPQ7ff45p/XAhB7Zdz4RyAbcBNzd4usWgVEA4Fp7LhY+ePomVf+fAvWh3vBJ00VKxoACxVDgN5czwpiXwxjP5DGUTjXEdAnoLkAULFuDy5cvo1asXJk+eDB8fH3Tq1EkBl2zI8ePH1TGr+fPnq8+XLFmC4OBg2B/BGjhwIPz8/DBo0CAcPHgQ7dq1w86dO/Hpp58iIiJCXR8WFqYEjhz3EoHDBlCAcBWQQGoQmN0AOLstNUY23Zjbr2XCnLAgjCoeiizej78NvtcfRfFK4AVUznI7Sd+mn86Lqw+90T/kJDzdH+8mWZRhoSH4rNRhHA9PBxE1g4uKAMmOiGh3tAqy1tOwKEBM9zUwjcEV82fBki5VTGMvDbUWAb0FSPPmzTFmzBgULFgQ586dQ5cuXbB69epEBYiIB9sRLXsBUrVqVcyYMQPFihVT1926dQsBAQEq6yE/3t7e6veSORk9ejTKlStnraCl0BsKkBSC42UkkCQBHr+KR3Mn0gNv7y+BCSWPIJfvw0SRvbSjHL4s++cjn++/mQEZvKLVY3sP3sqAqaeDManUIXglIj5k0OFHC+KlXHGF5/JY3v6HCmNK6cOYcipY1YP8l7gx40qmADFj1Mxj844BtZE7wNc8BtNSyxDQU4CcOHECDRs2VMepbO3evXtYtmwZihcv/lgGRI5YjR8/XnW1FyAlSpTAjz/+iKCgoEficOTIEVU/IhkWT09PhIaGYs6cOahQoYJl4uWIIxQgjtDjtSSQGAEev4qn8tOlbOp9H14JajsWVdyHjF7RuBfljia7KmB1ld8fqf8YHvoUCqSLwOt5L2D08afw8+Ws8HD7tz4kn/89TCtzWM2z72ZG/Hgpm6r9sLWvw3Jj/+2MyOAZhUFFTkLOtlupUYBYKZrG82XoS8XR9tn8xjOMFlmegJ4CRLIRkqno3LlzPFfJcJw/f14dp0p4BGv//v0YN27cYwJE6j2mTJmCUqVKqc/k6VgiRpo2bYoOHTqgZcuW6ve1atVS2RYKkDjcFCCW/zrTQd0J8PiV7shdbUIKEFeLuL7+1gjJjnlvVdJ3Us5GAgD0EiByNKpGjRqQJ13J8Stbk2NYIhi2bdum6j7sa0CSEiAfffSRKlYfPny4ynJIoblcL4XrImIkQ7Jy5UoMGTIEEydOVPOyUYBwDZCAcwnw+JVzeXK0RAlQgHBhpCYBbw937P/oBfh7e6bmNBybBB4joJcA2bx5sxIM8vSrhK1x48bo2bOnenqVFgFy584d9cSs3bt3I0uWLBg8eLB6CtbXX3+N6dOnqydjNWnSBDdu3FBF7HLEKzCQ79thBoT/ASABZxLg8Stn0uRYSRCgAOHSSG0CX7apgLrF+LSe1ObM8R8loJcAIfe0J0ABkvYxoAVWIrCwNXB0jZU8oi8GJEABYsCgWMykVyrmxact4s60s5GAXgRO79+Dv4+Hap6uQJnyyB1SVHN/djQOAQoQ48SClpidQGwsMCoYeJD042TN7iLtNwYBChBjxMHKVmRJ5429g1+wsov0jQRIIA0JUICkIXxObTECFw8C06tbzCm6Y0QCFCBGjIr1bFr1XjWUCAywnmP0iARIIM0JUICkeQhogGUIbB0PbBhqGXfoiHEJUIAYNzZWsqznC4Xxfu0QK7lEX0iABAxCgALEIIGgGRYgwMfvWiCI5nCBAsQccTK7lRXyZ8bSLs+a3Q3aTwIkYEACFCAGDApNMiGBqAfAyCAgOvG3fZvQI5psYAIUIAYOjoVM83B3w9FP6sHTw91CXtEVEiABIxCgADFCFGiD+QmcWA8siHvbKRsJpDYBCpDUJszxbQQWd66CSgWyEAgJkAAJOJUABYhTcXIwlyWwth+wa7rLuk/H9SVAAaIvb1ee7YMXi6BbrUKujIC+60gg4ugNPAzT/iRJ3yJZ4B2cQUcLOZWzCFCAOIskx3FtAjOeB87vcW0G9F43AhQguqF2+YmeK5wdcztUcnkOBKAPgZsrTyF823nNk2Vq9BTSV+NbxTUDM1BHChADBYOmmJRA5H3g0/xAVIRJHaDZZiNAAWK2iJnX3nTeHvjj4xfh7u5mXidouWkIpIUA2bhxI3r37o2hQ4fipZde0sTqu+++Q4sWLTT11dLJfrxOnTqhUaNGaNKkiZZLTduHAsS0oaPhhiHw1+/AzDqGMYeGWJ8ABYj1Y2wkD1d3r47ieTIaySTaYlECaSFAunXrhipVqmDDhg2YPXt2smRjY2NV/507dybbV0uHhOPdvn0bPj4+6sfKjQLEytGlb/oQ2P0VsKqXPnNxFhIAQAHCZaAngY8aF0f7qvn1nJJzuSgBvQXIzZs30apVK6xbtw7169fH3LlzkTNnTkW/S5cuKF68OP78809cunQJuXPnxuTJkyGCRcRKoUKFMGvWLHh5eaFv3744e/YsoqOj0b59e7Rt21aNUbZsWTWO9Pvll1+wfv16fPbZZ4iMjETlypUxcuRIvP/++4+MN2TIkPgMyA8//PBYf29vbyxevBhffvkloqKiEBgYiLFjxyr7zNQoQMwULdpqTALLuwAHFhrTNlplSQIUIJYMq2GdalImEBNfLWNY+2iYdQjoLUAWLFiAy5cvo1evXkpcSNZBjkBJE6EhAmXOnDnw8PBAw4YNMWDAACVKqlevjkOHDql+w4YNQ0REBEaMGIGwsDAlZORYlwiZihUrqqNaIlAuXLiAZs2aYdmyZciTJw+6du2K8uXLKwFkP57tCJZ89l/9f/rpJwQFBWHgwIHKvk8++cRUC4ECxFThorGGJDCpPHDthCFNo1HWJEABYs24GtWrwjkzYF3PGkY1j3ZZiIDeAqR58+YYM2YMChYsiHPnzqlsxerVq+MFiIgAyWhIE8FQu3ZtPP/8848IBsl6yI9kJqTVqVMHo0ePRrly5ZQAkUyFZEK+/fZblQWZPj3uiZn3799XwiE8PDxRASKiJrH+Mo985uvrq8ZZsWKFEjUilMzUKEDMFC3aajwCUoA+IjcQG2s822iRZQlQgFg2tIZ0zM0NOPpJfXh78oWEhgyQhYzSU4CcOHFCZTX8/f3jCd67d09t5iXLIRmQGjVq4OWXX44XJPJvERj2GYsjR45gwoQJKpPi6emJ0NBQJQYqVKigBMiSJUuQP39+JURkThEn9u369euJChA59pVYf6kZmTJlisqyuLm54datWyqjMm/ePFOtBAoQU4WLxhqOwJktwJxGhjOLBlmbAAWIteNrRO9YiG7EqFjPJj0FiAiBgIAAdO7cOR6kFKGfP38egwYN0ixAGjRogA4dOqBly7iXEdeqVUtlVWwCZOnSpciXL58SInJsaubMmaqfCAcRPHLsK7EjWA8fPky0/4EDBzBp0iQsWrQIGTJkwPLly9UPBYj1vg/0iASSJrBzKvDjABIiAV0JUIDoipuTARjbqjRalg8iCxJIVQJ6CRA5MiXZDNm0y/ErW5NjWCIktm3bhh49eiSaAZEaDxEX+/btU9kTyXKIcClRogRWrlwJKSKfOHGiulY+swmQixcvqvoQKSB/6qmn1PhyzWuvvfbIeLYaEClST6y/zLllyxaVUZEnZnXv3l0d45J5zNSYATFTtGir8Qis7AHsSf6xfcYznBaZmQAFiJmjZ07b36pWAIMbFTOn8bTaNAT0EiCbN2/G8OHD1dOvErbGjRujZ8+e6ihWYkew5EhWu3btcPDgQfV0KylGl7qOzJkzq3d33LhxQ2U75PqmTZvGCxCZZ+3atapYXZ5eVbVqVfV3qemwH0+OV9neA5JYfxEbb7/9thIf8uQrETJSnyJz9e/f3zSxpgAxTahoqCEJzG4AnN1mSNNolHUJUIBYN7ZG9ezZglnxTadnjGoe7bIIAb0EiEVwmdoNChBTh4/GpzmBMYWAu1fS3Awa4FoEKEBcK95G8DbAzwsHPqprBFNog4UJRBy9gYdhtzV76FskC7yDM2juz47GIUABYpxY0BKzEYi4CYzKZzaraa8FCFCAWCCIJnRh35C6yOzvZULLaTIJkIDRCFCAGC0itMc8BP76HZhZxzz20lLLEKAAsUwoTeXID92qonRQJlPZTGNJgASMSYACxJhxoVVmILD/a+D7d8xgKW20GAEKEIsF1CTuTH6tHBqVym0Sa2kmCZCAkQlQgBg5OrTN2ATWfwRs+8zYNtI6SxKgALFkWA3vVN96RfFOzX8fWWp4g2kgCZCAYQlQgBg2NDTM8AQWtgaOrjG8mTTQegQoQKwXUzN41LpSMEY2L2kGU2kjCZCAwQlQgBg8QDTPwASmVQUu/WlgA2maVQlQgFg1ssb2q2qhbPi6Y2VjG0nrSIAETEGAAsQUYaKRhiTAR/AaMiyuYBQFiCtE2Xg+Bmfxx+a+tYxnGC0iARIwHQEKENOFjAYbgkBMFDA8BxATbQhzaIRrEaAAca14G8VbD3c3nPhffbi5uRnFJNpBAiRgUgIUICYNHM1OYwI3zwKflUpjIzi9qxKgAHHVyKe93zsH1kaujL5pbwgtIAESMDUBChBTh4/GpxkBvgMkzdBzYoAChKsgrQj81KMGiuTim6fTij/nJQGrEKAAsUok6Ye+BEJXAYte13dOzkYC/xCgAOFSSCsCy7o+i3L5MqfV9JyXBEjAIgQoQCwSSLqhM4HdXwGreuk8KacjgTgCFCBcCWlFYG6HSniucPa0mp7zkgAJWIQABYhFAkk3dCbwy0jg11E6T8rpSIAChGsgbQlMea0cGvJt6GkbBM5OAhYgQAFigSDShTQgsLYfsGt6GkzMKUmAGRCugbQj8GnLUnilQt60M4AzkwAJWIIABYglwkgndCewsgewZ7bu03JCEhACPILFdZBWBAY3Koa3qhVIq+k5LwmQgEUIUIBYJJB0Q2cCy7sABxbqPCmnI4E4AhQgXAlpRaDXC4XRvXZIWk3PeUmABCxCgALEIoGkGzoToADRGTinsydAAcL1kFYEOtV4Ch82eDqtpue8JEACFiFAAWKRQNINnQksaQ8cWqbzpJyOBJgB4RpIWwKtKwVjZPOSaWsEZycBEjA9AQoQ04eQDqQJgYWtgaNr0mRqTkoCzIBwDaQVgZdK58Hnrcum1fSclwRIwCIEKEAsEki6oTMBChCdgXM6ewIUIFwPaUWgVtEcmN2uYlpNz3lJgAQsQoACxCKBpBs6E5jfHDj5s86TcjoSiCNgBgHi4e6GkyMaYPa2Mxi68lB86OoWy4mWFfLi7Xm7nyic+bL6I3sGH+w+cwNtquRHueBM6PHtfk1jBPh54efeNbH+8EUMWPaHpmtS2mlWu4pYceACvt93PqVDGPq6ivmzYEmXKoa2kcaRAAkYnwAFiPFjRAuNSGB2A+DsNiNaRptcgIBZBMihofVwOyISr83YiROXw1VkUipA5NGv3p7umLbp5BMLEBEsGX098UqlYNQZtwkPomJSbZXIPDJ+as6RasZrGLhY7oxY8351DT3ZhQRIgASSJkABwtVBAikhQAGSEmq8xkkEzCJADg+rp7If9UrkQpuvfntMgLi5Ab1fKIL6JXMjFrH4469bGPz9nwh/EIUZbSrg6MU7aFE+CLO3ncY7NQshMiYGS3b/hYu3IlAhf2Z4ebirTMil2w/QdcEenL95P1HCP3Srim7f7EPX5wpi+8mrWHXwb9WvQ9UCKB6YUY1TMHt6XL4dgW9+C8MrFfOiUI4MmLD+GH7YH5fJeLdWIWWLt4c7NoZewscrDiEmFo/Y2WfJAXSoViA+A9K0bCB6v1AYXp7u2HnyGvouPYiH0TFq/C7PFYSnhzvO37iPnt/uw9+3Ipy0OlJ3mLxZ/LGlb63UnYSjkwAJWJ4ABYjlQ0wHU4XAjOeB83tSZWgOSgLJETCLADk6vD5CPlyD1d2rY/y6Y9hw5NIjGZDGpfMoYdFi2jbcexiNya+Vw7nr9/Dpj6GY9kZ5ZEvvjTe/2oWIyBgMa1JcbdJtGRB5H0XLadtx4ko4Jr5aBudu3MfYn44+hq5QjvQY1bwUWk7fDjk+9E7Ngmg/53fVTzIj7z1fCLXHbcKdB1HY1q82thy/gn7fHVR9R7YoiTrjflU2961XFM2nblPiSGzbeeqaOl6W0E7bEazdZ65j5XvV8dKkLUoYzWhTEbvPXse3v5/DjgG11Zx/3biPT1uUQlRMLD5cnrpHw5JbU1o/z+zvhX1D6mrtzn4kQAIkkCgBChAuDBJICYFpVYFLf6bkSl5DAg4TMIsAOTa8PgoOXINnnsqKUS1K4YXxm1CrSI74GpAJr5TBoQu3MHPLacWkWqFsGNDgaTT8fIva2P/x101M3XRSfZZQgNQonA0d58bVkbSvmh8lAgPQe/GBx9gOqF8UYdfv4etdYeqzTX1qoeW0bbga/lAJkCoFs6rsiTSpbZiz/QxWH/wbOTP6YkOv51Dy458w8dWyOHbpDqb8ckL1k0JsyWC88sWOx+y0CRBfL3fULpoTnf6pdZF/x8RAZUB8PN3jj2g1KZMHLcvnVULLDE0yQMf+V98MptJGEiABAxOgADFwcGiagQlMKg9ci9uMsJFAahC4liEbzmXOi7D0WRDm7YMw91iERd1Fkbv5MfdQvdSY0qljShG6TYDIwNPfKI/9527i1JXweAEyt0MldcRp2d64Y04iIma2rYBnRvysNva/HruMRb+dU58lFCD2RehJFaW7u0FlG/y9PRH7j3ey+R/zU6gSPXJdqaAAfLAkTrgsersKvtx8EhtDLyN7eh/82rcWig35EbPbV0KZvJlw90GU6ie+XQt/gEaTtj5mp02AiIAJyZE+fmx7uO89H4I6xXIgNhaQAvkLN+/j9ZkUIE5dgByMBEjA0AQoQAwdHhpnWAITSgC34jZGbCSQEgKxbsCVjLkQljkQ59JljhMZiEFYdDjC7l/GvajH6xlyR6XHW3e6ot/FrCmZUtdrEgqQvJn98H23auqYlGQQ5ClY414ujcMXbuOrrXEZkOoh2dCvXtH4jf2mo5fVkaWUCpCaRbKjfdUCaDsrrv5EWvE8GTGmVWk0mLhFswCRY1JSjzJrW5yd9k2Ekr2dNgEimYL6JXLFH/fK6OeFdN4eKJ03E3rUKYxW07fjTkQUmpcLRItyQaYRIDyCpevXiJORgGUJUIBYNrR0LFUJUICkKl6rDB7j5oaLmYMQFpAbYekCcM7LB2FuUQiLvIO/7l9BRPSTFR5/fqwu9uasjUkXbPfzjUsqoQARS/u8WASNSuXB0Ut3lACRDXq350NULceDqGhMfb28Ouo0fv0xlVmw39gPblQMD6NiVH1IwoxHUhkQqSnZcfJq/PErG62t/Z5Hx7m/o1KBrJoyIHWezon364SoI1dSqyJvA4+OicXi3eces9MmQHacvIb1vZ5T9S0nL4er+pY/zt/C3QfReK5wdrw193f1ZK4pr5dHel9PNJtijqfqiZDc0u954y48WkYCJGAKAhQgpggTjTQcgTGFgLtXDGcWDdKfQJSHB/7OFIywTLkQ5p8RYV6eCIuNxLmoO/jr3mVExkQ6xag3bzyNSjeb4ltfd6y9mcEpY6bmIIkJEH9vD/zyQU0c+OtW/HtAJBvQqHRuuMENUrgtT5e6Hxn92MZe6kOmv1n+/+3dd3hUZfo38O9kMum9kQ4hlFACSSgaAREELIgisEhc9RWwuz+VXTu6ukpRWQtFuoUmNqyoIGAhFOmQhBYIZRLSey9T3uucLMGYhMwkM8mZc77Hy0vJPO3+PPNHbs5T8OupXBy4UNToHpDmEhDhl/v9s8dixFu/IK+splGo/57QF3q9Udy4bsoSLKGycArWlEGhsFOpcD6/As9+eQy5ZTUtJiDCPSC3RgdBSJzs7VTYdTYfz32ZJCYbH9w/BB5OGmSVVOGdn09jxX2D8fXhS5j340lrTolF2u4T5I6fnrzeIm2xEQpQQLkCTECUO/eMvD0C88OAmtL2tMC6NiRQZ++IDJ8waD27QOvsDq29GlpjLbS1xciqyofeqLdqNN56Z6z81A/Hb/h/mF/piHydxqr9sXEKtCQgHH/85SPXEYgCFKBAuwSYgLSLj5UVK8AlWLKb+moHZ6T7hEPr4Y90IclQ20FrrIG2pgjZVfniPRWd9Sw7PBAqh+FID3DA8wVdOmsY7JcCEPbVfDx9KCUoQAEKtEuACUi7+FhZsQK8B8Qmp77SyR0XhSTD3Q9aZ1ek26mgNVRBW1OIvOpCScY0obwH7l18GocnL0Ol5zHMLYyU5Dg5KGUI3DYgSNzPwocCFKBAewSYgLRHj3WVK7BuEpC2Q7nxSzjyUhcvaL2FNxm+0Dq6iMfXpuuFJKMAhTXFEh5506F5GZywaoMHyiOuw4mAIdB652J1fpBNxcDByktAuMVdOBWMDwUoQIH2CDABaY8e6ypX4OtHgGMblRt/J0fe0h0Z6dUFKK0r6+TRWa77xckx6LL5IE4lLIXe/jS+UflgV6mr5TpgSxQwU2Dm8AhxYz0fClCAAu0RYALSHj3WVa7AtleA3e8pN34rR96WOzKsPKQOb15cevV+Kmp7xGFX8HR4eP2EuaoRqDaqO3ws7JAClwWevLEnZo3tRRAKUIAC7RJgAtIuPlZWrMCud4Dt/1G/A2vdAAAgAElEQVRs+JYI3NJ3ZFhiTFJpw9Goxvr1vjBmZEKbsACXytTQq7ZgrnqcVIbIcShUYPatffDg9d0VGj3DpgAFLCXABMRSkmxHWQJHNwDfPKasmNsQrd7ODpleYf+7I8MTWgfhjgydxe/IaMPQJF3lnROxCP32APQ+gdg96BX4dNEiF5ewoJxr74Wb0oVfgIWLAC8/dyzZhVPZZZgwMBj/HNsLvm6OSLlUghe+SsLFgsomc91SuV5d3MWb2IXn/V/P4LC2fs+Qu5M9Pn0oHlOX70FFrXWPXJb0FxPAvDujcfc14VIfJsdHAQpIXIAJiMQniMOTqMDZbcD6KRIdXMcOS2enQYZv/R0Z6S4eSLfX1N+RUVeKS5W50Bl1HTsgG+9tZFU4Hl90ATAYkDtpNlIKg+ETkIjjbmFYX8gjeOfeGY3T2aVYu/dio5mO8HPFV48Nw7SVe3Emp0xMJPoGe+LeD/aZXG5RQixW/J6GwopazJ80APd/tF+s++rt/XD4YhG+O5Zp49+u9g9fMLp9YHD7G2ILFKCAogWYgCh6+hl8mwUyDgCrx7S5uq1VrNU4/u+OjICGi/jSDUKSIVzElweD0WBrIUlyvMLSq3WbgoAzF2BQa3Bg/BJUltdCX7kS23pMQ1K5oyTH3ZGDEn4B/uVULoSbxv/8hHo7I9LfDb+n5ok/jg3zwpK/x2HYG7+YXO6HJ0YgYdUfKK2qw2/PjMINC36FcPP37PF9cc/qxolMR8Yspb4+vH8IRkcFSGlIHAsFKGCDAkxAbHDSOGQJCBRfBN6T13KYKgcXaH271l/E5+QGrXBHhrEa6TXFyK6u/6WOj3UF3kyNQ8Sm+r91Lx07Ewfr4uDdpQhFlzZhge/dgEpl3QHYQOvCJXh2dkCEnxtgNOKT/Vos+y2t0cjdHO3xyu39UFmjwyvfHW8xqr+W++4fw8W3HiVVddj2z5EY/d/f8MmD1+Ldbam459quEMq/s+00jmeW2oCUdYb42cPxuCbCxzqNs1UKUEAxAkxAFDPVDNSiArpqYI7tLYcpd/YQL+JLFy7ic3KFVg1o9ZXiRXwF1UUWJWJj5gmIS6+WaAFd/ZK15KkrkJerg39oCsqqs/C6brh5Dcq09OOjeohvKD47kI5ufq5YM2Mo/vPdcWw5ni1G/OKtffDQ9d2x/3whHlx7UEwmmnuaK/faHf3x66kclFbr8PdrumLX2TxEBXqguk4PbWEldp3Jx3vTYsVlXkp9hLdE/YI9lBo+46YABSwkwATEQpBsRoECr/sD+lrJBV7k5gutdyi07j7IcHSFVmWA1lAJbVU+imuV+ze3kpuoPw1IbVRh46YQcemV8FTHjsUez4ni/ztqPkV5SF+8V9xTyiF02tieuLEngjyd8MJXyQ1jcNLY4d5ru2FSXChuWbizxbH9tZy/uyPmTOwPezs7vP3zabw1ZQDuWrEXi++Owxs/nUJqThkSnxuNEW82XtbVacF3QsdC/GHezp3QM7ukAAXkJMAERE6zyVg6VuDd/kBJesf2+b/e8j26QOsdAq2rN7SOjkhXGaHVV0BblYfyuopOGRM7bbvAnHOx6PXZgYYG0hIW4mKWPVzcq1GoXYrMwdOwqcC77R3IqObgbt5ISi9Brb5+39HT43rDw9keG/dr4e3igD1pBeLP1XYqnJl7K66Zux155TUNAsKeDlPKvTKhHw5ri/D9sUx8NH0o5v5wAmdzy/H7M6MwcsGvMhI1L5S0ebeKtnwoQAEKtEeACUh79FhX2QKrRgOXDlnFQLiIL8czBFrvYGhdvJDu4ACtSg9tXTnSq/NQpauySr9stOMFYmoD8eLC7IalV7qQHkjsPQvCvv6AsAvQJn2Fb/s+BG0VLyAUZuebx4fh11O5WPTLGYT7uIjH4z6/KUmcuDenDMDU5XvF5VKT40Lx/K1RGDJnu3iM7i39g/D5wXSM7OXfYrnLsx8VWL/x/PIJWsLdFwcvFmHf+QIxGbnz/d0d/0WRQI8hXs7Y/fxoCYyEQ6AABWxdgAmIrc8gx995AusmAWk72ty/cEdGlnco0j2DoXX1hNbeHlqVDtraUmRU56FWgsu72hwsKzYrICy9Wv9DBNTJqQ2fZ901Bydz6t92ePlsQ0leOt7yTaDg/wSE43bnTYpGvyAPcX/Hh7vP46Pd9UvXhL0f98V3g4eTvZiEvLb5hLgXJNLfFT/PGonIF3+8arnLyMLG839/k4KzeeXij4K9nPD21BjY26mwaMcZJJ7JV+R8xHf3xcaHrlVk7AyaAhSwrAATEMt6sjUlCXz9CHBs41Uj5h0ZSvpCmB/ry9oYRG842FDR4OKOvSMXoKZKD5WdATXFS+Daoz/m1cab3zhrUMDCAlMHh4n7YvhQgAIUaK8AE5D2CrK+cgW2vQLsfg+8I0O5X4H2RC4svZq9tADGqivL6QonPImjZb3EZn2C8pF5Yi0Mg27F+4Vd29MV61LAIgLCfpt/jO5hkbbYCAUooGwBJiDKnn9G3w6Bnanf4PUjC5FdrczlGO2gY1UAn3wfAfuUMw0WRgBHJq1AcWH9Mbx+wQeQcTwRqTEJ2FriRTMKdLrAwmmxuCOGt6B3+kRwABSQgQATEBlMIkPoHIED2QcwY+uMzumcvdq0wHMZMRi07srSKyGYihFTsE89qiEujWotyooK8EnkTBToNTYdLwcvD4GvHx8m3jDPhwIUoEB7BZiAtFeQ9RUrkF+Vj1GfX/mFUbEQDNwsgZ46X8xbUtpo6ZXQwKmEpcjMEt6DAG5elcg/vxxuAcGY73qHWe2zMAWsJXDwpbHwc3OwVvNslwIUUJAAExAFTTZDtbzADZ/dgILq+nsH+FDAFIF1v0TBcV9Ko6K1vQZjV/D0hp91CUvFxaTNcOsfj/kVMaY0yzIUsKqAcLJY0qs3WbUPNk4BCihHgAmIcuaakVpBYPqW6TiY03gpjRW6YZMyEZiVNRDxHze9O0absABns1waovTw/BG5F06hashErM4Pkkn0DMOWBXgEry3PHsdOAekJMAGR3pxwRDYk8Nre1/BF6hc2NGIOtbMExKVXK6pgLC1tNAS9TyB2D3oFurr6m701DjpU5C+FQa/DkYH3YVepa2cNmf1SoEFg5vAIvHxbX4pQgAIUsIgAExCLMLIRpQp8fvpzvP7H60oNn3GbIbDmtz5w3pvcpEbupNlIKbxyspBfSBYyUjZCrXHAstD7UQPegG4GM4taSeC/fxuIKYNCrdQ6m6UABZQmwAREaTPOeC0qwJOwLMop28Yey4vGDauPNInPoNbgwPglqCitP3pXePyC/kDGiT3wCI/EXPU42ZowMNsS+OGJEegX7GFbg+ZoKUAByQowAZHs1HBgtiDAk7BsYZY6d4yBejcsXm5osvRKGFXp2Jk4WBfXMECj0Qg11qCyuBBO0cOwoJy3Tnfu7LF3QUClAlLn3AKN2o4gFKAABSwiwATEIoxsRMkC474ch6yKLCUTMParCHy0uz9cdx5ttkTy1BXIy73y9sPLvwTZqR+IZYsGTcL6wi60pUCnC0QFumPLU9d3+jg4AApQQD4CTEDkM5eMpJMEnvn9GWy5sKWTeme3UhaYXtAXt6xManaI1bFjscdzYqPP/ENTkJ78s/izndEzcaycdy5IeX6VMrY7Y0Pw7l08Dlop8804KdARAkxAOkKZfchaYN2JdXjrwFuyjpHBmS8gLr36yB7GvPxmK6clLMTFLPtGn7k4b0Jh5kVxA/p7ITPq177woUAnC7x2Rz/cF9+tk0fB7ilAATkJMAGR02wylk4ROJZ3DPf8eE+n9M1OpSuwav8AeO443OwAdSE9kNh7Foz1J++Kj5NbDYrT3xf/3yMiCnMxSrrBcWSKEvjpyevRJ8hdUTEzWApQwLoCTECs68vWFSBQravG8E+Ho0Zfo4BoGaIpAvcU9cHty5seuXu5btZdc3Ayx7tRUwFhF6BN+kr8mTp2DN4r7mlKVyxDAasKuDiokfLqTbCz49s4q0KzcQooTIAJiMImnOFaR+DuH+5Gcn7Lv3Bap1e2KkUBL4MTVn3o1OLSK4OLO/aOXICaKn2j4Xv7/YqsM/VH9WYOnoZNBY0TFCnGyjHJX2BUVAA+un+I/ANlhBSgQIcKMAHpUG52JleBN/a/gQ0nN8g1PMZlhsCywwPhu/VQizUKJzyJo2W9Gn2usjNAX7kStVWV4s+/7fsQtFW8gNAMdha1ksDzt0ThkZGRVmqdzVKAAkoVYAKi1Jln3BYV2KHdgad+fcqibbIx2xOYUN4D9y4+1eLAjQCOTFqB4sIrR+8KhX2C8pF5Yq1Yz9nDC2/5Jthe8ByxLAW+evQ6xHXl2zhZTi6DokAnCjAB6UR8di0fgdLaUgzbOEw+ATESswXEpVcbPGDMyGyxbsWIKdinbrq5PCDkCLQpv4r13HvHYF5tvNn9swIFLC3gaG+H4/+5Cfa8gNDStGyPAooXYAKi+K8AASwlMPm7yUgtSrVUc2zHxgQWJ8egy+aDVx31qYSlyMwS3oM0fhw1n6Iktz5xMQ66FUsKu9pY9ByuHAXiu/ti40PXyjE0xkQBCnSyABOQTp4Adi8fgXcPvYsPUz6UT0CMxGQBcenV+6mA4U/n6v6ldm2vwdgVPL1Jm25elcg/v7zh52diE7Cl2MvkvlmQAtYSePqm3vjHqB7Wap7tUoACChZgAqLgyWfolhU4kH0AM7bOsGyjbE3yAo5GNdav973q0ishCG3CApzNcmkST0DYOWiTvqn/uUqFTyJnokCvkXzcHKD8BX54YgT6BXvIP1BGSAEKdLgAE5AOJ2eHchWo1dfihs9uQFldmVxDZFzNCLxzIhah3x64qo3eJxC7B70CXV3TNyRePtuQnVZ/hLNbQDDmu95BZwp0uoC3iwZH/j2u08fBAVCAAvIUYAIiz3llVJ0k8MQvT+DX9PrNxHzkLzCyKhyPL7pw1aVXgkLupNlIKQxuAqJx0KEifykM+vpTsdz6x2N+RYz84Rih5AWmDQ3DG5MGSH6cHCAFKGCbAkxAbHPeOGqJCnx++nO8/sfrEh0dh2VJAWHp1bpNQcCZC1dt1qDW4MD4JagobXz0rlDJLyQHGSlX7o+pGjIRq/ODLDlMtkWBNgmsuHcQbuoX2Ka6rEQBClCgNQEmIK0J8XMKmCFwqfwSbt50sxk1WNRWBd5MjUPEpv2tDr907EwcrItrtpxf8AFkHE9s+OzIwPuwq9S11TZZgALWFLBTAcf/czOcHXgZpjWd2TYFlCzABETJs8/YrSIw4esJuFB69b8Vt0rHbLTDBGJqA/HiwmxA1/Stxl8HkTx1BfJym5YzGo1QYw0qiwvFKmqNA5aF3o8a8Je+DptIdtSswPAeflj/wDXUoQAFKGA1ASYgVqNlw0oVWHp0KZYdW2az4V8fej3euP4NzNk7Bz+e/7HVON6/8X38dP4nbD63udWyciigNqqw/ocIqJNbv/OlOnYs9nhObDZsd99y5J1d2fCZR3gk5qq56VcO3xFbj+Hl2/pi5vAIWw+D46cABSQswAREwpPDodmmwPmS87j9m9ttc/AA3rnhHezL2ofR4aPx8LaHW43D3cEdNfoaCKeAKeF5WRuD6A1Xv3DwssOZu5cgPVPVLEuXsFRcTLqStDlFD8eC8mglEDJGiQskPjcaYd7OEh8lh0cBCtiyABMQW549jl2yArZ6K7qHgwc2jN8AYRnZtxO/xQNbH0BeVZ7oHB8cj2eGPANHtSPq9HV4++DbSLyUiD+/ARnoPxCzr50NN42bmJDM3TcXwv0ocnmEpVezlxbAWFXVakgtXTx4uaKr27coSE9raKd40GSsKwxotV0WoIA1BWLCvPDN48Os2QXbpgAFKAAmIPwSUMAKAquTV2Ph4YVWaNm6TU6LmgZ/Z38sPrIYDw94WHyz8fHxj8VOv77ja7y29zUcyT2CXt69cHefu/HqnlcbJSCbbt8k3gb/w7kfcFv32/DQwIdw+9e2+zbor9obN3c3aemVUK+liweFz4Tjd8tyFjVqfmf0TBwrd7DuBLN1CrQi8Ort/XD/dd3oRAEKUMCqAkxArMrLxpUqYKvLsDaO34gXd70IYfwhbiFYNHoRhLc5wrNq3CpcLL2Ij1M+RkZ5RsPU/vkNiL2dPfQGPYwwionMz1N+Ruy6WFl8DZ7LiMGgdaYtvbraxYMChl9IFjJSNja4CBvQ3wuZId6EzocCnSUgnH61f/YY+Lk5dtYQ2C8FKKAQASYgCplohtnxAra2DKu7Z3d8dcdXqNRVNmC52LsgYXMCThaehK+zr/hW5MbwG1FWW4b/Hvwvdl3a1egNyNiuY3FP33ugsdPATmWHKJ8oxKy1/Yv1eup8MW9JqUlLrwS8li4evAzrE5CIzNNXlqZ5RERhLkZ1/JeUPVLgTwLX9/TH2plDaUIBClDA6gJMQKxOzA6UKmBry7BmDZqF0tpSfJD8QcOU3dv3XgS7BePN/W82msZRYaMwb8Q8DNs4DItHLxZPwdqTuQdbp2zF1O+nim9QAl0DsWXyFlkkIOt+iYLjvhSTvspXu3hQaEBlZ4C+ciVqq64keurYMXivuKdJ7bMQBawl8M7UGEyKC7FW82yXAhSgQIMAExB+GShgJYHM8kzctOkmK7Vu2WaFtxXbpmzDAz8/ICYPlx9hGZawKX38pvFYedNK/PPXfyKnMgehbqH4/PbPMeyTYVhy4xIxATlVeAof3vwhbvz8RugMOjwR9wRmRs/EkPVDxL0ktvrMyhqI+I8PmTz8q108KDTi3aUIWac+atRe5uBp2FTgbXIfLEgBSws42tvh8Mtj4epob+mm2R4FKECBJgJMQPiloIAVBYRjbIU3A1J/hoUMw3NDn2t2w/iXt3+JRYcXwcfJBw9EPwC1nRo1uhpxo/oO7Y5GS7DmDp+LwYGDUVBVINZ5LOYxOKgdMG3zNKkTNDs+cenViioYS0tNHn9LFw9ebsA/NAXpyT83au+7vg/hYhUvIDQZmQUtLnDbgCAsuTvO4u2yQQpQgALNCTAB4feCAlYUEH5Bf+rXp6zYA5u2psCa3/rAeW+yyV1c7eLBy404aj5FSW5mQ5vOHl54yzfB5D5YkALWEBD2fgh7QPhQgAIU6AgBJiAdocw+FCtQZ6jD6M9Ho7imWLEGthr4Y3nRuGH1EbOGn5awEBezWl7C4uJejULt0kZtuveOwbzaeLP6YWEKWFIgyNMJe54fDRVPYbMkK9uiAAWuIsAEhF8PClhZ4N1D74p3Y/CxHYFAvRsWLzeYtfRKF9IDib1nwWhoOc6AsAvQJn3VqIBx0K1YUtjVdnA4UtkJzL61Dx68vrvs4mJAFKCAdAWYgEh3bjgymQjY0mZ0mZC3O4yPdveH686jZrWTddccnMy5+kZyL59tyE5rvKTrTGwCthR7mdUXC1PAUgIOajscenkM3J00lmqS7VCAAhRoVYAJSKtELECB9gtM3zIdB3NMu8Su/b2xhfYITC/oi1tWJpnVhMHFHXtHLkBNlb7FesLxuzXFS2DQ666UUanwSeRMFOj5y59Z4CxsMQHh2F3h+F0+FKAABTpSgAlIR2qzL8UKCMfUPrvzWcXGbyuBi0uvPrKHMS/frCEXTngSR8t6XbWOT1A+Mk+sbVTGLSAY813vMKsvFqaAJQW+emwY4sL5Bs6SpmyLAhRoXYAJSOtGLEGBdgsI92IId4LkVua2uy1LNXBrxK145bpX8NKul7Dt4raGZoX7Oyb1nCTeZJ6YkYjX976Oan11k26FiwbnDZ+H/n79kV6Wjhd3vYjThachtCvciC4c1/vv3f9u2IAf4x+DGdEz8MQvT1gqBIu3s2r/AHjuOGxWu0YARyatQHHhn95sNNOCX/ABZBxPbPSJa9RgvFEzxKz+WJgClhLoEeCG7f8caanm2A4FKEABkwWYgJhMxYIUaJ/AuhPr8NaBt9rXiIVq39f3PgwKHAR/Z398lPJRQwJyZ8878bdef8Oj2x9Fnb4O7495H3su7cGq5FVNev745o/xW/pvWH9iPcZHjseQwCFiMvP1HV9j8neTMT5iPPxd/MUN+EIys+aWNXhh5wvIKM+wUBSWbeaeoj64fbnpR+5e7r1ixBTsU49qdTAa1VqUFTZ+s1IVNwGri0JbrcsCFLCGwLw7o3H3NeHWaJptUoACFLiqABMQfkEo0EEC1bpq3PjFjSitNf1SO2sNrbdPb/Ftxapxq/D56c8bEpAB/gMgjDO1KFXsWrjJvLtnd8zeNbvRUIQb0j++5WOM+2IcjBDeAdQ/rhpXrL1lrZiAxAXEYWLPieJbkISoBHg5emHZsWXWCqld7XoZnLDqQyezl14JnZ5KWIrMrCsGzQ3EzasS+eeXN/noaMx9SCxxbdfYWZkCbRHwdtFg7ws3wknDCzDb4sc6FKBA+wSYgLTPj7UpYJaA1I7k/WsC8udgglyDsHD0QqxKWtVoiZZQZnT4aPy9z9/FpVfxwfFIL03H3H1zkV2RjY3jN2LitxNxTdA1uLnbzeKN6MvGLsMb+97AAwMeQEVdBebtm4eSmhKz7KxZeNnhgfDdesjsLmp7Dcau4Omt1usSloqLSZsblVNrHLAs9H7UgL8AtgrIAhYXePqm3vjHqB4Wb5cNUoACFDBFgAmIKUosQwELCeRU5GD81+NRo6+xUIvta6alBERIIoS9HZ+c+kRMHP78lkPo8Y4ed+Cla1/C49sfx/7s/RCWdAk/E958fD7hczyw9QHcFXUX8ivzEdslFtsvbsf0/tPFJVoxATHo5tEN7x99v32Dt1DtCeU9cO/iU21qTZuwAGezXFqt6+H5I3IvNO7DIzwSc9XjWq3LAhSwtICLgxoHZo+Bq2PLl2Zauk+2RwEKUODPAkxA+H2gQAcLvLb3NXyR+kUH99p8d1d7A+Lj5IPnhz6PopoizN83v1EDN4bfiEcGPoK/ff838efCHo+D9xzEqM9HIdIzUvwsuzIb36d9j3v63oMnf3kSW6dsxU1f3oQIzwg8M+QZPLb9sU43EJdebfCAMSPT7LHofQKxe9Ar0NVd5eZBABoHHSrylzY+fheAU/RwLCiPNrtfVqBAewUeH9UDz9zUu73NsD4FKECBNgswAWkzHStSoG0C50vO4/Zvbm9bZQvX+msCMiJkhLhJXBij8Agby1+Ofxm3f914vMIekkWjF4kJxeUE5NA9hzDi0xEorysXf6aCStwP8nzi87hUfgk/T/kZ474cJyYg/xr8L/xjxz8sHI35zS1OjkGXzW27nyV30mykFAa32qlfSBYyUjY2KVc8aDLWFQa0Wp8FKGBJAUd7O3Hvh4+rgyWbZVsUoAAFzBJgAmIWFwtTwDICwlG0v6b/apnG2tHKXxOQpwY9hT4+ffDP3/4pLhObfc1suDu44+nfnxYTB2Hz+a5Lu8QeN92+CcLJXt+c/UbcDzIhcgKmbZ7WMJq7et8FbydvLD9Wv/laWNb1yPZHMLjLYAz0H4h3Dr3TjpG3v6q49Or9VMBw9TcYzfVkUGtwYPwSVJRe/ehdoa5f0B/IOLGnSTM7o2fiWDl/CWz/TLIFcwSmD+uGVyb0M6cKy1KAAhSwuAATEIuTskEKtC4gnEA15fsprRe0UonPbvsMPbx7wF5lD4PRAOEf4YjcxEuJeOGaF3B96PXiZ8fyjkFYMpZTmYOpvaeK93s8+POD4qhC3UPx9si3xf+mFaeJp11dKL0gfiaceLV87HLc++O9qDPUiT8TNqs/OvBR8c+zE2eLS7Q663E0qrF+vW+bll4JYy4dOxMH6+JaHb7RaIQaa1BZXNiorLAB/b2QGYBK1WobLEABSwmo7VTY+/xoBHg4WapJtkMBClCgTQJMQNrExkoUaL/AM78/gy0XtrS/IbZgtsA7J2IR+u0Bs+tdrpA8dQXyclt/++HlX4Ls1A+a9OMREYW5aP3ukDYPkBUp0IzAffFd8dod/WlDAQpQoNMFmIB0+hRwAEoVEI6wve3r28Q3EHw6TmBkVTgeX3ShTUuvhFFWx47FHs+JJg04IOwktEk/NSlrHzsG7xb3NKkNFqKAJQScNWrsfWE0vFy47M8SnmyDAhRonwATkPb5sTYF2iUgpROx2hWIjVQWll6t2xQEnKlfKtaWJy1hIS5mmXZ8qavbtyhIT2vSTebgadhU4N2W7lmHAm0SeGpMLzw1hklvm/BYiQIUsLgAExCLk7JBCpgukF+VL54kVWuoNb0SS7ZZ4M3UOERs2t/m+rqQHkjsPQumvLRycqtBcXrzd5181/chXKziBYRtnghWNEvA19UBic+NgouDaYmzWY2zMAUoQIE2CDABaQMaq1DAkgJSux3dkrFJqS1x6dUSLaBrfe9GS+POumsOTuaY9uYiIOwCtElfNWnK2cMLb/kmSImGY5G5gLDvQ9j/wYcCFKCAVASYgEhlJjgOxQqU1ZaJ92Ncvj9DsRBWDFxtVGHjppB2Lb0yuLhj78gFqKnSmzRSb79fkXXmSJOy7r1jMK823qQ2WIgC7RUI9XbG78+MgnACFh8KUIACUhFgAiKVmeA4FC0g3Kfx1oG3FG1gzeDnnItFr8/afuqVMLbCCU/iaFkvk4apsjNAX7kStVWVTcobB92KJYX822iTIFmo3QJL7o7DbQOC2t0OG6AABShgSQEmIJbUZFsUaKOA3qDHpO8m4VzJuTa2wGotCcTUBuLFhdntWnplBHBk0goUF5q2fMsnKB+ZJ9Y2O6S0uAT8WOTFCaOA1QUGhHriu38Mt3o/7IACFKCAuQJMQMwVY3kKWElgb+ZePLTtISu1rsxmhaVX63+IgDo5tV0AFSOmYJ/a9Hs7AkKOQJvSzE33KhU+iZyJAr2mXeNhZQqYIvD9P4YjOtTTlKIsQwEKUKBDBZiAdCg3O6PA1QV4OaFlvyEva2MQveFguxs9lbAUmVnCexDTHkfNpyjJzWxS2C0gGPNd7zCtEZaiQDsE7jX5BQEAACAASURBVL4mHPPujG5HC6xKAQpQwHoCTECsZ8uWKWC2QFZ5FiZ+OxGVuqZ7B8xuTOEVhKVXs5cWwFhV1S6J2l6DsSt4usltuHlVIv/88mbLu0YNxhs1Q0xuiwUp0BYBLxcNdj4zCh7OfNPWFj/WoQAFrC/ABMT6xuyBAmYJrE5ejYWHF5pVh4WbCmzc3L3dS6+EVrUJC3A2y8Vk4oCwc9AmfdNs+aq4CVhdFGpyWyxIgbYIvD11ICbH8XvWFjvWoQAFOkaACUjHOLMXCpgsUKuvxd++/xs3pJss1rTgcxkxGLSu/Uuv9D6B2D3oFejqDCaPxstnG7LTkpstfzTmPiSWuJrcFgtSwFyB2HAvfP3YMHOrsTwFKECBDhVgAtKh3OyMAqYJ7Lq0C49uf9S0wizVSKCnzhfzlpS2e+mV0GjupNlIKQw2WVjjoENF/lIY9E1Py1JrHLAs9H7UgDegmwzKgmYJCHd9/DzrekT6u5lVj4UpQAEKdLQAE5COFmd/FDBR4LW9r+GL1C9MLM1ilwXW/RIFx30p7QYxqDU4MH4JKkpNO3pX6NAvJAcZKRua7dsjPBJz1ePaPS42QIGWBB4ZGYnnb4kiEAUoQAHJCzABkfwUcYBKFaisq8Tk7yYjozxDqQRmxz0rayDiPz5kdr3mKpSOnYmDdXFmteUXfAAZxxObreMUPRwLynkqkVmgLGyyQLiPC7bNuh6OGr5hMxmNBSlAgU4TYALSafTsmAKtCxzLO4b7t9wPncH0v4VvvVV5lhCXXq2ogrG01CIBJk9dgbxc092NRiPUWIPK4sJm+y8ePBnrCgIsMjY2QoE/C6hUwHeP884PfisoQAHbEWACYjtzxZEqVICnYpk28Wt+6wPnvc1v/jathSulqmPHYo/nRLOqufuWI+/syhbr7IyeiWPlDma1ycIUMEXgH6N74OlxvU0pyjIUoAAFJCHABEQS08BBUKBlAYPRgHt/vBdJ+UlkakHgsbxo3LD6iMV80hIW4mKWvVntdQlLxcWkzc3WETagvxcyAxD+qpoPBSwoEBXojs3/Nxz2ajsLtsqmKEABClhXgAmIdX3ZOgUsIpBelo5pm6ehtNYyy4ssMiiJNBKod8Pi5QaLLb3ShfRAYu9ZMJp+8q4o4er2LQrS05pV8YiIwlyMkogYhyEXAY1ahS1PXo/IAJ56JZc5ZRwUUIoAExClzDTjtHmBn87/hGd3PmvzcVg6gI9294frzqMWazbrrjk4meNtVnvC8btlOYtarGMfOwbvFvc0q00WpkBrAi/cEoWHR0a2VoyfU4ACFJCcABMQyU0JB0SBlgVm75qN79K+I9H/BKYX9MUtKy23NM3g4o69IxegpkpvlrFfSBYyUja2WCdz8DRsKjAvqTFrACysOIG4cC9sevQ6qLisT3Fzz4ApIAcBJiBymEXGoBgBHs17ZarFpVcf2cOYl2+x+S+c8CSOlvUyuz2fgERknj7QYr3v+j6Ei1U8HtVsWFZoVsDd0R7b/zUSXTycKEQBClDAJgWYgNjktHHQShYQjuYVNqUbYVQyA1btHwDPHYctZiBoHpm0AsWFph+9K3SusjNAX7kStVWVzY7F2cMLb/kmWGycbIgC62deg+E9/QhBAQpQwGYFmIDY7NRx4EoWWHp0KZYdW6ZYggnlPXDv4lMWjb9ixBTsU5u/Udy7SxGyTn3U4lg8esdgbm28RcfKxpQrwCN3lTv3jJwCchJgAiKn2WQsihFQ8tG8XgYnrNrgAWNGpkXn+1TCUmRmmf9WyT80BenJP7c4FuOgW7GksKtFx8rGlCkwtJsPPn3oWtjZ8ThnZX4DGDUF5CPABEQ+c8lIFCYgHM07+bvJqNJVKSryxckx6LL5oEVjru01GLuCp7epTUfNpyjJbTkZOhubgJ+KvdrUNitR4LKAr6sDdvxrJLxceJklvxUUoIDtCzABsf05ZAQKFvjm7Dd4effLihEQl169nwoYzLykoxUhbcICnM1yMdvRxb0ahdqlLddTqfBJ5EwU6DVmt80KFLgsILzwEE68ig3nSWr8VlCAAvIQYAIij3lkFAoWeGP/G9hwcoPsBRyNaqxf72vxpVd6n0DsHvQKdHXmJzUBYRegTfqqRXu3gGDMd71D9nPDAK0r8Nod/XFfPJfxWVeZrVOAAh0pwASkI7XZFwWsICDsB5n12yz8ov3FCq1Lp8l3TsQi9NuWj7pt60hzJ81GSmFwm6p7+WxDdlpyi3VdowbjjZohbWqblSggCNw1JAxvTh5ADApQgAKyEmACIqvpZDBKFajWVWPm1plIyrfcpXxSshxZFY7HF12w+NIrg1qDA+OXoKLUvKN3BRvh+N2a4iUw6FuuWxU3AauLQqVEybHYkMCgrt74/OF4qLnp3IZmjUOlAAVMEWACYooSy1DABgSKqovw9x//DmFzupweYenVuk1BwJkLFg+rdOxMHKyLa1O7PkH5yDyx9qp1j8bch8QS1za1z0rKFgj1dsYP/zcCni7cP6TsbwKjp4A8BZiAyHNeGZVCBbSlWkzfOh25lbmyEXgzNQ4Rm/ZbJZ7kqSuQl2v+2w9hMH7BB5BxPLHFcak1DlgWej9qwBvQrTJ5Mm7UxUGNzf83HN393WQcJUOjAAWULMAERMmzz9hlKZBalIr7froPFXUVNh+fuPRqiRbQtS1JuBpAdexY7PGc2GYjjWotygrzW6zvER6JuepxbW6fFZUpoFIBH08fipG9/JUJwKgpQAFFCDABUcQ0M0ilCezN3IvHtj8GndHyv7h3lKXaqMLGTSFWWXolxJCWsBAXs+zbFI6bVyXyzy+/al2n6OFYUB7dpvZZSbkCL9wShYdHRioXgJFTgAKKEGACoohpZpBKFPj27Ld4afdLNhv6nHOx6PWZ5U+9EkB0IT2Q2HsWjOafvCt6dglLxcWkzVe1LR48GesKAmzWnwPveIFpQ8LwBk+86nh49kgBCnS4ABOQDidnhxToOIGlR5di2bFlHdehhXqKqQ3EiwuzrbL0Shhi1l1zcDKn7Ze6eXj+iNwLp64a7c7omThWzlurLfSVkH0ztw0IwuKEWKiENVh8KEABCshcgAmIzCeY4VFg/r75+OTUJzYDISy9Wv9DBNTJqVYZs8HFHXtHLkBNlb5N7WscdKjIX3rV43eFDejvhcwA+Mtkm4yVVmlc3y5Yds8gHrertIlnvBRQsAATEAVPPkNXhoCtXVT4sjYG0RsONpocn+nTYefkiMJPNsJQUtKuiSuc8CSOlvVqcxt+IVnISNl41foeEVGYi1Ft7oMVlSMwrIcv1kwfCnu1nXKCZqQUoIDiBZiAKP4rQAAlCNjKRYXC0qvZSwtgrKpqmBbPCRNgqK2BS0wMXAYPRvmuXag9dx4l339v9tQZARyZtALFhfWb832CXVGcUwmDXvjEtMcv6A9knNhz1cL2sWPwbnFP0xpkKcUKxIZ54dOHroWjhkc1K/ZLwMApoFABJiAKnXiGrTyB0tpSPPXrUziQbZ2N3ZYQ3bi5e5OlV4Gvvoq8xYvh9/BDyFu4CF1efAF57y2ELi/P7C4rRkzBPnX9m4nIWH+4ejshvK8Pqsvr8Mv6kzDorp6IGI1GqLEGlcWFV+07K3YKvizmMapmT5CCKvQP8cBnD8XD1bFtJ7EpiIqhUoACMhRgAiLDSWVIFGhJoFZfi1m/zcLOjJ2SQ3ouIwaD1tUvvXIdMRyGklLUpKVB7esLY3UV/J98Crn//S+8p01D/rK2baw/lbAUmVn1SUb0DaGordLh4vEChPf1Rer+7FZNvPxLkJ36Qavlfuo7A2erHFstxwLKFBCSj40PXgt3J95yrsxvAKOmAAWYgPA7QAGFCegMOryQ+AK2XNgimch76nwxb0lpo6VXwgbu0EULUXvxIvJXrIR3QgJchwxG5ksvQ5eTY/bYa3sNxq7g6WI9OzsVQnp7ozi3EjFjwnHmQA6yz7W+tyQg7CS0ST9dtW9nDy+85Ztg9vhYQRkCQ7p5Y82MoXBx4JsPZcw4o6QABZoTYALC7wUFFCggLCV6+9DbWHN8jSSiX/dLFBz3pTQaiyYkBEaDAY4REfB76EGU79yJgo/XAIa2Xd6hTViAs1kuYh99hwfj0ukilBVWo9/wECT/nmGSg6vbtyhIT7tqWY/eMZhbG29SeyykLIHrIn3x0fQhcLTnng9lzTyjpQAF/irABITfCQooWODjlI/FRKQzn1lZAxH/8aEmyUeXZ54Rj7E1VFQgd9EiOPXqJSYhbXn0PoHYPegV6HQGwAh0jxH2fzgi60wxQqO8cXR7eqvNOrnVoDj9/VbLGQfdiiWFXVstxwLKEhjbpwuW3hMHDU+7UtbEM1oKUKBZASYg/GJQQOECX6Z+iTl/zIHe2LZ7MdrDJy69WlEFY2lpo2Y8xo9HVVIS6tLT4dSvH/yf+D/kLXkf1cnJbeoud9JspBQGwzvQBQ7O9gjs7onci6Vw83bC+aN50NW1/lYlIOwCtElftdr/2dgE/FTs1Wo5FlCOwK3RgViSECcu/eNDAQpQgAIAExB+CyhAAfye/ru4Ob3OUNehGmt+6wPnvX9JKoS9HwvfAwxGZL36KvTFxS2OSUhOqk+eFJdlCUu26nJzgbrGMRjUGhwYvwQVpfVH7wZFeqK8qBo9BndBbbUex3deMilmb79fkXXmyNXLqlT4JHImCvTcXGwSqgIK/W1wKN6aPIA3nCtgrhkiBShgugATENOtWJICshbYn7VfPKa3rK6sQ+J8LC8aN6xu+Rd6l6FD4ffIw6jcvx/5q1YD+qZvaJz694dT797iMi21j4/4tqRs2zaU//ZbQwylY2fiYF1cw5/j74xEfkY5zh7MgdHE6z9UdgboK1eitqryqjZuAcGY73pHh/ixE+kLzBrbC0/eyPtgpD9THCEFKNDRAkxAOlqc/VFAwgKpRal48OcHUVh99Xsu2htCoN4Ni5cbmiy9coiMRJdnnhbfeuQvWy6egOU6bBgqdu9uscsuLzyPukuXoMvLR+lPPyHg6X8h979X9rUkT12BvNz6tx92ahWCenjC0Vkj/jf5twyU5le3Go5PUD4yT6xttZxb1GDMrxnSajkWkLeARq3CO1NjMGFgsLwDZXQUoAAF2ijABKSNcKxGAbkKaEu1eGT7I0gva31jdlsNPtrdH647jzapHvjvl5E9bz6C/v0yHLp3h7GmRjyCV3gL0ujRaOBz992oTU9H7blzcBs1CrrcXLjExaL65CkUf/mlWLw6diz2eE5sqDpgdBiEVfi52lLYqe3Ek7BMeQJCjkCb8murRavjJmBVUWir5VhAvgIezvZYM30oYsO95RskI6MABSjQTgEmIO0EZHUKyFGgoKpAfBNypviMxcObXtAXt6xMarbdkHffQcHqD+AYGSnu7XDoHoGyrT83Kes1eTIqDhyAc//+cI4ZCGN1NfJXfwDDXzazpyUsxMWsK/ctDBkfAeEYrGM70tE9NgCn9maZFJ+j5lOU5Ga2WvZYzH3YWeLaajkWkKdAhJ+rmHyE+9Yf98yHAhSgAAWaF2ACwm8GBSjQrEBpbSke3vYwUvIb38/RHi5x6dVH9jDm5TfbjMrBAa7XXgvXYddBX1KKil27xNOw/voIb0d8Z85E4bp18Lj5ZhR9sgHeU+9C3pIlDUV1IT2Q2HsWjH864MrNxxG9hwaKZY7uSIfehNOv3LwqkX9+eathqzUOWBZ6P2rAOx5axZJhgcFdvfHh9CHw4O3mMpxdhkQBClhagAmIpUXZHgVkJCDcmv7e4fcsdmHhqv0D4LnjcBMht9Gj4XnbeKjd3ZHx9DNwHz1aLFPy9ddNynrcNh7GqmpU7NsHj1tuQU1aGqoOH4bXlCkNS6+ESll3zcHJnCvLYKLig+Dq5QgnV3tkninG+WNNkyC1xq5JUhIQdg7apG9anVWP8EjMVY9rtRwLyE/gweu747mbo2DPY3blN7mMiAIUsIoAExCrsLJRCshLYE/mHryQ+EK7NqffU9QHty9v5h4Pe3t0efYZceN44CuvoGD1KmiCQ5rdeO7Uvx88broJ+StXwevOO6EJCUbRJxvFzep/fgwu7tg7cgFqqq6cnDV0QgQuJBcg90KpmIRUV9RvTBeerv19UZxTKR7NW1etQ86FUuScr7+bxMtnG7LTWr9/xCl6OBaUR8tr4hnNVQXcHe2x+O5Y3NA7gFIUoAAFKGCGABMQM7BYlAJKFhD2hQhJyN6svWYzeBmcsOpDp2aXXqk0GoS89y6qk5JQvjMRhtpauA4diqKNG5v04z5mDIRlWkadDipHB9ScOStuWNc+8mij/R+FE57E0bJeDfU9/Z3hF+YOtb0d/MPdsH/zedRVNz7WN2ZMOAwGI/R1ejEZuZRaDI2DDhX5S2HQX0lWWgq+ePBkrCvgL6JmfzlstMLAUE8svWcQQrycbTQCDpsCFKBA5wkwAek8e/ZMAZsTMMKIz059hgUHF6BWX2vy+JcdHgjfrYdaLO9+0zh0efZZFG38FE5RUch67bUmG8rFyhoNwhYuhKGqEpkv/xvGykrxrpDSLVtRe+GCWES42uPIpBUoLrySNIRGeUNYgnX6j2yknyqsL/S/R7gV3TfUDQadQUxQUv50MaFfSA4yUjaYFOfO6Jk4Vu5gUlkWsm2BmcMj8MKtfbjkyrankaOnAAU6UYAJSCfis2sK2KrA2eKzePq3p5FWktZqCBPKe+DexaeaLef7wANw6hOFS/96GionJzgLN5ufOglDxV8u/NNo4DdjOkq370CdVgu/xx6F6zXXoCrlOFR2dsieM6eh/YoRU7BPPapJf3Z2KoT394WuVo+MU1eO3w3v5wtHZ3u4+zoh/WQhHJzscSm1/nO/4APIOJ7YaozCBvSFoTNgFA/55SNXAWHJ1Tt3xWBs3y5yDZFxUYACFOgQASYgHcLMTiggP4EafQ3e3P8mvkj9osXgxKVXGzxgzGh6hK2duzv8H38MOW+8Kdb3fWAmyhN3oeb06Sbt+UyfjrqMDFQePAjfmTNgrK5Bwdq1EJZv6QsKGpU/lbAUmVn1rzgiBvohuKc3aqt0COjqjrz0MhzYfL7hBnSvAGe4+zoj/1I5HJzUKMmtgp29CgadEUajEWqsQWVx65cyekREYS6aJj3ym3XlRjS4mzfeuysWod5ccqXcbwEjpwAFLCXABMRSkmyHAgoV+EX7C/6z9z/NblBfnByDLpsPNi+j0SB85QpkvfSyeJO5+9ix4sWD5Tt3Nikv3HZe8MEH8J42DWU7foG9vz+8p92F9EcebVS2ttdg7Aqe3vAzIZm49vZIZKUVi0nHhaTGJ1+5eDjgmju6o6KoBoGRntj5aaq4/0N43H3LkXd2pUmzah83Bu8W9TSpLAvZloCD2g7/HNcLD10fCR5yZVtzx9FSgALSFWACIt254cgoYDMCzW1QF5devZ8KGP50EQcA5wED4DFhAvTFxeLxub4z6hMGXX4BMl96CdA33hwufKYJCUHQa/8Rj9zNmTdfLB/85hvIfO75RkbahAU4m/WXS+BUwA0JvXHmUG6zN5+H9/OBSqUSl2BdTC5AWWG12GaXsFRcTNps0hxkxU7Bl8X+JpVlIdsR6BnghuX3DkKkv5vtDJojpQAFKGADAkxAbGCSOEQK2IKAsEH945SPsejwIqgNRqxf79tk6ZXL0KHwmjwJhR+vgSY8TLxMUDh+t/LQoWYTjz/HLbz1CHj2Gag9PKDLzkHVieMo/uzzhiJ6n0DsHvQKdC1cLtj72kBxE/rlR3j70ee6INRU6sT7QY5u14r/f/lxdfsWBemt73ERyv/UdwbOVjnawjRxjCYICG86HhkZiVlje0Oj5r4eE8hYhAIUoIBZAkxAzOJiYQpQoDWB4wXHkffRB+iy+qcmRUPefQeXnnkW0NX/om/n4YHQ996FdsbM1ppt+FzYrC4cxWsorb+n4/KTN/XfSM69sjlYpQJCensjT1vWKLG4XF44dvfoDi3cvBzRb0QIUg9koyirfvmVcPxuWc4ik8bk7OGFt3wTTCrLQtIXEPZ4LPv7IESHekp/sBwhBShAARsVYAJioxPHYVNA0gI6HQo3bEDeosUwVFQ0DDVs2TKkP9p430boooXIeOLJpuGoVHCNj0fN2bPQ5eY2fK7284M+v/FeDoNagwPjl6CitD6xEZKP7jH+0OsMCOjqAZWdCvu+O9eojx6DAiC8BTm9LxvCDejhfX1xam+WWMYvJAsZKU3vIWnO3KN3DObWxkt6Oji41gWENx0PjuiOJ8f0gqO9XesVWIICFKAABdoswASkzXSsSAEKtCagy8tD7tvvoOSbb8SiwQveQtnWn1G2fbv4Z/vAQAQ88QQyX3yxSVMBzz4LQ1kZXK+Lx6Vnn4Muqz45EH6ev2yZ+Nnlp3TsTBysi2v488DRYQju6SW+1Th3JK/h1Ku/duIZ4IzI2ADxAsLkXzPEhEV4fAISkXn6QGvhiZ8bB92KJYVdTSrLQtIUGNrNB/MnR3OvhzSnh6OiAAVkKMAERIaTypAoIDWByiNHxNOudNnZCHzl37D39kbtRa24DyTz2efEDel/fYRkJfOZZ+HYqyfcR49G/vIVcOzdG3UZ6U3uCUmeugJ5uVf2b3SL9kVdrQF2ahXC+/gg+fcMlObXby7/6+PgbC9eQnh574hw/K6xZgVqq/5yF0kLqGlxCfixyEtq5ByPCQK+rg54cXwfTI4LNaE0i1CAAhSggKUEmIBYSpLtUIACVxf407IsOxcXqL29UHPmLFp6PRHyztviBYXC50Fz54gJjLhca9Y/G/aQCB1Wx47FHs+JjfoWbj4X7v9Q29dvIN779ZXN5MKyq7ibu6IsvxrHfkkXT78SnrKC+gTFy78E2akfmDabKhU+iZyJAr3GtPIsJQkBYYlewtBwvHBLH7g72UtiTBwEBShAASUJMAFR0mwzVgpIQOCvy7JMGZKQgFQfPwFdYQHKtmxtVCUtYSEuZjX+JbLbAD94+Dkj7VAO9DojqivqGur0Gx6Ms4dzIdyAnnuhFGF9fJCy81LD5/6hKUhP/tmUYcEtIBjzXe8wqSwLSUPgmggfvHZHf/QOdJfGgDgKClCAAgoUYAKiwElnyBSQgoCwLCvn9ddRfeJkq8NxjotFwL/+hYt/v6dRWV1IDyT2ngVj46tGxDLC0bpDJ0TgRGImci5cOTFL+LmLuwMKssoRc2M4DHoDjm5Pb2jXUfMpSnKb3tze3CDdogZjfs2QVsfPAp0vICQcz9/SB6N6876Wzp8NjoACFFC6ABMQpX8DGD8FOlPAYEDxF18i9913m90H0mhoanWTu0Ky7pqDkznejYoJS6oGjApDzvkS8ZSrk3uzUFfd9HJDoZLwpqQktxJF2fX7PVzcq1GoXWqySHXcBKwq4v4Bk8E6oWCAuyP+Na43/jY4jDeZd4I/u6QABSjQnAATEH4vKECBThcQNqEXrlmLwnXrYCgvN2k8Bhd37B25ADVVjZOLqPggMfHIzyiDQW9Exqkik9oTCgWEXYA26SuTyx+LuQ87S1xNLs+CHSfg6qDGIzdE4sERkXDS8FjdjpNnTxSgAAVaF2AC0roRS1CAAh0kIBytW7h+AwrXrGn1jUjhhCdxtKxXo5H5hbnB3kGN0rwqxI7rit1fnjFr5F4+25Cdlgx3X3/UVFbAoNdDV1vTbBtqjQOWhd6PGqjN6oOFrSsgbCr/f/Hd8ND13eHhzMMBrKvN1ilAAQq0TYAJSNvcWIsCFLCigLGyEoWffoaCDz9scumg0K0RwJFJK1BceOXoXeHnwvKrgTeG4dSeLHGT+eGtF00epcrOgJriJTAaDRg45mZ4B4XgQtJRnD9Sfx+IvYMj3H194RMShnOHDsA9LAJz1eNMbp8FrSvg5aLBjGERuH9YBDx4spV1sdk6BShAgXYKMAFpJyCrU4AC1hMw1tSg+Msvkb9qtXiHyOWnYsQU7FOPatRxzNhwnP4jG44u9uLt55dSi1BR3Pzbi+ZG7BOUj8wTa8WPQnr3RWjf/jizfw8KL2WIPwuPjoF3YBBqKitRkKFFuUcI3iqPtl7wbNkkAT83B/EG83vju8HFgW+jTEJjIQpQgAKdLMAEpJMngN1TgAImCNTVofjbb5G/YiXq0tNxKmEpMrOE9yD1j3+4OzSOanGzedR1QeLeD3OXX/kFH0DG8UQx0SjJyRbfdmScPC627+TmjpCovvAODMaJxF9RWVKMksGTsbYgwITBs4g1BAI9nPDwyEjcfU04HO25x8MaxmyTAhSggLUEmIBYS5btUoACVhEo2roDvxzzRPa5K0frRgz0ExOQLt08kPj5GcSMCcfR7Vqz+teo1qKsMB8RMYPg7OEJe40DknZsEdvw8POHf7fu0NXUoK6mBpmpJ5E4YCaOljmY1QcLt19gcDdv3HttN9waHQSNuv6iST4UoAAFKGBbAkxAbGu+OFoKUOB/AoVZFUj5/RJO78tGbZUOwu3nOedLISQjdnYqnPrjypKt1tDcvCqRf345vLoEIaxfNFQqFdIOH0BFUaFYtd/IG1FdUQ6Vyg5nD+yFsAF9YegMGMFfgFuztcTnwtKqibEhmH5dBHp2cbNEk2yDAhSgAAU6UYAJSCfis2sKUKD9Aro6A84ezMHxxEviWxFndw2qyq7cfG5KD13CUnExaTMcnF1wzcS/4ei2H1FbWSmehCU8/UeNRXV5OQx6HbLTzsDeJxBz0XgPiin9sIx5Aj383XBvfFdMHhQKN8fGt92b1xJLU4ACFKCAlASYgEhpNjgWClCgXQJ/fStiamMenj+iNP8S4qckIGnHVngFBEKbcgx1NdViUtLr2mFI+W07+gwbiZO7foN93Bi8W9TT1OZZzgwBbxcNJgwMxsSYEMR1bXzJpBnNsCgFKEABCkhYgAmIhCeHQ6MABdomoNcZoD1eiLOHcnD+WD7qapq/CV1oXeOgQ0X+UvHthldgcEnlrgAACa1JREFUsHjSlaOrG07t/l3svFf8cKSnJCGs3wDxXhBhCVZW7BR8WezftsGxVhMBYRP52L5dcGdsKEb29oe9HZe28WtCAQpQQM4CTEDkPLuMjQIUQGvJiF9IFjJSNiKoZxS8g4Lh5uOLrDOnkH48WdRz9fKGrq4OQT16wdHFFaf3JuKnvjNwtsqRuu0QUKmA+O6+uDMuVNxQLtxczocCFKAABZQhwAREGfPMKClAAaDZZMQv6A9knNgD37CuCOnVB9WV5Ujdu6uJl7uvH6orKsQLCd/yTaBnGwSEJGNET3/c2LcLbowKgI8rTxFrAyOrUIACFLB5ASYgNj+FDIACFGirwLmjebhw9DucO7xXvPvD3tFRvAX9yJYfxCVZGidn6OtqxaVXTu4eqC4vg0evgZhbG9/WLhVXL8zbGaP7dMGNfQIQ392PR+cq7hvAgClAAQo0FWACwm8FBShAAQBlBfnixvP0E8lIP56E0rxchPcfCL/wbuL9H/YODjj803cwDr4VSwq60qwFAU9nDeIjfTGsh5/4b3c/V1pRgAIUoAAFGgkwAeEXggIUoEAzAkJCkiEkI2JCkgwnd3dkn01FWlwCfizyotn/BJw0dhjSzach4egX7AnuIefXgwIUoAAFribABITfDwpQgAImCAgJiZCMbC3xxqHsGqTllSOrpNqEmvIq0iPADcK/UYEeuKa7j7iRnA8FKEABClDAHAEmIOZosSwFKECBPwlU1elxLq8CZ3PLxYRE/De3HOfzK1CjM9islXDpX6R/faIh/Bv5v/929XGBmq83bHZeOXAKUIACUhFgAiKVmeA4KEAB2QgYjMClokoxMTnbkJhUiAlKYUWtZOL0d3NslGBcTjgCPZwkM0YOhAIUoAAF5CfABER+c8qIKEABCQvoDUYUVdahrFr4V9fw39KGP+tQWnXl8ys/v1xeB53BADcnDdwd7eHuZA83J+G/V/4s/L/wMw/hM0dNQxkP4ef/qyNsFufbDAl/UTg0ClCAAjIWYAIi48llaBSgAAUoQAEKUIACFJCaABMQqc0Ix0MBClCAAhSgAAUoQAEZCzABkfHkMjQKUIACFKAABShAAQpITYAJiNRmhOOhAAUoQAEKUIACFKCAjAWYgMh4chkaBShAAQpQgAIUoAAFpCbABERqM8LxUIACFKAABShAAQpQQMYCTEBkPLkMjQIUoAAFKEABClCAAlITYAIitRnheChAAQpQgAIUoAAFKCBjASYgMp5chkYBClCAAhSgAAUoQAGpCTABkdqMcDwUoAAFKEABClCAAhSQsQATEBlPLkOjAAUoQAEKUIACFKCA1ASYgEhtRjgeClCAAhSgAAUoQAEKyFiACYiMJ5ehUYACFKAABShAAQpQQGoCTECkNiMcDwUoQAEKUIACFKAABWQswARExpPL0ChAAQpQgAIUoAAFKCA1ASYgUpsRjocCFKAABShAAQpQgAIyFmACIuPJZWgUoAAFKEABClCAAhSQmgATEKnNCMdDAQpQgAIUoAAFKEABGQswAZHx5DI0ClCAAhSgAAUoQAEKSE2ACYjUZoTjoQAFKEABClCAAhSggIwFmIDIeHIZGgUoQAEKUIACFKAABaQmwAREajPC8VCAAhSgAAUoQAEKUEDGAkxAZDy5DI0CFKAABShAAQpQgAJSE2ACIrUZ4XgoQAEKUIACFKAABSggYwEmIDKeXIZGAQpQgAIUoAAFKEABqQkwAZHajHA8FKAABShAAQpQgAIUkLEAExAZTy5DowAFKEABClCAAhSggNQEmIBIbUY4HgpQgAIUoAAFKEABCshYgAmIjCeXoVGAAhSgAAUoQAEKUEBqAkxApDYjHA8FKEABClCAAhSgAAVkLMAERMaTy9AoQAEKUIACFKAABSggNQEmIFKbEY6HAhSgAAUoQAEKUIACMhZgAiLjyWVoFKAABShAAQpQgAIUkJoAExCpzQjHQwEKUIACFKAABShAARkLMAGR8eQyNApQgAIUoAAFKEABCkhNgAmI1GaE46EABShAAQpQgAIUoICMBZiAyHhyGRoFKEABClCAAhSgAAWkJsAERGozwvFQgAIUoAAFKEABClBAxgJMQGQ8uQyNAhSgAAUoQAEKUIACUhNgAiK1GeF4KEABClCAAhSgAAUoIGMBJiAynlyGRgEKUIACFKAABShAAakJMAGR2oxwPBSgAAUoQAEKUIACFJCxABMQGU8uQ6MABShAAQpQgAIUoIDUBJiASG1GOB4KUIACFKAABShAAQrIWIAJiIwnl6FRgAIUoAAFKEABClBAagJMQKQ2IxwPBShAAQpQgAIUoAAFZCzABETGk8vQKEABClCAAhSgAAUoIDUBJiBSmxGOhwIUoAAFKEABClCAAjIWYAIi48llaBSgAAUoQAEKUIACFJCaABMQqc0Ix0MBClCAAhSgAAUoQAEZCzABkfHkMjQKUIACFKAABShAAQpITYAJiNRmhOOhAAUoQAEKUIACFKCAjAWYgMh4chkaBShAAQpQgAIUoAAFpCbABERqM8LxUIACFKAABShAAQpQQMYCTEBkPLkMjQIUoAAFKEABClCAAlITYAIitRnheChAAQpQgAIUoAAFKCBjASYgMp5chkYBClCAAhSgAAUoQAGpCTABkdqMcDwUoAAFKEABClCAAhSQsQATEBlPLkOjAAUoQAEKUIACFKCA1ASYgEhtRjgeClCAAhSgAAUoQAEKyFiACYiMJ5ehUYACFKAABShAAQpQQGoCTECkNiMcDwUoQAEKUIACFKAABWQswARExpPL0ChAAQpQgAIUoAAFKCA1ASYgUpsRjocCFKAABShAAQpQgAIyFmACIuPJZWgUoAAFKEABClCAAhSQmgATEKnNCMdDAQpQgAIUoAAFKEABGQswAZHx5DI0ClCAAhSgAAUoQAEKSE2ACYjUZoTjoQAFKEABClCAAhSggIwFmIDIeHIZGgUoQAEKUIACFKAABaQmwAREajPC8VCAAhSgAAUoQAEKUEDGAkxAZDy5DI0CFKAABShAAQpQgAJSE2ACIrUZ4XgoQAEKUIACFKAABSggYwEmIDKeXIZGAQpQgAIUoAAFKEABqQkwAZHajHA8FKAABShAAQpQgAIUkLEAExAZTy5DowAFKEABClCAAhSggNQEmIBIbUY4HgpQgAIUoAAFKEABCshYgAmIjCeXoVGAAhSgAAUoQAEKUEBqAkxApDYjHA8FKEABClCAAhSgAAVkLMAERMaTy9AoQAEKUIACFKAABSggNQEmIFKbEY6HAhSgAAUoQAEKUIACMhb4/xFl+ANJdF1GAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "\n",
    "pio.templates.default = 'simple_white'\n",
    "\n",
    "def show_fig():\n",
    "    fig.show(renderer='png', width=800)\n",
    "\n",
    "fig = px.pie(df_continent_count,\n",
    "             values='num_of_companies',\n",
    "             names='continent_name',\n",
    "             title='Companies by continent breakdown')\n",
    "\n",
    "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76216cc5-178b-4500-922f-00f633e4dd62",
   "metadata": {},
   "source": [
    "Interesting, there appears to be 2 companies in Antarctica. Let's have a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07d04ac7-5a19-4c4d-aedd-afe08c401974",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1238022</th>\n",
       "      <td>Names Directory</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bouvet Island</td>\n",
       "      <td>NA - Bouvet Island</td>\n",
       "      <td>BVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antarctica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019999</th>\n",
       "      <td>QBO</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bouvet Island</td>\n",
       "      <td>NA - Bouvet Island</td>\n",
       "      <td>BVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antarctica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name          type primary_role combined_stock_symbols  \\\n",
       "1238022  Names Directory  organization      company                    NaN   \n",
       "2019999              QBO  organization      company                    NaN   \n",
       "\n",
       "                  city              region country_code country_name  \\\n",
       "1238022  Bouvet Island  NA - Bouvet Island          BVT          NaN   \n",
       "2019999  Bouvet Island  NA - Bouvet Island          BVT          NaN   \n",
       "\n",
       "        continent_name  \n",
       "1238022     Antarctica  \n",
       "2019999     Antarctica  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continents.query('continent_name==\"Antarctica\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5c03f6b3-6a1b-419d-a179-a5499197f028",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#### 7.7 What are the top 10 countries with the most companies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "540eff7d-5ffb-480e-b8ed-11eac8932a69",
   "metadata": {
    "tags": []
   },
   "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>index</th>\n",
       "      <th>num_of_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>1130976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>173206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>86203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>80353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>75397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>58166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Australia</td>\n",
       "      <td>53043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Japan</td>\n",
       "      <td>50465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>50395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Spain</td>\n",
       "      <td>42973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index  num_of_companies\n",
       "0   United States           1130976\n",
       "1  United Kingdom            173206\n",
       "2           India             86203\n",
       "3         Germany             80353\n",
       "4          Canada             75397\n",
       "5          France             58166\n",
       "6       Australia             53043\n",
       "7           Japan             50465\n",
       "8     Netherlands             50395\n",
       "9           Spain             42973"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_10_countries = df_continents['country_name'].value_counts().head(10).to_frame(name='num_of_companies').reset_index()\n",
    "\n",
    "df_top_10_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "da5ab5fb-392e-495f-b857-be6aed28fa98",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5PyDaiQACCCCQmAKBCCAaHOlT1IxmQEIDu4xmQPTJr5braDlPVkdGAURruDXw0Sfw4YfO18BLASN80BU6R4FFg+fUswbhZSgMaB9DejMgup7+hJZ2pN6EriU/WhoS+t6SjB49G40Aov0bWlalJ36FNh2Ht0PLpFJvMtdSGA16R48e7T6F1gAw9NCA8NcePXrUbZ6Wpay0xEoD74ULF7r2aZO8+lGDaC078mIA0fIwLWtKb5+L2qpP3lN/t4x+ntMAEpqpyWgGpFatWm5JWPihIKGAoP02Wsqm2cPQRvmslmDJXcvbwpfWhZetgK8lZBnNgIQCam4DSOoZEN0TKlN7mBSutJRN944eWBA61F7NBuUmgOSkf7VPRksrFX60pDKSWdis/hvF7xFAAAEEEPC6QCACiGYQNIBIvQdE68r1KbmWKGlQqwCiAYgGVqFNtHrCjjZxhz59zarDMgogof0HoQGwytEgWcuMtOFZy2zSO/Q0JG2KT/2FedqPogGpPk3Wp976lDi02VrlaKCo9uhTZ62L10BGA8DUn5Jr5kQD9pwEEO0/0V4UHRkFl/AZEC0h0iZjbWDWbEro0FOH5K5lWfr0V1YKfOFhROv/da3Qfo70rPTpuvZOKGjqCUkKZaGgoQCowZvW+WtGJKMAEr6HINIwlrouoYF9dsvS+Rrk6qECejpY6NCSKd2P2kid3pHTAKJwp3tCA+7wPSDab6KgIG/tddF7RPfaRRddlHz5UKDVDJ0e8KCQr+VK4ftNUtdVe1m++eYbV15or5Leawodeo/q9Vp2lHoPSGh2Q2VrD1BuA0jq97L+O6B7Rv8dUN/pvg7vO80+aQmfPsRQ3bUPI5J7I/U9Fkn/aq+TZsFSL4lTnfUe1awIBwIIIIAAAkEXCEQA0eBcgyQNTjWo0kBOAw4NfLT0Qss6dGiwrE/StW5ea961bEsDfw1a9elomTJl0u1vbVrVgEyHPnHXI3y1dEVLafSJtTYtK2xocKUn/OiaesKRPonVMg7tnQj/bpDUF9E+Bi3F0CBaX3T45ptvuk/J9UdPPNIgTgNFbezWkhoNmLTuXQM2zTZoZkBH6BHDep0GtFripAGkBn7ZDSAKCAoMmp3R3hQNGrNagqU6qH56wpI2HWsDs66tT9n12NTQJmstB9LvFLK0KV3naAZEQUWfBmd0KMRpL42MZaLXhvpVgUxlaqCrI/XgMDSg1lOaFPj0qGN9Cp7VMpv06pLTskJPSdJyPy1vkq82H2vZmwbeCm7RDCAqS8sNtQRLS9XkLmtdT6FH1nqvKKSqf3SO3kPavK6nXWlZkmYPVU8NkDU4132v8JTeE+P0PtI9qmCo94JCp2btNBum94xCiZbgKfTr5wo8mm3Rfa/2h5aD5TaAqNyePXu65VUKHboXNduhe1mPvNZ7SvtVVAf9W/eeQofep5ot0kyE/tuR1b2R0VOwMuvf0AcOej8rGGrjuWbF9N8t/Tcs/JHSQf8/H9qHAAIIIJC4AoEIIOo+DXr1eM7Q94BoIK99GeFPY1IA0YZPDXY0wNJgTE8R0iekoScjpXcrhJaOpPc7Dc40w6BDAy4FGm121YbW0Dp6rW/P7NB3I2gJhkKTBuEaHGvfSPj3gGjwp6VGGrBqE7gGV3o0anjZWualAYw+hdam7lAo03cahNbdR/LJruqqJU2qk/ZnKHzoiUSRBBC1O/Skp9B3LOhTeIWy0BN+Qn2lT4I1I6IBrZaA6ZyMQqDqpA3cepyq9omEPzFKgzfNoOjxspqBSS+A6GdaY69wp8fI6vUKZ1kNMjPqt5yWpfCiZXLax6KBvvYzaPO/+jK9JXq6fk5nQPRaBWPdv9qIrU/fNeDVJnMFCt37OjQID+1/0LW0DyEUEBXWdeje0qBeT43TTEfoO2NS+yjIazZST7xSuNFgX++v0JK80LX0HtGsnYK6+lQzWuoXHbkNIHpvq816KptmPjUbp5nB0P4K3ct6n2omSPtg9F7Te0X/rdB+KgUV/W9W90Z63wMSSf/KSLMlMtKHILrnNQMjXzajJ+7/GdNyBBBAIJEEfBdActM5CiCarWCddW4UeS0C3hQIbUIPf1KWN2tKrRBAAAEEEEhsAQJIYvc/rUcgMAIEkMB0JQ1BAAEEEAi4AAEk4B1M8xBIFAECSKL0NO1EAAEEEPC7QEIFEL93FvVHAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/9yD1RwABBBBAAAEEEEDARwIEEB91FlVFAAEEEEAAAQQQQMDvAgQQv/cg9UcAAQQQQAABBBBAwEcCBBAfdRZVRQABBBBAAAEEEEDA7wIEEL/3IPVHAAEEEEAAAQQQQMBHAgQQH3UWVUUAAQQQQAABBBBAwO8CBBC/92CU6v+nP/3Jtm7dGqXSKAYBBBBAAAEEEEAAgfQFCCDcGU6AAMKNgAACCCCAAAIIIBAPAQJIPJR9cA0CiA86iSoigAACCCCAAAIBECCABKATo9EEBZARM1fa7kNHolEcZSCAAALJAif+e9IK5M+HCAIIIBB1gUYXn21VyxePerkUGFsBAkhsfX1TugJIjX5Jtnbbft/UmYoigAACCCCAQGILTO1UyxpdfE5iI/iw9QQQH3ZaLKpMAImFKmUigAACCCCAQCwFCCCx1I1d2QSQ2Nn6qmQCiK+6i8oigAACCCCAgJkRQPx5GxBA/NlvUa81ASTqpBSIAAIIIIAAAjEWIIDEGDhGxRNAYgTrt2IJIH7rMeqLAAIIIIAAAgQQf94DBBB/9lvUa00AiTopBSKAAAIIIIBAjAUIIDEGjlHxBJAYwfqtWAKI33qM+iKAAAIIIIAAAcSf90CgAsiwYcOsSJEi9uCDD6bojQYNGthTTz1lNWvWzLCXNmzYYL1797a3337btm/fbj/++GOm56dXUPfu3a1p06bWsmXLNL+eNGmSvf7663bw4EFXx8aNG9vgwYPd3yO9XqTn5eRWJIDkRI3XIIAAAggggEBeChBA8lI/59cmgPx/u+PHj7twcOaZZ9rUqVPt2LFj1q1bt2zJZhRAFi1aZAogL730kpUuXdr2799vPXv2tOrVq1u/fv0ivl5O6xVJIwggkShxDgIIIIAAAgh4SYAA4qXeiLwuCRdAQoP+d99913bs2GGtWrWyHj16WGgG5LHHHnPhoGDBgta2bVsXEFauXOlmUH777TcrX768Pfvssy6ofPfdd9anTx/bt2+fValSxQ4fPuxmP1LPgOi1P//8s40YMSK5Z/SafPny2VdffZXmepopmTx5sikUlStXzsaOHWvffvttxPVavXq1jR492o4ePWqFCxe2gQMHWsOGDTO9Kwggkb9pOBMBBBBAAAEEvCFAAPFGP2S3FgkXQGrVqmV33nmnW26lEFCvXj375JNP7Ouvv05egqWlXGXKlHEzILt27bLmzZvbK6+8YpUqVbKXX37Znf/iiy9ar169XCAZMGCAbd682dq0aWOjRo1KE0DWrVtnd911l7Vv396uv/56F1YUcEJH+PU0O6I6LV261JWtZVoFChRw4SXSemkZmM7XkjPVKykpydUrs4MAkt23DucjgAACCCCAQF4LEEDyugdydv2EDCDTp0+3ypUrO7EaNWrYggUL3AxFaA9I+EB/1qxZtmTJEps2bZo7/5dffnED+88//9wFhSlTpiSXdeutt7qQkd4ekC+//NJmzpxpmp04dOiQ3XjjjW5mokSJEimCha5x5MgRK1q0qLvewoULbe7cue76kdbrnnvusYoVK5r+97zzzktzZ8yYMSPNz4YPH241+iXZ2m37c3Yn8SoEEEAAAQQQQCDOAgSQOINH6XKBCiAaRGu24KGHHkrBU6dOHXv++efdngvNgMyZM8cqVKjgzgn9W/s/0gsgWgql1yoohA4FCIUSbW5fvny5m6nQ0aVLF2vRokW6ASS8QpptGTNmjNtnogATHixOnjxpEyZMcMu+tETrwIEDVrZsWTeLEX5eZvXS61SG6nb66afboEGDXF1Dh8pKfWjGhAASpXcVxSCAAAIIIIBAXAQIIHFhjvpFAhVAtCxq/fr1bsN36NDMhkLGqlWr3LKq7AYQzY4sXrzYJk6cmAa/du3abmbikksucb/TfpJOnTqlCSDLli2zyy+/3EqVKpVchuqpTesfffRRimChYDN+/HjTzMtpp51m8+bNc39SB5DM6hVe0RUrVlj//v3dsjGFs4wOlmBF/b1FgQgggAACCCAQYwECSIyBY1R8oALI999/72YgHn74Yfc4XO2nePTRR93AWzMCOiIJICNHjnSbtzVw37t3r910000uEGhZk5ZeKRA88sgjLkCcf/75bg+IAsUdd9zhNn+nXoKlchSEtMFdIUSzGvq7llopbIRfT8ujtExLMxyaldE+E21u16xNJPVSXTp06OBmbfTELW20V30+/fRTy58/PwEkRm8kikUAAQQQQACB+AsQQOJvHo0rBiqACOSzzz5zy5s2btzoZhCuu+4669u3r/t7pAFkzZo1dt9997llS+PGjbN33nnHPYlKgUHlDBkyxO0D2bZtm3sKlr4zRMu7tHxK3+/RunXrFH2jp2fp9ZoJ0R6SU0891erXr28PPPCAW9oVfj0Fm65du7rwoRkbLQvTU7o0u1K3bt2I6qWwohmbEydOuL0kqqM2v2d2MAMSjbcTZSCAAAIIIIBAPAUIIPHUjt61AhdAokeTWCURQBKrv2ktAggggAACQRAggPizFwkg/uy3qNeaABJ1UgpEAAEEEEAAgRgLEEBiDByj4gkgMYL1W7EEEL/1GPVFAAEEEEAAAQKIP+8BAog/+y3qtSaARJ2UAhFAAAEEEEAgxgIEkBgDx6h4AkiMYP1WLAHEbz1GfRFAAAEEEECAAOLPe4AA4s9+i3qtCSBRJ6VABBBAAAEEEIixAAEkxsAxKp4AEiNYvxVLABYLhOEAACAASURBVPFbj1FfBBBAAAEEECCA+PMeIID4s9+iXmsCSNRJKRABBBBAAAEEYixAAIkxcIyKJ4DECNZvxSqAjHh1pf146Kjfqk59EUAgCgL58kWhkAyKOH7ipBUsEMMLxK7qni755ElPV4/KIRAXgfoXnW2Xlz8jLtfiItETIIBEz9LXJSmAbN261ddtoPIIIIAAAggggAAC3hcggHi/j+JSQwJIXJi5CAIIIIAAAgggkPACBJCEvwX+B0AA4UZAAAEEEEAAAQQQiIcAASQeyj64BgHEB51EFRFAAAEEEEAAgQAIEEAC0InRaAIBJBqKlIEAAggggAACCCCQlQABJCuhBPk9ASRBOppmIoAAAggggAACeSxAAMnjDvDK5d1jeGeutN2HjnilSoGoxxUVSljdC0sGoi00AgEEEEAAAQQQiIYAASQaigEogy8ijE0nPtaqirW/qkJsCqdUBBBAAAEEEEDAhwIEEB92WiyqTACJhaoZASQ2rpSKAAIIIIAAAv4VIID4t++iWnMCSFQ5kwsjgMTGlVIRQAABBBBAwL8CBBD/9l1Ua04AiSonASQ2nJSKAAIIIIAAAgEQIIAEoBOj0QQCSDQU05bBDEhsXCkVAQQQQAABBPwrQADxb99FteYEkKhyMgMSG05KRQABBBBAAIEACBBAAtCJ0WgCASQaisyAxEaRUhFAAAEEEEAgSAKeDSBNmza1/v3727XXXpvs/d1331njxo3tq6++yrQP/vKXv1jz5s2tZcuW9v7779sFF1xgpUuXzla/Va9e3RYvXpzmde3atbM77rjDla1jy5Ytdtddd9n48eOtZs2aVrVqVVu2bFm2r5dR5TKqR7YaE8HJBJAIkHJwCkuwcoDGSxBAAAEEEEAg0AKBDCAHDx60IkWKuD/du3d3f6pVq5atjowkgOzevdsUSAYPHmzXX3+9K3/fvn1WokQJy58/f7auRwCJCpfnCiGAeK5LqBACCCCAAAII5LGAbwPIxo0bbeDAgdawYUPbsGGD7dy504YOHWr16tWz0AzIrl277LnnnrNSpUq5c+vUqWPDhg2z9evX24kTJ6xHjx522223uS5YvXq1+12BAgXs5ptvtpdffjnTGRDNxNx+++3uj2ZEQkdoBmT//v0Z1u/777+3Pn36uLByxRVX2JEjR6xBgwbWpk2bTOuhGRm157///a+VLFnShg8f7mZ3FLAuvvhiNzOkGZlbbrnFChcubB999JH98MMPNm7cOPf7zA5mQGLzTiSAxMaVUhFAAAEEEEDAvwK+DSAaaGuZ1ZQpU6xu3bouLOjvs2fPTg4gWialpVyjR492MyCPPfaY/fTTT/bkk0+aZkk0UH/hhRfc4Lx+/fo2atQoFwRmzpxpjz76qK1atSrdJVi33nqrvfnmm3b55Ze7IBF+hAKIys+ofvfdd5+dd955NmDAABc4FCB0bZ2fUT0UOm666SabO3euVaxY0V555RWbP3++zZkzx1Teb7/9Zi+99JJt377dbrjhBtfm1q1b2/PPP2979uxxYYUAEv83KgEk/uZcEQEEEEAAAQS8LeDrAKLlT+vWrXPCmzZtsm7durnQEL4HJDyAXHPNNW5AXqNGDfeaxx9/3IoVK+YG/m3btk0u6+jRo1alShUXDlLvHdE1NduiQ2Fh5MiRGQaQjOpXu3ZtmzZtml1yySXutQoWXbt2ddfMqB6qy4oVK2zSpEnuNarjZZddZp999pkNGjTIVGaHDh3czE6lSpXc7MdZZ51lCxYscOFs4sSJyfWcMWNGmrtSAaVGvyRbu22/t+9Yn9WOAOKzDqO6CCCAAAIIIBBzAc8GEA3KNbvQpEmTZIRvv/3WmjVrZlp+pRmQzp0725o1a9zvw/+dUQDRAL948eJumZWOY8eOufJC13rvvfeSr6XB/fLly9MNIAowf/3rX61Vq1Zu9kIzIqEjfAYko/opeLzzzjvJZes8zdace+65rs3p1UNBYtu2bS40hQ7NwCxcuNDGjBljjRo1cku4dGg5lZaZ/eEPf3AzNXrt3/72t+TXJSUlpbmxRowYQQCJwduNABIDVIpEAAEEEEAAAV8LeDaA3H333W6GQYPz0KGnS2kAvnLlyhwFEO3b0NOqKleunKLT/v3vf7u9IJpN0PHLL7+4JVsZzYCEnoKlIHTnnXeaZhQUbnREEkBq1aplr776qgsKOlq0aGFdunSxSy+9NMN6KGhpBiQ0k5F6BiQ7ASS9O5Y9ILF5HxNAYuNKqQgggAACCCDgXwHPBhCFDe3D0JIpDe61wbpv377Wvn1769ixY8QBRBvK+/Xrl7y3QnsltNzo+PHjbuZAy680I6F9JNoboj0g2kuh32kmIr0lWOGP4VWQ0LIo7cfQ068iCSD33HOP2z/Sq1cv95hgLb/SHhDNxmRUD91i+v28efOsQoUKNnXqVPe439dee83tASGAePNNSADxZr9QKwQQQAABBBDIOwHPBhCRvPHGG27pkL7/o0yZMm62Qd+5kS9fvogDiGY8VMYDDzzgligp1Hz66adOXDMsQ4YMsYIFC7rZBW1SP3nypJuF0CZvbfAuV65cit5J/T0g+qWC0c8//+yCi2ZOFAy0CT2jJVhaLqbXaBbj6quvNj2tS8vAtAwrs3qoXD3R6vfff3ceWjalzewEkLx7A2V1ZQJIVkL8HgEEEEAAAQQSTcDTASTInaGnWoW+K0TBSkuwNIuRVwdLsGIjTwCJjSulIoAAAggggIB/BQggedB3Wm61d+9ee+qpp9xjc/U44KVLl9o555yTB7X53yUJILGhJ4DExpVSEUAAAQQQQMC/AgSQPOi7H3/80S0J01Ot9IWBPXv2dE/UysuDABIbfQJIbFwpFQEEEEAAAQT8K0AA8W/fRbXmBJCociYXRgCJjSulIoAAAggggIB/BQgg/u27qNacABJVTgJIbDgpFQEEEEAAAQQCIEAACUAnRqMJBJBoKKYtgxmQ2LhSKgIIIIAAAgj4V4AA4t++i2rNCSBR5WQGJDaclIoAAggggAACARAggASgE6PRBAJINBSZAYmNIqUigAACCCCAQJAECCBB6s1ctEUBZMSrK+3HQ0dzUQovTS1Q/bziVvfCksAggAACCCCAAAII/H8BAgi3ghNQANm6dSsaCCCAAAIIIIAAAgjEVIAAElNe/xROAPFPX1FTBBBAAAEEEEDAzwIEED/3XhTrTgCJIiZFIYAAAggggAACCGQoQADh5nACBBBuBAQQQAABBBBAAIF4CBBA4qHsg2sQQHzQSVQRAQQQQAABBBAIgAABJACdGI0mEECioUgZCCCAAAIIIIAAAlkJEECyEkqQ37vH8M5cabsPHfF8i2tXPNOuOv8sz9eTCiKAAAIIIIAAAgikFSCAcFc4AT99EeFTt15uba4oT88hgAACCCCAAAII+FCAAOLDTotFlQkgsVClTAQQQAABBBBAAIHUAgQQ7glmQLgHEEAAAQQQQAABBOImQACJG7W3L8QMiLf7h9ohgAACCCCAAAJBESCABKUnc9kOAkguAXk5AggggAACCCCAQEQCBJCImIJ/EgEk+H1MCxFAAAEEEEAAAS8IEEC80AseqAMBxAOdQBUQQAABBBBAAIEEECCAeKCTX3/9dXvzzTctKSkpW7Xp3r27NW3a1Fq2bGlVq1a1ZcuWWenSpbNVRuhkAkiO2HgRAggggAACCCCAQDYFCCDZBIvF6dEIIPv27bMSJUpY/vz5c1RFAkiO2HgRAggggAACCCCAQDYFCCDZBIvF6eEBZOPGjTZw4EBr2LChbdiwwXbu3GlDhw61evXq2XfffWd9+vQxhY0qVarY4cOH3exH6hkQlTd58mQ7fvy4lStXzsaOHWtlypTJtOoEkFj0LGUigAACCCCAAAIIpBYggHjgnggPIFu2bLHmzZvblClTrG7durZ48WL399mzZ1uvXr2sfPnyNmDAANu8ebO1adPGRo0alSKAFC5c2IWVpUuXunMHDx5sBQoUsBEjRhBAPNDXVAEBBBBAAAEEEEh0AQKIB+6A1AGkXbt2tm7dOlezTZs2Wbdu3WzVqlV21VVXuTBSuXJl97tbb73V2rdvn2YG5MiRI1a0aFF3zsKFC23u3Lk2bdq05JbOmDEjTauHDx9uNfol2dpt+z0gknkVnrr1cmtzRXnP15MKIoAAAggggAACCKQVIIB44K5IHUA6d+5sa9ascTXTjEjo35dccoktX77czWzo6NKli7Vo0SJFAClVqpRNmDDBVq5cafny5bMDBw5Y2bJlU2xwT2+zu2ZICCAeuBmoAgIIIIAAAgggEHABAogHOjjSAFK7dm03k6EgoqNVq1bWqVOnFAFk/fr1Nn78eJs1a5addtppNm/ePPcnqydssQfEAzcCVUAAAQQQQAABBBJAgADigU6ONIDosbvnn3++2wOioHHHHXfY6NGjUwQQzZCsXr3abUI/ePCg2zeizepz5szJtKUEEA/cCFQBAQQQQAABBBBIAAECiAc6OdIAsm3bNvcUrB9//NGqV69uJ0+etMaNG1vr1q2TvwdEm9C7du3qwoeefNW7d2/r0aOHmy0ZNGhQhq0lgHjgRqAKCCCAAAIIIIBAAggQQBKgkyNpIgEkEiXOQQABBBBAAAEEEMitAAEkt4IBeT0BJCAdSTMQQAABBBBAAAGPCxBAPN5B8aoeASRe0lwHAQQQQAABBBBIbAECSGL3f3LrCSDcCAgggAACCCCAAALxECCAxEPZB9cggPigk6giAggggAACCCAQAAECSAA6MRpNIIBEQ5EyEEAAAQQQQAABBLISIIBkJZQgvyeAJEhH00wEEEAAAQQQQCCPBQggedwBXrk8AcQrPUE9EEAAAQQQQACBYAsQQILdvxG3TgFkxKsr7cdDRyN+TV6dWPOPJezq88/Kq8tzXQQQQAABBBBAAIFcCBBAcoEXpJcqgGzdujVITaItCCCAAAIIIIAAAh4UIIB4sFPyokoEkLxQ55oIIIAAAggggEDiCRBAEq/P020xAYQbAQEEEEAAAQQQQCAeAgSQeCj74BoEEB90ElVEAAEEEEAAAQQCIEAACUAnRqMJBJBoKFIGAggggAACCCCAQFYCBJCshBLk9wSQBOlomokAAggggAACCOSxAAEkjzvAK5d3j+GdudJ2HzrilSqlqMdl5c6wayud48m6USkEEEAAAQQQQACByAUIIJFbBfpMr38R4YAbK9lfG14Q6D6gcQgggAACCCCAQCIIEEASoZcjaCMBJAIkTkEAAQQQQAABBBDItQABJNeEwSiAABKMfqQVCCCAAAIIIICA1wUIIF7voTjVjwASJ2gugwACCCCAAAIIJLgAASTBb4BQ8wkg3AgIIIAAAggggAAC8RAggMRD2QfXIID4oJOoIgIIIIAAAgggEAABAkgAOjEaTSCAREORMhBAAAEEEEAAAQSyEiCAZCUU9vt33nnHXnjhBdu0aZMVKlTILrroIrv33nutfv362SjFm6cSQLzZL9QKAQQQQAABBBAImgABJMIeXbFihfXr188eeugha9y4sQsgy5cvt+HDh9u0adOsWrVqEZbkzdMIIN7sF2qFAAIIIIAAAggETYAAEmGPNm/e3G677Tbr2LFjilfs3LnTSpUqZQUKFLDDhw/bsGHDbP369XbixAnr0aOHe42O6tWrW/fu3W3KlCmmmZQ6derY/fffb6tWrbJvvvnGHnzwQXvvvffc3/Ply2cvv/yynXLKKbZu3TpXpsouXLiwPfLII3bVVVfZxo0bbeDAgdawYUPbsGGDqR5Dhw6133//3caMGWOLFy9OrmfLli2tZ8+e1qRJkwxbSwCJ8EbgNAQQQAABBBBAAIFcCRBAIuDToL5KlSq2evVqO+ecczJ8xWOPPWY//fSTPfnkk3bw4EG75ZZb3JKtypUrW61ataxNmzY2YMAAy58/v/t3ly5drFu3bvb3v//dzaS89dZbVqFCBevQoYP9+c9/tptuusn96dq1qylELFiwwCZMmGDLli2zLVu2mEKRAk3dunVd4NDfX331VbvmmmtsxowZVqlSJfvhhx+sWbNmtnbtWhdgMjoIIBHcCJyCAAIIIIAAAgggkGsBAkgEhLt27bJ69eq5vR8FCxZ0r9BMiEKAjgYNGtgTTzzhBv7PP/+81ahRw/388ccft2LFirmZDgWOyZMnu5kQHfr3zJkz3T6SDz74wAWQJUuWuN8NHjzYFAg6depkx44dc9dUaNm9e7eb8VA9dO127dq5GRId+pnCjGZUNBNyxhlnuCVj06dPty+++MLNioQOhZPUh65fo1+Srd22PwKR+J8y4MZK9teGF8T/wlwRAQQQQAABBBBAIKoCBJAIOI8fP+5mQFauXGlly5Z1r9BMh36uvSFaOjVx4kR3TvHixd1yLB0KD5p9UCBQ4Jg9e7b98Y9/TA4g8+bNs/Lly7vZidGjR9v8+fPd73S+ZkI0Q6KZDYUIzcJoWZeCxldffeUCSOfOnW3NmjXuNeH//vjjj61///6uXu3bt3flKLiEjqSkpDStHjFiBAEkgnuBUxBAAAEEEEAAAQRyJ0AAidDvjjvucE+70j6O8GPRokX2j3/8wwUQbU4fP368W3KV+lAAmTNnjgsWOvTvrAKIlnBpdkXB5MILL3T7PBo1apRlADl58qSr66OPPur2liikaNN8ZgdLsCK8ETgNAQQQQAABBBBAIFcCBJAI+bTU6e6773Z7OJo2beo2iL///vs2cuRItz9Dy6xGjRplv/32m1tOpdkRLXvSPo2qVau6wJHdAKIQoRkMBQgtw3r66adt0qRJbtP5jh07MpwBUZM0o6IlXdrsrnpldRBAshLi9wgggAACCCCAAALRECCAZEPxo48+snHjxtmXX37p9mRcfPHFbsO4QoYOPalKsw6ffvqp+7cCxJAhQ1x4yEkA0dIpLaXSkqqSJUta37597bnnnnNLu7TnJKMlWLq26tiqVSubOnWq26Se1UEAyUqI3yOAAAIIIIAAAghEQ4AAEg1FD5axZ88e9xQubUoP7UnJrJoEEA92IlVCAAEEEEAAAQQCKEAACWCnag+InqSlDe76pvZIDgJIJEqcgwACCCCAAAIIIJBbAQJIbgU99vq9e/e6PSmXXXaZPfvss1a0aNGIakgAiYiJkxBAAAEEEEAAAQRyKUAAySVgUF5OAAlKT9IOBBBAAAEEEEDA2wIEEG/3T9xqRwCJGzUXQgABBBBAAAEEElqAAJLQ3f9/jSeAcCMggAACCCCAAAIIxEOAABIPZR9cgwDig06iiggggAACCCCAQAAECCAB6MRoNIEAEg1FykAAAQQQQAABBBDISoAAkpVQgvxeAWTEqyvtx0NHPdniS8ucbtdeco4n60alEEAAAQQQQAABBCIXIIBEbhXoMxVAtm7dGug20jgEEEAAAQQQQACBvBcggOR9H3iiBgQQT3QDlUAAAQQQQAABBAIvQAAJfBdH1kACSGROnIUAAggggAACCCCQOwECSO78AvNqAkhgupKGIIAAAggggAACnhYggHi6e+JXOQJI/Ky5EgIIIIAAAgggkMgCBJBE7v2wthNAuBEQQAABBBBAAAEE4iFAAImHsg+u4R7DO3Ol7T50xDO1LVoov914aRmrcNYpnqkTFUEAAQQQQAABBBDInQABJHd+gXm1F7+I8OzTilhS59p2SZnTAuNMQxBAAAEEEEAAgUQXIIAk+h3w/9tPAOFGQAABBBBAAAEEEIiHAAEkHso+uAYBxAedRBURQAABBBBAAIEACKQbQN577z2rU6eOFSxYMABNpAmRCBBAIlHiHAQQQAABBBBAAIHcCqQbQKpWrWrFihWz5s2bW6tWreyyyy7L7XV4vccFCCAe7yCqhwACCCCAAAIIBEQg3QBy5MgRW716tS1dutTefvttO/vss61ly5Z2880327nnnhuQptOMcAECCPcDAggggAACCCCAQDwEstwDcuzYMfvwww/trbfesmXLltnFF19st912m910001WpEiReNSRa8RBgAASB2QugQACCCCAAAIIIGBZBpBdu3bZm2++6WZDNm7caA0bNrTvv//eDh06ZBMmTLBKlSrBaGZff/21jR071j766CM7ceKEc+nZs6fVq1cvLj7Vq1e3xYsXW+nSpXN0PQJIjth4EQIIIIAAAggggEA2BdINIIcPH3aD2QULFtjHH39sl156qbVp08btCSlevLi7xAsvvOBmRRROEv1QSJNN69atrUOHDs5Is0UjRoywyZMn25VXXhlzIgJIzIm5AAIIIIAAAggggEAUBNINIAocGkRrz4eCx4UXXpjmUr/99ptVq1bNvvrqqyhUw99FjBw50rZt22YvvfRSioa88847VqZMGTcbsm7dOhs2bJgp3BUuXNgeeeQRu+qqq9ys0sCBA93M0oYNG2znzp02dOhQN3Oyd+9eGzBggG3fvt3NqnTq1Mnuuusudw3t0VF5BQoUcP308ssvJ8+AvP766y74HD9+3MqVK+dmZlSPzA5mQPx9D1J7BBBAAAEEEEDALwLpBpBXX33VfZpftGjRTNvx66+/2imnnOKXtsasntqg37FjRxfWMjq0Z6Zr165uM79mlrR8TbMkW7ZscbMnU6ZMsbp167oQob/Pnj3bhg8fbnogwKhRo2zHjh3WtGlTW7lypZUsWdLq16/vft6gQQObOXOmPfroo7Zq1SoXbhRetGSufPnyNnjwYBdSNBtDAInZLUDBCCCAAAIIIIAAAhEKpBtAqlSpYitWrMjxfoIIrx2Y06699lo3G6FQkNGhzfz6XpX8+fPb7t273YzHpk2bXABp166dmyHRoZ9169bNhQnNeuiPQoWOJk2a2JNPPmlnnHGGtW3bNvk1R48eNfWZZkW0B0ShJRQeFy5caHPnzrVp06YlV23GjBlpqqmwU6Nfkq3dtt8z/XL2aUUsqXNtu6TMaZ6pExVBAAEEEEAAAQQQyJ1AugFk4sSJbqP5vffeSwiJwPfOO+90y6AUJDI6NLMxffp0+/33312oUNDQ8jUFkM6dO9uaNWvcS8P/rXOeeeYZ27NnjwsvmzdvdkFCIaZPnz6mL4wMHfquluXLl1upUqXc7IpmSvLly2cHDhywsmXLWlJSUvK54X8P/VAzJASQCDqbUxBAAAEEEEAAAQRyJZBuAGncuLHt37/f7VcoVKhQmm9E//zzz3N10aC9WCFBm/Vfe+21FE1btGiR86tVq5ZbKjV//ny3n0b7PBo1apRlAGnWrJkLJ5rt0KHXjBkzxu3P0aOQP/vsM/fzX375xe3H0QzI+vXrbfz48TZr1iw77bTTbN68ee5PeqEjvLLsAQnaXUl7EEAAAQQQQAABbwqkG0D05YMaOGd0ZLbUyJvNjG2tNMtw/fXXm4Jb9+7d7ayzznJL2DSroP0cWg7Vvn17N8uhmYynn37aJk2a5Dada29HRjMgCi5Tp051y6sUZh5++GEbN26cXX311W6/iJZjKdho87uCiWZENAuiIKJN6AcPHrRevXq5IDlnzpxMEQggsb1HKB0BBBBAAAEEEEDgfwLpBpB+/fq5JyelPjSQ7d27d5qnPYFp7ilYTzzxhK1du9ZOnjzpQsMDDzxgejyujv79+7tZEm0g79u3rz333HOmfSF6TUYBRJvLtRyuRIkSbvP6Tz/95Dana0+Hlmc99thj7lqaDXnllVdcyChWrJjb7K7woSdfqb969OhhrVq1skGDBmXYVQQQ7mIEEEAAAQQQQACBeAikCCAaROuPvkBPy3hSH99++6099dRT9uWXX8ajblwjjgIEkDhicykEEEAAAQQQQCCBBVIEkHfffdd9waCeyHTqqaemYdGn69poff/99ycwWTCbTgAJZr/SKgQQQAABBBBAwGsC6S7B0n4FLenhSBwBAkji9DUtRQABBBBAAAEE8lIg3QCiCmlvwb59+9x3SqQ+9AV3HMESIIAEqz9pDQIIIIAAAggg4FWBdAPIW2+95Z64pKc7pXds3brVq+2hXjkUIIDkEI6XIYAAAggggAACCGRLIN0Aoke76ksIa9eubUWKFElToL5tmyNYAgSQYPUnrUEAAQQQQAABBLwqkG4AufHGG23JkiVerTP1ioEAASQGqBSJAAIIIIAAAgggkEYg3QCi75HQEiz2eiTOHUMASZy+pqUIIIAAAggggEBeCqQbQF5++WWbMWOGNWnSxEqVKmX58uVLUccuXbrkZZ25dgwEFEBGvLrSfjx0NAal56zIooXy2/WVS1mFs/6QswJ4FQIIIIAAAggggIDnBNINIPrW7UKFCmVYWX3jNkewBBRAeLhAsPqU1iCAAAIIIIAAAl4UyPAxvF6sLHWKnQABJHa2lIwAAggggAACCCDwfwLpBpDly5dnaHT8+HFr2rQphgETIIAErENpDgIIIIAAAggg4FGBdANIrVq1UlRXX0qo7wTRI3nLlStnS5cu9WhzqFZOBQggOZXjdQgggAACCCCAAALZEYh4Cdbhw4dt4sSJVqFCBbv11luzcw3O9YEAAcQHnUQVEUAAAQQQQACBAAhEHEBCbb355ptt4cKFAWg6TQgXIIBwPyCAAAIIIIAAAgjEQyBbAUSzINddd519+OGH8agb14ijgALI0KQVtuvAkbhdtUq5063ZZWXidj0uhAACCCCAAAIIIJD3AukGkAceeCBNzY4ePWqff/65Va5c2S3F4giWQF58EWGfJn+y+5tcFCxIWoMAAggggAACCCCQqUC6AWTo0KFpXqQN6BUrVrRbbrnFTjnlFFgDJkAACViH0hwEEEAAAQQQQMCjAtlaguXRNlCtKAgQQKKASBEIIIAAAggggAACWQpkGEC+/vprmz9/vu3cudO0/EpPv2rVqpVpoMoRPAECSPD6lBYhgAACCCCAAAJeFEg3gCxbtszuu+8+q1atmp1//vmu3gokX3zxhU2bNs1q167txbZQohhf5QAAIABJREFUp1wIEEBygcdLEUAAAQQQQAABBCIWSDeANGvWzHr27JnmG88XLFhgSUlJ9sYbb0R8AU70hwABxB/9RC0RQAABBBBAAAG/C6QbQKpWrWqfffaZFSxYMEX7fv/9d9O3pK9fv97v7ab+qQQIINwSCCCAAAIIIIAAAvEQSDeAXH/99fbkk0+6JVjhh4JHv379bMWKFfGom6+v0a5dO1u3bp3ly5cvRTvef/99K1mypOfaRgDxXJdQIQQQQAABBBBAIJAC6QaQV155xZ555hn3yF0NTE+ePGlbtmyxefPmWffu3a1bt26BxIhmoxRA7rjjDmvZsmU0i41ZWQSQmNFSMAIIIIAAAggggECYQIZPwVq8eLHNnj3b/vOf/9ixY8fcU7Bat27tnoTFkbVAZgFEIe6iiy5ygW706NFWqVIlGzBggG3fvt1OnDhhnTp1srvuustdpHr16m7W6d1337UdO3Y4/x49erjfaU/Os88+a1oapwcDqKzChQvbypUr7amnnrLffvvNypcv784588wzM600ASTrPuUMBBBAAAEEEEAAgdwLRPw9IJoFSb2cKPeXD24JmQUQPWFs7969NnXqVCtWrJgNHz7cjhw5YqNGjXIho2nTpi5ElCpVyu25ufPOO6137962b98+q1evnn3yySe2f/9+N0M1d+5cK1u2rAslV1xxhZtxad68uWkWS8Hm5Zdfdue/+OKLBJDg3m60DAEEEEAAAQQQ8I1AmgDy+uuv2zfffGODBg1KbsTx48ft1ltvdUuvbrzxRt80Li8rqgCiZWv6BvnQoW+QV7BQAKlSpYpbzqZDsx76o9kLHU2aNHF7cGrUqOECyPTp061y5crud/qZZj4++OADe+edd2zixInu55rtKFCggAskS5YscY9L1vHLL79YzZo17fPPP7dChQq5n82YMSMNjUJQjX5Jtnbb/rix9WnyJ7u/yUVxux4XQgABBBBAAAEEEMh7gRQBZPny5W5wPGTIEOvQoUOK2mnQqiU++l990s6RuYACSIsWLeyGG25IPjF//vx21llnOWPNZOgcHZs2bXJ7bvbs2eOePLZ582YXIBQcFEDmzJnjlsDpCP176dKl9u9//9sFlfBj8uTJ9vzzz1uJEiWSf3zo0CEXSs455xz3Mz1KOfUxYsQIAgg3NQIIIIAAAggggEDMBVIEkNtvv92FC+05SO/QEiHNjrz00ksxr5jfL5DVEqz69evbbbfd5pqp713p3LmztW3b1v27UaNGNmbMmEwDyMcff2wKIaG+OHDggP3666+mn2v/TmhmJFJH9oBEKsV5CCCAAAIIIIAAArkRSBFAtOFZ+xJSP343dAE9VlbLhtauXZubaybEa7MTQDSrIXcty1q0aJE9/PDDNm7cOFNIyWgGREu7tFdES+b0bfXaI6LXt2nTxm666SabNWuWVaxY0S290mb3Rx55JFN3AkhC3JY0EgEEEEAAAQQQyHOBFAHk0ksvdXsILr744nQrtnHjRvep/RdffJHnFfd6BbITQGbOnOlmLLRsSpvIf/rpJ/cEMvWFnnqV3hIsLcnSTIdmpbRHp06dOu7v2keivSFjx451G9tPO+00t6ROy7kyOwggXr+jqB8CCCCAAAIIIBAMgRQBRE9P0ifoegxsesf48eNN+0QWLlwYjNbTimQBAgg3AwIIIIAAAggggEA8BFIEEC0D0mZoPbpVS3/CDwWPXr162eDBg9NsUI9HRblGbAUIILH1pXQEEEAAAQQQQACB/wmkCCB6FGyfPn3c0h5tRtegVD/T0qsvv/zSfe/E448/bnqaE0ewBAggwepPWoMAAggggAACCHhVIN0vItR3VSxbtsx9C7q+fFD7DbThuW7dul5tB/XKpQABJJeAvBwBBBBAAAEEEEAgIoGIvwk9otI4ybcCBBDfdh0VRwABBBBAAAEEfCVAAPFVd8WusgSQ2NlSMgIIIIAAAggggMD/CRBAuBucAAGEGwEBBBBAAAEEEEAgHgIEkHgo++AaBBAfdBJVRAABBBBAAAEEAiBAAAlAJ0ajCQogQ5NW2J6DR6JRXERlXFr2dGt6WZmIzuUkBBBAAAEEEEAAgWAIEECC0Y+5boUCyNatW3NdDgUggAACCCCAAAIIIJCZAAGE+8MJEEC4ERBAAAEEEEAAAQTiIUAAiYeyD65BAPFBJ1FFBBBAAAEEEEAgAAIEkAB0YjSaQACJhiJlIIAAAggggAACCGQlQADJSihBfk8ASZCOppkIIIAAAggggEAeCxBA8rgDvHJ5AohXeoJ6IIAAAggggAACwRYggAS7fyNuXegxvLsOxPYxvKcWKWg3Vytr5UsUi7hunIgAAggggAACCCAQHAECSHD6MlctidcXEZ5bophN63ylXXD2qbmqLy9GAAEEEEAAAQQQ8KcAAcSf/Rb1WhNAok5KgQgggAACCCCAAALpCBBAuC2cAAGEGwEBBBBAAAEEEEAgHgIEkHgo++AaBBAfdBJVRAABBBBAAAEEAiBAAAlAJ0ajCQSQaChSBgIIIIAAAggggEBWAgSQrIQS5PcEkATpaJqJAAIIIIAAAgjksQABJI87wCuXJ4B4pSeoBwIIIIAAAgggEGwBAkgu+ve2226zX3/91d58881clGK2fft2+/HHH61mzZrZKqd79+7WtGlTa9mypVWtWtWWLVtmpUuXzlYZoZMJIDli40UIIIAAAggggAAC2RQggGQTLHT61q1b7fHHH7cCBQpYjx49rHr16jksyWzq1Kl27Ngx69atW7bKCA8g+/btsxIlSlj+/PmzVQYBJEdcvAgBBBBAAAEEEEAghwIEkBzCKXxceOGFVqRIEfvnP/9pw4cPdyWtX7/eBgwY4GYjUv979erVNnr0aDt69KgVLlzYBg4c6F7fs2dPK1iwoLVt29aaNWtmDz74oHssrmZFpk+fbq+//rpNnjzZjh8/buXKlbOxY8damTJlLKMZkIzOz6ypzIDk8EbgZQgggAACCCCAAALZEiCAZIvrfyefOHHCrrvuOlu4cKGbAbnhhhtsxYoVLlRkFkC0XGrEiBFuqdXmzZstKSnJRo0aZcOGDXOBQjMgW7ZscUFEP2/evLnt37/f6tWrZ0uXLrXy5cvb4MGD3TVVTnoBRHXI6HwCSA46m5cggAACCCCAAAIIRFWAAJIDznfffdfmzZtn48aNc6/u37+/NW7c2G688cZMA0jHjh2tYsWKds8999h5552XfOXUAaR169b2+eefJy+nOnLkiBUtWtSdr9Azd+5cmzZtWoYzIBmdH7rgjBkz0rRaMzg1+iXZ2m37cyAS+UvOLVHMpnW+0i44+9TIX8SZCCCAAAIIIIAAAoERIIDkoCt79eplCiGaidChGZGrr77aJk2alGkA0ZKqCRMm2PLly+3000+3QYMGWYMGDdLMgHTq1Mnef/99V/bJkyfda1auXGn58uWzAwcOWNmyZd3sSXozIKVKlcrw/FBT9drUh2ZUCCA5uBl4CQIIIIAAAggggEC2BAgg2eIyO3jwoFt+pf0cWu6kQ3sz6tata2+99ZZ999131rdvX7ckS4fO0+A+tCckdDn9XjMnn3zyift9+BKszp0725o1a9ypS5YssfHjx9usWbPstNNOczMv+pNRANESsIzOz6yp7AHJ5o3A6QgggAACCCCAAAI5EiCAZJNt5syZ9vHHHycvvwq9XGGiSpUqbj/I9ddfbx999JGdcsopNnToUFu7dq0tWLDAOnToYM8//7x7VO6OHTvc43M//fRTtzFdYUZlaA9IeADRcimFGG1CV/jR7Mvhw4dtzpw56c6AaHYlo/MJINnsbE5HAAEEEEAAAQQQiLoAASSbpNogfvfdd7sN4uGHBv5aKjV//nwbOXKkffDBB25W45prrrHXXnvNLbtSaJg4caJbsqU9HX369HFhRbMd9913n1uOde+996YIINqE3rVrVxc+VF7v3r3dY39btWpl3377bZrvAVGQyeh8LfnK6GAGJJs3AqcjgAACCCCAAAII5EiAAJIjtuC9iAASvD6lRQgggAACCCCAgBcFCCBe7JU8qBMBJA/QuSQCCCCAAAIIIJCAAgSQBOz09JpMAOFGQAABBBBAAAEEEIiHAAEkHso+uAYBxAedRBURQAABBBBAAIEACBBAAtCJ0WgCASQaipSBAAIIIIAAAgggkJUAASQroQT5PQEkQTqaZiKAAAIIIIAAAnksQADJ4w7wyuUJIF7pCeqBAAIIIIAAAggEW4AAEuz+jbh1BJCIqTgRAQQQQAABBBBAIBcCBJBc4AXppQogQ5NW2J6DR2LarD8UKWgtLi9r5UsUi+l1KBwBBBBAAAEEEEDAmwIEEG/2S9xrpQCydevWuF+XCyKAAAIIIIAAAggklgABJLH6O8PWEkC4ERBAAAEEEEAAAQTiIUAAiYeyD65BAPFBJ1FFBBBAAAEEEEAgAAIEkAB0YjSaQACJhiJlIIAAAggggAACCGQlQADJSihBfk8ASZCOppkIIIAAAggggEAeCxBA8rgDvHJ5AohXeoJ6IIAAAggggAACwRYggAS7fyNuXegxvLsOxO4xvGWKF7UOV1WIuE6ciAACCCCAAAIIIBA8AQJI8Po0Ry2KxxcRNrzobJvW+coc1Y8XIYAAAggggAACCARDgAASjH7MdSsIILkmpAAEEEAAAQQQQACBCAQIIBEgJcIpBJBE6GXaiAACCCCAAAII5L0AASTv+8ATNSCAeKIbqAQCCCCAAAIIIBB4AQJI4Ls4sgYSQCJz4iwEEEAAAQQQQACB3AkQQHLnF5hXE0AC05U0BAEEEEAAAQQQ8LQAAcTT3RO/yhFA4mfNlRBAAAEEEEAAgUQWIIDEufeHDRtmr732mv33v/+11atXW+nSpeNcg/QvRwDxRDdQCQQQQAABBBBAIPACBJA86mIN+AkgeYTPZRFAAAEEEEAAAQTyTIAAkkf04QHk9ddft8mTJ9vx48etXLlyNnbsWCtTpox16dLFdN7mzZvt559/tkqVKtnw4cOtUKFCtm7dOtNsyuHDh61w4cL2yCOP2FVXXWUbN260gQMHWsOGDW3Dhg22c+dOGzp0qNWrVy/TljIDkkc3ApdFAAEEEEAAAQQSTIAAkkcdHgogCg8KB0uXLrXy5cvb4MGDrUCBAjZixAjr3r277d+/31599VXLnz+/tWvXzjp27GgtWrSwm266ybp27WotW7a0BQsW2IQJE2zZsmW2ZcsWa968uU2ZMsXq1q1rixcvdn+fPXs2ASSP+prLIoAAAggggAACCPyfAAEkj+6G8BmQI0eOWNGiRV1NFi5caHPnzrVp06a5AFK7dm3r1KmT+92zzz7rZkI083Hs2DErWLCgCya7d+92Mx6bNm1yAURBRTMkOvSzbt262apVq5JbOmPGjDSt1sxKjX5Jtnbb/piJNLzobJvW+cqYlU/BCCCAAAIIIIAAAt4XIIDkUR+FAkipUqXc7MXKlSstX758duDAAStbtqwlJSW5AHLddddZmzZtXC1feukl+/LLL+2ZZ55xMxvTp0+333//3U6cOOGCxldffeUCSOfOnW3NmjXuNan/rZ+p7NSHZlwIIHl0M3BZBBBAAAEEEEAggQQIIHHqbC2PqlChgl188cV29OhRq1Klin366af2wQcf2Pjx423WrFl22mmn2bx589yfUACpWbOm2wuiQ3tDtOejZ8+e1qBBA5s/f75deOGFbp9Ho0aNIg4g6TWZPSBxuhG4DAIIIIAAAgggkOACBJA43QCPP/64bd261YUNLbOaOnWq2/eh5VB6GpY2oR88eNB69erlQsacOXPcDMjevXvtlVdecbW85ZZb3O8vuOACa9++vZvl0DKsp59+2iZNmuQ2ne/YsSPLGRACSJw6ncsggAACCCCAAAIIpBEggMTpplCoeOCBB+zDDz90m81HjRpl1apVc5vMtZlc4UNPvurdu7f16NHDWrVqZd9++61VrFjRPvnkE9u1a5fbrK6lUtqk3r9/f/v444+tZMmS1rdvX3vuuefcvpAnnniCABKnPuUyCCCAAAIIIIAAAtkXIIBk3yxur9AMSNOmTd2TrmJ9sAQr1sKUjwACCCCAAAIIICABAoiH7wMCiIc7h6ohgAACCCCAAAII5EiAAJIjtvi8iAASH2euggACCCCAAAIIIBA/AQJI/Kw9fSWWYHm6e6gcAggggAACCCAQGAECSGC6MncNIYDkzo9XI4AAAggggAACCEQmQACJzCnwZxFAAt/FNBABBBBAAAEEEPCEAAHEE92Q95UggOR9H1ADBBBAAAEEEEAgEQQIIInQyxG0kQASARKnIIAAAggggAACCORagACSa8JgFKAAMjRphe05eCRmDSpTvJjdWfu8mJVPwQgggAACCCCAAALeFyCAeL+P4lJDBZCtW7fG5VpcBAEEEEAAAQQQQCBxBQggidv3KVpOAOFGQAABBBBAAAEEEIiHAAEkHso+uAYBxAedRBURQAABBBBAAIEACBBAAtCJ0WgCASQaipSBAAIIIIAAAgggkJUAASQroQT5PQEkQTqaZiKAAAIIIIAAAnksQADJ4w7wyuUJIF7pCeqBAAIIIIAAAggEW4AAEuz+jbh1ocfw7joQu8fwnndWMftzLR7DG3GncCICCCCAAAIIIBBAAQJIADs1J02KxxcRNrusjL1wZ42cVI/XIIAAAggggAACCAREgAASkI7MbTMIILkV5PUIIIAAAggggAACkQgQQCJRSoBzCCAJ0Mk0EQEEEEAAAQQQ8IAAAcQDneCFKhBAvNAL1AEBBBBAAAEEEAi+AAEk+H0cUQsJIBExcRICCCCAAAIIIIBALgUIILkEDMrLCSBB6UnagQACCCCAAAIIeFuAAOLt/olb7QggcaPmQggggAACCCCAQEILJEwAOX78uF1yySXWsWNHGzp0aHKnL1++3N544w2bOHFipjfC+++/bxdccIGVLl3a7rvvPqtfv77ddtttOb55Nm7caD179rS33347x2WEvzC35RFAotINFIIAAggggAACCCCQhUBCBZBq1arZ6aefbklJSXbhhRc6mkgDSPfu3U1/VAYBJGfvK74HJGduvAoBBBBAAAEEEAiSQEIFkMsvv9yGDBliy5Yts6lTp6YJIIcPH7Zhw4bZ+vXr7cSJE9ajRw83yzFp0iR77rnnrFSpUjZw4EBbtGiRCzCaFdmxY4fVqlXLnn32WStYsKB9/fXX9sgjj9iuXbusUKFC9vjjj5uuqxmKBx980DTT8OOPP7pyQjMge/futQEDBtj27dvddTt16mR33XWXq1/16tWtX79+9u6777prtWrVytVLh2ZtZs6cacWLF7emTZu6mRzNqKxevdpGjx5tR48etcKFC7trNWzYMNP7lhmQIL2taQsCCCCAAAIIIOBdgYQKIFWqVHFBoGXLlta7d29r3LhxihmQxx57zH766Sd78skn7eDBg3bLLbfYCy+8YJUrV3YDfA3qQzMgu3fvtilTprjQ0bx5c3v00UetTp06ruzbb7/d/fn888+tW7dutmrVKtu2bZu1bdvWRo0a5c4PXzI1fPhwO3LkiPudQoautXLlShd4FG7uvPNOV999+/ZZvXr17JNPPrGdO3e6cLRkyRIrWbKkCyn/+te/XADR60eMGGE1a9a0zZs3uxkflZ3ZQQDx7puUmiGAAAIIIIAAAkESSKgAcumll9pXX31la9eutcGDB9vixYvtvffeS94Dcs0119jzzz9vNWrUcH2s2YtixYrZ/fffnyaA6JzOnTu78zQjoTBzxRVXWIsWLVwQKFCggPudQoxmIM4880xr3bq1CyX58+dPEUA066E/mq3Q0aRJExeCdA0FkOnTp7sQpEM/W7BggZvlULAJ7V3RvzV7owCifS4VK1a0e+65x84777w09+uMGTPS/EwhqEa/JFu7bX/M7m+WYMWMloIRQAABBBBAAAHfCCRkAFHv3HvvvVa1alU7//zzkwOIZki0nCkUHo4dO2bNmjVzm9ZTz4CEb0IP7Qm5+OKL3ayENqqHjt9++83NjmgDu5ZWadmWjvAZkE2bNtkzzzxje/bscTMqmrWYNm2am8FQAJkzZ45VqFDBvS7077feesvNqiio6FCw6dOnjwsgWuI1YcIEN7ujPS+DBg2yBg0aJNdJMyKpD82YEEB8876loggggAACCCCAgG8FEjaA/Oc//3FLojRo1/4KzSRoFmP8+PHJsw3hvRpJAFEoUWD57LPP0twQW7ZscTMma9asSRNA9Br9TvXR0ahRIxszZkymAUTl6M+LL77oXqMlWyNHjkzzVK0VK1ZY//793bKtULBK725lCZZv38NUHAEEEEAAAQQQ8JVAwgYQ9dLYsWPdMiwNvhVAtE9CMxZajqTH9ioEaL+GZkpuvvlmt89CISP1U7DC/609IF26dHFLsfbv3+/2YuiP9mxkFEA0q6FN8ZqB0Qb3hx9+2MaNG+euldEMyK+//ur2hqj+Z511llsmppmTN9980zp06OCWkmkmRntKVKdPP/3ULf3K6CCA+Op9S2URQAABBBBAAAHfCiR0APnll1/s+uuvt8suu8wFED0FS8ulNFjXoQCgp2ZpWZRmRv72t7/ZAw88YP/85z9TfA9IeADRsigt2frhhx/cjIOWXWlDemYzIHqSla5fokQJFxa0EX727Nk2d+5c99Sr9JZgaUmWnrz197//3S2z+vOf/+z2img2R+erPO0rKVq0qJvlUTszOwggvn0PU3EEEEAAAQQQQMBXAgkTQHzVK3lQWQJIHqBzSQQQQAABBBBAIAEFCCAJ2OnpNZkAwo2AAAIIIIAAAgggEA8BAkg8lH1wDQKIDzqJKiKAAAIIIIAAAgEQIIAEoBOj0QQCSDQUKQMBBBBAAAEEEEAgKwECSFZCCfJ7AkiCdDTNRAABBBBAAAEE8liAAJLHHeCVyxNAvNIT1AMBBBBAAAEEEAi2AAEk2P0bcesIIBFTcSICCCCAAAIIIIBALgQIILnAC9JLCSBB6k3aggACCCCAAAIIeFeAAOLdvolrzRRAhiatsD0Hj8TsuueeWcza1TovZuVTMAIIIIAAAggggID3BQgg3u+juNRQAWTr1q1xuRYXQQABBBBAAAEEEEhcAQJI4vZ9ipYTQLgREEAAAQQQQAABBOIhQACJh7IPrkEA8UEnUUUEEEAAAQQQQCAAAgSQAHRiNJpAAImGImUggAACCCCAAAIIZCVAAMlKKEF+TwBJkI6mmQgggAACCCCAQB4LEEDyuAO8cnkCiFd6gnoggAACCCCAAALBFiCABLt/I25d6DG8uw5E7zG8fyhSwFpcXsbOO/MPEdeDExFAAAEEEEAAAQSCLUAACXb/Rty6WHwRYbnixWxa5yvtT+ecGnE9OBEBBBBAAAEEEEAg2AIEkGD3b8StI4BETMWJCCCAAAIIIIAAArkQIIDkAi9ILyWABKk3aQsCCCCAAAIIIOBdAQKId/smrjUjgMSVm4shgAACCCCAAAIJK0AASdiuT9lwAgg3AgIIIIAAAggggEA8BAgg8VD2wTUIID7oJKqIAAIIIIAAAggEQIAAEoBOjEYTCCDRUKQMBBBAAAEEEEAAgawECCBZCcXg91u3brXRo0fbl19+aSdPnrRzzz3X+vbta3Xq1Mnx1apWrWrLli2z0qVL56gMAkiO2HgRAggggAACCCCAQDYFCCDZBIvG6dddd53dfffddvvtt1u+fPlsyZIlNnDgQFu1apUVL148R5fYt2+flShRwvLnz5+j1xNAcsTGixBAAAEEEEAAAQSyKUAAySZYbk///fffrXLlyvbBBx/Y2WefnVzcN998YxUqVHCzIoMGDbLatWubZkoOHTpkQ4YMsVq1atnevXttwIABtn37djtx4oR16tTJ7rrrLldGaAZk//79Lsw0bNjQNmzYYDt37rShQ4davXr1Mq06ASS3PcvrEUAAAQQQQAABBCIRIIBEohTlc7p27Wp79uxxAeLqq6+2c845J/kKGzdutJYtW9pLL71kDRo0sPfee88ee+wxW758uQ0fPtyOHDlio0aNsh07dljTpk1t5cqVVqpUqeQAcvDgQWvevLlNmTLF6tata4sXL3Z/nz17NgEkyv1IcQgggAACCCCAAALZFyCAZN8s1684evSozZo1y5YuXWr/+te/7Pzzz7e//vWvLlAogLRv394+++wzd53jx4/bJZdcYmvXrrUzzjjDzXwULlzY/a5Jkyb25JNPWo0aNVIEkHbt2tm6devcOZs2bbJu3bq55V2hY8aMGWnaoHBTo1+Srd22P9ftCxVQrngxm9b5SvvTOadGrUwKQgABBBBAAAEEEPC3AAEkj/tPMxoKIlomlZSU5MJFjx493MxH6NDyqgULFrjZj2eeecbNnhQsWNA2b95s06ZNs5o1a6YIIJ07d7Y1a9a4l2/ZssXC/62f6TqpjxEjRhBA8vhe4PIIIIAAAggggEAiCBBA4tzL2pOhWYnGjRunuLJCgmY0qlWr5janr1+/3m1Q12xJlSpV3AyIZkZ0Xtu2bd1rGzVqZGPGjMl2AEmvyewBifONwOUQQAABBBBAAIEEFSCAxLnjtdm8TZs27jG8ehqWnlr18ccf27333mtaGqXH8rZu3drNdGhJ1vz5823SpEluL4c2ok+dOtUFkkWLFtnDDz9s48aNs/r162drBoQAEudO53IIIIAAAggggAACyQIEkDy4Gd5//3177rnn7Ouvv3YBRN8D0r17dxdItAekZ8+ebjZEG8w1C6KwcsUVV9jMmTNt4sSJ7nG72qj+008/uc3lc+fOdWFF3wOiTehZLcEigORBp3NJBBBAAAEEEEAAASdAAPHYjRAKIG+//XZca8YSrLhyczEEEEAAAQQQQCBhBQggHut6AojHOoTqIIAAAggggAACCERVgAASVc7cF0YAyb0hJSCAAAIIIIAQ7n2jAAAXXElEQVQAAgh4V4AA4t2+iWvNWIIVV24uhgACCCCAAAIIJKwAASRhuz5lwwkg3AgIIIAAAggggAAC8RAggMRD2QfXIID4oJOoIgIIIIAAAgggEAABAkgAOjEaTSCAREORMhBAAAEEEEAAAQSyEiCAZCWUIL8ngCRIR9NMBBBAAAEEEEAgjwUIIHncAV65vALI0KQVtufgkahV6Q9FCthNVcvaeWeeErUyKQgBBBBAAAEEEEDA3wIEEH/3X9RqrwCydevWqJVHQQgggAACCCCAAAIIpCdAAOG+cAIEEG4EBBBAAAEEEEAAgXgIEEDioeyDaxBAfNBJVBEBBBBAAAEEEAiAAAEkAJ0YjSYogHAggAACCCCAAAIIZC3AsvWsjTI7gwCSO7/AvHrGjBl28uRJ69ixY2Da5NeGvPjii1aqVClr3bq1X5sQmHo//fTTVqlSJWvWrFlg2uTXhowcOdKuueYaa9SokV+bEJh6DxkyxJo3b25XXXVVYNrk14b069fPOnToYJdffrlfm0C9E1SAAJKgHZ+62QQQ79wIBBDv9AUBxDt9QQDxTl8QQLzTFwQQ7/QFNcmeAAEke16BPZsA4p2uJYB4py8IIN7pCwKId/qCAOKdviCAeKcvqEn2BAgg2fMK7NkEEO90LQHEO31BAPFOXxBAvNMXBBDv9AUBxDt9QU2yJ0AAyZ5XYM8mgHinawkg3ukLAoh3+oIA4p2+IIB4py8IIN7pC2qSPQECSPa8OBsBBBBAAAEEEEAAAQRyIUAAyQUeL0UAAQQQQAABBBBAAIHsCRBAsufF2QgggAACCCCAAAIIIJALAQJILvB4KQIIIIAAAggggAACCGRPgACSPS/ORgABBBBAAAEEEEAAgVwIEEBygReUl06aNMlmzpxpx44dsxtuuMGGDh1qBQsWDErz8rwdR48etYceeshWr15tp5xyinXt2tVuv/32NPU6fPiwPfzww+68woULu2+37d69uzuvXbt2tnv3bnvnnXcsX758ya9dtGiR9e3b16ZNm2Z16tTJ87Z6vQKR9kV4O9q3b29nnXWWjRs3jr6IYgdnpy90f0+cONH+X3t3Amxj/cdx/BsJkUqmSaXUiGwhopFlCGWPkChrkp1KjBZLIVtkayGlJGvSYinKyE7WImEqhpRdWcr2n89v5ty59/7PPee513Hucx/v34xpcs95nt/z+p5zn+fz/H6/x5kzZ6xx48bWq1cv9z3gexGbgnitxdGjR935YevWrZY5c2bn37ZtW74XsSlDxK3IvEuXLlaxYkXr16+fe+3Zs2etcOHC1qBBAxs6dGiS9+tJZdOnT7dt27ZxPo9DfdhF6gUIIKk3C9Q7Vq9ebX369LFp06a5i+OOHTta9erVTRddtNgIjB492nbs2GHDhg2zgwcPupP2pEmTrFChQkl2oJPK4cOH3Ynk0KFD1qhRI3fRW7ZsWfeeffv22ahRo6x06dIJ71NA2bhxo40YMYIA4qFcXmsR2tTs2bNtzJgxVqJEiSQBhFp4wI7yEq+1WLlypbvonTJligvm3bt3dwGkaNGifC8uvgxuC15rod9RFy5csP79+5vCSP369d3vnjJlylCLGNUi3GbWr1/vQkfBggUtZ86cSQJIqVKl3A2SBQsWWLZs2dzbFdQffvhh279/v23atIkAcglrw6bTLkAASbtdIN6pX2p58+a19u3bu+NZvHixuzjWiAgtNgI1a9a0wYMHW8mSJd0GBw0a5E4iXbt2TbKDRYsWuVCSL18+9/cKF1WrVrUmTZq4k/vtt99u2bNndyd/tePHj1vdunXt1ltvdcGREZDo9fJaC23pyJEjzr1ly5a2Zs2aJAGEWkS3jvYKr7Xo2bOnC91Nmzb9v03yvYim7O3nXmuhG1P6o4tbtc6dO1v58uWtWbNm/I7yRp2mV/3++++WJ08eN9J94MCBJAFEN0eqVavmZi/UqlXLbX/JkiU2Z84cmzdvHiMgaRLnTfEQIIDEQ9nH+9DFlU4e+uWltmvXLjf1Z8WKFT7udcbqmobIdRf3uuuucx2fOnWqrV271kaOHJnigWg6Vo0aNezDDz+0AgUKuJN7u3bt3J3gpUuXWpYsWWzmzJnu5LJ9+3YCiMePRGpqobvsurOrsKi7i4mnYFELj+ARXua1FgrZtWvXtvnz55u+Fw0bNrROnTq5LfO9uPg6aAteazF27Fh3jtAorUZrFQonTJjA76jYlCHqVsaNG/d/AUQjgfr7WbNmuWmKavrHCTWTQQGRKVhRWXlBOgkQQNIJ3i+71d11ncwrV67suqSpJTrZb9iwwS9dzND90FB4kSJFbMuWLQnD47oztXDhwoSTRfIDPH36tKtJ8eLF3XST0IWW7gTrBKPAqJGRFi1aWI8ePWz48OEEEA+fktTUQiMeCogKiwofyQMItfAAHuElqamFfjcVK1bMTWFUANHnX/66aaIAQi3iV4tTp0659Wu6I3/y5El3U0QXu/yOurgaeH13SgFE55dKlSolTMPSiIhmM+h7QwDxqsvr4i1AAIm3uM/216pVK3cS1xC82s6dO01/t2zZMp/1NON2R3cXly9fbrlz53YHoeltmtOrudPJm6ZVaZF6uXLlXLgItdCF1t69e91CdC1q191HnWSaN29OAPH48fBSCz2MQXfZtd5Go0+68x4ugFALj+gpvMxLLfTWOnXquCCuiyo13YXXGqm+ffsmBBBqEZ9adOvWze688063GPrEiRNuAbqmZNWrV49aXFwJPL07pQCiUXCt5dRNK420awrWkCFD7K677iKAeJLlRekhQABJD3Uf7XPAgAF27bXXmk4sanPnznVzRzXXlBYbAY0oaerU/fff7zaoE4XWeXTo0CHJDvQkGo1qaB6vpsYlbqEAotGUKlWquJCiNQq6+0gA8V4nL7XYvHmz88+aNavbsAKJaqM1PAqP1MK7d6RXeqmF3q+1UJpO8uijjyYEkGPHjrkQTi3iWwsteNb5IX/+/G7HGpHdvXu3W9dGLWJTi0hbiRRAVq1aZePHj7dcuXK5WuhpWQSQS18T9pB2AQJI2u0C8c5169a5x7jOmDHDcuTI4S6AdUGrJzDRYiOgk4ZGPPTfPXv2OF956ySuk4ZOGAoWetqS7uyGHrEYLoBoTYLuBuvpZQqJWrROAPFeJ6+1SLzFlEZAqIV393Cv9FoLTVfUa7Ue6ty5c27kTyFeQTx00Ust4lMLTdnVAvQ2bdq4YK4REE0Hbd26NbW4uBJ4enekAHL+/HkX1PV4an1n9JhkAognVl6UTgIEkHSC99NuJ06caJMnT3bPFNeCz969e1umTJn81MUM3RedqHW3VovHFfI0fUHPbVfTyJMerag1H5rDqyecJLbXfPfEd3p1oaVpV1r3oQtjNQKI94+H11p4DSDUwrt98lemphaadqUAosfwKnToO6SWOIBQi0tfCy1A19S3P/74w+2sQoUK7vdTqC5aj8PvqLTXIaV3Dhw40K1HUwDXY5D173QpDMpei9A1BUtNI1H6XoVuYhFAYl8Lthg7AQJI7CzZEgIIIIAAAggggAACCEQRIIDwEUEAAQQQQAABBBBAAIG4CRBA4kbNjhBAAAEEEEAAAQQQQIAAwmcAAQQQQAABBBBAAAEE4iZAAIkbNTtCAAEEEEAAAQQQQAABAgifAQQQQAABBBBAAAEEEIibAAEkbtTsCAEEEEAAAQQQQAABBAggfAYQQAABBBBAAAEEEEAgbgIEkLhRsyMEEEAAAQQQQAABBBAggPAZQAABBBBAAAEEEEAAgbgJEEDiRs2OEEAAAQQQQAABBBBAgADCZwABBBBAAAEEEEAAAQTiJkAAiRs1O0IAAQQQQAABBBBAAAECCJ8BBBBAAAEEEEAAAQQQiJsAASRu1OwIAQQQQAABBBBAAAEECCB8BhBAAAEEEEAAAQQQQCBuAgSQuFGzIwQQQAABBBBAAAEEECCA8BlAAAEELmOB48ePW+nSpe2rr76yggULpovEqlWrrGfPnpY5c2ZbsmRJuvQhnjv95ptvrE+fPrZ27dp47pZ9IYAAAr4RIID4phR0BAEELneBL7/80nr06GHPPfecPfPMM0k42rVrZ2XKlLH27dvHlMkPAeTpp5+2a665xvr37285c+YMe3xLly61iRMn2pYtW+zs2bN22223WaNGjaxly5aWKVOmmJpc6o0dPnzYfv31Vxf8aAgggMDlKEAAuRyrzjEjgIAvBRRA+vXrZ//995/Nnz/fbrnlloR+BjmANG3a1KpUqZJiuJoyZYoLJ82aNbMGDRrYVVddZRs2bLA333zTKlWqZMOHD/dlPekUAggggEB4AQIInwwEEEDAJwIKIO+//77dcccdduLECXvrrbfCBpCXX37ZTp48aSNGjEj4+X333WeDBg2y6tWrW/369a1OnTq2evVq27Vrl2XLls1GjhxpH330ka1fv96997XXXrOKFStaaARk6NChboRh9+7dVqxYMdP/58uXz21/+fLl7v07duxwIxRt2rSxtm3bup9pn8eOHXN/Nm7caJpOlbydPn3ahgwZYosWLXL7Lly4sL3wwgt2zz332OOPP24//PCDXXnllXbTTTfZt99+m+TtGi1QPzt06GCdO3dO8jPt7+2333YBRP1as2aN24+O+YYbbrCHHnrIjSZpapf6eeTIEbv66qvd69RfOf7111/26aefuv+2aNHC7UetZs2aVq9ePTdNSqMVFy5ccGYVKlRwP9+0aZMNHjzYtm3b5nyrVatmffv2deFo8eLF7rVdu3Z15tp20aJFXb3Uz+RTsCL57tu3z/VTdVMrVaqUDRw40PLmzeuTTy3dQAABBFIvQABJvRnvQAABBC6JgALIu+++a++9957VqFHD3njjDTcyoJZ4BCRaANHUJAWLTz75xK6//np3Yb1161abMGGCm/Yzbtw4W7hwoX3++ecJAaREiRL2+uuvW+7cue3555+3v//+22bOnGl79uyx2rVr26uvvupCjS7GFUC6d+9uDRs2tGHDhtns2bOtY8eO7nW68E/eNKqjEQsFKm1fF+JffPGFW++hi/fHHnvMqlatGnYEZM6cOfbiiy+6kJI9e/YU3ffv328PPvigu1hXvxRCWrdubZrepf6qnwoDCngy0OjJBx984Ka6aVrbunXrrHnz5i5Ayaxu3bp29OhRmzp1qgtiOkaNwqxcudKFGIUiBT2FDAWMVq1auTD11FNPmaaLKcho/7I8deqUs1MdNGUscQCJ5qvty0iG58+fd0FKtR07duwl+QyyUQQQQCAeAgSQeCizDwQQQMCDgALIO++84y7OJ0+e7P5oKlbWrFlTHUC0XqR3795ur7r4/v77713gUNNFtC66N2/enBBANHKgC3c1XYQ/+eSTbqTg448/du+dPn16whGoj7rI1s+07QULFri7/uGaRg6KFy/uRlA0OqP2zz//WNmyZV3Y0ohCpAAyevRok8vXX38dUVB90utkF2oaGdExzJgxw/VzxYoVpkCjpv5rFEcjC1p/cu7cObv77rvdaIj6qwAiQ41qqGndyb333utCoUY7NDKj0QyNeKgpIGiERcEmtG2Fply5crmfayQmR44cNmDAgCQBREEikq+CTf78+d321dRPjejQEEAAgYwsQADJyNWj7wggECiBxAFEF5qPPPKIu9jt1q1bqgOIRiN0B15NF/EagdDdf7XQ3f7t27cnBBCNdpQsWdL9XHflNSKhi/lJkyYlXLQnxtYUIF1o68JeoyuhbScviEYHHnjgARcgNLUs1DSyo9EHhY9IAWTMmDH22WefpRhwQtt76aWX3LQ1BZ1QU9hQCNEUJ/VToyKasqWm6WnyUd9DrVChQi5UKXgogCiQhQz1GvVZIxwaKdFUMY3oaMraFVdc4fat49T25dKpUye3YD7U9NQrhRhNbUs8AqKpaKFQFM5XtdK2NPqj9S4aSVF4oyGAAAIZWYAAkpGrR98RQCBQAokDiA5Md+c1EjFv3jw3719ThzRyEW4Kli6atSZBowyagpU8gGi9hMJESgFk7ty5VqRIkSQBRKFBweLgwYM2fvz4sNa6sP/ll1/c9K5wLVIA0dQoHV+kAKJRGz2iV2sxQqMJifdz5swZy5Ili6UUQDRlSe8NF0C0/59++inFAKIQoilcoaYAoNCk9TZaH6JpaQqJWr+i+ii4hQKI1qtohClaAHnllVci+ur9msKlUZLvvvvOfRYUgBRcaAgggEBGFSCAZNTK0W8EEAicQPIAogPs1auXHThwwK0D0DoNBRAtcNaah9A6AE1p0uJkhYS0BhBNHapVq5YzDU3BUmiZNm2aWweReIqVAommLWlqWLQAoilYWmyukQgtClcLTcHSxbou6iMFEC0W13qLJk2auJCRuGn0QiMUCk8KKvqv/j2TUNM+dSyzZs1KUwBRvxUs1LSQXgFQIzKaaqXjTrzgXv3T+pbUBhCt94nkK2utq9Eoi5rW7mhqnUa0aAgggEBGFSCAZNTK0W8EEAicQLgAorUGChW6y6+LbQUQPZZWIw5aH6IF0Zp2pCdYjRo1Ks0BRHf1tR2tU9C/RaIpYBoxOXTokJt6pEXmWo+gC2JNCdLUsC5dukQNICqS1j1oNEfrNDSKofUmmsKkp2JpDUWkAKL3a/2JwocWfetRvJqOpO3peDUCoUXqf/75p5s2prUSelTvzz//7NZ4aPqa3pOWERAtQlfg0MiQ+q6woPUzGtmQhaao6cldGiXStCrVSIvVNQXL6whIJF+ZV65c2S1e10iRmqbTaUpZaD1P4L4EHBACCFwWAgSQy6LMHCQCCGQEgXABRP3WugRdWOuJSgogWm/w7LPP2o8//mh58uRxU3J0cawnU+nxsamZgqWAU65cObd4Whfbe/fudYuwdcF+8803OzYt3lZo2Llzp3tClKYfaf+aehRtBETvV38VQpYtW2b//vuvG63RNDL9Y4Jq0QKIXqMnZil0acqUngal9SRPPPGENW7cOKG0CjU6jt9++81uvPFG93NdvGv0IC0BRE8i00iDFrJrFEJT3MqXL+/2p8XpCgEKQ9qHgoKecKVjUz28BpBovnrcr0a8FKjkrXU6ClwFChTICB9p+ogAAgiEFSCA8MFAAAEEEEAgmYDWf2haVWjkASAEEEAAgdgJEEBiZ8mWEEAAAQQCIkAACUghOQwEEPClAAHEl2WhUwgggAAC6SlAAElPffaNAAJBFyCABL3CHB8CCCCAAAIIIIAAAj4SIID4qBh0BQEEEEAAAQQQQACBoAsQQIJeYY4PAQQQQAABBBBAAAEfCRBAfFQMuoIAAggggAACCCCAQNAFCCBBrzDHhwACCCCAAAIIIICAjwQIID4qBl1BAAEEEEAAAQQQQCDoAgSQoFeY40MAAQQQQAABBBBAwEcCBBAfFYOuIIAAAggggAACCCAQdAECSNArzPEhgAACCCCAAAIIIOAjAQKIj4pBVxBAAAEEEEAAAQQQCLoAASToFeb4EEAAAQQQQAABBBDwkQABxEfFoCsIIIAAAggggAACCARdgAAS9ApzfAgggAACCCCAAAII+EiAAOKjYtAVBBBAAAEEEEAAAQSCLkAACXqFOT4EEEAAAQQQQAABBHwkQADxUTHoCgIIIIAAAggggAACQRcggAS9whwfAggggAACCCCAAAI+EiCA+KgYdAUBBBBAAAEEEEAAgaALEECCXmGODwEEEEAAAQQQQAABHwkQQHxUDLqCAAIIIIAAAggggEDQBQggQa8wx4cAAggggAACCCCAgI8ECCA+KgZdQQABBBBAAAEEEEAg6AIEkKBXmONDAAEEEEAAAQQQQMBHAgQQHxWDriCAAAIIIIAAAgggEHQBAkjQK8zxIYAAAggggAACCCDgIwECiI+KQVcQQAABBBBAAAEEEAi6AAEk6BXm+BBAAAEEEEAAAQQQ8JEAAcRHxaArCCCAAAIIIIAAAggEXYAAEvQKc3wIIIAAAggggAACCPhI4H87CCKpcsHEpQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df_top_10_countries.sort_values('num_of_companies', ascending=True), x='num_of_companies', y='index',\n",
    "             orientation='h',\n",
    "             labels={'index': 'Country', 'num_of_companies': 'Number of Companies'},\n",
    "             title='Top 10 countries with the most companies')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b863fd71-b559-441f-b56d-14a27ec58a63",
   "metadata": {},
   "source": [
    "#### 7.8 What are the top 10 cities with the most companies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2ef0b356-db51-44da-889d-18c0fada7735",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>region</th>\n",
       "      <th>num_of_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>53904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>49347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>26597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>20634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>19412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>17010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Paris</td>\n",
       "      <td>Ile-de-France</td>\n",
       "      <td>16175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>16047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>15147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Central Region</td>\n",
       "      <td>12996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city          region  num_of_companies\n",
       "0         London         England             53904\n",
       "1       New York        New York             49347\n",
       "2  San Francisco      California             26597\n",
       "3    Los Angeles      California             20634\n",
       "4          Tokyo           Tokyo             19412\n",
       "5        Chicago        Illinois             17010\n",
       "6          Paris   Ile-de-France             16175\n",
       "7        Houston           Texas             16047\n",
       "8        Toronto         Ontario             15147\n",
       "9      Singapore  Central Region             12996"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_10_cities = df_continents[['city', 'region']].value_counts().head(10).to_frame(name='num_of_companies').reset_index()\n",
    "\n",
    "df_top_10_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6781f5b6-d122-4b3a-a133-733c73ef0eed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df_top_10_cities.sort_values('num_of_companies', ascending=True), x='num_of_companies', y='city',\n",
    "             orientation='h',\n",
    "             labels={'city': 'City', 'num_of_companies': 'Number of Companies'},\n",
    "             title='Top 10 cities with the most companies')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e62208d-51fc-4d0a-b8b3-8f18d285b3a3",
   "metadata": {},
   "source": [
    "#### 7.9 In the U.S., how many companies are private vs. public?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dab987cd-9c6e-4de2-8a2f-52e07bf3bdee",
   "metadata": {
    "tags": []
   },
   "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>index</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>private</td>\n",
       "      <td>1121987</td>\n",
       "      <td>99.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>public</td>\n",
       "      <td>8989</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  num_of_companies      %\n",
       "0  private           1121987  99.21\n",
       "1   public              8989   0.79"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a new column 'status' to denote whether the company is 'private' or 'public' based on the 'combined_stock_symbols' value\n",
    "df['status'] = df['combined_stock_symbols'].apply(lambda x: 'private' if pd.isnull(x) else 'public')\n",
    "\n",
    "# USA only\n",
    "df_usa = df.query('country_code==\"USA\"')\n",
    "\n",
    "df_status_count = df_usa['status'].value_counts().head(10).to_frame(name='num_of_companies').reset_index()\n",
    "df_status_count['%'] = round( df_status_count['num_of_companies'] / df_status_count['num_of_companies'].sum() * 100, 2 )\n",
    "\n",
    "df_status_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ad9756b5-c98b-42cd-af85-7939efea033b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.pie(df_status_count,\n",
    "             values='num_of_companies',\n",
    "             names='index',\n",
    "             title='Private vs. Public')\n",
    "\n",
    "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c89f99f-51ea-4d87-bfc8-e4a7b69a52b9",
   "metadata": {},
   "source": [
    "#### 7.10 In the U.S., what is the breakdown percentages by state?\n",
    "\n",
    "Let's add the state abbreviations data courtesy of census.gov:\n",
    "\n",
    "- https://www.census.gov/library/reference/code-lists/ansi.html#state\n",
    "- https://www2.census.gov/geo/docs/reference/state.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "096dceec-7bb4-466f-90e0-e03fa27dbaf3",
   "metadata": {
    "tags": []
   },
   "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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8185</th>\n",
       "      <td>Smule</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253315</th>\n",
       "      <td>Vera Pax</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677084</th>\n",
       "      <td>PSD Markup</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fresno</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414675</th>\n",
       "      <td>ViviPet</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chino</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2448076</th>\n",
       "      <td>Riggins Law, PC</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name          type primary_role combined_stock_symbols  \\\n",
       "8185               Smule  organization      company                    NaN   \n",
       "2253315         Vera Pax  organization      company                    NaN   \n",
       "1677084       PSD Markup  organization      company                    NaN   \n",
       "2414675          ViviPet  organization      company                    NaN   \n",
       "2448076  Riggins Law, PC  organization      company                    NaN   \n",
       "\n",
       "                  city      region country_code   status  \n",
       "8185     San Francisco  California          USA  private  \n",
       "2253315       Sheridan     Wyoming          USA  private  \n",
       "1677084         Fresno  California          USA  private  \n",
       "2414675          Chino  California          USA  private  \n",
       "2448076       Pasadena  California          USA  private  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cd4ea17b-1036-4916-8b14-4e329710754f",
   "metadata": {
    "tags": []
   },
   "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>state</th>\n",
       "      <th>state_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  state_name\n",
       "0    AL     Alabama\n",
       "1    AK      Alaska\n",
       "2    AZ     Arizona\n",
       "3    AR    Arkansas\n",
       "4    CA  California"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_states = pd.read_csv('https://www2.census.gov/geo/docs/reference/state.txt', sep='|', header=0, names=[\n",
    "    'state_code', 'state', 'state_name', 'gnisid'\n",
    "])\n",
    "\n",
    "# keep only state abbr and state name\n",
    "df_usa_states = df_usa_states[['state', 'state_name']]\n",
    "\n",
    "df_usa_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ab1899-a172-43e5-8fe7-9fba3572ccac",
   "metadata": {},
   "source": [
    "Let's merge this with the U.S. dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9fa14b7e-ca92-4f57-9eeb-ebe33da6e6a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_usa = df_usa.merge(df_usa_states, left_on=['region'], right_on=['state_name'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b647cdf3-615c-40e2-9040-1ccbe877d25e",
   "metadata": {
    "tags": []
   },
   "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",
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       "    }\n",
       "\n",
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       "        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>name</th>\n",
       "      <th>type</th>\n",
       "      <th>primary_role</th>\n",
       "      <th>combined_stock_symbols</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>country_code</th>\n",
       "      <th>status</th>\n",
       "      <th>state</th>\n",
       "      <th>state_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wetpaint</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zoho</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Digg</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Meta</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>nasdaq:META</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>public</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Omnidrive</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name          type primary_role combined_stock_symbols        city  \\\n",
       "0   Wetpaint  organization      company                    NaN    New York   \n",
       "1       Zoho  organization      company                    NaN  Pleasanton   \n",
       "2       Digg  organization      company                    NaN    New York   \n",
       "3       Meta  organization      company            nasdaq:META  Menlo Park   \n",
       "4  Omnidrive  organization      company                    NaN   Palo Alto   \n",
       "\n",
       "       region country_code   status state  state_name  \n",
       "0    New York          USA  private    NY    New York  \n",
       "1  California          USA  private    CA  California  \n",
       "2    New York          USA  private    NY    New York  \n",
       "3  California          USA   public    CA  California  \n",
       "4  California          USA  private    CA  California  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5fb011ef-b25d-4422-b54b-978c64240d43",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>combined_stock_symbols</th>\n",
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       "      <td>New York</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zoho</td>\n",
       "      <td>organization</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>New York</td>\n",
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       "      <td>private</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
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       "    <tr>\n",
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       "      <td>Meta</td>\n",
       "      <td>organization</td>\n",
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       "      <td>nasdaq:META</td>\n",
       "      <td>Menlo Park</td>\n",
       "      <td>USA</td>\n",
       "      <td>public</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Omnidrive</td>\n",
       "      <td>organization</td>\n",
       "      <td>company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>USA</td>\n",
       "      <td>private</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name          type primary_role combined_stock_symbols        city  \\\n",
       "0   Wetpaint  organization      company                    NaN    New York   \n",
       "1       Zoho  organization      company                    NaN  Pleasanton   \n",
       "2       Digg  organization      company                    NaN    New York   \n",
       "3       Meta  organization      company            nasdaq:META  Menlo Park   \n",
       "4  Omnidrive  organization      company                    NaN   Palo Alto   \n",
       "\n",
       "  country_code   status state  state_name  \n",
       "0          USA  private    NY    New York  \n",
       "1          USA  private    CA  California  \n",
       "2          USA  private    NY    New York  \n",
       "3          USA   public    CA  California  \n",
       "4          USA  private    CA  California  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop 'region' column\n",
    "df_usa = df_usa.drop(columns='region')\n",
    "\n",
    "df_usa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4d8809cc-9907-4390-84d3-36b736e693e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>state</th>\n",
       "      <th>state_name</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>207439</td>\n",
       "      <td>18.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>95323</td>\n",
       "      <td>8.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TX</td>\n",
       "      <td>Texas</td>\n",
       "      <td>87026</td>\n",
       "      <td>7.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FL</td>\n",
       "      <td>Florida</td>\n",
       "      <td>77057</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IL</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>47038</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MA</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>37950</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PA</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>37115</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GA</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>32683</td>\n",
       "      <td>2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NJ</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>31982</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OH</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>31595</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state     state_name  num_of_companies      %\n",
       "0    CA     California            207439  18.34\n",
       "1    NY       New York             95323   8.43\n",
       "2    TX          Texas             87026   7.69\n",
       "3    FL        Florida             77057   6.81\n",
       "4    IL       Illinois             47038   4.16\n",
       "5    MA  Massachusetts             37950   3.36\n",
       "6    PA   Pennsylvania             37115   3.28\n",
       "7    GA        Georgia             32683   2.89\n",
       "8    NJ     New Jersey             31982   2.83\n",
       "9    OH           Ohio             31595   2.79"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_state_count = df_usa[['state', 'state_name']].value_counts().to_frame(name='num_of_companies').reset_index()\n",
    "\n",
    "df_usa_state_count['%'] = round( df_usa_state_count['num_of_companies'] / df_usa_state_count['num_of_companies'].sum() * 100, 2 )\n",
    "\n",
    "df_usa_state_count.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "26e0b4fc-8fd1-4553-b75d-3cac78dba7e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df_usa_state_count.head(20).sort_values('num_of_companies', ascending=True), x='num_of_companies', y='state_name',\n",
    "             orientation='h',\n",
    "             labels={'state_name': 'State', 'num_of_companies': 'Number of Companies'},\n",
    "             title='Top 20 states with the most companies')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ec297ff8-be82-4284-ad0e-a492bf50b425",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.choropleth(df_usa_state_count,\n",
    "                    locations='state', \n",
    "                    locationmode='USA-states',\n",
    "                    scope='usa',\n",
    "                    color='num_of_companies',\n",
    "                    color_continuous_scale='matter',\n",
    "                    title='Number of U.S. companies by state',\n",
    "                    labels={'state': 'State', 'num_of_companies': 'Number of Companies'},\n",
    "                    hover_name='state_name'\n",
    "                   )\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a669cb-1930-4b57-ae1f-521044b814cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 7.11 In the U.S., what is the breakdown percentages by city?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "441396ac-b8dd-4544-a9b5-8aeed7650f15",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>49347</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>26597</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>20634</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>17010</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>16047</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>12990</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>CA</td>\n",
       "      <td>11663</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>11625</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>10889</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>10742</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city state  num_of_companies     %\n",
       "0       New York    NY             49347  4.36\n",
       "1  San Francisco    CA             26597  2.35\n",
       "2    Los Angeles    CA             20634  1.82\n",
       "3        Chicago    IL             17010  1.50\n",
       "4        Houston    TX             16047  1.42\n",
       "5         Austin    TX             12990  1.15\n",
       "6      San Diego    CA             11663  1.03\n",
       "7        Atlanta    GA             11625  1.03\n",
       "8         Dallas    TX             10889  0.96\n",
       "9          Miami    FL             10742  0.95"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_city_count = df_usa[['city', 'state']].value_counts().to_frame(name='num_of_companies').reset_index()\n",
    "\n",
    "df_usa_city_count['%'] = round( df_usa_city_count['num_of_companies'] / df_usa_city_count['num_of_companies'].sum() * 100, 2 )\n",
    "\n",
    "df_usa_city_count.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "51b087b6-4b38-45af-b3ec-c66545027d48",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df_usa_city_count.head(20).sort_values('num_of_companies', ascending=True), x='num_of_companies', y='city',\n",
    "             orientation='h',\n",
    "             labels={'city': 'City', 'num_of_companies': 'Number of Companies'},\n",
    "             title='Top 20 cities with the most companies')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3068bc-829c-4811-882d-0645c2f8613f",
   "metadata": {},
   "source": [
    "Let's add the city lat/long coordinates to plot the cities as a bubble map:\n",
    "\n",
    "[simplemaps](https://simplemaps.com/data/us-cities) provides a U.S. cities database with this data; the Basic version will do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "69d8964a-6089-453d-a88a-6a1c785c8af9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4218789"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download the zip file\n",
    "url = 'https://simplemaps.com/static/data/us-cities/1.76/basic/simplemaps_uscities_basicv1.76.zip'\n",
    "\n",
    "response = requests.get(url)\n",
    "\n",
    "open('data/simplemaps_uscities.zip', 'wb').write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f606f9bd-2c8d-4865-8101-113143922b12",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data/simplemaps_uscities.zip\n",
      "  inflating: data/license.txt        \n",
      "  inflating: data/uscities.csv       \n",
      "  inflating: data/uscities.xlsx      \n"
     ]
    }
   ],
   "source": [
    "# unzip the file\n",
    "!unzip data/simplemaps_uscities.zip -d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dc674073-6343-4921-9a79-771508a30bf9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data:\n",
      "crunchbase.tar.gz\n",
      "organizations.csv\n",
      "uscities.csv\n"
     ]
    }
   ],
   "source": [
    "# remove some files\n",
    "!rm data/simplemaps_uscities.zip data/license.txt data/uscities.xlsx\n",
    "!ls -R1 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "53e509cc-e08b-44bf-92a9-9a181fdd2ba1",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>40.6943</td>\n",
       "      <td>-73.9249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>34.1141</td>\n",
       "      <td>-118.4068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>41.8375</td>\n",
       "      <td>-87.6866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>25.7840</td>\n",
       "      <td>-80.2101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>32.7935</td>\n",
       "      <td>-96.7667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city state_id      lat       lng\n",
       "0     New York       NY  40.6943  -73.9249\n",
       "1  Los Angeles       CA  34.1141 -118.4068\n",
       "2      Chicago       IL  41.8375  -87.6866\n",
       "3        Miami       FL  25.7840  -80.2101\n",
       "4       Dallas       TX  32.7935  -96.7667"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cities with lat/long coords\n",
    "df_usa_cities_db = pd.read_csv('data/uscities.csv', usecols=['city', 'state_id', 'lat', 'lng'])\n",
    "\n",
    "df_usa_cities_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ec6eeb36-021b-4819-84fd-91790bef1be2",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>40.6943</td>\n",
       "      <td>-73.9249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>34.1141</td>\n",
       "      <td>-118.4068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>41.8375</td>\n",
       "      <td>-87.6866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>25.7840</td>\n",
       "      <td>-80.2101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>32.7935</td>\n",
       "      <td>-96.7667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city state      lat       lng\n",
       "0     New York    NY  40.6943  -73.9249\n",
       "1  Los Angeles    CA  34.1141 -118.4068\n",
       "2      Chicago    IL  41.8375  -87.6866\n",
       "3        Miami    FL  25.7840  -80.2101\n",
       "4       Dallas    TX  32.7935  -96.7667"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename column\n",
    "df_usa_cities_db.rename(columns={'state_id': 'state'}, inplace=True)\n",
    "\n",
    "df_usa_cities_db.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5feaa299-1190-476e-9920-b8eb3705efdf",
   "metadata": {},
   "source": [
    "Let's create a dataframe of the top 100 cities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1048e44b-f013-47db-a3e3-740e030d80a0",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>num_of_companies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>49347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>26597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>20634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>17010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>16047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city state  num_of_companies\n",
       "0       New York    NY             49347\n",
       "1  San Francisco    CA             26597\n",
       "2    Los Angeles    CA             20634\n",
       "3        Chicago    IL             17010\n",
       "4        Houston    TX             16047"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 100 cities with most companies\n",
    "df_usa_cities_top_100 = df_usa[['city', 'state']].value_counts().to_frame(name='num_of_companies').reset_index().head(100)\n",
    "\n",
    "df_usa_cities_top_100.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c12ef677-9b9e-4dd6-8133-32aec50ff4d7",
   "metadata": {},
   "source": [
    "Now let's merge `df_usa_cities_db` with it so we'll have the cities' lat/long coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5a132b92-704f-4a05-9355-d50561ced17c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_usa_cities_top_100 = df_usa_cities_top_100.merge(df_usa_cities_db, on=['city', 'state'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5ab3fe72-ec79-4b09-82b4-3b394174e077",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>49347</td>\n",
       "      <td>40.6943</td>\n",
       "      <td>-73.9249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>26597</td>\n",
       "      <td>37.7558</td>\n",
       "      <td>-122.4449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>20634</td>\n",
       "      <td>34.1141</td>\n",
       "      <td>-118.4068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>17010</td>\n",
       "      <td>41.8375</td>\n",
       "      <td>-87.6866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>16047</td>\n",
       "      <td>29.7860</td>\n",
       "      <td>-95.3885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city state  num_of_companies      lat       lng\n",
       "0       New York    NY             49347  40.6943  -73.9249\n",
       "1  San Francisco    CA             26597  37.7558 -122.4449\n",
       "2    Los Angeles    CA             20634  34.1141 -118.4068\n",
       "3        Chicago    IL             17010  41.8375  -87.6866\n",
       "4        Houston    TX             16047  29.7860  -95.3885"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_cities_top_100.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a08f286a-c338-40cf-ae50-d99afec0984f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                0\n",
       "state               0\n",
       "num_of_companies    0\n",
       "lat                 2\n",
       "lng                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if there are any NaN values\n",
    "df_usa_cities_top_100.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c5f60902-670d-4574-ad29-58d46bd6d7b0",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>St Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>3833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>1810</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city state  num_of_companies  lat  lng\n",
       "30    St Louis    MO              3833  NaN  NaN\n",
       "75  Saint Paul    MN              1810  NaN  NaN"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 cities did not join, let's see what those 2 cities are:\n",
    "df_usa_cities_top_100[df_usa_cities_top_100['lat'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f856916f-7842-4fc8-97d9-e7d06ac6b022",
   "metadata": {},
   "source": [
    "The 2 cities are `St Louis, MO` and `Saint Paul, MN`.\n",
    "\n",
    "From the `df_usa_cities_db` df, let's see why those 2 cities didn't join:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "46a523d6-d19f-4e2f-82ac-6b0071f70698",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>St. Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>38.6359</td>\n",
       "      <td>-90.2451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2662</th>\n",
       "      <td>Lake St. Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>38.7846</td>\n",
       "      <td>-90.7886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                city state      lat      lng\n",
       "22         St. Louis    MO  38.6359 -90.2451\n",
       "2662  Lake St. Louis    MO  38.7846 -90.7886"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_cities_db[ (df_usa_cities_db.city.str.contains(' Louis')) & (df_usa_cities_db.state.str.contains('MO')) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3260d1aa-1adb-438d-b403-f9b5f41907ec",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>St. Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>44.9478</td>\n",
       "      <td>-93.1039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>South St. Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>44.8877</td>\n",
       "      <td>-93.0411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>West St. Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>44.9018</td>\n",
       "      <td>-93.0858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>North St. Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>45.0137</td>\n",
       "      <td>-92.9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6162</th>\n",
       "      <td>St. Paul Park</td>\n",
       "      <td>MN</td>\n",
       "      <td>44.8359</td>\n",
       "      <td>-92.9949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                city state      lat      lng\n",
       "143         St. Paul    MN  44.9478 -93.1039\n",
       "2179  South St. Paul    MN  44.8877 -93.0411\n",
       "2194   West St. Paul    MN  44.9018 -93.0858\n",
       "3414  North St. Paul    MN  45.0137 -92.9995\n",
       "6162   St. Paul Park    MN  44.8359 -92.9949"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_cities_db[ (df_usa_cities_db.city.str.contains(' Paul')) & (df_usa_cities_db.state.str.contains('MN')) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d93d1c-45eb-4fd3-a7dc-59ea4a0d31b6",
   "metadata": {},
   "source": [
    "They didn't join due to mis-abbreviations. Let's add the lat/long coordinates manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0d4f56e9-3749-49e8-82c3-6c975b0d08f5",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>St Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city state  lat  lng\n",
       "30  St Louis    MO  NaN  NaN"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find by row index location\n",
    "df_usa_cities_top_100.loc[[30], ['city', 'state', 'lat','lng']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1daf4949-f847-47ba-b15f-b49d49598d66",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city state  lat  lng\n",
       "75  Saint Paul    MN  NaN  NaN"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find by row index location\n",
    "df_usa_cities_top_100.loc[[75], ['city', 'state', 'lat','lng']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ab01baa3-d538-497f-acc4-520210dfd5c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# update the lat/long coords manually\n",
    "st_louis_mo = df_usa_cities_db.loc[[22], ['lat','lng']].values.tolist()\n",
    "st_paul_mn = df_usa_cities_db.loc[[143], ['lat','lng']].values.tolist()\n",
    "\n",
    "df_usa_cities_top_100.loc[[30], ['lat','lng']] = st_louis_mo\n",
    "df_usa_cities_top_100.loc[[75], ['lat','lng']] = st_paul_mn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4aeeba9e-5eda-4c57-bcc2-744bec47f687",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>St Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>38.6359</td>\n",
       "      <td>-90.2451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city state      lat      lng\n",
       "30  St Louis    MO  38.6359 -90.2451"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check\n",
    "df_usa_cities_top_100.loc[[30], ['city', 'state', 'lat','lng']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f0f9e780-34cd-4fe6-90ef-96e9dfd89672",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Saint Paul</td>\n",
       "      <td>MN</td>\n",
       "      <td>44.9478</td>\n",
       "      <td>-93.1039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city state      lat      lng\n",
       "75  Saint Paul    MN  44.9478 -93.1039"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check\n",
    "df_usa_cities_top_100.loc[[75], ['city', 'state', 'lat','lng']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9c5f727e-ac13-4b1a-bcd4-2ab88581995d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                0\n",
       "state               0\n",
       "num_of_companies    0\n",
       "lat                 0\n",
       "lng                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm there aren't anymore NaN values\n",
    "df_usa_cities_top_100.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "66438b2d-d0ac-476b-9e19-728bc77dbda3",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>49347</td>\n",
       "      <td>40.6943</td>\n",
       "      <td>-73.9249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>MN</td>\n",
       "      <td>5308</td>\n",
       "      <td>44.9635</td>\n",
       "      <td>-93.2678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Beverly Hills</td>\n",
       "      <td>CA</td>\n",
       "      <td>1845</td>\n",
       "      <td>34.0786</td>\n",
       "      <td>-118.4021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>NM</td>\n",
       "      <td>1940</td>\n",
       "      <td>35.1054</td>\n",
       "      <td>-106.6465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Bethesda</td>\n",
       "      <td>MD</td>\n",
       "      <td>1397</td>\n",
       "      <td>38.9866</td>\n",
       "      <td>-77.1188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city state  num_of_companies      lat       lng\n",
       "0        New York    NY             49347  40.6943  -73.9249\n",
       "20    Minneapolis    MN              5308  44.9635  -93.2678\n",
       "74  Beverly Hills    CA              1845  34.0786 -118.4021\n",
       "69    Albuquerque    NM              1940  35.1054 -106.6465\n",
       "99       Bethesda    MD              1397  38.9866  -77.1188"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_cities_top_100.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3943d07f-a83f-4503-9a31-a82a154637c5",
   "metadata": {},
   "source": [
    "Let's create a new column consisting of `City, State` for use in the map hover:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "79ad1dc7-8682-490d-b88f-803e0dde28eb",
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>num_of_companies</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>city_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grand Rapids</td>\n",
       "      <td>MI</td>\n",
       "      <td>1971</td>\n",
       "      <td>42.9619</td>\n",
       "      <td>-85.6562</td>\n",
       "      <td>Grand Rapids, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Anaheim</td>\n",
       "      <td>CA</td>\n",
       "      <td>1498</td>\n",
       "      <td>33.8390</td>\n",
       "      <td>-117.8574</td>\n",
       "      <td>Anaheim, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Knoxville</td>\n",
       "      <td>TN</td>\n",
       "      <td>1669</td>\n",
       "      <td>35.9692</td>\n",
       "      <td>-83.9496</td>\n",
       "      <td>Knoxville, TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>CA</td>\n",
       "      <td>2851</td>\n",
       "      <td>37.7904</td>\n",
       "      <td>-122.2166</td>\n",
       "      <td>Oakland, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA</td>\n",
       "      <td>2086</td>\n",
       "      <td>47.5951</td>\n",
       "      <td>-122.1535</td>\n",
       "      <td>Bellevue, WA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city state  num_of_companies      lat       lng        city_state\n",
       "68  Grand Rapids    MI              1971  42.9619  -85.6562  Grand Rapids, MI\n",
       "95       Anaheim    CA              1498  33.8390 -117.8574       Anaheim, CA\n",
       "81     Knoxville    TN              1669  35.9692  -83.9496     Knoxville, TN\n",
       "43       Oakland    CA              2851  37.7904 -122.2166       Oakland, CA\n",
       "63      Bellevue    WA              2086  47.5951 -122.1535      Bellevue, WA"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_usa_cities_top_100['city_state'] = df_usa_cities_top_100['city'] + ', ' + df_usa_cities_top_100['state']\n",
    "\n",
    "df_usa_cities_top_100.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b6c4322c-89e1-4510-b6fa-ccd00ade16c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter_geo(df_usa_cities_top_100,\n",
    "                    lat='lat', lon='lng',\n",
    "                    locationmode='USA-states',\n",
    "                    scope='usa',\n",
    "                    color='num_of_companies',\n",
    "                    color_continuous_scale='matter',\n",
    "                    title='Top 100 U.S. cities with the most companies',\n",
    "                    labels={'city': 'City', 'state': 'State', 'num_of_companies': 'Number of companies'},\n",
    "                    hover_data={'lat': False, 'lng': False, 'num_of_companies': ':,.0f'},\n",
    "                    hover_name='city_state'\n",
    "                   )\n",
    "\n",
    "fig.update_geos(landcolor='#dadada', subunitcolor='#aaa')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a8f0f7b0-a20f-4a61-89f5-75a179ffdd91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# add and format hover over text\n",
    "df_usa_cities_top_100['text'] = df_usa_cities_top_100['city_state'] + '<br>Number of companies: ' + (df_usa_cities_top_100.num_of_companies.apply(lambda x: '{:,.0f}'.format(x)))\n",
    "\n",
    "# scale bubbles by top 3, next 5, next 15, next 27, last 50\n",
    "limits = [(0,3),(3,8),(8,23),(23,50),(50,100)]\n",
    "colors = ['royalblue', 'crimson', 'lightseagreen', 'orange', 'lightgrey']\n",
    "scale = 200\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "for i in range(len(limits)):\n",
    "    lim = limits[i]\n",
    "    df_sub = df_usa_cities_top_100[lim[0]:lim[1]]\n",
    "    fig.add_trace(go.Scattergeo(\n",
    "        locationmode = 'USA-states',\n",
    "        lat = df_sub['lat'],\n",
    "        lon = df_sub['lng'],\n",
    "        text = df_sub['text'],\n",
    "        hoverinfo='text',\n",
    "        marker = dict(\n",
    "            size = df_sub['num_of_companies']/scale,\n",
    "            color = colors[i],\n",
    "            line_color='rgb(40,40,40)',\n",
    "            line_width=0.5,\n",
    "            sizemode = 'area'\n",
    "        ),\n",
    "        name = '{0} - {1}'.format(lim[0],lim[1])))\n",
    "\n",
    "fig.update_layout(\n",
    "        title_text = 'Top 100 U.S. cities with the most companies',\n",
    "        showlegend = True,\n",
    "        geo = dict(\n",
    "            scope = 'usa',\n",
    "            landcolor = 'rgb(217, 217, 217)',\n",
    "        )\n",
    "    )\n",
    "\n",
    "fig.update_geos(landcolor='#dadada', subunitcolor='#aaa')\n",
    "\n",
    "show_fig()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f3eb2d-8bc6-4066-9ed4-7659d8efbc8d",
   "metadata": {},
   "source": [
    "The above bubble map represents the top cities in the tiers:\n",
    "\n",
    "- Top 3\n",
    "- \\#4 - \\#8\n",
    "- \\#9 - \\#23\n",
    "- \\#24- \\#50\n",
    "- Last 50"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}