SQL Query Challenges.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQL Challenges, Class Day 6\n",
    "\n",
    "The following notebook includes 4 questions from the SQL challenges given in class as well as the table and normally distributed visualization for question 10. Details [here](https://docs.google.com/document/d/1BQJTwEZtaTSCl6a8pCJV4aFx2sbzJaJJ-5b62mnSlpY/edit).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "np.random.seed(1547)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which top 3 categories of crime are most likely to go unresolved in Mission District?\n",
    "```mysql\n",
    "SELECT COUNT(*) resolution, category FROM tutorial.sf_crime_incidents_cleandate \n",
    "WHERE resolution = 'NONE'\n",
    "GROUP BY category, resolution\n",
    "ORDER BY resolution DESC\n",
    "LIMIT 3\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resolution</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7582</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2377</td>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1379</td>\n",
       "      <td>ASSAULT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   resolution       category\n",
       "0        7582  LARCENY/THEFT\n",
       "1        2377   NON-CRIMINAL\n",
       "2        1379        ASSAULT"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_1 = pd.read_csv('datasets/sql_challenges-top_3_crime_cat-6475ffb44a32-2018-01-27-07-29-39.csv')\n",
    "query_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a query that finds the top 5 tallest players, as well as their position, height, school, and the conference they belong to.\n",
    "\n",
    "```mysql\n",
    "SELECT full_school_name, position, height, teams.conference FROM benn.college_football_players players\n",
    "JOIN benn.college_football_teams teams\n",
    "ON players.full_school_name = teams.school_name\n",
    "GROUP BY position, full_school_name, position, height, teams.conference\n",
    "ORDER BY height DESC\n",
    "LIMIT 5\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_school_name</th>\n",
       "      <th>position</th>\n",
       "      <th>height</th>\n",
       "      <th>conference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>OL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Big Sky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>OL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Big Sky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>TE</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Big Sky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>DL</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Big Sky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>OL</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Big Sky</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  full_school_name position  height conference\n",
       "0     North Dakota       OL    80.0    Big Sky\n",
       "1     North Dakota       OL    78.0    Big Sky\n",
       "2     North Dakota       TE    78.0    Big Sky\n",
       "3     North Dakota       DL    77.0    Big Sky\n",
       "4     North Dakota       OL    77.0    Big Sky"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_2 = pd.read_csv('datasets/sql_challenges-top_5_height-35b056af87ff-2018-01-27-06-59-09 (1).csv')\n",
    "query_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a table that displays the average height of players at each position.\n",
    "```mysql\n",
    "SELECT avg(height), position FROM benn.college_football_players \n",
    "GROUP BY position\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg</th>\n",
       "      <th>position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72.322258</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.583639</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.862385</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.620588</td>\n",
       "      <td>QB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.225914</td>\n",
       "      <td>DT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.089286</td>\n",
       "      <td>NT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74.744102</td>\n",
       "      <td>DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75.424460</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>75.322823</td>\n",
       "      <td>TE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>74.894040</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>73.965105</td>\n",
       "      <td>DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>71.015521</td>\n",
       "      <td>FB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>71.582575</td>\n",
       "      <td>WR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>69.649168</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>72.064240</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>72.447514</td>\n",
       "      <td>P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>75.254706</td>\n",
       "      <td>OL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>70.627081</td>\n",
       "      <td>CB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>77.134185</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28.055556</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>72.223058</td>\n",
       "      <td>LS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          avg position\n",
       "0   72.322258       LB\n",
       "1   70.583639       DB\n",
       "2   70.862385        K\n",
       "3   73.620588       QB\n",
       "4   74.225914       DT\n",
       "5   74.089286       NT\n",
       "6   74.744102       DE\n",
       "7   75.424460        G\n",
       "8   75.322823       TE\n",
       "9   74.894040        C\n",
       "10  73.965105       DL\n",
       "11  71.015521       FB\n",
       "12  71.582575       WR\n",
       "13  69.649168       RB\n",
       "14  72.064240        S\n",
       "15  72.447514        P\n",
       "16  75.254706       OL\n",
       "17  70.627081       CB\n",
       "18  77.134185       OT\n",
       "19  28.055556        ?\n",
       "20  72.223058       LS"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_3= pd.read_csv('datasets/sql_challenges-avg_height-e59a1440aafb-2018-01-27-06-38-13.csv')\n",
    "query_3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Companies from which 10 cities have the  highest average acquisition amount?\n",
    "\n",
    "```mysql\n",
    "SELECT price_amount, company_city, company_name FROM tutorial.crunchbase_acquisitions \n",
    "WHERE price_amount != 0 AND company_city is NOT NULL\n",
    "GROUP BY price_amount, company_city, company_name\n",
    "ORDER BY price_amount DESC\n",
    "LIMIT 10\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_amount</th>\n",
       "      <th>company_city</th>\n",
       "      <th>company_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.500000e+10</td>\n",
       "      <td>Overland Park</td>\n",
       "      <td>Nextel Communications</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.810000e+10</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Alltel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.840000e+10</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>Goodrich Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.600000e+10</td>\n",
       "      <td>510 Lake Cook Road</td>\n",
       "      <td>Beam Inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.390000e+10</td>\n",
       "      <td>Plano</td>\n",
       "      <td>EDS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.370000e+10</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Ziggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.050000e+10</td>\n",
       "      <td>South San Francisco</td>\n",
       "      <td>Onyx Pharmaceuticals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.020000e+10</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Autonomy Corporation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000e+10</td>\n",
       "      <td>New York</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.400000e+09</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Tokyo Electron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price_amount         company_city           company_name\n",
       "0  3.500000e+10        Overland Park  Nextel Communications\n",
       "1  2.810000e+10          Little Rock                 Alltel\n",
       "2  1.840000e+10            Charlotte   Goodrich Corporation\n",
       "3  1.600000e+10   510 Lake Cook Road               Beam Inc\n",
       "4  1.390000e+10                Plano                    EDS\n",
       "5  1.370000e+10              Utrecht                  Ziggo\n",
       "6  1.050000e+10  South San Francisco   Onyx Pharmaceuticals\n",
       "7  1.020000e+10        San Francisco   Autonomy Corporation\n",
       "8  1.000000e+10             New York     Paramount Pictures\n",
       "9  9.400000e+09                Tokyo         Tokyo Electron"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_4 = pd.read_csv('datasets/sql_challenges-top_10_acquistions-4dedc328ff50-2018-01-30-01-35-58.csv')\n",
    "query_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For companies in SF with names that start with the letter ‘a’, what percentage of companies raise a funding total between between 25 and 30 million? Create a visualization that shows this on a graph of a normal distribution.  \n",
    "```mysql\n",
    "SELECT name, funding_total_usd FROM tutorial.crunchbase_companies \n",
    "WHERE funding_total_usd > 0 \n",
    "AND city = 'San Francisco' \n",
    "AND (name LIKE 'a%' or name LIKE 'A%')\n",
    "ORDER BY name ASC\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\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>funding_total_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>about.me</td>\n",
       "      <td>5675000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Academia.edu</td>\n",
       "      <td>17800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Achievers</td>\n",
       "      <td>49122326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACT Biotech</td>\n",
       "      <td>26980000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adBrite</td>\n",
       "      <td>40400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Addoway</td>\n",
       "      <td>110000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Addvocate</td>\n",
       "      <td>2390000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Addy</td>\n",
       "      <td>300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Adeze</td>\n",
       "      <td>5500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Adinch Inc</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ad Infuse</td>\n",
       "      <td>17600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Aditive</td>\n",
       "      <td>3301958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Adku</td>\n",
       "      <td>1500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>adRise</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AdRocket</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Adteractive</td>\n",
       "      <td>5100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdTonik</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AdverCar</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Advice Wallet</td>\n",
       "      <td>395000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Adynxx</td>\n",
       "      <td>7540560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AFAR</td>\n",
       "      <td>16000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AgileMD</td>\n",
       "      <td>2000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AimWith</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Airbnb</td>\n",
       "      <td>326441000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AirPR</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Airstone Labs</td>\n",
       "      <td>2511000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Airtime Media</td>\n",
       "      <td>7958625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Aktana</td>\n",
       "      <td>11637424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Aligo</td>\n",
       "      <td>7920000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>AllBusiness.com</td>\n",
       "      <td>22400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>AppRedeem</td>\n",
       "      <td>700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Appthority</td>\n",
       "      <td>6250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Apture</td>\n",
       "      <td>4600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Aravo Solutions</td>\n",
       "      <td>39868500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Arch Rock Corporation</td>\n",
       "      <td>15000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Ardica Technologies</td>\n",
       "      <td>1875000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Aria Retirement Solutions</td>\n",
       "      <td>4650000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Aria Systems</td>\n",
       "      <td>81200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ark</td>\n",
       "      <td>5250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Artify It</td>\n",
       "      <td>800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ARTtwo50</td>\n",
       "      <td>145000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Aruspex</td>\n",
       "      <td>3770000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Asana</td>\n",
       "      <td>38200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Ascendify</td>\n",
       "      <td>1000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Asseta</td>\n",
       "      <td>535000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Astrid</td>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Atom Entertainment</td>\n",
       "      <td>22900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Atooma</td>\n",
       "      <td>397200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Attachments.me</td>\n",
       "      <td>2500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Attendify</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Attune Foods</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Auctomatic</td>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Audiodraft</td>\n",
       "      <td>400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Augmedix</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>August</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Automattic</td>\n",
       "      <td>30600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Avocado�</td>\n",
       "      <td>2150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>AwayFind</td>\n",
       "      <td>800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>awe.sm</td>\n",
       "      <td>5250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Ayla</td>\n",
       "      <td>170000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name  funding_total_usd\n",
       "0                    about.me          5675000.0\n",
       "1                Academia.edu         17800000.0\n",
       "2                   Achievers         49122326.0\n",
       "3                 ACT Biotech         26980000.0\n",
       "4                     adBrite         40400000.0\n",
       "5                     Addoway           110000.0\n",
       "6                   Addvocate          2390000.0\n",
       "7                        Addy           300000.0\n",
       "8                       Adeze          5500000.0\n",
       "9                  Adinch Inc          1000000.0\n",
       "10                  Ad Infuse         17600000.0\n",
       "11                    Aditive          3301958.0\n",
       "12                       Adku          1500000.0\n",
       "13                     adRise          2000000.0\n",
       "14                   AdRocket          1000000.0\n",
       "15                Adteractive          5100000.0\n",
       "16                    AdTonik           500000.0\n",
       "17                   AdverCar          2000000.0\n",
       "18              Advice Wallet           395000.0\n",
       "19                     Adynxx          7540560.0\n",
       "20                       AFAR         16000000.0\n",
       "21                    AgileMD          2000000.0\n",
       "22                    AimWith             5000.0\n",
       "23                     Airbnb        326441000.0\n",
       "24                      AirPR          1000000.0\n",
       "25              Airstone Labs          2511000.0\n",
       "26              Airtime Media          7958625.0\n",
       "27                     Aktana         11637424.0\n",
       "28                      Aligo          7920000.0\n",
       "29            AllBusiness.com         22400000.0\n",
       "..                        ...                ...\n",
       "51                  AppRedeem           700000.0\n",
       "52                 Appthority          6250000.0\n",
       "53                     Apture          4600000.0\n",
       "54            Aravo Solutions         39868500.0\n",
       "55      Arch Rock Corporation         15000000.0\n",
       "56        Ardica Technologies          1875000.0\n",
       "57  Aria Retirement Solutions          4650000.0\n",
       "58               Aria Systems         81200000.0\n",
       "59                        Ark          5250000.0\n",
       "60                  Artify It           800000.0\n",
       "61                   ARTtwo50           145000.0\n",
       "62                    Aruspex          3770000.0\n",
       "63                      Asana         38200000.0\n",
       "64                  Ascendify          1000000.0\n",
       "65                     Asseta           535000.0\n",
       "66                     Astrid           400000.0\n",
       "67         Atom Entertainment         22900000.0\n",
       "68                     Atooma           397200.0\n",
       "69             Attachments.me          2500000.0\n",
       "70                  Attendify           200000.0\n",
       "71               Attune Foods          5000000.0\n",
       "72                 Auctomatic           400000.0\n",
       "73                 Audiodraft           400000.0\n",
       "74                   Augmedix           100000.0\n",
       "75                     August         10000000.0\n",
       "76                 Automattic         30600000.0\n",
       "77                   Avocado�          2150000.0\n",
       "78                   AwayFind           800000.0\n",
       "79                     awe.sm          5250000.0\n",
       "80                       Ayla           170000.0\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_5= pd.read_csv('datasets/sql_challenges-sf_companies-0df8a524c950-2018-01-30-06-13-54.csv')\n",
    "query_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 23.,  11.,   9.,   4.,   6.,   1.,   3.,   0.,   1.,   1.]),\n",
       " array([  5.00000000e+03,   1.16824240e+06,   2.33148480e+06,\n",
       "          3.49472720e+06,   4.65796960e+06,   5.82121200e+06,\n",
       "          6.98445440e+06,   8.14769680e+06,   9.31093920e+06,\n",
       "          1.04741816e+07,   1.16374240e+07]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEJCAYAAACE39xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEwZdkpow6JLUxP8Bdfos1p9ebKsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109361860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q_10_df = pd.read_csv('datasets/sql_challenges-sf_companies-0df8a524c950-2018-01-30-06-13-54.csv')\n",
    "\n",
    "\n",
    "#twenty_thirty = q_10_df[q_10_df[\"funding_total_usd\"].between(25000000, 30000000, inclusive=True)]\n",
    "# df[df['closing_price'].between(99, 101, inclusive=True)]                                                              \n",
    "dataset = q_10_df[q_10_df[\"funding_total_usd\"] < 15000000][\"funding_total_usd\"]\n",
    "plt.hist(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_sample(dataset, n=50):\n",
    "    \"\"\"Grabs a random subsample of size 'n' from dataset.\n",
    "    Outputs the mean of the subsample.\"\"\"\n",
    "    sample = np.random.choice(dataset, size=n)\n",
    "    return np.mean(sample)\n",
    "\n",
    "def create_sample_distribution(dataset, size=1000):\n",
    "    \"\"\"Creates a dataset of subsample means.  The length of the dataset is specified by the 'size' \n",
    "    keyword argument. Should return the entire sample distribution as a numpy array.  \"\"\"\n",
    "    sample_ave = np.array([])\n",
    "    for i in range(0, size):\n",
    "        ave = get_sample(dataset)\n",
    "        sample_ave = np.append(sample_ave, ave)   \n",
    "    return sample_ave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   7.,   29.,  114.,  188.,  216.,  218.,  129.,   71.,   23.,    5.]),\n",
       " array([ 1525091.56 ,  1755534.378,  1985977.196,  2216420.014,\n",
       "         2446862.832,  2677305.65 ,  2907748.468,  3138191.286,\n",
       "         3368634.104,  3599076.922,  3829519.74 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c82LkRv0iAPAAnV/F/4/O/5S3AH8PPA0cpDOp67vGf4zOucojtFfHW30bnXdoPA98rKt+\nKZ0dewb4EnBeq7+73Z5p91+61DZGPA+/SefXv4PAU+2yjc450APAc+16TRsfdP54y/Ntria71vUH\n7d81A9zcVZ+k84R4Hvhr3vpA3Iq3MYZ5OKv2CeBXgH9r/95DwF8Mu/dhzs8Y5uHrbX84BPwDb70z\np+TzYtQXPxkrScX5yVhJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6TiDHpJKs6gl6Ti/h+hmp3Kq/w8\npgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e490cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_ave = create_sample_distribution(dataset)\n",
    "plt.hist(sample_ave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 58.74196903  58.7311976   56.00203188  58.16024043  59.116559\n",
      "  59.30631672  59.2910708   60.12903845  57.3965312   59.16814456\n",
      "  58.60968455  58.72103456  59.83724874  57.87881907  58.73137461\n",
      "  56.34420734  57.66054827  58.71027937  58.91641596  55.91581164\n",
      "  56.97475107  58.66331407  59.76178385  58.84811905  56.22595611\n",
      "  58.0010839   57.47736851  58.17104526  57.96695501  60.03415566\n",
      "  58.09937352  59.07277071  58.46857847  58.91686305  58.2568759\n",
      "  59.63262367  57.60026119  57.42678244  58.78404063  58.89570402\n",
      "  58.81008394  59.2006895   59.56881041  56.6183192   57.61729647\n",
      "  58.35517281  57.30680688  58.34100402  58.38506598  58.03144795\n",
      "  56.49238947  59.60008357  59.74163415  60.90137344  57.84183237\n",
      "  59.82087674  57.7028729   59.86469535  58.84320465  58.60327611\n",
      "  59.181802    58.94431482  57.1399537   56.29971489  58.3819987\n",
      "  58.93259025  59.55328837  59.17966252  57.88824708  57.60317462\n",
      "  58.3573191   58.85563995  59.49766682  59.10608736  56.76290479\n",
      "  57.66460231  57.95306349  57.64729699  58.59803912  57.63879386\n",
      "  57.52794261  58.1786056   56.47170802  58.06697754  59.27652324\n",
      "  58.39021197  56.95821174  57.88066541  57.81681999  56.35620341\n",
      "  58.49889278  60.51716374  58.44669247  55.84779959  58.86335659\n",
      "  59.31522989  59.68396444  58.94314633  59.0820904   57.54189287\n",
      "  58.20263306  58.56163105  57.60390767  58.02349057  57.61505939\n",
      "  58.05306053  58.27360588  55.34576998  59.09941471  57.71733545\n",
      "  59.31370843  57.64936492  57.13638958  57.76412259  58.27922227\n",
      "  57.69327189  58.50190267  58.24913887  56.17645976  58.89686689\n",
      "  58.14190283  60.3206666   57.74257437  57.5552771   57.72307203\n",
      "  59.1431545   57.60126033  59.62471109  57.98399009  57.42163911\n",
      "  57.75646901  60.18360919  58.7008128   58.30898261  57.70085481\n",
      "  56.84588778  56.61180383  59.49928387  56.59969018  57.53512355\n",
      "  59.57295138  58.77055703  58.08724244  58.79816046  58.96450937\n",
      "  57.97152716  57.62791587  58.51552431  58.48951617  58.39419466\n",
      "  58.68403444  59.76048721  59.54007896  57.48196303  57.58304927\n",
      "  59.65957912  57.62200208  57.46852828  59.32101299  57.37878981\n",
      "  57.66469861  58.04696864  59.21468966  58.06912389  59.70296147\n",
      "  59.70384587  58.01522298  59.61394127  57.30603954  58.52783588\n",
      "  56.51585968  55.98237043  59.25134446  56.98857136  58.43604165\n",
      "  59.11989205  57.51061274  57.46380972  58.0050602   59.70793591\n",
      "  59.0762124   56.88021391  56.58569002  59.67419709  59.01442638\n",
      "  59.6192792   58.49820567  59.39189767  58.02589269  58.24436209\n",
      "  57.68110433  58.52433566  58.52347118  58.27573713  56.34944256\n",
      "  57.52142178  59.35590058  58.63986228  57.46217114  57.56468565\n",
      "  58.78079598  57.66003703  57.0348677   58.70098393  57.86952634\n",
      "  58.23231098  57.95327411  57.8140946   56.55879196  59.10428306\n",
      "  58.74125024  58.11314516  56.9317372   59.07267461  56.78858327\n",
      "  58.87851369  59.55324055  58.73835601  59.25172464  58.89736368\n",
      "  56.8020139   58.00519121  57.40318111  58.71165239  59.42237322\n",
      "  58.51682844  57.29624768  57.27526508  58.90058814  58.5989611\n",
      "  59.86302097  60.67844559  58.25963522  59.16275164  58.00194157\n",
      "  57.54390524  57.92228794  58.14118039  58.28611371  59.07420284\n",
      "  57.90997434  57.5166039   56.95193794  56.60541183  57.68507652\n",
      "  58.1091929   58.10175739  59.01636331  58.33389259  58.55227073\n",
      "  58.68346039  56.02701424  60.0954001   59.07368894  57.00082419\n",
      "  59.84876904  59.23313142  57.24832826  58.09527423  58.35425182\n",
      "  59.13867663  57.98841936  59.16134147  58.14346889  58.69247128\n",
      "  58.98821991  57.07630948  59.39833036  57.88695388  57.53405631\n",
      "  56.56103285  57.83226253  58.37100057  57.98179613  59.53910969\n",
      "  57.83372187  58.09854468  57.88211403  58.59413983  59.38772605\n",
      "  55.86160432  59.97476823  57.33008339  58.02940733  57.33213613\n",
      "  59.72618568  58.34215695  58.13400466  59.32212243  58.5242226\n",
      "  58.21631453  57.40394242  57.94179351  55.77550151  56.86339331\n",
      "  56.81039148  57.15504639  56.90150531  59.9023535   56.84128467\n",
      "  58.00623759  57.61989257  58.33672267  57.79241194  57.72262967\n",
      "  58.82566884  57.54896158  57.96796908  60.76222229  58.73272167\n",
      "  59.57878853  57.10946775  58.6581416   58.53171826  58.32192588\n",
      "  56.68373291  58.42872533  59.73160363  59.06709735  58.24670365\n",
      "  59.66835832  56.41947775  57.3297172   58.47032622  57.78667744\n",
      "  58.29804447  58.54065688  57.6330476   57.40517413  58.97473053\n",
      "  59.20816467  56.88173866  60.08515771  57.0613658   58.72838642\n",
      "  58.40259862  57.70026328  59.12945924  59.36299916  58.61659155\n",
      "  57.44009471  58.87008637  58.77893591  58.20149146  59.54499227\n",
      "  58.84367578  59.49892163  56.79913341  57.9121749   58.9277783\n",
      "  58.13561281  57.71541418  58.87655122  58.11444623  58.19105006\n",
      "  59.07284741  57.51649271  58.01561415  57.76400682  58.58292401\n",
      "  58.50195334  57.63159653  59.11051794  58.19199108  59.24991545\n",
      "  59.00727505  58.79183173  56.44551434  57.44396872  59.61958082\n",
      "  57.75851853  57.71831611  58.61502107  56.56170372  57.90190764\n",
      "  58.52600879  58.62537486  57.02674107  59.33677457  58.46710118\n",
      "  56.16762255  60.08177705  58.20624972  58.34736324  59.31478743\n",
      "  58.76288155  58.19780622  59.42672018  57.62811036  57.06107225\n",
      "  57.94988768  58.26576759  58.42911415  55.77538689  58.8201142\n",
      "  58.54963925  57.09704842  58.57747625  57.76632539  58.5260552\n",
      "  57.97370462  57.80161497  57.9212477   60.25748254  59.85243373\n",
      "  59.47424261  57.53469732  58.2847834   60.05274186  57.23941956\n",
      "  59.12075658  57.9031253   59.26081946  59.13523115  58.33376668\n",
      "  58.38565917  58.01114965  59.48001431  58.37083746  57.06690514\n",
      "  57.52443958  56.8933537   58.85799546  59.18316311  57.08160323\n",
      "  59.58705508  57.31397377  56.05395653  56.80286101  58.17185194\n",
      "  57.59525662  59.58368956  59.60309648  58.96191307  58.23736612\n",
      "  58.58075633  57.09841999  57.72963323  59.33057253  58.13779381\n",
      "  58.57963701  58.832894    57.60337202  57.93577097  59.1261034\n",
      "  57.39064545  58.2899643   58.78145989  58.59821597  58.92697589\n",
      "  58.18383359  59.50453808  58.98349989  56.29788634  58.15951934\n",
      "  56.99406772  56.54402753  58.65063282  57.17199889  59.52729943\n",
      "  58.60598911  59.35447954  56.8458038   55.62793927  57.55735102\n",
      "  58.49874423  57.96563558  59.58898061  57.32252929  57.78329782\n",
      "  57.2143987   57.99158784  58.68201552  55.69580449  57.05088408\n",
      "  56.9389671   59.54976915  58.81626902  57.91989955  58.88354219\n",
      "  58.55283016  58.45194195  57.80505099  56.75869857  59.401562\n",
      "  57.99745907  56.52755875  59.05679393  58.34489545  59.40779412\n",
      "  59.68516249  58.36624289  57.95762669  58.12337252  58.96353089\n",
      "  58.74038352  59.47153168  57.55303882  59.04084868  57.93550853\n",
      "  58.83438336  57.42504386  56.99349377  59.48343305  59.16272615\n",
      "  57.35248479  57.42894513  58.92719551  59.42077688  57.71987847\n",
      "  57.87988293  57.83092457  59.7099193   58.60330904  59.2354938\n",
      "  58.68745974  58.94197752  57.8176546   56.69471522  57.40313486\n",
      "  58.78924437  58.9339498   58.35107788  57.04183932  56.3733811\n",
      "  59.2005035   58.70207739  56.71671537  57.7636359   57.78560489\n",
      "  56.46418925  59.33342352  58.08841739  57.33433586  57.91459576\n",
      "  59.0096828   57.5822455   56.60848291  56.06774794  57.90949764\n",
      "  58.49513995  57.72530074  58.08363212  60.25232786  56.58255011\n",
      "  57.58792246  58.91427388  57.99605358  58.79520375  57.60743302\n",
      "  59.60173433  58.59721403  57.61750043  58.98884817  57.25238318\n",
      "  58.47500044  58.81892979  59.50749188  58.67225295  58.13722179\n",
      "  59.4790363   57.44700108  56.59769264  58.31776815  58.0178427\n",
      "  58.93182489  56.63279158  59.08109574  60.34818637  58.35776895\n",
      "  59.82599265  57.39725816  57.24136746  58.45635842  59.54030774\n",
      "  57.64966519  58.42242969  57.7331435   59.44257287  58.64390112\n",
      "  56.49121088  57.94832053  58.9298107   57.79845346  58.90865536\n",
      "  55.92572473  58.99600216  56.36247517  56.92292601  58.59226753\n",
      "  58.64095829  57.14339804  58.88087539  56.89750992  60.09063711\n",
      "  59.25575282  58.28538019  58.27466448  57.62262087  58.21260463\n",
      "  59.17883436  57.69961904  57.68188187  56.60761457  59.18076941\n",
      "  57.339045    58.46681153  58.18256681  58.42312736  59.68508912\n",
      "  57.77262077  56.4948084   57.23227432  59.80809471  58.77805478\n",
      "  57.68599579  59.25842872  57.61627393  57.98906781  57.636032    57.0858159\n",
      "  59.03039807  57.41893432  58.22738487  58.36339237  58.71669483\n",
      "  59.15243808  57.79982253  58.45549519  58.03276306  59.90787036\n",
      "  56.20360944  59.20010687  57.62958672  58.42394865  58.46162225\n",
      "  57.57870871  59.49839389  59.82444221  58.57000878  57.43066332\n",
      "  59.00992193  57.77647261  58.09716417  56.67839696  58.03819667\n",
      "  57.02981017  58.14054942  57.64629754  59.49456427  58.0230979\n",
      "  59.17583649  59.35554964  59.09987507  56.34504159  57.79276673\n",
      "  57.49202571  56.55810438  58.76045627  59.84592185  57.82178429\n",
      "  59.49739491  59.58092962  58.85005613  59.22867546  59.92737879\n",
      "  59.64852912  57.42413437  57.12533443  58.53810558  58.48516822\n",
      "  58.21743203  58.69569491  58.42744742  56.87171401  57.69950587\n",
      "  57.45997921  58.57675735  56.71963712  59.0349403   58.51078931\n",
      "  59.30077676  59.79088648  58.614627    57.70406969  58.22954069\n",
      "  57.45324693  57.67171493  59.99619184  56.16355661  59.70496067\n",
      "  58.25296569  59.03837548  59.26624235  56.71537586  56.43605345\n",
      "  58.04582122  60.21001869  58.33414635  59.57753159  59.63078795\n",
      "  57.87439313  58.99008664  59.30682422  57.51004831  57.74679615\n",
      "  55.81479658  60.08865101  59.38465206  56.40607593  58.89835443\n",
      "  57.010611    57.45652842  59.7268025   58.70576851  58.47498114\n",
      "  56.04435151  56.25768241  56.63922328  58.54845494  57.71751547\n",
      "  59.0833795   61.07010642  58.64540073  59.33925162  59.13828505\n",
      "  56.88478674  59.08773796  57.28858932  58.59269573  59.40965877\n",
      "  59.02270172  57.55044689  58.51633363  60.49303264  59.67003026\n",
      "  58.1127976   57.72525121  57.72434755  55.19033684  58.90570775\n",
      "  57.07089559  58.74118733  58.57022851  55.96415817  56.96784282\n",
      "  58.55205834  56.92628018  57.34150446  60.10175313  57.11400306\n",
      "  57.07906353  57.41974043  59.31927576  56.47668949  59.5737618\n",
      "  58.63660655  59.77351726  58.65872705  59.95255075  58.03308383\n",
      "  58.46557259  57.13451801  58.81572078  56.90121681  57.04339044\n",
      "  57.67540459  57.87938875  57.17335266  58.37338537  57.07936115\n",
      "  59.15441366  58.99199391  57.78174717  58.33550496  58.67890692\n",
      "  57.95970378  56.84326499  56.60927402  56.48516493  57.21118906\n",
      "  58.19362009  58.56901834  58.2290644   57.41136602  58.22575316\n",
      "  57.86136817  57.98367744  57.50526305  57.67614747  58.13941886\n",
      "  59.4867481   57.9291759   58.71182149  57.14056391  57.91758895\n",
      "  58.28771254  57.17297373  58.03118863  56.66201908  57.2998273\n",
      "  56.57578219  59.21803608  57.49988688  57.84119636  57.39015813\n",
      "  59.9101166   57.71496714  59.68075455  57.42358181  56.97234728\n",
      "  58.1054931   57.40771195  60.23374359  59.0466107   58.50189767\n",
      "  58.52420225  56.87258979  57.87378391  58.62969107  58.72991444\n",
      "  57.79176863  58.16777304  57.67769235  59.54130188  56.2383385\n",
      "  57.23912195  58.57060083  56.86685284  57.77826718  58.82569615\n",
      "  56.22867739  57.77337854  57.87081871  58.99395441  57.53582986\n",
      "  58.46340866  59.87215517  57.63365922  58.15739693  59.2159914\n",
      "  59.13078892  59.88997117  60.31528497  58.92951059  58.34845327\n",
      "  56.87137832  58.74693167  59.5989925   58.70877746  57.26777202\n",
      "  58.71860392  58.06631369  58.92485478  57.4309447   58.81667444\n",
      "  58.4370003   58.73538665  58.35727524  57.38198087  57.43285529\n",
      "  59.18100042  57.82137377  58.29318762  57.6304783   58.95456028\n",
      "  57.08853057  60.47716366  59.73266379  59.36182354  58.61768569\n",
      "  58.49305323  55.07512354  59.34034858  59.09916002  57.70602582\n",
      "  60.2918209   58.19555514  58.25068005  58.65058105  56.90155531\n",
      "  57.81721693  58.2004417   58.42136068  59.0633504   56.98975078\n",
      "  58.1753161   57.39699073  58.49920418  58.82771357  58.89930367\n",
      "  57.51699137  57.66289051  58.69860516  57.33163763  59.56957733\n",
      "  57.16243071  59.20816909  56.32178778  58.46090908  58.49813615\n",
      "  57.39957496  57.68062696  58.22752759  57.11226463  58.46930559\n",
      "  57.47332655  58.98706744  59.01598442  60.10783004  58.51548534\n",
      "  59.33551441  58.45360212  58.1859777   57.76409434  58.8657443\n",
      "  57.71158607  59.51838532  58.18171679  58.47358502  59.50308795\n",
      "  57.47171117  57.917725    56.89500483  59.47668011  57.27710638\n",
      "  58.18737949  59.56725986  57.6122533   58.66541115  57.60791841\n",
      "  57.60108181  58.8008987   56.6702428   57.95098614  60.41881392\n",
      "  58.5725953   58.69177355  57.82063879  58.18403344  57.7361717\n",
      "  58.97998666  58.1041899   56.95986442  57.26183091  57.81106754\n",
      "  58.34486216  58.69919097  58.91738185  58.17104927  56.60921018\n",
      "  58.57105953  57.13783467  57.3960021   58.28632224  59.40800411\n",
      "  57.54476234  57.20591222  58.77127364  57.16341215  57.69015809\n",
      "  57.61113741  58.24522506  59.20397651  59.06075644  57.74929687\n",
      "  57.96376338  56.33591255  58.68676482  57.02598486  57.86375687\n",
      "  56.46856961  57.38324604  57.57344008  57.25095713  58.1762476\n",
      "  57.93140981  58.6293548   57.01718381  58.51436898  57.13877839\n",
      "  56.76130362  59.16842714  57.69660088  58.3173569   58.3796383\n",
      "  58.3282198   58.0685397   57.66401015  58.71895289  56.70353172\n",
      "  57.03130072  56.67414127  58.66756098  57.55631843  58.86319779\n",
      "  57.63820353  58.5769061   55.33668969  56.31474581]\n"
     ]
    }
   ],
   "source": [
    "# z score = (x - x-bar) / standard deviation\n",
    "\n",
    "sample_std = np.std(sample_ave)\n",
    "thir_z = (30000000 - sample_ave) / sample_std\n",
    "# print(thir_z)\n",
    "twen_z = (25000000 - sample_ave) / (sample_std)\n",
    "print(twen_z)\n",
    "total_z = thir_z - twen_z\n",
    "# total_z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.634% of companies receive total funding amounts between 25 & 30 million."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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