EDA - SAT Verbal and Math Scores .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 1\n",
    "\n",
    "## Step 1: Open the `sat_scores.csv` file. Investigate the data, and answer the questions below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. What does the data describe?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The verbal and math scores for each state as well as the rate. However, I am not sure what the rate is rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Does the data look complete? Are there any obvious issues with the observations?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing observations, but there is an \"All\" entry in the State column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Create a data dictionary for the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### Data Source: \n",
    "    GA\n",
    "##### Data Describes: \n",
    "    SAT scores\n",
    "##### Variables Names and Types: \n",
    "    State (string), Rate (String), Verbal (String), Math (String)\n",
    "##### Notes:\n",
    "    1. Missing Observations: None\n",
    "    2. One row has entry as ALL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Load the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Load the data into a list of lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import csv\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "################################\n",
    "\n",
    "sat_score_data = '../assets/sat_scores.csv'\n",
    "sat_rows = []\n",
    "\n",
    "with open(sat_score_data, 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    \n",
    "    for row in reader:\n",
    "        sat_rows.append(row)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. Print the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['State', 'Rate', 'Verbal', 'Math'], ['CT', '82', '509', '510'], ['NJ', '81', '499', '513'], ['MA', '79', '511', '515'], ['NY', '77', '495', '505'], ['NH', '72', '520', '516'], ['RI', '71', '501', '499'], ['PA', '71', '500', '499'], ['VT', '69', '511', '506'], ['ME', '69', '506', '500'], ['VA', '68', '510', '501'], ['DE', '67', '501', '499'], ['MD', '65', '508', '510'], ['NC', '65', '493', '499'], ['GA', '63', '491', '489'], ['IN', '60', '499', '501'], ['SC', '57', '486', '488'], ['DC', '56', '482', '474'], ['OR', '55', '526', '526'], ['FL', '54', '498', '499'], ['WA', '53', '527', '527'], ['TX', '53', '493', '499'], ['HI', '52', '485', '515'], ['AK', '51', '514', '510'], ['CA', '51', '498', '517'], ['AZ', '34', '523', '525'], ['NV', '33', '509', '515'], ['CO', '31', '539', '542'], ['OH', '26', '534', '439'], ['MT', '23', '539', '539'], ['WV', '18', '527', '512'], ['ID', '17', '543', '542'], ['TN', '13', '562', '553'], ['NM', '13', '551', '542'], ['IL', '12', '576', '589'], ['KY', '12', '550', '550'], ['WY', '11', '547', '545'], ['MI', '11', '561', '572'], ['MN', '9', '580', '589'], ['KS', '9', '577', '580'], ['AL', '9', '559', '554'], ['NB', '8', '562', '568'], ['OK', '8', '567', '561'], ['MO', '8', '577', '577'], ['LA', '7', '564', '562'], ['WI', '6', '584', '596'], ['AR', '6', '562', '550'], ['UT', '5', '575', '570'], ['IA', '5', '593', '603'], ['SD', '4', '577', '582'], ['ND', '4', '592', '599'], ['MS', '4', '566', '551'], ['All', '45', '506', '514']]\n"
     ]
    }
   ],
   "source": [
    "print sat_rows\n",
    "#for i in range (5):\n",
    "    #print sat_rows[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### 6. Extract a list of the labels from the data, and remove them from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = sat_rows[0]\n",
    "#print labels ## labels list\n",
    "data_nolabel = sat_rows[1:-2] ## In here I removed the label and the row with entry ALL\n",
    "#print data_nolabel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7. Create a list of State names extracted from the data. (Hint: use the list of labels to index on the State column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CT', 'NJ', 'MA', 'NY', 'NH', 'RI', 'PA', 'VT', 'ME', 'VA', 'DE', 'MD', 'NC', 'GA', 'IN', 'SC', 'DC', 'OR', 'FL', 'WA', 'TX', 'HI', 'AK', 'CA', 'AZ', 'NV', 'CO', 'OH', 'MT', 'WV', 'ID', 'TN', 'NM', 'IL', 'KY', 'WY', 'MI', 'MN', 'KS', 'AL', 'NB', 'OK', 'MO', 'LA', 'WI', 'AR', 'UT', 'IA', 'SD', 'ND']\n"
     ]
    }
   ],
   "source": [
    "state_names =[]\n",
    "\n",
    "for i in data_nolabel: ## Using the data without label\n",
    "    if i[0] != \"All\": ## Printing all the state names except ALL\n",
    "        state_names.append(i[0])\n",
    "print state_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 8. Print the types of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
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      "4\n",
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      "4\n",
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      "4\n",
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      "4\n",
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      "4\n",
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      "4\n",
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      "4\n",
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      "4\n",
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      "4\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
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      "4\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
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      "4\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "4\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "<type 'str'>\n",
      "<type 'str'>\n"
     ]
    }
   ],
   "source": [
    "for row in data_nolabel:\n",
    "    print len(row)\n",
    "    print type (row[0])\n",
    "    print type (row[1])\n",
    "    print type (row[2])\n",
    "    print type (row[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 9. Do any types need to be reassigned? If so, go ahead and do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['CT', 82.0, 509.0, 510.0], ['NJ', 81.0, 499.0, 513.0], ['MA', 79.0, 511.0, 515.0], ['NY', 77.0, 495.0, 505.0], ['NH', 72.0, 520.0, 516.0], ['RI', 71.0, 501.0, 499.0], ['PA', 71.0, 500.0, 499.0], ['VT', 69.0, 511.0, 506.0], ['ME', 69.0, 506.0, 500.0], ['VA', 68.0, 510.0, 501.0], ['DE', 67.0, 501.0, 499.0], ['MD', 65.0, 508.0, 510.0], ['NC', 65.0, 493.0, 499.0], ['GA', 63.0, 491.0, 489.0], ['IN', 60.0, 499.0, 501.0], ['SC', 57.0, 486.0, 488.0], ['DC', 56.0, 482.0, 474.0], ['OR', 55.0, 526.0, 526.0], ['FL', 54.0, 498.0, 499.0], ['WA', 53.0, 527.0, 527.0], ['TX', 53.0, 493.0, 499.0], ['HI', 52.0, 485.0, 515.0], ['AK', 51.0, 514.0, 510.0], ['CA', 51.0, 498.0, 517.0], ['AZ', 34.0, 523.0, 525.0], ['NV', 33.0, 509.0, 515.0], ['CO', 31.0, 539.0, 542.0], ['OH', 26.0, 534.0, 439.0], ['MT', 23.0, 539.0, 539.0], ['WV', 18.0, 527.0, 512.0], ['ID', 17.0, 543.0, 542.0], ['TN', 13.0, 562.0, 553.0], ['NM', 13.0, 551.0, 542.0], ['IL', 12.0, 576.0, 589.0], ['KY', 12.0, 550.0, 550.0], ['WY', 11.0, 547.0, 545.0], ['MI', 11.0, 561.0, 572.0], ['MN', 9.0, 580.0, 589.0], ['KS', 9.0, 577.0, 580.0], ['AL', 9.0, 559.0, 554.0], ['NB', 8.0, 562.0, 568.0], ['OK', 8.0, 567.0, 561.0], ['MO', 8.0, 577.0, 577.0], ['LA', 7.0, 564.0, 562.0], ['WI', 6.0, 584.0, 596.0], ['AR', 6.0, 562.0, 550.0], ['UT', 5.0, 575.0, 570.0], ['IA', 5.0, 593.0, 603.0], ['SD', 4.0, 577.0, 582.0], ['ND', 4.0, 592.0, 599.0]]\n"
     ]
    }
   ],
   "source": [
    "## Yes columns with index 1,2,3\n",
    "for i in data_nolabel: \n",
    "    i[1] = float(i[1])\n",
    "    i[2] = float(i[2])\n",
    "    i[3] = float(i[3])\n",
    "print data_nolabel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 10. Create a dictionary for each column mapping the State to its respective value for that column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'WA': [53.0, 527.0, 527.0], 'DE': [67.0, 501.0, 499.0], 'DC': [56.0, 482.0, 474.0], 'WI': [6.0, 584.0, 596.0], 'WV': [18.0, 527.0, 512.0], 'HI': [52.0, 485.0, 515.0], 'FL': [54.0, 498.0, 499.0], 'WY': [11.0, 547.0, 545.0], 'NH': [72.0, 520.0, 516.0], 'NJ': [81.0, 499.0, 513.0], 'NM': [13.0, 551.0, 542.0], 'TX': [53.0, 493.0, 499.0], 'LA': [7.0, 564.0, 562.0], 'NB': [8.0, 562.0, 568.0], 'NC': [65.0, 493.0, 499.0], 'ND': [4.0, 592.0, 599.0], 'TN': [13.0, 562.0, 553.0], 'NY': [77.0, 495.0, 505.0], 'PA': [71.0, 500.0, 499.0], 'RI': [71.0, 501.0, 499.0], 'NV': [33.0, 509.0, 515.0], 'VA': [68.0, 510.0, 501.0], 'CO': [31.0, 539.0, 542.0], 'AK': [51.0, 514.0, 510.0], 'AL': [9.0, 559.0, 554.0], 'AR': [6.0, 562.0, 550.0], 'VT': [69.0, 511.0, 506.0], 'IL': [12.0, 576.0, 589.0], 'GA': [63.0, 491.0, 489.0], 'IN': [60.0, 499.0, 501.0], 'IA': [5.0, 593.0, 603.0], 'OK': [8.0, 567.0, 561.0], 'AZ': [34.0, 523.0, 525.0], 'CA': [51.0, 498.0, 517.0], 'ID': [17.0, 543.0, 542.0], 'CT': [82.0, 509.0, 510.0], 'ME': [69.0, 506.0, 500.0], 'MD': [65.0, 508.0, 510.0], 'MA': [79.0, 511.0, 515.0], 'OH': [26.0, 534.0, 439.0], 'UT': [5.0, 575.0, 570.0], 'MO': [8.0, 577.0, 577.0], 'MN': [9.0, 580.0, 589.0], 'MI': [11.0, 561.0, 572.0], 'KS': [9.0, 577.0, 580.0], 'MT': [23.0, 539.0, 539.0], 'SC': [57.0, 486.0, 488.0], 'KY': [12.0, 550.0, 550.0], 'OR': [55.0, 526.0, 526.0], 'SD': [4.0, 577.0, 582.0]}\n"
     ]
    }
   ],
   "source": [
    "d = {} ## mapping dictionary\n",
    "for row in data_nolabel:\n",
    "    #print row\n",
    "    key = row[0]\n",
    "    values = row[1:]\n",
    "    d[key] = values\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 11. Create a dictionary with the values for each of the numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Math': [510.0, 513.0, 515.0, 505.0, 516.0, 499.0, 499.0, 506.0, 500.0, 501.0, 499.0, 510.0, 499.0, 489.0, 501.0, 488.0, 474.0, 526.0, 499.0, 527.0, 499.0, 515.0, 510.0, 517.0, 525.0, 515.0, 542.0, 439.0, 539.0, 512.0, 542.0, 553.0, 542.0, 589.0, 550.0, 545.0, 572.0, 589.0, 580.0, 554.0, 568.0, 561.0, 577.0, 562.0, 596.0, 550.0, 570.0, 603.0, 582.0, 599.0], 'State': ['CT', 'NJ', 'MA', 'NY', 'NH', 'RI', 'PA', 'VT', 'ME', 'VA', 'DE', 'MD', 'NC', 'GA', 'IN', 'SC', 'DC', 'OR', 'FL', 'WA', 'TX', 'HI', 'AK', 'CA', 'AZ', 'NV', 'CO', 'OH', 'MT', 'WV', 'ID', 'TN', 'NM', 'IL', 'KY', 'WY', 'MI', 'MN', 'KS', 'AL', 'NB', 'OK', 'MO', 'LA', 'WI', 'AR', 'UT', 'IA', 'SD', 'ND'], 'Rate': [82.0, 81.0, 79.0, 77.0, 72.0, 71.0, 71.0, 69.0, 69.0, 68.0, 67.0, 65.0, 65.0, 63.0, 60.0, 57.0, 56.0, 55.0, 54.0, 53.0, 53.0, 52.0, 51.0, 51.0, 34.0, 33.0, 31.0, 26.0, 23.0, 18.0, 17.0, 13.0, 13.0, 12.0, 12.0, 11.0, 11.0, 9.0, 9.0, 9.0, 8.0, 8.0, 8.0, 7.0, 6.0, 6.0, 5.0, 5.0, 4.0, 4.0], 'Verbal': [509.0, 499.0, 511.0, 495.0, 520.0, 501.0, 500.0, 511.0, 506.0, 510.0, 501.0, 508.0, 493.0, 491.0, 499.0, 486.0, 482.0, 526.0, 498.0, 527.0, 493.0, 485.0, 514.0, 498.0, 523.0, 509.0, 539.0, 534.0, 539.0, 527.0, 543.0, 562.0, 551.0, 576.0, 550.0, 547.0, 561.0, 580.0, 577.0, 559.0, 562.0, 567.0, 577.0, 564.0, 584.0, 562.0, 575.0, 593.0, 577.0, 592.0]}\n"
     ]
    }
   ],
   "source": [
    "## Enumerate command\n",
    "## In this command lines I am also creating the variables I need to continue in my analysis\n",
    "d = {} ## mapping dictionary\n",
    "\n",
    "for i, h in enumerate (labels): \n",
    "    col = []\n",
    "    for row in data_nolabel:\n",
    "        cell = row[i]\n",
    "        col.append(cell)\n",
    "    d[h] = col\n",
    "print d ## Dictionary\n",
    "\n",
    "## Variables for future analysis\n",
    "if 'State' in d:\n",
    "    state = d['State']\n",
    "    del d['State']\n",
    "if 'Math' in d:\n",
    "    math = d['Math']\n",
    "if 'Rate' in d:\n",
    "    rate = d['Rate']\n",
    "if 'Verbal' in d:\n",
    "    verbal = d['Verbal']\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Describe the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 12. Print the min and max of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The max value of variable Math is: 603.0\n",
      "The max value of variable Rate is: 82.0\n",
      "The max value of variable Verbal is: 593.0\n",
      "The min value of variable Math is: 439.0\n",
      "The min value of variable Rate is: 4.0\n",
      "The min value of variable Verbal is: 482.0\n"
     ]
    }
   ],
   "source": [
    "var_list = [math, rate, verbal]\n",
    "var_names = ['Math', 'Rate', 'Verbal']\n",
    "\n",
    "max_value = []\n",
    "for i in var_list:\n",
    "    max_value.append(np.max(i))\n",
    "min_value = []\n",
    "for i in var_list:\n",
    "    min_value.append(np.min(i))\n",
    "\n",
    "for x, y in map(None, var_names, max_value):\n",
    "    print \"The max value of variable %s is: %s\" % (x, y)\n",
    "\n",
    "for x, y in map(None, var_names, min_value):\n",
    "    print \"The min value of variable %s is: %s\" % (x, y)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 13. Write a function using only list comprehensions, no loops, to compute Standard Deviation. Print the Standard Deviation of each numeric column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "St.Dev of Verbal is 233.460960334\n",
      "St.Dev of Rate is 191.940667916\n",
      "St.Dev of Math is 255.860157117\n"
     ]
    }
   ],
   "source": [
    "def stddev(x):\n",
    "    \"\"\"returns the standard deviation\"\"\"\n",
    "    mn = np.mean(x)\n",
    "    variance = sum([(e-mn)**2 for e in x]) ## Variance\n",
    "    return np.sqrt(variance) ## standard deviations\n",
    "\n",
    "print \"St.Dev of Verbal is\", stddev(verbal)\n",
    "print \"St.Dev of Rate is\", stddev(rate)\n",
    "print \"St.Dev of Math is\", stddev(math)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Visualize the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "##### 14. Using MatPlotLib and PyPlot, plot the distribution of the Rate using histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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dQtn/RflD4SfNsbclZdWO3ShLst2VMpd4Y1M9JDUcWZX6QGauzcxnU944L6Ccufxayhvr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11674f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11,7))\n",
    "rate.sort\n",
    "plt.hist(rate, bins =20)\n",
    "plt.title('SAT Rate Scores - Histogram\\n', fontsize = 20)\n",
    "plt.ylabel('Frequency\\n', fontsize = 16)\n",
    "plt.xlabel(\"Rate\\n\", fontsize = 16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 15. Plot the Math distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Wn6q1FHP09d8DO2fmm1ra7Il9XbWIWA98LTMPaFn2STyuV515+vpI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c92050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11,7))\n",
    "math.sort\n",
    "plt.hist(math, bins =20, alpha=0.7, color ='r')\n",
    "plt.title('SAT Math Scores - Histogram\\n', fontsize = 20)\n",
    "plt.ylabel('Frequency\\n', fontsize = 16)\n",
    "plt.xlabel('Math\\n', fontsize = 16)\n",
    "plt.show()\n",
    "\n",
    "## Probability density Function Graph\n",
    "# d['Math'].sort()\n",
    "# hmean = np.mean(d['Math'])\n",
    "# print \"Mean\", hmean\n",
    "# hstd = np.std(d['Math'])\n",
    "# print hstd \n",
    "# pdf = stats.norm.pdf(d['Math'], hmean, hstd)\n",
    "# print pdf\n",
    "# plt.plot(d['Math'], pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 16. Plot the Verbal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XUm4wsBvlKiDtZ/1P5qKmjudFxH0ofVvvSzkBc23zemfyfmxni61mNVtcpS1AZt6VmYdT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\n3wP2jogFEbGM8oXwk2HUvBU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104020a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(11,7))\n",
    "verbal.sort\n",
    "plt.hist(verbal, bins =20, alpha=0.7, color ='grey')\n",
    "plt.title('SAT Verbal Scores - Histogram\\n', fontsize =20)\n",
    "plt.ylabel(\"Frequency\\n\", fontsize = 16)\n",
    "plt.xlabel('Verbal\\n', fontsize =16)\n",
    "plt.show()\n",
    "\n",
    "## Probability density Function Graph\n",
    "# verbal.sort()\n",
    "# hmean = np.mean(verbal)\n",
    "# print \"Mean\", hmean\n",
    "# hstd = np.std(verbal)\n",
    "# print hstd\n",
    "# pdf = stats.norm.pdf(verbal, hmean, hstd)\n",
    "# print pdf\n",
    "# plt.plot(verbal, pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 17. What is the typical assumption for data distribution?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The data is normally distributed, or is at least symetric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 18. Does that distribution hold true for our data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "No"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 19. Plot some scatterplots. **BONUS**: Use a PyPlot `figure` to present multiple plots at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GMmhzhlNJkmpuaPUa5v/XpWy1bgiAubffxtwvnrPZcTPhlHe7MN5g1XR28LS+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\nmZlZw3A4NTMzM7OG4XBqZmZmZg3jL2R7bo3NLSQyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196a1d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# edgecolors='white',alpha = 1,\n",
    "fig = plt.figure(figsize=(11,7))\n",
    "plt.scatter(verbal, math, color ='red', s= 100,  marker='v')\n",
    "plt.title('Correlation (SAT Verbal - Math Scores) \\n', fontsize=18)\n",
    "plt.ylabel(\"Math Scores\", fontsize = 14 )\n",
    "plt.xlabel(\"Verbal Scores\", fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmaZbklC4odtzG4fWv7FVYAioLBbVspbt/R07ik6ZjAkCRJkqRZlGOyYGjL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sig/QZAdW46i+EjGb9DCVreqtq2G1JGh2ysmZd+Wky/D9yrX/ZrSv9rWufPQXWzCmg9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\nv2ZCin2+TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c81710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xt/vHgb+vvcreOL43f9E758FZs9Kpd2W7n0zy17XWn92kTwCYOkO0AWB0J9NspXlNKeXf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\nLnlU0Gq9TEi/fgx4qrtPTvvpocTYZeKqYVpqtQ4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196dc290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_state = [i+1 for i in range(len(state_names))] ## Creating index for each state\n",
    "fig = plt.figure(figsize=(40,15))\n",
    "\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "my_xticks = state_names ## asing label names (state names using the state names list)\n",
    "plt.xticks(index_state, my_xticks)\n",
    "plt.scatter(index_state, math, marker='H', s=100, color = 'red', alpha = 0.6)\n",
    "plt.title('SAT Scores by State\\n', fontsize=20)\n",
    "plt.ylabel(\"Math Scores\", fontsize = 16 )\n",
    "#plt.xlabel(\"Verbal Scores\", fontsize = 14)\n",
    "\n",
    "ax2 = plt.subplot(2,2,3)\n",
    "my_xticks = state_names ## asing label names (state names using the state names list)\n",
    "plt.xticks(index_state, my_xticks)\n",
    "plt.scatter(index_state, verbal, marker ='D', s=100, color = 'green', alpha = 0.6)\n",
    "# plt.title('SAT Verbal Scores by State) \\n', fontsize=18)\n",
    "plt.ylabel(\"Verbal Scores\", fontsize = 16 )\n",
    "# plt.xlabel(\"States\", fontsize = 16)\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure(figsize=(20,7))\n",
    "my_xticks = state_names ## asing label names (state names using the state names list)\n",
    "plt.xticks(index_state, my_xticks)\n",
    "plt.scatter(index_state, rate, marker ='D', s=100, color = 'blue', alpha = 0.6)\n",
    "plt.title('SAT Participation Rate by State \\n', fontsize=20)\n",
    "plt.ylabel(\"Rate\", fontsize = 16 )\n",
    "# plt.xlabel(\"States\", fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eD3wNeCzwAHAK6azgJUAXcCewLMY40kYNbNq0tZah1sXs2T20w3aA29Ks2mVb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\nEREREZEMKcgSERERERHJ0P8Dc5mUJoez72wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11918df50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,6))\n",
    "# ## STATES and Verbal Scores\n",
    "# ax1 = plt.subplot(221)\n",
    "# plt.plot(index_state, verbal)\n",
    "\n",
    "# ## STATES and Math Scores\n",
    "# ax2 = plt.subplot(222)\n",
    "# plt.scatter(index_state, math)\n",
    "\n",
    "## Verbal and Rates\n",
    "ax1 = plt.subplot(1,2,1)\n",
    "plt.scatter(verbal, rate, color ='red')\n",
    "plt.title('Correlation SAT Verbal Scores - Rate \\n', fontsize =18)\n",
    "plt.ylabel(\"Rate\\n\", fontsize = 15)\n",
    "plt.xlabel(\"SAT Verbal Scores\\n\", fontsize = 15)\n",
    "\n",
    "\n",
    "## Math and Rates\n",
    "ax2 = plt.subplot(1,2,2)\n",
    "plt.scatter(math,rate, marker='v', color='green', s= 50)\n",
    "plt.title('Correlation SAT Math Scores - Rate\\n', fontsize =18)\n",
    "plt.xlabel(\"SAT Math Scores\\n\", fontsize = 15)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Verbal</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CT</td>\n",
       "      <td>82</td>\n",
       "      <td>509</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NJ</td>\n",
       "      <td>81</td>\n",
       "      <td>499</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MA</td>\n",
       "      <td>79</td>\n",
       "      <td>511</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NY</td>\n",
       "      <td>77</td>\n",
       "      <td>495</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NH</td>\n",
       "      <td>72</td>\n",
       "      <td>520</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Rate  Verbal  Math\n",
       "0    CT    82     509   510\n",
       "1    NJ    81     499   513\n",
       "2    MA    79     511   515\n",
       "3    NY    77     495   505\n",
       "4    NH    72     520   516"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pd.read_csv('../assets/sat_scores.csv')\n",
    "new_data.columns = ['State', 'Rate', 'Verbal', 'Math']\n",
    "new_data.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x119199690>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rq3vcnshhGAbPjZgECkUG+pvNolU1Ubbamj4djuP0A/8UGAH+AvDLwN9xXXdu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21ul99HIpFjpL+DqWSB+UT7gFfFccpcSLrMzFx7kNeV17IfHB4nV6wVdSu91jdau/0trdZGOo/V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\nkiRJKfj/Om7egOWwnsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11976efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.pairplot(new_data, size=4, hue='State', plot_kws={'s': 75})\n",
    "sns.pairplot(new_data,size=4, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 20. Are there any interesting relationships to note?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. There is a positive correlation between the verbal and math SAT scores.\n",
    "2. The correlation is negative between the participation rate and the math and verbal scores.\n",
    "3. The math, verbal and participation rate data does not present a symmetric distribution.\n",
    "4. North and South Dakota have the highest SAT Verbal and Math Scores but have the lowest participation rates.\n",
    "5. On the contrary, the data shows that the east coast states have a greater SAT participation rate. \n",
    "6. Math scores in between the 510 and 530 present the highest SAT participation rates (between 75 and 85 percent). This math score range coincides with the math scores of the majority of the east coast states.\n",
    "7. Verbal scores between 490 and 510 have the highest participation rates  (between 75 to 85 percent). This verbal score range coincides with the verbal scores of the majority of the east coast states.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 21. Create box plots for each variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118cb3850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad4b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aab8410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,6))\n",
    "\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "plt.boxplot(verbal, 0, 'rs', 0, 0.75, showmeans=True)\n",
    "plt.title('SAT Verbal Scores\\n', fontsize = 18)\n",
    "\n",
    "fig = plt.figure(figsize=(20,6))\n",
    "ax2 = plt.subplot(2,2,3)\n",
    "plt.boxplot(math, 0, 'rs', 0, 1.5, showmeans=True)\n",
    "plt.title('SAT Math Scores\\n', fontsize = 18)\n",
    "\n",
    "fig = plt.figure(figsize=(9,3))\n",
    "plt.boxplot(rate, 0, 'rs', 0, 1.5, showmeans=True)\n",
    "plt.title('SAT Participation Rate\\n', fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### BONUS: Using Tableau, create a heat map for each variable using a map of the US. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## SAT Math Scores \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://snag.gy/Pqkkb.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAT Verbal Scores\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://snag.gy/xOcEm.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAT Participation Rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://snag.gy/6udFn.jpg)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}