Homework 6 Answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 6: Stroop Test Analysis with Pandas\n",
    "\n",
    "Perform the same analysis as in Homework 3, on the same data.  While this is the same dataset, and the same analysis sequence, the different data organization (a data frame) will have a different \"feel\" to it than before.  As you work through the notebook, notice the differences and similarities in your code as when you were just working with 1D Numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While exploring pandas, you may have |also noticed pandas.np.  Yep, it's NumPy! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy\n"
     ]
    }
   ],
   "source": [
    "print(pd.np.__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Don't worry about whether you use the numpy that pandas imports or that you import--they are exactly the same, and numpy will only actually be imported once.  This is a general rule for all python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.np is np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set Paths\n",
    "The Data for this homework assignment is actually the same as the data from last week, except we'll be working directly from the original CSV File, which contains all of the data.  CSV Files are text files that store data in rows and columns.\n",
    "\n",
    "The Data File: 'Homework Sample Data/Homework 6'/'StroopData.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Homework Sample Data/Homework 6/StroopData.csv'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = path.join('Homework Sample Data', 'Homework 6')\n",
    "filename =  path.join(data_dir, 'StroopData.csv')\n",
    "filename"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the CSV File into an Pandas DataFrame\n",
    "\n",
    "This is a lot of data! To display just the first 5 rows of the data (useful in these notebooks), use the **head()** method on the dataframe.  Note: The **Condition** column says whether the word displayed matches (Congruent) or doesn't match (Incongruent) its color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ClassID</th>\n",
       "      <th>UserID</th>\n",
       "      <th>NumTrial</th>\n",
       "      <th>Condition</th>\n",
       "      <th>ColorORWord</th>\n",
       "      <th>WordDisplayed</th>\n",
       "      <th>ColorOfStimulus</th>\n",
       "      <th>ColorOfResponse</th>\n",
       "      <th>ReactionTime</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>1</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>2</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>3</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>RED</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>4</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>5</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClassID  UserID  NumTrial Condition ColorORWord WordDisplayed  \\\n",
       "0     6110  156772         1      ConW           C        YELLOW   \n",
       "1     6110  156772         2      ConW           C         GREEN   \n",
       "2     6110  156772         3      ConW           C           RED   \n",
       "3     6110  156772         4      ConW           C        YELLOW   \n",
       "4     6110  156772         5      ConW           C         GREEN   \n",
       "\n",
       "  ColorOfStimulus ColorOfResponse  ReactionTime Gender  Age  \n",
       "0               Y               Y        1389.0      M   26  \n",
       "1               G               G        2043.0      M   26  \n",
       "2               R               R        1389.0      M   26  \n",
       "3               Y               Y        1092.0      M   26  \n",
       "4               G               G        1310.0      M   26  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(filename)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How Many Trials were there in total across all subjects?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2880"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How Many Unique Subject IDs were there in this study?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(df['UserID']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New Approach: If you change a column's type to **Category**, python will treat numbers the same way it treats strings!  (Note: this isn't completely implemented by Pandas yet, but is a standard feature in dataframes across languages and statistical software) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       2880\n",
       "unique        32\n",
       "top       156885\n",
       "freq          90\n",
       "Name: UserIDCat, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UserIDCat'] = df['UserID'].astype('category')\n",
    "df['UserIDCat'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the mean response time, across all subjects and conditions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2136.1878472222224"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ReactionTime'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot a histogram of the response times\n",
    "(Don't forget to make sure that **%matplotlib inline** has been run already in this notebook, or the plot may not show up!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ffc456a20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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cl5sgIiIR0md7QCUlJdTV1dHe3s7SpUvJy8ujsrISn8/HE088AZydiFBQUEBWVhZTp06l\nsLCQxMRECgoKsNlsACxevJitW7cGp2FPmDChrzZBREQiyGaev0tyBWhoaADA+OwjOp9eHRxPemQd\n/uzvWhUrRDzslsdDRlDOSFPOyIqXnMOGDYvKcmNmFpyIiFxZVEAiImIJFZCIiFhCBSQiIpZQAYmI\niCVUQCIiYgkVkIiIWEIFJCIillABiYiIJVRAIiJiCRWQiIhYQgUkIiKWUAGJiIglVEAiImIJFZCI\niFhCBSQiIpZQAYmIiCVUQCIiYgkVkIiIWCKxr1ZUXl7O3r17SUlJYf369QCcPHmSTZs20dzcTEZG\nBoWFhdjtdgAqKytxu90YhkF+fj7jx48H4NChQ5SVldHV1cXEiRPJz8/vq00QEZEI6rM9oFmzZrFm\nzZqQsR07djB27FhKSkpwuVxUVlYCcPToUfbs2UNxcTFFRUVUVFRgmiYAFRUVLFmyhJKSEo4dO8a+\nffv6ahNERCSC+qyARo8ezcCBA0PGampqmDFjBgAzZ86kuro6OD5t2jQMwyAjI4PMzEzq6+tpbW3l\nzJkz5OTkADB9+vTge0REJL5Yeg6ora0Nh8MBgMPhoK2tDQCv10taWlrwdU6nE6/Xi9frJTU1NTie\nmpqK1+vt29AiIhIRYZ8DeuONN7j55psZPHhw1MLYbLaILq+2tpba2trg47y8PJKTkwH4ygjddMNI\nxP6X56yWlJQUzBmr4iEjKGekKWdkxUtOgO3btwd/drlcuFyuXi8z7AI6cOAAL7/8Mi6Xi+nTpzN5\n8mSuuuqqXq3c4XDQ2toa/D0lJQU4u8fT0tISfJ3H48HpdOJ0OvF4PBeMX8rFPqT29nYADL8vZNzv\n9wWfs1pycnLMZLmUeMgIyhlpyhlZ8ZQzLy8v4ssN+xDcww8/TFlZGRMmTODf//3f+fGPf8yzzz5L\nXV1d2CszTTM4mQBg0qRJVFVVAVBVVUVubi4Aubm57N69G5/PR1NTE42NjeTk5OBwOLDb7dTX12Oa\nJjt37mTy5Mlhr19ERGJHt6ZhJycnc8stt3DLLbdw5MgRSktLcbvdpKWlMWfOHObNm0f//v0v+t6S\nkhLq6upob29n6dKl5OXlMX/+fIqLi3G73aSnp1NYWAhAVlYWU6dOpbCwkMTERAoKCoKH5xYvXszW\nrVuD07AnTJjQy49ARESsYDPP3yUJw/79+9m1axfV1dVkZ2czY8YM0tLSeOONN2hra+Pxxx+PVtaI\naGhoAMD47CM6n14dHE96ZB3+7O9aFStEPOyWx0NGUM5IU87Iipecw4YNi8pyw94D+u1vf8vu3bux\n2+1Mnz6dDRs2hJx/ueaaa7j33nujElJERC4/YRdQV1cXDz74YPA7OBcsKDGRp59+OmLBRETk8hZ2\nAS1YsICkpKSQsZMnT9LZ2RncExo+fHhk04mIyGUr7FlwzzzzzAVf+vR6vcHruomIiHRH2AXU0NDA\niBEjQsZGjBjB//7v/0Y8lIiIXP7CLqDBgwfT2NgYMtbY2Bg33+IVEZHYEvY5oFmzZrFhwwbuvPNO\nhg4dSmNjI6+88gqzZ8+OZj4REblMhV1A8+fPJzExkd/97nd4PB5SU1OZPXs2t956azTziYjIZSrs\nAkpISOAHP/gBP/jBD6KZR0RErhDduhRPQ0MDn3/+OR0dHSHjOgwnIiLdFXYB/fGPf+TVV1/l29/+\nNv369Qt5TgUkIiLd1a37AT311FN8+9vfjmYeERG5QoQ9DTspKUlXOhARkYgJu4B+9KMf8etf/5oT\nJ04QCARCfomIiHRX2IfgysrKAPjzn/98wXOvvPJK5BKJiMgVIewCKi0tjWYOERG5woRdQOnp6QAE\nAgHa2toYMmRI1EKJiMjlL+wCOnXqFBUVFbz33nvBKyLU1NRQX1/PnXfeGc2MIiJyGQp7EsK2bduw\n2+2UlZWRmHi2t0aNGsXu3bujFk5ERC5fYe8B7d+/n+eeey5YPnD2CtltbW29DvH666/jdrux2WyM\nGDGCZcuW0dHRwaZNm2hubiYjI4PCwkLsdjsAlZWVuN1uDMMgPz+f8ePH9zqDiIj0rbD3gOx2O+3t\n7SFjLS0tvT4X5PV6+c///E/WrVvH+vXr8fv9vPPOO+zYsYOxY8dSUlKCy+WisrISgKNHj7Jnzx6K\ni4spKiqioqIC0zR7lUFERPpe2AU0Z84cNmzYwIEDBzBNk08++YStW7fyve99r9chAoEAHR0d+P3+\n4C2+a2pqmDFjBgAzZ86kuroagJqaGqZNm4ZhGGRkZJCZmUl9fX2vM4iISN8K+xDc7bffTlJSEs8/\n/zx+v5/y8nLmzp3LvHnzehXA6XRy6623smzZMvr168e4ceMYN24cbW1tOBwOABwOR/BQn9frZdSo\nUSHv//qtwkVEJPaFXUA2m4158+b1unC+7tSpU9TU1FBWVobdbmfjxo3s2rXrouvvrtraWmpra4OP\n8/Lygndw/coI3XTDSMQeI3d3TUpKivk7zcZDRlDOSFPOyIqXnADbt28P/uxyuXC5XL1eZtgFdODA\ngUs+d9111/U4wP79+8nIyGDQoEEA3HDDDXz88cc4HA5aW1uDv6ekpABn93haWlqC7/d4PDidzosu\n+2If0rnzWIbfFzLu9/suOMdlleTk5JjJcinxkBGUM9KUM7LiKWdeXl7Elxt2AZWXl4c8/vLLL/H5\nfKSmpvbqKglpaWl8+umndHZ2ctVVV7F//36ys7Pp378/VVVVzJ8/n6qqKnJzcwHIzc1l8+bN3Hrr\nrXi9XhobG8nJyenx+kVExBphF9DWrVtDHgcCAV599VUGDBjQqwA5OTlMmTKF1atXYxgGI0eOZO7c\nuXR0dFBcXIzb7SY9PZ3CwkIAsrKymDp1KoWFhSQmJlJQUNCjw3MiImItm9mLOcx+v58lS5awbdu2\nSGaKqoaGBgCMzz6i8+nVwfGkR9bhz/6uVbFCxMNueTxkBOWMNOWMrHjJOWzYsKgsN+xp2Bfz4Ycf\nkpDQq0WIiMgVKuxDcEuXLg153NnZSWdnJwUFBREPJSIil7+wC2jlypUhj/v160dmZmbw8jgiIiLd\nEXYBjRkzJpo5RETkChN2AW3ZsiWs2WYrVqzoVSAREbkyhD2DYODAgVRXVxMIBHA6nQQCAaqrq7Hb\n7QwdOjT4S0REJBxh7wEdO3aMRx55hO9+969TlQ8ePMirr77KP/zDP0QlnIiIXL7C3gP65JNPuOaa\na0LGcnJy+OSTTyIeSkRELn9hF9B3vvMdXn75ZTo7O4Gz07D/5V/+hZEjR0Yrm4iIXMbCPgS3bNky\nNm/ezD333MOgQYM4efIk2dnZ3H///dHMJyIil6mwCygjI4MnnniClpYWTpw4wZAhQ0hLS4tmNhER\nuYx16zo67e3t1NXVUVdXR1paGl6vF4/HE61sIiJyGQu7gOrq6li1ahW7du3i1VdfBaCxsTGuLkQq\nIiKxI+wCeuGFF1i1ahVr1qzBMAzg7Cy4zz77LGrhRETk8hV2ATU3NzN27NiQscTERPx+f8RDiYjI\n5S/sAsrKymLfvn0hY/v372fEiBERDyUiIpe/sGfBLVq0iHXr1jFx4kQ6Ozv553/+Z95//30eeuih\naObrM7bERIzPPjr7wJmOf4hm+ImIRFPYBTRq1CieeeYZdu3aRf/+/UlLS+Opp54iNTU1mvn6TvuX\ndJb8P+Ds3VFRAYmIRFVYBRQIBHj88cdZs2YNt99+e7QziYjIFSCsAkpISKCpqQnTNKMS4vTp0zz7\n7LN88cUX2Gw2li5dSmZmJps2baK5uZmMjAwKCwuDN7+rrKzE7XZjGAb5+fmMHz8+KrlERCR6wp6E\n8MMf/pBt27bR3NxMIBAI+dVbv/nNb5g4cSLFxcU888wzDB8+nB07djB27FhKSkpwuVxUVlYCcPTo\nUfbs2UNxcTFFRUVUVFRErRhFRCR6wj4H9NxzzwGwc+fOC5575ZVXehzg9OnTHDx4kOXLlwNgGAZ2\nu52amhoee+wxAGbOnMljjz3GwoULqampYdq0aRiGQUZGBpmZmdTX119wpW4REYlt31hAra2tOBwO\nSktLoxKgqamJ5ORkysrKOHLkCFdffTX5+fm0tbXhcDgAcDgctLW1AeD1ehk1alTw/U6nE6/XG5Vs\nIiISPd9YQD/96U958cUXSU9PB2D9+vU8+OCDEQsQCAQ4fPgwixcvJjs7mxdeeIEdO3Zc8Lpwbgf+\ndbW1tdTW1gYf5+XlkZycDMBXRuimn798w0jE/pfXWSEpKSmYM1bFQ0ZQzkhTzsiKl5wA27dvD/7s\ncrlwuVy9XuY3FtDXz6+c/xd6JDidTlJTU8nOzgZgypQp7NixA4fDEdz7am1tJSUlJfj6lpaW4Ps9\nHg9Op/Oiy77Yh9Te3g6A4feFjJ+/nX6/L/g6KyQnJ1u6/nDEQ0ZQzkhTzsiKp5x5eXkRX+43TkLo\nyZ5HdzgcDlJTU2loaADOXl0hKyuLSZMmUVVVBUBVVRW5ubkA5Obmsnv3bnw+H01NTTQ2NpKTkxPV\njCIiEnnfuAfk9/s5cOBA8HEgEAh5DHDdddf1KsS9997Lli1b8Pl8DB06lGXLlhEIBCguLsbtdpOe\nnk5hYSFw9pJAU6dOpbCwkMTERAoKCqJekiIiEnnfWEApKSmUl5cHHw8aNCjksc1m6/UEhZEjR/LL\nX/7ygvFHH330oq9fsGABCxYs6NU6RUTEWt9YQFu3bu2LHCIicoXp1h1RRUREIkUFJCIillABiYiI\nJVRAIiJiCRWQiIhYQgUkIiKWUAGJiIglVEAiImIJFZCIiFhCBSQiIpZQAYmIiCVUQCIiYgkVkIiI\nWEIFJCIillABiYiIJVRAIiJiCRWQiIhYQgUkIiKW+MZbcveVQCBAUVERTqeT1atXc/LkSTZt2kRz\nczMZGRkUFhZit9sBqKysxO12YxgG+fn5jB8/3uL0IiLSXTGzB/TGG28wfPjw4OMdO3YwduxYSkpK\ncLlcVFZWAnD06FH27NlDcXExRUVFVFRUYJqmVbFFRKSHYqKAPB4PH3zwAXPmzAmO1dTUMGPGDABm\nzpxJdXV1cHzatGkYhkFGRgaZmZnU19dbkltERHouJgroxRdfZNGiRdhstuBYW1sbDocDAIfDQVtb\nGwBer5e0tLTg65xOJ16vt28Di4hIr1l+Dmjv3r2kpKQwcuRIamtrL/m688spXLW1tSHLzMvLIzk5\nGYCvjNBNP3/5hpGI/S+vs0JSUlIwZ6yKh4ygnJGmnJEVLzkBtm/fHvzZ5XLhcrl6vUzLC+jgwYPU\n1NTwwQcf0NnZyZkzZ9iyZQsOh4PW1tbg7ykpKcDZPZ6Wlpbg+z0eD06n86LLvtiH1N7eDoDh94WM\nn38eye/3BV9nheTkZEvXH454yAjKGWnKGVnxlDMvLy/iy7X8ENzdd99NeXk5paWlrFq1iuuuu46V\nK1cyadIkqqqqAKiqqiI3NxeA3Nxcdu/ejc/no6mpicbGRnJycizcAhER6QnL94AuZf78+RQXF+N2\nu0lPT6ewsBCArKwspk6dSmFhIYmJiRQUFPTo8JyIiFgrpgpozJgxjBkzBoBBgwbx6KOPXvR1CxYs\nYMGCBX0ZTUREIszyQ3AiInJlUgGJiIglVEAiImIJFZCIiFhCBSQiIpZQAYmIiCVUQCIiYgkVkIiI\nWEIFJCIillABiYiIJVRAIiJiCRWQiIhYQgUkIiKWUAGJiIglVEAiImKJmLofUF9IePdPANjSMixO\nIiJyZbvi9oC6XthM1wubwe+3OoqIyBXtiisgERGJDVfcIbhw2BITMT776OwDZzr+IWnWBhIRuQxZ\nXkAej4fS0lLa2tqw2WzMmTOHefPmcfLkSTZt2kRzczMZGRkUFhZit9sBqKysxO12YxgG+fn5jB8/\nPrKh2r+ks+T/AZD0yDpQAYmIRJzlBWQYBvfccw8jR46ko6OD1atXM378eNxuN2PHjuX2229nx44d\nVFZWsnDhQo4ePcqePXsoLi7G4/Hwi1/8gs2bN2Oz2azeFBER6QbLzwE5HA5GjhwJQP/+/Rk+fDge\nj4eamhpmzJgBwMyZM6murgagpqaGadOmYRgGGRkZZGZmUl9fb1V8ERHpIcsL6HxNTU0cOXKEUaNG\n0dbWhsPO03G3AAAKsklEQVThAM6WVFtbGwBer5e0tL8eEnM6nXi9XkvyiohIz1l+CO6cjo4ONm7c\nSH5+Pv3797/g+Z4cYqutraW2tjb4OC8v75LLO//x+T8bRiL25ORur7s3kpKSSO7jdXZXPGQE5Yw0\n5YyseMkJsH379uDPLpcLl8vV62XGRAH5/X42bNjA9OnTmTx5MnB2r6e1tTX4e0pKCnB2j6elpSX4\nXo/Hg9PpvOhy/68PyTTNSz4+/2e/30d7e3vPNqyHkpOT+3yd3RUPGUE5I005Iyuecp7/D/hIiYlD\ncOXl5WRlZTFv3rzg2KRJk6iqqgKgqqqK3NxcAHJzc9m9ezc+n4+mpiYaGxvJycmxIraIiPSC5XtA\nBw8eZNeuXYwYMYKHH34Ym83GXXfdxfz58ykuLsbtdpOenk5hYSEAWVlZTJ06lcLCQhITEykoKNAM\nOBGROGR5AY0ePZpXXnnlos89+uijFx1fsGABCxYsiGYsERGJspg4BCciIlceFZCIiFhCBSQiIpZQ\nAYmIiCVUQCIiYgnLZ8HFOt2aQUQkOlRA30S3ZhARiQodghMREUuogERExBIqIBERsYQKSERELKEC\nEhERS6iARETEEiogERGxhApIREQsoS+i9pBxogW8zX8d0FUSRES6RQXUU95mOp9eHXyoqySIiHSP\nDsGJiIgl4nYPaN++fbzwwguYpsmsWbOYP39+1Nd5/oVJbb6uqK9PRORyFpcFFAgEeP755/n5z3/O\nkCFDKCoqYvLkyQwfPjy6Kz7vwqT9fro2rLeEnCvSeSIRkaC4PARXX19PZmYm6enpJCYmctNNN1Fd\nXW1ppnN7R8ZnH50tnXP+cq6o8+nVoZMWRESucHG5B+T1eklNTQ0+djqd1NfXW5iI0L2jf9qA8Zey\n6e2hOu1BiUhvxPLfIXFZQL1x1d0/OfuDYURvJZc4VBdyczsI7z+G82bbaaadiHRbDP8dYjNN07Q6\nRHd98skn/OEPf2DNmjUA7NixA+CCiQi1tbXU1tYGH+fl5fVdSBGRy8j27duDP7tcLlwuV6+XGZfn\ngHJycmhsbKS5uRmfz8e7775Lbm7uBa9zuVzk5eUFf53/AcayeMgZDxlBOSNNOSMrnnKe/3dpJMoH\n4vQQXEJCAosXL+aJJ57ANE1mz55NVlaW1bFERKQb4rKAACZMmEBJSYnVMUREpIeMxx577DGrQ/Sl\njIwMqyOEJR5yxkNGUM5IU87IupJzxuUkBBERiX9xOQlBRETinwpIREQsEbeTELrDiguXlpeXs3fv\nXlJSUli/fj0AJ0+eZNOmTTQ3N5ORkUFhYSF2ux2AyspK3G43hmGQn5/P+PHjATh06BBlZWV0dXUx\nceJE8vPzAfD5fJSWlnLo0CGSk5MpLCwkLa17XzDzeDyUlpbS1taGzWZjzpw5zJs3L+ZydnV1sXbt\nWnw+H36/nylTpnDHHXfEXM5zAoEARUVFOJ1OVq9eHZM5ly9fjt1ux2azYRgGv/zlL2My5+nTp3n2\n2Wf54osvsNlsLF26lMzMzJjK2dDQwKZNm7DZbJimyfHjx/nRj37E9OnTYyrn66+/jtvtxmazMWLE\nCJYtW0ZHR4e1Gc3LnN/vN1esWGE2NTWZXV1d5oMPPmgePXo06uv96KOPzMOHD5sPPPBAcOx3v/ud\nuWPHDtM0TbOystJ86aWXTNM0zS+++MJ86KGHTJ/PZx4/ftxcsWKFGQgETNM0zaKiIvPTTz81TdM0\nn3rqKfODDz4wTdM0/+u//svctm2baZqm+e6775rFxcXdznjixAnz8OHDpmma5pkzZ8z777/fPHr0\naMzlNE3T7OjoME3z7J/nz372M/PTTz+NyZymaZqvvfaaWVJSYj799NOmacben7tpmuby5cvN9vb2\nkLFYzFlaWmq+9dZbpmmaps/nM0+dOhWTOc/x+/3mj3/8Y7O5uTmmcno8HnP58uVmV1eXaZqmuXHj\nRtPtdlue8bI/BGfVhUtHjx7NwIEDQ8ZqamqYMWMGADNnzgzmqKmpYdq0aRiGQUZGBpmZmdTX19Pa\n2sqZM2fIyckBYPr06cH3VFdXB5c1ZcoU9u/f3+2MDoeDkSNHAtC/f3+GDx+Ox+OJuZwA/fr1A87u\nDfn9/mCeWMvp8Xj44IMPmDNnTnAsFnOapon5tflHsZbz9OnTHDx4kFmzZgFgGAZ2uz3mcp5v//79\nDB06lLS0tJjLGQgE6OjowO/309nZidPptDzjZX8ILpYuXNrW1obD4QDO/uXf1tYWzDhq1KiQjF6v\nF8MwQrKnpqbi9XqD7zn3XEJCAgMHDuTkyZMMGjSoR9mampo4cuQIo0aNismcgUCARx55hOPHj/O3\nf/u35OTkxGTOF198kUWLFnH69OngWCzmtNlsPPHEEyQkJDB37lzmzJkTczmbmppITk6mrKyMI0eO\ncPXVV5Ofnx9zOc+3e/dubr75ZiC2/tydTie33nory5Yto1+/fowbN45x48ZZnvGyL6BYZrPZIras\nr/9rtjs6OjrYuHEj+fn59O/f/4LnYyFnQkICv/rVrzh9+jTr16/niy++uOA1Vuc8d85v5MiRIdcg\n/DqrcwL84he/YMiQIXz55Zc88cQTDBs27ILXWJ0zEAhw+PBhFi9eTHZ2Ni+88ELwuo/nszrnOT6f\nj5qaGhYuXHjR563MeerUKWpqaigrK8Nut7Nx40Z27dp1wev6OuNlfwjO6XTS0vLX+/N4vV6cTqcl\nWRwOB62trQC0traSkpJy0Ywejwen04nT6cTj8Vwwfu49554LBAKcOXOmR/9q8/v9bNiwgenTpzN5\n8uSYzXmO3W5nzJgx7Nu3L+ZyHjx4kJqaGlasWEFJSQkHDhxgy5YtMZcTYMiQIQAMHjyYyZMnU19f\nH3M5nU4nqampZGdnA2cP6xw+fDjmcp6zb98+rr76agYPHgzE1v9H+/fvJyMjg0GDBpGQkMANN9zA\nxx9/bHnGy76Awr1waTR8/Tj7pEmTqKqqAqCqqiqYIzc3l927d+Pz+WhqaqKxsZGcnBwcDgd2u536\n+npM02Tnzp3BksjNzeXtt98GYM+ePVx33XU9ylheXk5WVhbz5s2L2Zxffvll8JBWZ2cn+/fvZ/jw\n4TGX8+6776a8vJzS0lJWrVrFddddx8qVK2Mu51dffUVHRwdwdu/3ww8/ZMSIETGX0+FwkJqaSkND\nA3D2L9GsrKyYy3nOO++8w0033RR8HEs509LS+PTTT+ns7MQ0zZj5LK+IKyHs27eP3/zmN8ELl/bF\nNOySkhLq6upob28nJSWFvLw8Jk+eTHFxMS0tLaSnp1NYWBicqFBZWclbb71FYmLiBVMet27dGpzy\neO+99wJnT8Zv2bKFzz//nOTkZH760592+1IZBw8eZO3atYwYMQKbzYbNZuOuu+4iJycnpnL+z//8\nD1u3biUQCGCaJtOmTePv/u7vOHnyZEzlPF9dXR2vvfZacBp2LOVsamrimWeewWaz4ff7+Zu/+Rvm\nz58fczkBPv/8c5577jl8Ph9Dhw5l2bJlBAKBmMv51VdfsWzZMkpLSxkwYABAzH2ef/jDH9i9ezeG\nYTBy5EiWLFlCR0eHpRmviAISEZHYc9kfghMRkdikAhIREUuogERExBIqIBERsYQKSERELKECEhER\nS6iARETEEiogERGxxP8HbM5qK5InZOMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ffc471fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(y='ReactionTime', bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Outliers\n",
    "Make a \"Good Trials\" column, with False values for response times greater than 5000 msecs.  There is no way they should have taken that long!  Then, plot the RT histogram with only the good columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ClassID</th>\n",
       "      <th>UserID</th>\n",
       "      <th>NumTrial</th>\n",
       "      <th>Condition</th>\n",
       "      <th>ColorORWord</th>\n",
       "      <th>WordDisplayed</th>\n",
       "      <th>ColorOfStimulus</th>\n",
       "      <th>ColorOfResponse</th>\n",
       "      <th>ReactionTime</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>GoodTrials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>1</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>2</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>3</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>RED</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>4</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6110</td>\n",
       "      <td>156772</td>\n",
       "      <td>5</td>\n",
       "      <td>ConW</td>\n",
       "      <td>C</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>G</td>\n",
       "      <td>G</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClassID  UserID  NumTrial Condition ColorORWord WordDisplayed  \\\n",
       "0     6110  156772         1      ConW           C        YELLOW   \n",
       "1     6110  156772         2      ConW           C         GREEN   \n",
       "2     6110  156772         3      ConW           C           RED   \n",
       "3     6110  156772         4      ConW           C        YELLOW   \n",
       "4     6110  156772         5      ConW           C         GREEN   \n",
       "\n",
       "  ColorOfStimulus ColorOfResponse  ReactionTime Gender  Age GoodTrials  \n",
       "0               Y               Y        1389.0      M   26       True  \n",
       "1               G               G        2043.0      M   26       True  \n",
       "2               R               R        1389.0      M   26       True  \n",
       "3               Y               Y        1092.0      M   26       True  \n",
       "4               G               G        1310.0      M   26       True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GoodTrials'] = df['ReactionTime'] < 5000\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ffc3fdcf8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O7N6926nff/7znyI2Nlb4+voKvV4vpkyZ4nTWWnPxbtq0Scjlcml6ypQp4sYbbxQqlUoE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\n+Y9SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ffbeaf630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['GoodTrials']].plot.hist(y='ReactionTime', title='Response Times over all Subjects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform the Response Times\n",
    "\n",
    "You should see that the responses are not normally distributed (if you can't see this, try using a higher number of **bins** in your histogram function).  While this isn't really a problem, it's very convenient when you have normally-distributed data.  Let's try transforming it to get something a bit nicer for statistical analysis...\n",
    "\n",
    "  - Make a new column with the **log** of the response times.  \n",
    "  - Plot a new histogram!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ffbd06e48>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cP34cAwYMQFpaGqqqqqDT6QAAOp0OVVVVAACHw4HBgwe7p9fr9XA4HHLFJSIi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\nA8UqtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ffbd03f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LogRT'] = np.log(df['ReactionTime'])\n",
    "df[df['GoodTrials']].plot.hist(y='LogRT', title='Response Times over all Subjects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reject Bad Data\n",
    "\n",
    "Now, that's a little nicer!  But wait... there's are some trials that seem to be on the low end, as well-- those with **log response times less than 5.7**.  After much consideration and deliberation with my supervisor, I've decided to remove those trials from my whole analysis, too! \n",
    "\n",
    "Make those too-slow trials also \"False\" in the \"Good Trials\" column, and plot the new, cleaner histogram!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ffbca3160>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+HkqlEqGhofdk98tERA2dxa5ImjVrhrfffhtLlizB4sWLYWdnh0cffRRxcXHw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\nLEwkRERkFiYSIiIyy/8D1hkOdvB2RYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ffbc094e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['GoodTrials'] &= df['LogRT'] > 5.7\n",
    "df[df['GoodTrials']].plot.hist(y='LogRT', title='Response Time Distribution over all Subjects\\nafter Outlier Removal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall, what was the accuracy of the responses? \n",
    "  - How many trials in total were correct (stimcolor matched respcolor), \n",
    "  - how many were incorrect, and \n",
    "  - what percent accuracy was there, overall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ClassID  UserID  NumTrial Condition ColorORWord WordDisplayed  \\\n",
      "0     6110  156772         1      ConW           C        YELLOW   \n",
      "1     6110  156772         2      ConW           C         GREEN   \n",
      "2     6110  156772         3      ConW           C           RED   \n",
      "3     6110  156772         4      ConW           C        YELLOW   \n",
      "4     6110  156772         5      ConW           C         GREEN   \n",
      "\n",
      "  ColorOfStimulus ColorOfResponse  ReactionTime Gender  Age GoodTrials  \\\n",
      "0               Y               Y        1389.0      M   26       True   \n",
      "1               G               G        2043.0      M   26       True   \n",
      "2               R               R        1389.0      M   26       True   \n",
      "3               Y               Y        1092.0      M   26       True   \n",
      "4               G               G        1310.0      M   26       True   \n",
      "\n",
      "      LogRT Correct  \n",
      "0  7.236339    True  \n",
      "1  7.622175    True  \n",
      "2  7.236339    True  \n",
      "3  6.995766    True  \n",
      "4  7.177782    True  \n"
     ]
    }
   ],
   "source": [
    "df['Correct'] = df['ColorOfStimulus'] == df['ColorOfResponse']\n",
    "print(df.head())\n",
    "df = df[df['GoodTrials']]  # Analyze only the kept trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2629"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Correct'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Incorrect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Correct'] == False).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97011070110701103"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Correct'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than using section headers for every single result, it's probably better to use formatted strings to report your results in a nicer format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Accuracy: 97.01%'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = df['Correct'].mean()\n",
    "'Accuracy: {:.2%}'.format(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matched vs Unmatched Colors: \n",
    "\n",
    "  - What was the mean log response time for Congruent trials (the stimulus word and color matched each other)?  \n",
    "  - What was the mean log response time for Incongruent trials (when they did not match)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Condition\n",
       "ConW    7.043540\n",
       "IncW    7.594469\n",
       "Name: LogRT, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conds = df.groupby('Condition')\n",
    "conds.LogRT.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a Bar Graph of Log Response Time\n",
    "The bar graph should show Mean Log Response Times for the Congruent and Incongruent conditions, with Standard-Deviation Error Bars.  Put text labels on the x access showing which bars go with which condition.\n",
    "\n",
    "For help and an example, take a look at http://matplotlib.org/examples/api/barchart_demo.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Condition\n",
       "ConW    0.335484\n",
       "IncW    0.354580\n",
       "Name: LogRT, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IPPnkk+jRoweWLl1603atvf8Vxo4di+vXr2Ps2LE21btu3TqsWrUKCQkJGDhwIKKiojBh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1d7d62358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond_rt = conds.LogRT\n",
    "cond_rt.mean().plot.bar(legend=True, yerr=cond_rt.std(), \n",
    "                        title='Mean Response Times by Condition')\n",
    "cond_rt.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make a Bar Graph of Response Time\n",
    "\n",
    "The logged data doesn't make a very good impression of just how big an effect this is, sadly.  Let's make the same bar graph, but this time with the original response times, in milliseconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1d7e80b70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/D3PS6/WwWCywWCxwc3OT2t3c3GCxWOyxDkREdAfqfBx/SUkJoqOjER4eDicn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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1d7d2e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond_rt = conds.ReactionTime\n",
    "cond_rt.mean().plot.bar(legend=True, yerr=cond_rt.std(), \n",
    "                        title='Mean Response Times by Condition')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change in Performance over Time\n",
    "\n",
    "In experiments like this, we assume that each trial is independent of each other.  Often, this isn't the case, and not taking that into account can sometimes lead to some wrong conclusions.  Let's see what we can find...\n",
    "\n",
    "### How did reaction time change over the course of a session? \n",
    "\n",
    "Were earlier trials faster or slower than later trials in a session, overall?  Make a scatter plot of the response times over the trial numbers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1d7bfec88>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VOvbAAw9g2rRpuOGGG/Czn/0MGRkZ+Pbbb/Huu+9i8eLFyM/PZ47917/+FbNnz8YPfvAD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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1d7bf4400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='NumTrial', y='LogRT', title='Response Time Over Session Trials')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about between each subject?  Do all subjects show this difference in response time between the Congruent and Incogruent conditions, or is this just coming from a few subjects?\n",
    "\n",
    "We could use **Masking** and **For-Loops** to split up the data into each user's responses, then reassemble the processed data into a new array or dataframe. This would look very similar, practice, to what we've done up until now (see Homework 2 Answers for example of this approach).  \n",
    "\n",
    "However, this means creating a lot of new variables and essentially breaking apart our nicely-organized data.  Instead, I'll demonstrate a different approach, called **Split-Apply-Combine**.  There are lots of ways to do this; here, I'll demonstrate a simple pipeline that will perform it in two steps:\n",
    "   1. Use **groupby()** to split the data into the groups you're interested in.\n",
    "   2. Use **agg(functions)** or aggregation shortcut functions like **mean()** or **std()** to compute values from each group and produce a new Series or DataFrame from them.\n",
    "      \n",
    "For more information on different approaches for **Split-Apply-Combine** in Pandas: see https://pandas-docs.github.io/pandas-docs-travis/groupby.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(156874, 'IncW'),\n",
       " (156863, 'ConW'),\n",
       " (156856, 'ConW'),\n",
       " (156772, 'IncW'),\n",
       " (156871, 'IncW'),\n",
       " (156880, 'IncW')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjrt = df.groupby(('UserID', 'Condition')).LogRT\n",
    "list(subjrt.groups.keys())[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply / Combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserID  Condition\n",
       "156772  ConW         6.971163\n",
       "        IncW         7.351251\n",
       "156839  ConW         6.455973\n",
       "        IncW         6.759741\n",
       "156856  ConW         6.871654\n",
       "        IncW         7.014764\n",
       "Name: LogRT, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjrt = subjrt.agg(np.mean)\n",
    "subjrt.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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></th>\n",
       "      <th>LogRT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserID</th>\n",
       "      <th>Condition</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">156772</th>\n",
       "      <th>ConW</th>\n",
       "      <td>6.971163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IncW</th>\n",
       "      <td>7.351251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">156839</th>\n",
       "      <th>ConW</th>\n",
       "      <td>6.455973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IncW</th>\n",
       "      <td>6.759741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156856</th>\n",
       "      <th>ConW</th>\n",
       "      <td>6.871654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     LogRT\n",
       "UserID Condition          \n",
       "156772 ConW       6.971163\n",
       "       IncW       7.351251\n",
       "156839 ConW       6.455973\n",
       "       IncW       6.759741\n",
       "156856 ConW       6.871654"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjrt = pd.DataFrame(subjrt)\n",
    "subjrt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we have many-dimensional data: UserID x Condition x MeanLogRT. However, DataFrames only let you view data in 2D.  Use stack(), unstack(), and pivot() to transpose multidimensional data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">LogRT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition</th>\n",
       "      <th>ConW</th>\n",
       "      <th>IncW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156772</th>\n",
       "      <td>6.971163</td>\n",
       "      <td>7.351251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156839</th>\n",
       "      <td>6.455973</td>\n",
       "      <td>6.759741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156856</th>\n",
       "      <td>6.871654</td>\n",
       "      <td>7.014764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156857</th>\n",
       "      <td>6.897377</td>\n",
       "      <td>7.475482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156858</th>\n",
       "      <td>6.897377</td>\n",
       "      <td>7.475482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LogRT          \n",
       "Condition      ConW      IncW\n",
       "UserID                       \n",
       "156772     6.971163  7.351251\n",
       "156839     6.455973  6.759741\n",
       "156856     6.871654  7.014764\n",
       "156857     6.897377  7.475482\n",
       "156858     6.897377  7.475482"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjrt.unstack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All at Once: What does Split-Apply-Combine look like in practice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">STD</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition</th>\n",
       "      <th>ConW</th>\n",
       "      <th>IncW</th>\n",
       "      <th>ConW</th>\n",
       "      <th>IncW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156772</th>\n",
       "      <td>0.268299</td>\n",
       "      <td>0.144569</td>\n",
       "      <td>6.971163</td>\n",
       "      <td>7.351251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156839</th>\n",
       "      <td>0.498677</td>\n",
       "      <td>0.592312</td>\n",
       "      <td>6.455973</td>\n",
       "      <td>6.759741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156856</th>\n",
       "      <td>0.305584</td>\n",
       "      <td>0.314003</td>\n",
       "      <td>6.871654</td>\n",
       "      <td>7.014764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156857</th>\n",
       "      <td>0.228352</td>\n",
       "      <td>0.380566</td>\n",
       "      <td>6.897377</td>\n",
       "      <td>7.475482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                STD                Mean          \n",
       "Condition      ConW      IncW      ConW      IncW\n",
       "UserID                                           \n",
       "156772     0.268299  0.144569  6.971163  7.351251\n",
       "156839     0.498677  0.592312  6.455973  6.759741\n",
       "156856     0.305584  0.314003  6.871654  7.014764\n",
       "156857     0.228352  0.380566  6.897377  7.475482"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subjRTs = df.groupby(('UserID', 'Condition')).LogRT.agg({'Mean': np.mean, 'STD': np.std}).unstack()\n",
    "subjRTs.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a histogram of the difference in mean log reaction times between conditions for each subject!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1d621dcc0>"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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//jwSEhKwadMmzJ49GwBw6NAhLFiwAHFxcbhy5Qp2796Ns2fPyl8eKiKEwMiRI6HT6RAb\nG4spU6Zg0KBBaNiwodyme/fuaNSoEaZPn47hw4dX+k1+3LhxmDdvHg4fPoy//voLJ06cwMiRI3Hv\n3j0MHjwYANCpUyc4OTlhxowZSElJwa+//opPPvmk3P4WLFiAXbt2ITExEePGjUNWVhbGjRtXbtuw\nsDDUr18fzz//PPbs2YPLly/j2LFjiIiIkE8jnjRpEgoKCjB48GD88ccfuHTpEn7++Wfs3r0bwP3f\nKSEEfv75Z9y8eRN379595Pv927DMoZe/t5s3b8oXEjo6OgovLy/RrVs3sWrVKvlCwqKiIjFr1izh\n6+sr7O3tRVBQkNi8ebNBPyqVSnz77bcG4/r06SNGjx4tD2dmZopXXnlFPpV09uzZZQ6aPzycmZkp\nnn/+eeHi4mJwGm+jRo3KnMY7fvx44enpKRwcHESHDh3Enj175NcfPgOmVNOmTeUDluUp7zReIe5f\n4BUYGChCQ0PLTPNwtkfp0qWLUKlUQqVSCTs7O+Hp6Sl69uwpli1bJgoKCgzaGnsQ/cGzdkqdOXNG\nvPjii6J27dpCo9GIpk2bivHjx4vs7GwhxP2D6M2bNy8347Zt20Tnzp2FRqMRLi4uon379gbvr2vX\nrmVOBPjwww9F06ZNDcZt2LBBPh24Tp064oUXXjA4A2j16tWibdu2wsHBQbi5uYnOnTvLJzjEx8eL\nfv36ibp168qnE8+aNeuRF9iVLq+FCxeKunXrCicnJ/Hyyy+LzMzMMm0jIyOFSqUSJ06cqLC/Ulu3\nbhUDBw4U9evXFw4ODqJevXri2WefFbt37zZot2PHDtG8eXOh0WhE165dxa5du8qchaVSqcTPP/8s\n2rdvL5/G++DJBOWdFHHr1i0xfvx44ePjI+zt7UX9+vXF0KFDxZkzZ+Q2SUlJYtCgQcLV1VU4OTmJ\ndu3aif/85z/y61OmTBFeXl5P1Gm8khDmeSLhypUrcfLkSbi4uODzzz8HAGzcuBEnTpyAjY0NvLy8\nEB4ebnBwjohqrnfeeQf79+/HiRMnLB3FQEJCAlq2bImzZ89WuFuLjGO2XVg9e/Yss3umdevWiIyM\nxKJFi1CvXj1s377d6P4ePhOopmF+y6nJ2QHrz3/nzh0cO3YMX3/9NaZNm1bmdUvmv3nzJmJiYqDV\nass9ucAY1r78K1Od+c1WQAIDA8vsB23durV8W4emTZvi1q1bRvfHD9GyanL+mpwdsP78zz//PHr1\n6oVXXnkFw4cPL/O6JfO/9NJLWL9+Pb766is4Ojoq6sPal39lqjO/1ZzG+/vvvyMkJMTSMYioig4e\nPGjpCBXav3+/pSP8rVjFWVg//vgj1Gq1Sc//JyKi6mW2g+gAcOPGDXz22WfyQXTg/rUPe/fuxfvv\nv//Ic+B1Op3Bpld1nMtPRPQkiomJkX8OCgqq9NTtiph1F5b4/7ezLnX69Gn89NNP+Oijjyq9gKq8\nN3nt2jWT5DQHrVZb6fUa1qwm56/J2QHmt7Sant/b27vavoCbrYAsXboUCQkJyMnJwYQJExAaGopt\n27ahuLhYvrCradOmGDt2rLkiERFRFZitgDz4IKBSPXv2NNfsiYiomlnFQXQiIqp5WECIiEgRq7kO\nhIhMx9nZ2Wqe2a1Wq8s8i74mqSn5hRDIzc016TxYQIieAJIk1egzh+jxmaPIcRcWEREpwgJCRESK\nsIAQEZEiLCBE9ERJTU1FQECAfFeMmzdvYsiQIQgMDJQfqfz2228jKCgIAwYMsGRUq8eD6ERkUU8/\n/TRu3rwJGxsbaDQadOnSBREREXB2dkZYWBj+/PNPSJKEgoICSJIEOzs7AMCQIUMQERFh0FdMTAym\nTZsm36rd3d0dnTp1wqRJk9C4cWMAgI+PD5KSkuRpNm7cCHd3dyQmJgIAjh07hkOHDuHEiRNwcHAw\nxyKosbgFQkQWJUkSvvnmGyQlJWH37t1ITEzE0qVLAQAbNmzA+fPnkZSUhMGDByM8PBxJSUlISkoq\nUzxKBQcHIykpCYmJidi8eTMcHBzQr18/nD9/vtz2qampaNasmTx85coV1K9fX1HxKCkpeexpajIW\nECKyuNLdSR4eHujRo4fBFoJSkiShQYMGWLBgATp16oTIyEgAwNWrV+Hr6wu9Xo+3334bW7ZsQXR0\nNAICArBx40a8++67OHHiBAICArB48WIAwO7du/HMM8+gRYsWGDRoEM6dOyfPp1OnToiOjkafPn3Q\nrFkz6PV6pKen44033kDr1q3xj3/8A19//bXcfvHixRg/fjymTp2KgIAA9O7dG/Hx8fLr165dk6dt\n1aoV3nvvPfm1zZs3o0ePHggKCsKIESOQmppa5eVUFSwgRGQ1rl27ht9//x3t2rWr1n779++PY8eO\nycOlF1V+8cUXBls2I0aMQEREBJ566ikkJSXhnXfewdmzZzFt2jQsWrQIOp0OI0aMwOjRo1FUVCT3\n9+9//xsbN25EQkICJEnCqFGj0LJlS5w6dQrff/891q5diwMHDsjtd+/ejcGDByMxMRF9+vTB7Nmz\nAQB6vR4jR45E/fr1cezYMZw4cQIDBw4EAPznP/9BVFQU1q5di/j4eHTs2BHh4eHVupweFwsIEVnc\nmDFjEBAQgI4dO6Jhw4bl3ny1Kry8vHD79m1F03777bcICwtDmzZtIEkShg4dCjs7O5w8eVJuM2bM\nGNStWxf29vY4ffo0MjMzMXXqVKjVatSvXx/Dhw/Hv//9b7l9x44d0aNHD7m/0i2akydPIiMjA3Pn\nzoWDgwPs7OzQoUMHAPeP1UyePBn+/v5QqVSYNGkSdDqdRbdCeBCdiCzu66+/RkhICI4ePYrRo0fj\nzJkzaNu2bbX1n5aWBldXV0XTXr16FT/88AP+9a9/Abi/u62oqAhpaWlym3r16hm0T0tLk59fJISA\nXq/H008/LbepU6eO/LOjoyMKCgqg1+tx/fp1+Pr6QqUq+93+6tWreP/99/Hxxx/L/UqShLS0NPj4\n+Ch6b1XFAkJWS511E8i8Ue39FqhtoC4pLvuCWx2U1Pao9vlR5UqPgXTq1AmjRo3C/Pnz8cMPP1Rb\n/7/++is6duyoaFpvb29MmTIFkydPrrDNg/cZ8/b2RoMGDRQ9G97b2xupqanQ6/VlioiPjw+mTp2K\nQYMGPXa/psICQtYr8wYKP51httnZzfwMYAGxuDfeeAOrV6/GqVOnFB0LKS1Ger0eqampWLVqFY4e\nPYodO3aUaWOM1157DWPHjkWXLl3Qrl075OXl4Y8//kDnzp2h0WjKtG/Xrh2cnZ0RHR2N119/Hba2\ntkhOTkZ+fj7atGnzyMzt2rWDp6cnFixYgH/+859QqVQ4c+YMOnTogBEjRmDRokVo0aIFmjVrhjt3\n7uDAgQMWvVaFx0CIyKIevkuwm5sbQkNDsWLFike2q8jJkycREBCAwMBAvPzyy7h79y527txpcKru\n49yZuHXr1li0aBHmzp2LoKAgdO3aFVu2bKmwL5VKhfXr10On06Fz585o3bo1pk+f/sibWZb2oVKp\nsG7dOly8eBEdOnRAhw4d5ML33HPPYeLEiQgPD0fz5s3Rp08fxMbGGv0+TEESj1OKrQyfiW455siv\nTjln9i2QEv/mZpufUkqW/cPTmGr3oIy7Ay2uovXE29u72ubBXVhETyIT7x7k7sAnA3dhERGRIiwg\nRESkCAsIEREpwgJCRBYXERGBtWvXWjqGyZw7dw4vvviipWNUOxYQIrKozMxMbN26FSNGjJDH5ebm\n4v3330fHjh0REBCAkJAQfPjhh8jKyjJ7vk6dOuHQoUNV6qN58+ZwcXHBnj17qimVdWABISKLiomJ\nQa9evWBvbw8AKCoqQmhoKJKTk/Hdd98hKSkJO3bsgJubG06fPm3htMoNHjwYGzZssHSMasXTeIme\nRG517p9qa8L+jbVv3z4MHz5cHt6yZQuuX7+OrVu3yg+GcnNzw5QpU+Q2ycnJmDlzJhISElCvXj3M\nmDEDzzzzDID7TxPUaDS4cuUK/vzzTzRr1gwrVqxAgwYNqvy2YmJisGnTJrRv3x6bN2+Gi4sLPvnk\nE/Ts2RMAcPv2bXz88cfYv38/8vPz0blzZ6xZswYA0LlzZ0ybNg1FRUWwtbWtchZrwAJC9AQqqe1h\nNddpJCYmwt/fXx4+dOgQevToIRePhxUXF2PkyJF49dVXsXnzZvz55594/fXX8euvv8pPHfzpp5/w\n7bffomXLlpg6dSo+++yzMle2K3X69Gm88sorOHv2LDZs2IBp06bhxIkTAIDJkydDq9UiNjYWGo0G\nx48fl6erW7cubG1tkZKSgsDAwGrJYmnchUVEFnXnzh04OTnJw1lZWfD09Kyw/cmTJ3Hv3j1MnDgR\nNjY2CAkJQZ8+fQxul96vXz+0bt0aKpUKgwcPhk6nq7a8Pj4+GDZsGCRJwssvv4z09HTcvHkTGRkZ\n2L9/Pz777DNotVqo1WqDO/ACgJOTE+7cuVNtWSzNbFsgK1euxMmTJ+Hi4oLPP/8cwP0DZUuWLMGN\nGzfg6ekpb3oS0ZPDxcUFd+/elYdr166NjIyMCtunpaWVuR2Hr68vrl+/Lg8/fLv0B/t/0MyZM/Hj\njz9CkiRMnjwZkyZNqjTvg8WtdCvp7t27yMrKgqurK7RabYXT3r17F7Vq1ap0HjWF2bZAevbsiTlz\n5hiM2759O1q1aoWlS5ciKCgI27ZtM1ccIrISzZs3x3//+195uGvXrti/fz/u3btXbvu6deuWuQ9e\namqqwTM5jPXpp5/Kz1w3png8ire3N27fvl3hfcrS0tJQVFRksLuupjNbAQkMDDTYTAWA48ePo3v3\n7gCAHj16IC4uzlxxiMhK9OrVC0eOHJGHX3rpJXh7e+PNN99EcnIyhBDIzMzE8uXL5cfdOjo6Ijo6\nGsXFxThy5Aj27Nlj8essPD090bNnT8yePRvZ2dkoLi7Gn3/+Kb9+9OhRhISE/G0OoAMWPgaSnZ0t\nPyXM1dUV2dnZloxDRBYwdOhQ/P777ygoKAAA2NnZYfPmzfD398fw4cMRGBiIF154AVlZWWjXrh1s\nbW2xbt067Nu3D61atcLcuXOxbNky+QD649yq3RiV9ffg68uWLYNarUb37t3Rpk0b+QwsAPjxxx8R\nFhZWrdkszarOwqruD56IrJ+bmxuGDh2KjRs3YsyYMQAAZ2dnfPjhh/jwww/LnaZp06YVPrFw8eLF\nBsOdO3eu0t6NP/74Q/45NDQUoaGhBq9fuXJF/tnFxQVLliwp08e5c+eQnZ2Nvn37Ks5hjSxaQFxd\nXXH79m35fxcXlwrb6nQ6gzMpQkNDH3mwytrZ2dkxfyUK1OZdPdVqG2hqwGeiZNmr1WoTpakeM2aY\n77kvltC8eXODs8TMQa1WV7iexMTEyD8HBQXJz29/XGb9DRVCGDxK8qmnnkJsbCwGDRqE2NhYBAcH\nVzhteW+SD2SyHLM8UKq855abUElJcY34TJQ+UIqeLCUlJeWuJ1qttsxWlFJmKyBLly5FQkICcnJy\nMGHCBISGhmLQoEH44osv8Pvvv6NOnTp4++23zRWHiIiqyGwFZOrUqeWOf++998wVgYiIqhGvRCci\nIkVYQIiISBGrOo2XiExDCGE1B9LVajVKSkosHUOxmpL/wROWTIUFhOgJkJuba+kIMp6B+PfBXVhE\nRKQICwgRESnCAkJERIqwgBARkSIsIEREpAgLCBERKcICQkREirCAEBGRIiwgRESkCAsIEREpwluZ\nVDPb9FTob6ZX2q5ArYa6ivfTUXl5o8ijbpX6ICJSigWkmpWcOoqirevNMi/b16cCLCBEZCHchUVE\nRIqwgBARkSIsIEREpAgLCBERKcICQkREirCAEBGRIiwgRESkCAsIEREpwgJCRESKsIAQEZEiLCBE\nRKQICwgRESnCAkJERIpYxd14t23bhoMHD0KlUqFBgwYIDw+HjY1VRCMiogpYfAvkxo0b2Lt3LxYu\nXIjPP/8cJSUlOHz4sKVjERFRJSxeQBwdHWFjY4P8/HyUlJSgoKAAtWvXtnQsIiKqhMX3Ezk7O2PA\ngAEIDw+Hvb09WrdujdatW1s6FhERVcLiBSQ9PR07d+5EdHQ0NBoNIiMjcejQIXTp0sWgnU6ng06n\nk4dDQ0MNF2pzAAAUjElEQVSh1WrNHfeRhBC4J0lmm59KUkHj7AzJjPMsZWdnZ/LlX6A27+qpVttA\nY2XrVHnMsexNifktLyYmRv45KCgIQUFBivqxeAFJSUlBQEAAnJ2dAQBPP/00kpKSyhSQ8t5kTk6O\n2XIaSyWE2ealF3rk5uaabX4P0mq1Jl/+6pJik/b/sJKSYqtcpx5mjmVvSsxvWVqtFqGhodXSl8WP\ngXh7e+PChQsoLCyEEALx8fHw8fGxdCwiIqqExbdA/Pz80L17d8ycORMqlQp+fn7o06ePpWMREVEl\nLF5AAGDgwIEYOHCgpWMQEdFjsPguLCIiqplYQIiISBEWECIiUoQFhIiIFDG6gPzyyy+4c+eOKbMQ\nEVENYvRZWGfPnsWmTZsQFBSEbt26oUOHDrC1tTVlNiIismJGF5B3330XOTk5OHz4MHbu3InVq1fj\n6aefRrdu3dCiRQtTZiQiIiv0WNeBaLVaPPfcc3juuedw+fJlREVF4ffff4eHhwd69+6N/v37w8HB\nwVRZiYjIijz2hYTx8fE4ePAg4uLi0LhxY0ycOBGenp7YuXMnFixYgI8//tgUOYmIyMoYXUC++eYb\nHDlyBBqNBt26dUNkZCTc3Nzk15s0aYLRo0ebJCQREVkfowtIUVERpk2bhiZNmpTfkY0NPv3002oL\nRkRE1s3oAjJ48GDY2dkZjMvNzUVhYaG8JcK76BIRPTmMvg5k0aJFyMzMNBiXmZmJzz//vNpDERGR\n9TO6gFy7dg0NGjQwGNegQQOkpqZWeygiIrJ+RheQWrVqIS0tzWBcWlpajX+0IxERKWP0MZCePXsi\nMjISw4YNg5eXF9LS0vD999+jV69epsxHRERWyugCMmjQINjY2GDDhg24desW3N3d0atXLwwYMMCU\n+YiIyEoZXUBUKhWfHEhERLLHuhL92rVruHTpEvLz8w3GczcWEdGTx+gC8uOPP2Lr1q1o2LAh7O3t\nDV5jASEievIYXUB++eUXLFiwAA0bNjRlHiIiqiGMPo3Xzs6OV5oTEZHM6ALyyiuv4Ouvv0ZWVhb0\ner3BPyIievIYvQsrOjoaALB3794yr33//ffVl4iIiGoEowtIVFSUKXMQEVENY3QBqVOnDgBAr9cj\nOzsbtWvXNlkoIiKyfkYXkLt372LNmjU4evSofEX68ePHkZycjGHDhpkyIxERWSGjD6KvXr0aGo0G\n0dHRsLG5X3eaNWuGI0eOmCwcERFZL6O3QOLj47Fq1Sq5eAD379CbnZ1d5RB5eXn48ssvceXKFUiS\nhAkTJqBp06ZV7peIiEzH6AKi0WiQk5NjcOzj5s2b1XIs5F//+hfatWuHd955ByUlJSgoKKhyn0RE\nZFpG78Lq3bs3IiMjcfbsWQghcP78eaxYsQJ9+/atUoC8vDwkJiaiZ8+eAAC1Wg2NRlOlPomIyPSM\n3gJ58cUXYWdnh7Vr16KkpAQrV65Enz590L9//yoFyMjIgFarRXR0NC5fvozGjRtj9OjRZZ6/TkRE\n1sXoAiJJEvr371/lgvEwvV6PixcvYsyYMfD398e6deuwfft2hIaGGrTT6XTQ6XTycGhoqNU9DVEI\ngXuSZLb5qSQVNM7OkMw4z1J2dnYmX/4F6se6WXSVqdU20FjZOlUecyx7U2J+y4uJiZF/DgoKQlBQ\nkKJ+jP4NPXv2bIWvtWzZUtHMAcDNzQ3u7u7w9/cHAHTq1Anbt28v0668N5mTk6N4vqaiEsJs89IL\nPXJzc802vwdptVqTL391SbFJ+39YSUmxVa5TDzPHsjcl5rcsrVZb5gu6UkYXkJUrVxoM37lzB8XF\nxXB3d6/SVequrq5wd3fHtWvX4O3tjfj4ePj6+iruj4iIzMPoArJixQqDYb1ej61bt8LR0bHKIUaP\nHo3ly5ejuLgYXl5eCA8Pr3KfRERkWop3MqtUKgwZMgTjx4+v8nPR/fz8EBERUaU+iIjIvIw+jbc8\nZ86cgUpVpS6IiKiGMnoLZMKECQbDhYWFKCwsxNixY6s9FBERWT+jC8jkyZMNhu3t7VGvXj1e9EdE\n9IQyuoC0aNHClDmIiKiGMbqALF++3KgL1iZNmlSlQEREVDMYfQTcyckJcXFx0Ov1cHNzg16vR1xc\nHDQaDby8vOR/RET0ZDB6C+T69euYOXMmmjdvLo9LTEzE1q1b8frrr5skHBERWS+jt0DOnz9f5hkd\nTZo0wfnz56s9FBERWT+jC0ijRo2wadMmFBYWArh/Gu/mzZvh5+dnqmxERGTFjN6FFR4ejmXLlmHk\nyJFwdnZGbm4u/P39MWXKFFPmIyIiK2V0AfH09MT8+fNx8+ZNZGVloXbt2vDw8DBlNiIismKPdR+S\nnJwcJCQkICEhAR4eHsjMzMStW7dMlY2IiKyY0QUkISEBb731Fg4ePIitW7cCANLS0rB69WqThSMi\nIutldAFZt24d3nrrLcyZMwdqtRrA/bOwUlJSTBaOiIisl9EF5MaNG2jVqpXBOBsbG5SUlFR7KCIi\nsn5GFxBfX1+cPn3aYFx8fDwaNGhQ7aGIiMj6GX0WVlhYGD777DO0a9cOhYWF+Oqrr3DixAlMnz7d\nlPmIiMhKGV1AmjVrhkWLFuHgwYNwcHCAh4cHFixYAHd3d1PmIyIiK2VUAdHr9fj4448xZ84cvPji\ni6bORERENYBRx0BUKhUyMjIghDB1HiIiqiGMPog+dOhQrF69Gjdu3IBerzf4R0RETx6jj4GsWrUK\nAHDgwIEyr33//ffVl4iIiGqESgvI7du34erqiqioKHPkISKiGqLSXVhTp04FANSpUwd16tTB+vXr\n5Z9L/xER0ZOn0gLy8IFznU5nsjBERFRzVFpAJEkyRw4iIqphKj0GUlJSgrNnz8rDer3eYBgAWrZs\nWf3JiIjIqlVaQFxcXLBy5Up52NnZ2WBYkiQeYCciegJVWkBWrFhhjhzQ6/WYNWsW3NzcMGPGDLPM\nk4iIlHusJxKa0i+//AIfHx9LxyAiIiNZRQG5desWTp06hd69e1s6ChERGckqCsj69esRFhbGM76I\niGoQo29lYionT56Ei4sL/Pz8oNPpKrxho06nM7gGJTQ0FFqt1lwxjSKEwD0zFkGVpILG2dlshbfo\n+lXob2bc/1klwU5v2ptrCjM/7VKttoHGytap8tjZ2Vnduv+gB9eTcl+v5nVH5eEJ23q+1dZfZax9\n+RsjJiZG/jkoKAhBQUGK+rF4AUlMTMTx48dx6tQpFBYW4t69e4iKisKkSZMM2pX3JnNycswZ1Sgq\nM96xWC/0yM3NNdv81OnXUPip+U5wsJ/6gdnmBQAlJcVWuU49TKvVWnVOc68ndjM/Q76zi9nmZ+3L\nvzJarRahoaHV0pfFC8irr76KV199FQCQkJCAHTt2lCkeRERkfaziGAgREdU8Ft8CeVCLFi3QokUL\nS8cgIiIjcAuEiIgUYQEhIiJFWECIiEgRFhAiIlKEBYSIiBRhASEiIkVYQIiISBEWECIiUoQFhIiI\nFGEBISIiRVhAiIhIERYQIiJShAWEiIgUYQEhIiJFWECIiEgRFhAiIlKEBYSIiBRhASEiIkVYQIiI\nSBEWECIiUoQFhIiIFGEBISIiRVhAiIhIERYQIiJShAWEiIgUYQEhIiJFWECIiEgRFhAiIlLExtIB\nbt26haioKGRnZ0OSJPTu3Rv9+/e3dCwiIqqExQuIWq3GyJEj4efnh/z8fMyYMQNt2rSBj4+PpaMR\nEdEjWHwXlqurK/z8/AAADg4O8PHxQWZmpmVDERFRpSxeQB6UkZGBy5cvo2nTppaOQkRElbCaApKf\nn4/Fixdj1KhRcHBwsHQcIiKqhMWPgQBASUkJIiMj0a1bN3To0KHcNjqdDjqdTh4ODQ2FVqs1V0Sj\nCCFwT5LMNj+VpILG2RmSmeZZoDbv6mKu91VKrbaBxsrWqfLY2dlZ3br/IHOvJ+b+3Kx9+RsjJiZG\n/jkoKAhBQUGK+rGKArJy5Ur4+vo+8uyr8t5kTk6OqaM9NpUQZpuXXuiRm5trtvmpS4rNNi/gfkE2\np5KSYqtcpx6m1WqtOqe51xNzf27Wvvwro9VqERoaWi19WbyAJCYm4uDBg2jQoAHeffddSJKE4cOH\no23btpaORkREj2DxAhIYGIjvv//e0jGIiOgxWc1BdCIiqllYQIiISBEWECIiUoQFhIiIFGEBISIi\nRVhAiIhIERYQIiJShAWEiIgUYQEhIiJFWECIiEgRFhAiIlKEBYSIiBRhASEiIkVYQIiISBGL386d\n6EmkzroJZN5QNG2B2uaxH9okOWkh7prnIUhScZFZ5iPPz8YG6pRzZptfgdYF6pxs88zMrQ5KanuY\nZ14KsIAQWULmDRR+OsNss7Of+gEKl35ktnmZVc4ds703AHB460MULvnQLPOym/kZYMUFhLuwiIhI\nERYQIiJShAWEiIgUYQEhIiJFWECIiEgRFhAiIlKEBYSIiBRhASEiIkVYQIiISBEWECIiUoQFhIiI\nFGEBISIiRaziZoqnT5/GunXrIIRAz549MWjQIEtHIiKiSlh8C0Sv12Pt2rWYM2cOIiMjcfjwYaSm\nplo6FhERVcLiBSQ5ORn16tVDnTp1YGNjg5CQEMTFxVk6FhERVcLiBSQzMxPu7u7ysJubGzIzMy2Y\niIiIjGEVx0D+TtQt2wP2DpW2U0kS9EJUbV6NA6GvUg9ERMpJQlTxr1gVnT9/Hlu2bMGcOXMAANu3\nbweAMgfSdToddDqdPBwaGmq+kEREfyMxMTHyz0FBQQgKClLUj8V3YTVp0gRpaWm4ceMGiouLcfjw\nYQQHB5dpFxQUhNDQUPnfgwugJmJ+y6nJ2QHmt7S/Q/4H/5YqLR6AFezCUqlUGDNmDObPnw8hBHr1\n6gVfX19LxyIiokpYvIAAQNu2bbF06VJLxyAiosdg8V1YSlVls8saML/l1OTsAPNbGvP/j8UPohMR\nUc1UY7dAiIjIslhAiIhIEas4iG6M3NxcLFmyBDdu3ICnpyfefvttaDSaMu22bduGgwcPQqVSoUGD\nBggPD4eNjeXfpjH5r127hiVLlkCSJAghkJ6ejldeeQX9+/e3UOr/MXb55+Xl4csvv8SVK1cgSRIm\nTJiApk2bWiDx/xibfeLEidBoNJAkCWq1GhERERZIW5ax+YH795abNWsW3NzcMGPGDDMnLZ8x+YuK\nivDBBx+guLgYxcXFCA4OxquvvmqhxIaMyX/r1i1ERUUhOzsbkiShd+/eVvF7Cxi//qxcuRInT56E\ni4sLPv/8c+M6FzXEhg0bxPbt24UQQmzbtk1s3LixTJuMjAwxceJEUVRUJIQQYvHixSI2NtasOSti\nTP4HlZSUiDfffFPcuHHDHPEqZWz+qKgosW/fPiGEEMXFxeLu3btmy1gRY7NPnDhR5OTkmDOaUR5n\n3dmxY4dYunSp+PTTT80Vr1LG5s/PzxdC3F/3Z8+eLc6dO2e2jI9iTP6srCxx8eJFIYQQ9+7dE1Om\nTBFXr141Z8wKGbv8z507Jy5evCj++c9/Gt13jdmFdfz4cXTv3h0A0KNHj3JvuOjo6AgbGxvk5+ej\npKQEBQUFqF27trmjlsuY/A+Kj4+Hl5cXPDw8zBGvUsbkz8vLQ2JiInr27AkAUKvVFX5TNidjl70Q\nAsIKzykxNv+tW7dw6tQp9O7d25zxKmVsfnt7ewD3t0b0ej2cnZ3NlvFRjMnv6uoKPz8/AICDgwN8\nfHys5p5+xi7/wMBAODk5PVbflt+3Y6Ts7Gy4uroCuP9hZWdnl2nj7OyMAQMGIDw8HPb29mjdujVa\nt25t7qjlMib/g44cOYKQkBBzRDOKMfkzMjKg1WoRHR2Ny5cvo3Hjxhg9ejTs7OzMHdeAsctekiTM\nnz8fKpUKvXv3Rp8+fcwZs0LG5l+/fj3CwsKQl5dnzniVMja/Xq/HzJkzkZ6ejr59+1rNBcWP+7ub\nkZGBy5cvW3zXbanHzf84rKqAzJs3z+DNCSEgSRKGDRtWpq0kSWXGpaenY+fOnYiOjoZGo0FkZCQO\nHTqELl26mDR3qarmL1VcXIzjx4/jtddeM0nOilQ1v16vx8WLFzFmzBj4+/tj3bp12L59u1nuW1Yd\ny37evHmoXbs27ty5g3nz5sHX1xeBgYEmy/zwvKuSv3TftZ+fH3Q6ndm3pKpj+atUKixcuBB5eXn4\n5JNPkJCQgBYtWpgs84Oq63c3Pz8fixcvxqhRo+DgUPlNVatLdeV/XFZVQN57770KX3N1dcXt27fl\n/11cXMq0SUlJQUBAgLzp+/TTTyMpKclsBaSq+UudPn0ajRs3Rq1atUwRs0JVze/m5gZ3d3f4+/sD\nADp16iTfHNPUqmPZl+7urFWrFjp27Ijk5GSzFZCq5k9MTMTx48dx6tQpFBYW4t69e4iKisKkSZNM\nGVtWXes+AGg0GrRr1w4pKSlmKyDVkb+kpASRkZHo1q0bOnToYKqo5arO5f84aswxkKeeegqxsbEA\ngNjY2HJvuOjt7Y0LFy6gsLAQQgjEx8fDx8fHzEnLZ0z+UocOHbKq3VeAcfldXV3h7u6Oa9euAbh/\nHMcadkMYk72goAD5+fkA7n+LPHPmDOrXr2/OmBUyJv+rr76KlStXIioqCm+99RZatmxptuJRGWPy\n37lzR971VlhYiPj4ePmYgqUZ+7u7cuVK+Pr6Ws3ZV6Ue52/P4x4HrDFXoufm5uKLL77AzZs3UadO\nHbz99ttwcnJCVlYWVq1ahZkzZwIAfvrpJ8TGxkKlUsHPzw/jx4+3mtN4jclfUFCA8PBwREVFwdHR\n0cKp/8fY/JcuXcKqVatQXFwMLy8vhIeHW/xAujHZMzIysGjRIkiShJKSEnTt2rXMIwUsxdhlXyoh\nIQE7duywqtN4K8v/119/YcWKFfIfsK5du2LgwIGWjg7AuPyJiYn44IMP0KBBA0iSBEmSMHz4cLRt\n29bS8Y1ef5YuXYqEhATk5OTAxcUFoaGh8gkxFakxBYSIiKxLjdmFRURE1oUFhIiIFGEBISIiRVhA\niIhIERYQIiJShAWEiIgUYQEhIiJFWECIiEiR/wcG6Jr9Rz1vSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1d6202eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(subjRTs.Mean.ConW - subjRTs.Mean.IncW).plot.hist(label='RT Difference\\n(Con - Inc)', \n",
    "                                                  legend=True,\n",
    "                                                 title='Condition RT Differences by Subject')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you think, should anyone be rejected?  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Participant Response Speed vs. Size of Stroop Effect\n",
    "\n",
    "Make a scatter plot showing the relationship between how quickly subjects responded for congruent and incongruent stimuli!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1d6090d68>"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OXyuFP8+2ohaIazNRM/BzsYW/aztotRIUCsE9DmpxWDyImkl1wWDdoJaIfR5E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\niGT7fzF/VtYK0bW/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1d642d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "subjRTs['Mean'].plot.scatter(x='ConW', y='IncW', s=50, ax=ax,\n",
    "                      title='Mean Log Subject Response Time by Condition')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Figure to File\n",
    "\n",
    "Save the figure using **fig.savefig()** to your data directory as a png file!  Great job!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig(path.join(data_dir, 'myfig.svg'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}