surveys/meetup-2_May-30-2016/notebook/Meetup-2_Data-Analysis_v0.01.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Meetup #2 Feedback Survey Analysis\n",
    "\n",
    "Following the second MENA Devs meetup that occured on the **30th of May 2016**, we have distributed an online feedback survey to all attendees.\n",
    "\n",
    "Below is a modest analysis of the feedback received given the small sample size.\n",
    "\n",
    "### Questions asked in the survey:\n",
    "\n",
    "```\n",
    "Q1: \"Overall how satisfied were you with the event?\"\n",
    "Q2: \"... the talks?\"\n",
    "Q3: \"... the venue?\"\n",
    "Q4: \"... the food?\"\n",
    "Q5: \"How did you first hear about our event?\"\n",
    "Q6: \"How well did the event meet your expectations?\"\n",
    "Q7: \"How likely are you to attend another one of our events?\"\n",
    "```\n",
    "\n",
    "### Findings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# General syntax to import specific functions in a library: \n",
    "##from (library) import (specific library function)\n",
    "from pandas import DataFrame, read_csv\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import sys\n",
    "import datetime as dt\n",
    "\n",
    "# Enable inline plotting\n",
    "%matplotlib inline\n",
    "\n",
    "# Style the plots\n",
    "plt.pyplot.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version 3.5.1 (default, Apr 18 2016, 11:46:32) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 7.3.0 (clang-703.0.29)]\n",
      "Pandas version 0.18.1\n",
      "Matplotlib version 1.5.1\n"
     ]
    }
   ],
   "source": [
    "print('Python version ' + sys.version)\n",
    "print('Pandas version ' + pd.__version__)\n",
    "print('Matplotlib version ' + plt.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Loading the CSV file and transforming into a DataFrame\n",
    "file_location = r'../feedback_survey.csv'\n",
    "# Read the CSV file and add custom header names\n",
    "raw_data = pd.read_csv(file_location, header=0, names=['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q5</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <td>MENA Devs Slack group</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>In the media (Facebook)</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>In the media (Facebook)</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>In the media (Facebook)</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <th>12</th>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>MENA Devs Slack group</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Q1  Q2  Q3  Q4                       Q5  Q6  Q7\n",
       "0    4   4   3   3    MENA Devs Slack group   4   4\n",
       "1    4   4   2   3    MENA Devs Slack group   4   5\n",
       "2    4   4   4   4    MENA Devs Slack group   5   5\n",
       "3    4   3   1   2    MENA Devs Slack group   3   4\n",
       "4    5   4   5   5  In the media (Facebook)   5   5\n",
       "5    5   4   4   5    MENA Devs Slack group   5   5\n",
       "6    5   5   5   4  In the media (Facebook)   5   5\n",
       "7    4   3   2   3    MENA Devs Slack group   4   5\n",
       "8    3   4   2   1    MENA Devs Slack group   3   5\n",
       "9    4   4   3   4    MENA Devs Slack group   4   5\n",
       "10   3   4   3   3    MENA Devs Slack group   3   5\n",
       "11   4   5   4   4  In the media (Facebook)   4   5\n",
       "12   3   3   4   3    MENA Devs Slack group   2   4\n",
       "13   4   3   5   5    MENA Devs Slack group   4   5\n",
       "14   5   4   2   4    MENA Devs Slack group   3   3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the raw data as answered by the survey takers. \n",
    "\n",
    "At a glimpse you can see that the total number of answers received is **15** and that most of the knowledge of the event came from the **Slack Group**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q6</th>\n",
       "      <th>Q7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.066667</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.266667</td>\n",
       "      <td>3.533333</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.639940</td>\n",
       "      <td>1.279881</td>\n",
       "      <td>1.125463</td>\n",
       "      <td>0.915475</td>\n",
       "      <td>0.617213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Q1         Q2         Q3         Q4         Q6         Q7\n",
       "count  15.000000  15.000000  15.000000  15.000000  15.000000  15.000000\n",
       "mean    4.066667   3.866667   3.266667   3.533333   3.866667   4.666667\n",
       "std     0.703732   0.639940   1.279881   1.125463   0.915475   0.617213\n",
       "min     3.000000   3.000000   1.000000   1.000000   2.000000   3.000000\n",
       "25%     4.000000   3.500000   2.000000   3.000000   3.000000   4.500000\n",
       "50%     4.000000   4.000000   3.000000   4.000000   4.000000   5.000000\n",
       "75%     4.500000   4.000000   4.000000   4.000000   4.500000   5.000000\n",
       "max     5.000000   5.000000   5.000000   5.000000   5.000000   5.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read a bit into the data and see what insights we can gather by looking at the **mean (average)** and the **standard deviation**.\n",
    "\n",
    "By looking at the mean only, it seems that:\n",
    "1. The majority is 'quite satisfied' by the overall event and they are 'highly likely' to give us another chance and attend again in the future\n",
    "2. The talks were satisfactory though not great\n",
    "2. There seems to be a slight issue with the talks, venue, and the food (but this is inconclusive yet before we look at the stds)\n",
    "\n",
    "By looking at the stds, it seems that:\n",
    "1. Attendees are generally in agreement that the overall event was satisfactory\n",
    "2. Attendees are also in agreement regarding the talks\n",
    "3. Attendees are not in agreement regarding the venue, food and expectations\n",
    "4. Attendees are in agreement regarding their likelihood of attending another meetup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10bdaea58>"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KUQYS+Mj7GoQQsoMQERERUS9YFEqAz+dDcXFxj/sDlZaWwufz4cCBA9i7dy9+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6fCtXmzJSRrRZT2ReSvtWDT+fnmcn26vpXS84NTJt/f08HOtWWODEa5MBGE45ZgvGAwMGXx0/0d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\nvozOmMqi0fCHvKJeM1NqwNuTTavpo+vPxdSfGnpSxsQgbq4Gb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc84da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = raw_data.plot(kind='area', figsize=(20,10), stacked=True, xticks=raw_data.index)\n",
    "plot.set_title('Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c369550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EGgAAQEEINAAAgIIQaAAAAAUh0AAAAApCoAEAABSEQAMAACgIgQYAAFAQAg0AAKAgBBoA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\naCIiIiIiIhFCAZqIiIiIiEiE+BdGOPF4iQ0TwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd937b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "referral_channels = raw_data['Q5'].groupby(raw_data['Q5']).count()\n",
    "rf_plot = referral_channels.plot(kind='bar', figsize=(15, 8), rot=0)\n",
    "rf_plot.set_title('Referral Channels')\n",
    "rf_plot.set_xlabel('How did you first hear about our event?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this instance it seems that the MENA-Devs Slack Group was more effective in communicating the event to our community."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Conclusions\n",
    "\n",
    "The sample size is quite small to draw major conclusions, however, there are a few areas that we believe we can work on:\n",
    "\n",
    "1. Identify other possible venues to host the event\n",
    "2. Identify other possible options for food catering\n",
    "3. Add more tech talks to the meetup agenda\n",
    "4. Work more on increasing outreach through social media channels\n",
    "5. Identify areas where the event did not meet the attendees expectations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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