Extreme_Makeover_DSI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extreme Makeover: DSI Edition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ee/c4/bf82dc7ee81ac798cd64d8ec0884c07d59f82f45f4faf878a860c1c1d487/matplotlib-2.2.3-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (13.8MB)\n",
      "\u001b[K    100% |████████████████████████████████| 13.8MB 2.4MB/s ta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: six>=1.10 in /usr/local/Cellar/ipython/6.5.0/libexec/vendor/lib/python3.7/site-packages (from matplotlib) (1.11.0)\n",
      "Collecting cycler>=0.10 (from matplotlib)\n",
      "  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
      "Requirement already satisfied: pytz in /usr/local/lib/python3.7/site-packages (from matplotlib) (2018.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/Cellar/ipython/6.5.0/libexec/vendor/lib/python3.7/site-packages (from matplotlib) (2.7.3)\n",
      "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/68/f2/21ec13269a420c063a3d7d8c87dac030da7b00fc6b27fa88cfb1c72a645b/kiwisolver-1.0.1-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (107kB)\n",
      "\u001b[K    100% |████████████████████████████████| 112kB 8.2MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 (from matplotlib)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6a/8a/718fd7d3458f9fab8e67186b00abdd345b639976bc7fb3ae722e1b026a50/pyparsing-2.2.0-py2.py3-none-any.whl (56kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 5.0MB/s ta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.7/site-packages (from matplotlib) (1.15.1)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.0.0)\n",
      "Installing collected packages: cycler, kiwisolver, pyparsing, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.0.1 matplotlib-2.2.3 pyparsing-2.2.0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk \n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "workshop_data = pd.read_csv(\"workshops.csv\")\n",
    "timestamp = pd.to_datetime(workshop_data['Timestamp'])\n",
    "workshop_data['Timestamp'] = timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to people find DSI?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113ecab38>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=workshop_data, y = 'how did you hear about us', order = workshop_data['how did you hear about us'].value_counts().iloc[:5].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How often do members return?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Histogram of Number of Workshop Attendences by Email')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = sns.distplot(workshop_data['email'].value_counts(), kde= False)\n",
    "fig = a.figure\n",
    "fig.suptitle(\"Histogram of Number of Workshop Attendences by Email\", size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the academic standing of the workshop participants?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,\"Proportion of Attendees' Academic Standing\")"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aca = workshop_data[[\"Timestamp\", \"Workshop_Type\", 'first attendance', \"academic standing\"]].dropna(subset=['academic standing'])\n",
    "acag = aca.groupby('academic standing').count().reset_index().sort_values('Timestamp')\n",
    "acap = aca.groupby('academic standing').count().agg(lambda x: x/len(aca)).reset_index().sort_values('Timestamp')\n",
    "\n",
    "g = sns.catplot(x='academic standing', y='Timestamp', kind='bar', data=acap)\n",
    "g.set_axis_labels(\"Academic Standing\", \"Proportion\")\n",
    "fig = g.fig\n",
    "fig.suptitle(\"Proportion of Attendees' Academic Standing\", size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What do people think?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'10 Most Positive Words and Their Counts; Most and Least Positive Words Used')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lexicon = pd.read_csv('vader_lexicon.txt',\n",
    "                       sep='\\t', \n",
    "                       header=None,\n",
    "                       engine='python',\n",
    "                       names=['token', 'polarity', 'sd', 'raw'])\n",
    "\n",
    "sent = lexicon.drop({'sd','raw'},axis=1).set_index('token')\n",
    "polarity = workshop_data[[\"Timestamp\", \"no_punc\", \"polarity\", \"Workshop_Type\"]].drop(workshop_data.index[1634:]).dropna()\n",
    "\n",
    "words = [i for i in nltk.tokenize.word_tokenize(polarity['no_punc'].str.cat(sep=' ')) if i not in stop]\n",
    "word_dist = nltk.FreqDist(words)\n",
    "freq = pd.DataFrame.from_dict(data=dict(word_dist), orient='index', columns=[\"freq\"])\n",
    "\n",
    "polarity2 = freq.join(lexicon.set_index('token')).dropna()\n",
    "p2graph = (polarity2[(polarity2[\"freq\"] > 1)]\n",
    "           .sort_values('polarity').iloc[0:2,:]\n",
    "           .append(polarity2[(polarity2[\"freq\"] > 1)]\n",
    "                   .sort_values('polarity', ascending=False)\n",
    "                   .iloc[:,:].sort_values(\"polarity\")))\n",
    "\n",
    "#### ----\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(15,8))\n",
    "\n",
    "sns.color_palette(\"husl\", 8)\n",
    "sns.barplot(x=\"index\", y='freq', \n",
    "            data=freq.sort_values(\"freq\", ascending=False).iloc[0:10,:].reset_index(),\n",
    "            palette=sns.color_palette(\"rainbow\", 10), \n",
    "            alpha=0.7, ax=ax1)\n",
    "\n",
    "sns.barplot(x=\"index\", y='polarity', \n",
    "            data=p2graph.reset_index(), \n",
    "            palette=\"RdYlGn\", \n",
    "            ax=ax2, alpha=0.7)\n",
    "\n",
    "ax1.set_ylabel(\"Word Count\")\n",
    "ax1.set_xlabel(\"Word\")\n",
    "ax2.set_xlabel(\"Word\")\n",
    "ax2.set_ylabel(\"Polarity\")\n",
    "f.suptitle(\"10 Most Positive Words and Their Counts; Most and Least Positive Words Used\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion of "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1008 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### proportion new, proportion not new \n",
    "\n",
    "atten = workshop_data[[\"Timestamp\", \"Workshop_Type\", \"past attendance\", \"first attendance\"]].dropna(how='all')\n",
    "\n",
    "atten[\"past\"] = atten[\"past attendance\"].map(dict(Yes=1, No=0))\n",
    "atten[\"first\"] = atten[\"first attendance\"].map(dict(Yes=1, No=0))\n",
    "\n",
    "\n",
    "atten1 = atten.set_index(\"Timestamp\").loc['2016-07-01':'2017-01-01'].reset_index()\n",
    "atten2 = atten.set_index(\"Timestamp\").loc['2017-01-02':'2017-06-01'].reset_index()\n",
    "atten3 = atten.set_index(\"Timestamp\").loc['2017-06-02':'2018-01-01'].reset_index()\n",
    "atten4 = atten.set_index(\"Timestamp\").loc['2018-01-02':'2018-06-01'].reset_index()\n",
    "\n",
    "lis = []\n",
    "for i in [atten1, atten2, atten3, atten4]:\n",
    "    lis.append(pd.Series(i.groupby(\"Workshop_Type\").first().sort_values(\n",
    "        \"Timestamp\").index).str.replace(\"Sign.*|Workshop|Copy of|Post.*|Board.*|SIGN IN|3.*\", \"\"))\n",
    "\n",
    "lis[3] = pd.Series(list(lis[3])[:8] + ['Python 0 #3', 'Python 0 #4'] + list(lis[3][8:])).str.replace(\"Workshop\", \"\")\n",
    "lis[2] = lis[2].str.replace(\"\\(.*|Copy of|Workshop\", \"\")\n",
    "\n",
    "### ---\n",
    "\n",
    "f, axes = plt.subplots(2, 2, figsize=(18,14))\n",
    "\n",
    "f.suptitle(\"First Meeting?\")\n",
    "\n",
    "a1g = (atten1[~(atten1[\"Timestamp\"]\n",
    "                .dt.strftime('%Y-%m-%d') == ('2016-10-25'))]\n",
    "                .resample(\"D\", on=\"Timestamp\").mean().dropna()\n",
    "                .rename(columns={\"past\":\"No\", \"first\":\"Yes\"}).sort_index(ascending=False)\n",
    "                .plot(kind='barh', title='Fall 2016', \n",
    "                      color=['darkslateblue', 'lightsteelblue'], \n",
    "                      width=.7,ax=axes[0,0])).set_yticklabels(list(lis[0])[::-1])\n",
    "\n",
    "a2g = (atten2.resample(\"D\", on=\"Timestamp\").mean().dropna()\n",
    "                .rename(columns={\"past\":\"No\", \"first\":\"Yes\"}).sort_index(ascending=False)\n",
    "                .plot(kind='barh', title='Spring 2017',\n",
    "                      color=['darkslateblue', 'lightsteelblue'], \n",
    "                      width=.65, ax=axes[1,0])).set_yticklabels(list(lis[1])[::-1])\n",
    "\n",
    "a3g = (atten3.resample(\"D\", on=\"Timestamp\").mean().dropna()\n",
    "                .rename(columns={\"past\":\"No\", \"first\":\"Yes\"}).sort_index(ascending=False)\n",
    "                .plot(kind='barh', title='Fall 2017', color=['darkslateblue', 'lightsteelblue'],\n",
    "                      width=1,ax=axes[0,1])).set_yticklabels(list(lis[2])[::-1])\n",
    "\n",
    "a4g = (atten4.resample(\"D\", on=\"Timestamp\").mean().dropna()\n",
    "                .rename(columns={\"past\":\"No\", \"first\":\"Yes\"}).sort_index(ascending=False)\n",
    "                .plot(kind='barh', title='Spring 2018',color=['darkslateblue', 'lightsteelblue'],\n",
    "                      width=1,ax=axes[1,1])).set_yticklabels(list(lis[3])[::-1])\n",
    "\n",
    "plt.subplots_adjust(hspace=.3, wspace=0.5)\n",
    "axes[0,0].set_ylabel(\"\")\n",
    "axes[0,1].set_ylabel(\"\")\n",
    "axes[1,0].set_ylabel(\"\")\n",
    "axes[1,1].set_ylabel(\"\")"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}