PaletteGrouping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d069d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas.tools.rplot as rplot\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import operator\n",
    "from matplotlib.artist import setp\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.mpl_style = 'default'\n",
    "pd.options.display.max_rows = 30\n",
    "plt.set_cmap('Paired')\n",
    "matplotlib.style.use('ggplot')\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_raw = pd.DataFrame.from_csv(\"omit/ai2_data_with_pgroups_raw.csv\")\n",
    "df_groups_raw = pd.DataFrame.from_csv(\"omit/pgroups_raw.csv\")\n",
    "\n",
    "df_filter_a = pd.DataFrame.from_csv(\"omit/ai2_data_with_pgroups_FilterA.csv\")\n",
    "df_groups_filter_a = pd.DataFrame.from_csv(\"omit/pgroups_FilterA.csv\")\n",
    "\n",
    "blocks = [name for name in df_raw.columns if name[:2]==\"b_\"] #111 total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_csv(\"omit/ai2_data.csv\")\n",
    "df = df.fillna(0) #replacing NaN w/ 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#For has_multiplier()\n",
    "loop_blocks = ['b_controls_forRange', 'b_controls_while', 'b_controls_forEach']\n",
    "list_blocks = [ 'b_lists_create_with', 'b_lists_from_csv_table', 'b_lists_from_csv_row']\n",
    "proc = 'has_procedure'\n",
    "loop = 'has_loop'\n",
    "li = 'has_list'\n",
    "mult_cols = [proc, loop, li]\n",
    "\n",
    "#Functions for data manipulation\n",
    "\n",
    "#Given dataframe, return dataframe w/ new column col_name that sums given features\n",
    "def add_agg_column(df_inp, col_name, features):\n",
    "    df_tmp = df_inp.copy()\n",
    "    df_tmp[col_name] = 0 #defining column\n",
    "    for ind, row in df_tmp[features].iterrows(): #iterating through rows\n",
    "        result = int(row.values.sum()) #TODO: Debug here. was having trouble with NaN so removed cast to Int. \n",
    "        df_tmp.loc[ind, col_name] = result\n",
    "    return df_tmp\n",
    "\n",
    "# given path to 2 column csv, return dictionary where key is first col\n",
    "# key_as_first: true if first col is key (false is 2nd is)\n",
    "# 1st col: component/block name\n",
    "# 2nd col: component/block category\n",
    "def csv_to_dict(path, key_as_first=True):\n",
    "    reader = csv.reader(open(path, 'r'))\n",
    "    d = {}\n",
    "    for row in reader:\n",
    "        if key_as_first:\n",
    "            k, v = row\n",
    "            d[k] = v\n",
    "        else:\n",
    "            v, k = row\n",
    "            if k in d:\n",
    "                d[k]+=[v] #add to list\n",
    "            else:\n",
    "                d[k]=[v]\n",
    "    return d\n",
    "\n",
    "def add_groups_col(df_inp):\n",
    "    col_name = 'palette_groups'\n",
    "    col_name_counter = 'num_palette_groups'\n",
    "    col_basic = 'agg_basic'\n",
    "    agg_groups = ['agg_lego', 'agg_media', 'agg_storage', \n",
    "                'agg_connect', 'agg_social', 'agg_sensor', 'agg_drawing']\n",
    "    df_tmp = df_inp.copy()\n",
    "    \n",
    "    df_tmp[col_name] = \"\" #defining column\n",
    "    df_tmp[col_name_counter] = 0 #defining column\n",
    "    \n",
    "#     index = 0;\n",
    "    for ind, row in df_tmp.iterrows(): #iterating through rows\n",
    "        groups = \"\"\n",
    "        num_counter = 0\n",
    "        for grp in agg_groups:\n",
    "            if row[grp]>0:\n",
    "                groups+=grp[4:]+\",\"\n",
    "        #lego in own group\n",
    "        if 'lego' in groups:\n",
    "            df_tmp.loc[ind, col_name] = 'lego'\n",
    "            df_tmp.loc[ind, col_name_counter] = 1\n",
    "        elif len(groups)>0:    \n",
    "            df_tmp.loc[ind, col_name] = groups[:-1] #removing trailing comma\n",
    "            df_tmp.loc[ind, col_name_counter] = len(groups[:-1].split(\",\"))\n",
    "        else:\n",
    "            df_tmp.loc[ind, col_name]= col_basic[4:]\n",
    "            df_tmp.loc[ind, col_name_counter] = 1\n",
    "#         index = index+1\n",
    "    return df_tmp\n",
    "\n",
    "#Given a dataframe, return dataframe w/ 3 new columns: has_procedures, has_loop, has_list\n",
    "def has_multipliers(df_inp):\n",
    "    df_tmp = df_inp.copy()\n",
    "    \n",
    "    #defining column\n",
    "    for col in mult_cols:\n",
    "        df[col] = 0\n",
    "    \n",
    "#     index=0\n",
    "    for ind, row, in df_tmp.iterrows():\n",
    "        #existence of procedure, called more than once, called more often than defined (proc. used properly)\n",
    "        num_procs_def = row['b_procedures_defnoreturn'] + row['b_procedures_defreturn']\n",
    "        num_procs_call = row['b_procedures_callnoreturn'] + row['b_procedures_defnoreturn']\n",
    "        if num_procs_def>0 and num_procs_call>1 and num_procs_call>num_procs_def:\n",
    "            df_tmp.loc[ind, proc] = 1\n",
    "            \n",
    "        #existence of any loop block\n",
    "        for lp in loop_blocks:\n",
    "            if row[lp]>0:\n",
    "                df_tmp.loc[ind, loop] = 1\n",
    "                break\n",
    "        \n",
    "        for lis in list_blocks:\n",
    "            if row[lis]>0:\n",
    "                df_tmp.loc[ind, li] = 1\n",
    "                break\n",
    "#         index = index+1\n",
    "    return df_tmp\n",
    "\n",
    "#Given dataframe, add column that lists(string really) blocks in project\n",
    "def add_blocks_list(df_inp):\n",
    "    #get all columns w/ blocks\n",
    "    blocks = [name for name in df_inp.columns if name[:2]==\"b_\"]\n",
    "    \n",
    "    col_name = 'blocks'\n",
    "    df_tmp = df_inp.copy()\n",
    "    \n",
    "    df[col_name] = \"\" #defining new column\n",
    "    \n",
    "    for ind, row in df_tmp.iterrows(): #iterating through rows\n",
    "        blocks_str = \"\"\n",
    "        for blk_col in blocks:\n",
    "            if row[blk_col]>0:\n",
    "                blocks_str+=blk_col[2:]+\", \" #removing \"b_\" at beginning\n",
    "        df_tmp.loc[ind, col_name] = blocks_str[:-2] #removing trailing \", \"\n",
    "    return df_tmp   \n",
    "\n",
    "#Given dataframe, add column that lists(string really) unique components\n",
    "def add_components_list(df_inp):\n",
    "    #get all columns w/ blocks\n",
    "    components = [name for name in df_inp.columns if name[:2]==\"c_\"]\n",
    "    \n",
    "    col_name = 'components'\n",
    "    df_tmp = df_inp.copy()\n",
    "    \n",
    "    df[col_name] = \"\" #defining new column\n",
    "    \n",
    "    for ind, row in df_tmp.iterrows(): #iterating through rows\n",
    "        cmp_str = \"\"\n",
    "        for cmp_col in components:\n",
    "            if row[cmp_col]>0:\n",
    "                cmp_str+=cmp_col[2:]+\", \" #removing \"c_\" at beginning\n",
    "        df_tmp.loc[ind, col_name] = cmp_str[:-2] #removing trailing \", \"\n",
    "    return df_tmp\n",
    "    \n",
    "\n",
    "# df_with_pgroups: big df with agg cols, num palette columns, palette columns\n",
    "# df_count: simple df (7x2) where cols are 'count' and 'group name'\n",
    "# df_groups: df used for plotting w/ each pgroup as col and each row as num_blocks_unique for project in group\n",
    "# path: where to put files\n",
    "# suffix: string to put at end of file names\n",
    "def save_agg_dfs(df_with_pgroups, df_count, df_groups, path, suffix):\n",
    "    df_with_pgroups.to_csv(path+\"/ai2_data_with_pgroups_\"+suffix+\".csv\")\n",
    "    df_count.to_csv(path+\"/pgroup_count_\"+suffix+\".csv\")\n",
    "    df_groups.to_csv(path+\"/pgroups_\"+suffix+\".csv\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Random cell for debugging helper functions above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Testing has_multipliers()\n",
    "# df_test = has_multipliers(df_work)\n",
    "# df_test[loop_blocks+list_blocks+['has_loop', 'has_list']][df_test.has_loop>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dictionaries that map components to groups\n",
    "cat_to_comps = csv_to_dict(\"component_category.csv\", False)\n",
    "cat_to_comps.pop('layout')\n",
    "\n",
    "#removing too common components (to reduce overlap of groups)\n",
    "cat_to_comps['sensor'].remove('c_Clock') #removing b/c very common component\n",
    "cat_to_comps['media'].remove('c_Sound')\n",
    "\n",
    "# cat_to_comps['lego']\n",
    "\n",
    "# cat_to_comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#adding aggregate columns for palette groups\n",
    "#Takes ~ 1-2 min to run\n",
    "agg_cols = []\n",
    "\n",
    "#raw data\n",
    "for key in cat_to_comps:\n",
    "    agg_cols.append('agg_'+key)\n",
    "    df = add_agg_column(df, 'agg_'+key, cat_to_comps[key])\n",
    "\n",
    "#adding 'num_palette_groups', 'palette_groups' columns\n",
    "df = add_groups_col(df)\n",
    "\n",
    "#adding multiplier counts\n",
    "df = has_multipliers(df)\n",
    "\n",
    "#add column that lists all blocks (in string)\n",
    "df = add_blocks_list(df)\n",
    "\n",
    "#add column that lists all components (in string)\n",
    "df = add_components_list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#removing \"certainly nonfunctional projects\"\n",
    "df_work = df[df.num_blocks>=2][df.num_components>0] #removing \"certainly non-functional\"\n",
    "# df_work[['num_blocks','num_components']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4121"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#current dataframe for analysis (update this to update ref to dataframes below)\n",
    "df_this = df_work\n",
    "# df_this = df_raw\n",
    "len(df_this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#separating df by group (overlap exists)\n",
    "\n",
    "agg_cols = ['agg_basic', 'agg_lego', 'agg_media', 'agg_storage', 'agg_connect', 'agg_social', 'agg_sensor', 'agg_drawing']\n",
    "\n",
    "df_basic = df_this[df_this.agg_lego==0][df_this.agg_media==0][df_this.agg_storage==0][df_this.agg_connect==0][df_this.agg_social==0][df_this.agg_sensor==0][df_this.agg_drawing==0].reset_index()\n",
    "df_lego = df_this[df_this.agg_lego>0].reset_index()\n",
    "df_media = df_this[df_this.agg_media>0].reset_index()\n",
    "df_storage = df_this[df_this.agg_storage>0].reset_index()\n",
    "df_connect = df_this[df_this.agg_connect>0].reset_index()\n",
    "df_social = df_this[df_this.agg_social>0].reset_index()\n",
    "df_sensor = df_this[df_this.agg_sensor>0].reset_index()\n",
    "df_draw = df_this[df_this.agg_drawing>0].reset_index()\n",
    "df_all = df_this.reset_index()\n",
    "\n",
    "#make sure these align!\n",
    "groups = [df_all, df_storage, df_draw, df_connect, df_social, df_sensor, df_lego, df_media, df_basic]\n",
    "group_names = ['all', 'storage','drawing', 'connect', 'social', 'sensor', 'lego', 'media', 'basic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "975\n",
      "869\n",
      "24\n",
      "{'intermediate': 104, 'advanced': 59, 'basic': 706}\n",
      "-----------\n",
      "intermediate104\n",
      "advanced59\n",
      "basic706\n"
     ]
    }
   ],
   "source": [
    "#Seeing what group tutorials fit into\n",
    "\n",
    "# Key:Tutorial name, value: search terms\n",
    "tutorials = {\"hello_purr\":\"purr\",\n",
    "             \"talk_to_me\":\"talktome|talk_to_me\",\n",
    "             \"ball_bounce\":\"bounce\",\n",
    "             \"digital_doodle\" : \"doodle\",\n",
    "             \"magic_8_ball\": \"magic\",\n",
    "             \"paintpot\" : \"paint\",\n",
    "#              \"i_have_dream\" : \"dream\",\n",
    "             \"pic_call\": \"piccall|pic_call\", #tutorial doesn't work in AI2\n",
    "             'get_gold': \"gold\",\n",
    "             \"video_wall\": \"videowall|video_wall\",\n",
    "             \"stockquotes\": \"stockquotes|stock_quotes\",\n",
    "             \"mini_golf\" : \"minigolf|mini_golf\",\n",
    "             \"space_invaders\" : \"spaceinvader|space_invader\",\n",
    "             \"no_text\":\"notext|driving\",\n",
    "             \"mole_mash\": \"mash\",\n",
    "             \"sharing\": \"sharing\",\n",
    "             \"photo_booth\":\"photobooth|photo_booth\",\n",
    "             \"colored_dots\": \"colored\",\n",
    "             \"pizza_party\": \"pizza\",\n",
    "             \"nfc_cups\": \"cup\",\n",
    "             \"where_car\": \"where\",\n",
    "             \"map_it\": \"mapit|map_it\",\n",
    "             \"loc_sensor\":\"location\",\n",
    "             \"quiz_me\":\"quizme\",\n",
    "             \"spikes\":\"ohmyspikes|oh_my_spikes\"}\n",
    "\n",
    "difficulty = {\"basic\": [\"hello_purr\", \"ball_bounce\", \"digital_doodle\", \"talk_to_me\",\"magic_8_ball\",\"mole_mash\",\"paintpot\",\"pic_call\"],\n",
    "              \"intermediate\": [\"get_gold\", \"video_wall\",\"stockquotes\",\"mini_golf\",\"space_invaders\",\"no_text\",\"quiz_me\",\"sharing\",\"photo_booth\"],\n",
    "              \"advanced\": [\"colored_dots\",\"pizza_party\",\"nfc_cups\",\"where_car\",\"map_it\",\"spikes\", \"loc_sensor\"]}\n",
    "\n",
    "tutorials_count = {}\n",
    "total_tutorials = 0\n",
    "total_working_tutorials = 0\n",
    "\n",
    "#ids of tutorial recreations (to be removed), separated by level\n",
    "ids_by_level = {'basic': [],\n",
    "               'intermediate': [],\n",
    "               'advanced': []}\n",
    "\n",
    "for entry in tutorials:\n",
    "    search_term = tutorials[entry]\n",
    "    \n",
    "    #adding ids of tutorials to remove_ids\n",
    "    working = df_this[df_this.project_name.str.contains(search_term)]['id'].values.tolist()\n",
    "    \n",
    "    #determining difficulty level of project\n",
    "    if entry in difficulty['basic']:\n",
    "        ids_by_level['basic'] += working\n",
    "    elif entry in difficulty['intermediate']:\n",
    "#         print entry\n",
    "        ids_by_level['intermediate'] += working\n",
    "    elif entry in difficulty['advanced']:\n",
    "        ids_by_level['advanced'] += working\n",
    "    else:\n",
    "        print 'NO DIFFICULTY ASSIGNED: ' + entry\n",
    "    \n",
    "    remove_ids += working\n",
    "    \n",
    "    count_work = len(working)\n",
    "    count_all = len(df_raw[df_raw.project_name.str.contains(search_term)])\n",
    "    tutorials_count[entry] = count_work, count_all\n",
    "    \n",
    "    total_tutorials += count_all\n",
    "    total_working_tutorials += count_work\n",
    "\n",
    "print total_tutorials\n",
    "print total_working_tutorials\n",
    "print len(tutorials)\n",
    "# ( len(df_this) - len(df_this_notut) ) == total_working_tutorials #sanity check. should always be true\n",
    "\n",
    "#disjoint sets\n",
    "df_this_notut = df_this_notut[~df_this_notut.id.isin(remove_ids)]\n",
    "df_tut = df_this[df_this.id.isin(remove_ids)]\n",
    "\n",
    "## of recreations for each difficulty\n",
    "sums = {'basic':0,\n",
    "       'intermediate':0,\n",
    "       'advanced':0}\n",
    "\n",
    "#number of recreations for each difficulty\n",
    "for level in difficulty: \n",
    "    for tut_name in difficulty[level]:\n",
    "#         if level=='intermediate':\n",
    "#             print tut_name\n",
    "        sums[level]+=tutorials_count[tut_name][0]\n",
    "\n",
    "print sums\n",
    "    \n",
    "print \"-----------\"\n",
    "for lis in ids_by_level:\n",
    "    print lis + str(len(ids_by_level[lis]))\n",
    "    \n",
    "\n",
    "################\n",
    "search_criteria = tutorials['no_text']\n",
    "\n",
    "# df_result = df_this[df_this.project_name.str.contains(search_criteria)][['project_name', 'palette_groups', 'components', 'num_components']]\n",
    "# groups = df_result.groupby(df_result.palette_groups).groups\n",
    "\n",
    "# #key: group, value: frac of tutorials in that group\n",
    "# freq = {}\n",
    "# for group in groups:\n",
    "#     count = df_result[df_result.palette_groups==group][['palette_groups']].count()\n",
    "#     freq[group] = count/len(df_result)\n",
    "#     print group + \": \" + str(count) \n",
    "\n",
    "# df_result\n",
    "\n",
    "###############\n",
    "# Looking at project names\n",
    "#looking at names of tutorial recreations\n",
    "# for x in df_this[df_this.id.isin(remove_ids)]['project_name']:\n",
    "#     print x\n",
    "\n",
    "#looking at names of \"not\" tutorials\n",
    "# for x in df_this_notut['project_name']:\n",
    "#     print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Count of Tutorial Recreations by difficulty, functionality group\n",
    "\n",
    "#tuples representing (level, group) where no tutorials exist\n",
    "no_tutorials = [('basic', 'storage'), ('basic','connect'), ('basic', 'social'), ('basic', 'lego'),\n",
    "               ('intermediate','sensor'), ('intermediate','lego'), \n",
    "               ('advanced','social'), ('advanced','lego'), ('advanced','media'), ('advanced','basic')]\n",
    "\n",
    "#list of IDs to add back into dataset\n",
    "ids_to_keep = []\n",
    "\n",
    "#finding projects labelled as recreations that are not (b/c tutorial does not exist in that level, group)\n",
    "for i in range(len(groups)):\n",
    "    group_name = group_names[i]\n",
    "    dfg = groups[i] #dataframe\n",
    "    for level in ids_by_level:\n",
    "        ids = ids_by_level[level]\n",
    "        if (level, group_name) in no_tutorials:\n",
    "#             print level +\",\" + group_name + \", \" + str(len(dfg[dfg.id.isin(ids)]))\n",
    "            ids_to_keep += dfg[dfg.id.isin(ids)]['id'].tolist()\n",
    "#         print group_name + \"\\t\" + level + \"\\t\" + str(len(dfg[dfg.id.isin(ids)]))\n",
    "\n",
    "updated_remove_ids = []\n",
    "for pid in remove_ids:\n",
    "    if pid not in ids_to_keep:\n",
    "        updated_remove_ids.append(pid)\n",
    "\n",
    "df_tut = df_this[df_this.id.isin(updated_remove_ids)]\n",
    "df_test= df_this[~df_this.id.isin(updated_remove_ids)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all\tintermediate\t95\n",
      "all\tadvanced\t54\n",
      "all\tbasic\t683\n",
      "storage\tintermediate\t5\n",
      "storage\tadvanced\t48\n",
      "storage\tbasic\t0\n",
      "drawing\tintermediate\t70\n",
      "drawing\tadvanced\t5\n",
      "drawing\tbasic\t447\n",
      "connect\tintermediate\t9\n",
      "connect\tadvanced\t26\n",
      "connect\tbasic\t0\n",
      "social\tintermediate\t6\n",
      "social\tadvanced\t0\n",
      "social\tbasic\t0\n",
      "sensor\tintermediate\t0\n",
      "sensor\tadvanced\t35\n",
      "sensor\tbasic\t142\n",
      "lego\tintermediate\t0\n",
      "lego\tadvanced\t0\n",
      "lego\tbasic\t0\n",
      "media\tintermediate\t4\n",
      "media\tadvanced\t0\n",
      "media\tbasic\t198\n",
      "basic\tintermediate\t11\n",
      "basic\tadvanced\t0\n",
      "basic\tbasic\t81\n"
     ]
    }
   ],
   "source": [
    "#making sure we don't count ids in ids_to_keep\n",
    "updated_ids_by_level={}\n",
    "for level in ids_by_level:\n",
    "    unique_vals = list(set(ids_by_level[level])) #unique\n",
    "    for x in ids_to_keep:\n",
    "        if x in unique_vals:\n",
    "            unique_vals.remove(x)\n",
    "    updated_ids_by_level[level] = unique_vals\n",
    "\n",
    "for i in range(len(groups)):\n",
    "    group_name = group_names[i]\n",
    "    dfg = groups[i] #dataframe\n",
    "    for level in updated_ids_by_level:\n",
    "        ids = updated_ids_by_level[level]\n",
    "        print group_name + \"\\t\" + level + \"\\t\" + str(len(dfg[dfg.id.isin(ids)]))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1939"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to remove tutorial recretions\n",
    "df_this = df_this[~df_this.id.isin(updated_remove_ids)]\n",
    "len(df_raw)-len(df_this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#duplicate of above cell(need to rerun b/c df_this updated\n",
    "#separating df by group (overlap exists)\n",
    "\n",
    "agg_cols = ['agg_basic', 'agg_lego', 'agg_media', 'agg_storage', 'agg_connect', 'agg_social', 'agg_sensor', 'agg_drawing']\n",
    "\n",
    "df_basic = df_this[df_this.agg_lego==0][df_this.agg_media==0][df_this.agg_storage==0][df_this.agg_connect==0][df_this.agg_social==0][df_this.agg_sensor==0][df_this.agg_drawing==0].reset_index()\n",
    "df_lego = df_this[df_this.agg_lego>0].reset_index()\n",
    "df_media = df_this[df_this.agg_media>0].reset_index()\n",
    "df_storage = df_this[df_this.agg_storage>0].reset_index()\n",
    "df_connect = df_this[df_this.agg_connect>0].reset_index()\n",
    "df_social = df_this[df_this.agg_social>0].reset_index()\n",
    "df_sensor = df_this[df_this.agg_sensor>0].reset_index()\n",
    "df_draw = df_this[df_this.agg_drawing>0].reset_index()\n",
    "df_all = df_this.reset_index()\n",
    "\n",
    "#make sure these align!\n",
    "groups = [df_all, df_storage, df_draw, df_connect, df_social, df_sensor, df_lego, df_media, df_basic]\n",
    "group_names = ['all', 'storage','drawing', 'connect', 'social', 'sensor', 'lego', 'media', 'basic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e722e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_names_no_all = ['storage','drawing', 'connect', 'social', 'sensor', 'lego', 'media', 'basic']\n",
    "#Number of projects that fit into each palette group\n",
    "\n",
    "#(ignoring all overlap, so some projects double counted)\n",
    "data = {'group':cat_to_comps.keys(),\n",
    "       'count': []} #supposed to be named 'count' or something but that shows up on pie chart\n",
    "\n",
    "num_unique = {}\n",
    "\n",
    "for name in group_names_no_all:\n",
    "# for val in data['group']:\n",
    "    val = name\n",
    "    count = df_this[df_this.palette_groups.str.contains(val)]['palette_groups'].count()\n",
    "    data['count'].append(count)\n",
    "\n",
    "# data \n",
    "# df_count = pd.DataFrame(data=data['count'], index=data['group'], columns=['count'])\n",
    "df_count = pd.DataFrame(data=data['count'], index=group_names_no_all, columns=['count'])\n",
    "df_count_plot = pd.DataFrame(data=data['count'], index=group_names_no_all, columns=['']) #change column name to blank so not shown in pie\n",
    "\n",
    "df_count_t = df_count.transpose()\n",
    "\n",
    "\n",
    "# print df_count_t.values.sum()\n",
    "# df_count_t.plot(kind='barh', stacked=True, figsize=(12,4))\n",
    "plot = df_count_plot.plot(kind='pie', subplots=True, figsize=(12,12), legend=False, fontsize=30, colormap='Paired', autopct='%.1f')\n",
    "# print df_count.values.sum() #number of categories projects fit into\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    all  storage  drawing  connect  social  sensor  lego  media  basic\n",
      "0  3.00    19.00     3.00    18.00   12.00   18.00  5.00   5.00   8.00\n",
      "1  8.00     3.00    17.00    12.00    5.00    3.00  5.00  18.00   3.00\n",
      "2 17.00    17.00     4.00     5.00   22.00   13.00  3.00  13.00   6.00\n"
     ]
    }
   ],
   "source": [
    "groups = [df_all, df_storage, df_draw, df_connect, df_social, df_sensor, df_lego, df_media, df_basic]\n",
    "group_names = ['all', 'storage','drawing', 'connect', 'social', 'sensor', 'lego', 'media', 'basic']\n",
    "\n",
    "agg_groups = {'all': df_all,\n",
    "             'basic': df_basic,\n",
    "             'lego': df_lego,\n",
    "             'media': df_media,\n",
    "             'storage': df_storage,\n",
    "             'connect': df_connect,\n",
    "             'social': df_social,\n",
    "             'sensor': df_sensor,\n",
    "             'drawing': df_draw,\n",
    "             }\n",
    " \n",
    "#new dataframe where each group is column (rows don't correspond to anything really)\n",
    "df_groups = pd.DataFrame()\n",
    "df_groups_f = pd.DataFrame()\n",
    "counter = 0\n",
    "# for ignore, agg_df in agg_groups.iteritems():\n",
    "#     name = ignore\n",
    "for agg_df in groups:\n",
    "    name = group_names[counter] # need this to ensure columns of graphs same order\n",
    "    col = agg_df['num_blocks_unique']\n",
    "    df_groups.insert(len(df_groups.columns), name, col)\n",
    "    \n",
    "    #Sanity check (had this issue, resolved by calling reset_index() for df_all)\n",
    "    if len(col)!=len(df_groups[name].dropna()):\n",
    "        print 'WARNING: Not all projects being transferred to df_groups'\n",
    "    counter+=1\n",
    "# print 'all'\n",
    "# df_groups.insert(len(df_groups.columns), 'all', agg_all['num_blocks_unique']                 \n",
    "# df_groups.to_csv(\"temp.csv\")\n",
    "print df_groups.head(3)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3289\n",
      "2569\n",
      "4282\n",
      "1.30191547583\n",
      "0.781088476741\n",
      "0.218911523259\n"
     ]
    }
   ],
   "source": [
    "#number of projects that fit into one group\n",
    "num_projects = len(df_this) \n",
    "num_projects_single = len(df_this[~df_this.palette_groups.str.contains(',')]) #number of projects put into 1 palette group\n",
    "num_groupings = df_count.values.sum() #number of groups projects fit into\n",
    "\n",
    "print num_projects\n",
    "print num_projects_single\n",
    "print num_groupings\n",
    "\n",
    "print float(num_groupings)/num_projects\n",
    "\n",
    "print float(num_projects_single)/num_projects # proportion of projects in 1 group\n",
    "print 1.-float(num_projects_single)/num_projects # proportion of projects in multiple groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#To show positive skew\n",
    "# df_all['num_blocks_unique'].hist(data,bins=100,range=(0,60),facecolor=\"r\", histtype = 'step')\n",
    "\n",
    "\n",
    "# y,binEdges=np.histogram(df_all['num_blocks_unique'], bins=100)\n",
    "# bincenters = 0.5*(binEdges[1:]+binEdges[:-1])\n",
    "# plt.plot(bincenters,y,'-')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save_agg_dfs(df, df_count, df_groups, \"omit\", \"SOMETHING\")\n",
    "# df_groups.describe()\n",
    "# df_groups.median()\n",
    "\n",
    "#trying to get outliers...failed...\n",
    "# upper = df_groups.transform(lambda x: x.quantile(q=.75) + (1.5*x.quantile(q=.75)-x.quantile(q=.25)) )\n",
    "\n",
    "df_this_outliers = df_this[abs(df_this.num_blocks_unique - df_this.num_blocks_unique.mean()) >= 3*(df_this.num_blocks_unique.std())] \n",
    "df_this_no_outliers = df_this[abs(df_this.num_blocks_unique - df_this.num_blocks_unique.mean()) <= 3*(df_this.num_blocks_unique.std())] \n",
    "df_this_no_outliers['num_blocks_unique'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x1157b3b10>,\n",
       " <matplotlib.text.Text at 0x10f088f90>,\n",
       " <matplotlib.text.Text at 0x11064b390>,\n",
       " <matplotlib.text.Text at 0x10d67e4d0>,\n",
       " <matplotlib.text.Text at 0x10d67e2d0>,\n",
       " <matplotlib.text.Text at 0x110996190>,\n",
       " <matplotlib.text.Text at 0x110996f10>]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "IiIiIhItFqtEREREJFosVomIiIhItFisEhEREZFosVglIiIiItH6PwEgcx5NjzwQAAAAAElFTkSu\n",
       "QmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157c2290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_groups.plot(kind='box', figsize=(11.326,7), sym='b.', rot= 90, fontsize=20)\n",
    "\n",
    "fig = plt.figure(figsize=(11.326,7))\n",
    "ax, bp = df_groups.boxplot(return_type='both')\n",
    "plt.set_cmap('Paired')\n",
    "plt.setp(bp['boxes'], lw=2)\n",
    "plt.setp(bp['whiskers'], lw=2, linestyle='solid')\n",
    "plt.setp(bp['medians'], lw=4)\n",
    "plt.setp(bp['caps'], lw=2)\n",
    "plt.setp(bp['fliers'], marker='.')\n",
    "plt.set_cmap('Paired')\n",
    "ax.set_xlabel('Functionality Group')\n",
    "ax.set_ylabel('Number of Unique Blocks')\n",
    "ax.set_xticklabels(df_groups.columns, rotation=90, fontsize=20)\n",
    "ax.set_yticklabels(range(0,61,10), fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'all': 90, 'lego': 1, 'media': 26, 'basic': 41, 'storage': 11, 'connect': 14, 'social': 10, 'sensor': 12, 'drawing': 6}\n"
     ]
    }
   ],
   "source": [
    "#number of outliers, max (minus outliers)\n",
    "max_vals={}\n",
    "num_outliers = {}\n",
    "for i in range(len(group_names)):\n",
    "    dfg = groups[i]\n",
    "    name = group_names[i]\n",
    "#     print name\n",
    "    range_iqr = dfg.num_blocks_unique.quantile([0.25,0.75])\n",
    "    iqr = range_iqr[.75]-range_iqr[.25]\n",
    "    max_val = dfg.num_blocks_unique.quantile([0.75])+ 1.5*iqr\n",
    "    max_val = max_val[0.75]\n",
    "    max_vals[name] = max_val\n",
    "    num_outliers[name] = len(dfg[dfg.num_blocks_unique>max_val])\n",
    "# print max_vals\n",
    "print num_outliers\n",
    "# df_all[df_all.num_blocks_unique>max_vals['all']][['project_name', 'palette_groups', 'num_blocks_unique' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114645a10>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "hHByEvZCCCGEk/sHxVe2og+6EegAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca68cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#relationship between intricacy and # of projects by group\n",
    "\n",
    "df_test = df_count.copy()\n",
    "s = df_test.sum()\n",
    "s.name = 'all'\n",
    "df_test = df_test.append(s)\n",
    "df_test['avg'] = df_groups.mean()\n",
    "df_test['med'] = df_groups.median()\n",
    "df_test = df_test.drop('all')\n",
    "\n",
    "# df_test\n",
    "# df_groups[~['all']]\n",
    "df_ratio = df_test['count'] / df_test['avg']\n",
    "plot = df_ratio.plot(kind='bar', figsize=(8,5), fontsize=16,)\n",
    "plot.set_ylabel('Number of Projects / Mean # of Unique Blocks')\n",
    "plot.set_xlabel('Functionality Group')\n",
    "\n",
    "df_ratio2 = df_test['count'] / df_test['med']\n",
    "plot = df_ratio2.plot(kind='bar', figsize=(8,5), fontsize=16,)\n",
    "plot.set_ylabel('Number of Projects / Median # of Unique Blocks')\n",
    "plot.set_xlabel('Functionality Group')\n",
    "\n",
    "# df_ratio.plot(kind='bar', x='c', y='d', color='DarkGreen');\n",
    "\n",
    "# df_ratio_med = df_test['count'] / df_test['med']\n",
    "# df_ratio_med.plot(kind='bar')\n",
    "\n",
    "# df_ratio_2 = df_test['avg'] / df_test['count']\n",
    "# df_ratio_2.plot(kind='bar')\n",
    "\n",
    "\n",
    "#LEARNABILITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#ratio to measure learnability, capability\n",
    "tmp = pd.DataFrame(data=df_groups.describe())\n",
    "low = tmp.loc['25%',:]- tmp.loc['min',:]\n",
    "iqr = tmp.loc['75%',:] - tmp.loc['25%',:]\n",
    "\n",
    "ratio = 2.5*iqr / low #normalize somehow to get this to work\n",
    "\n",
    "#results not very definitive of anything (lego and storage next to each other...yikes)\n",
    "# ratio.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drawing   0.04\n",
      "storage   0.15\n",
      "basic     0.25\n",
      "social    0.25\n",
      "connect   0.27\n",
      "lego      0.28\n",
      "all       0.30\n",
      "sensor    0.38\n",
      "media     0.45\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Pearson Mode Skewness\n",
    "skew = (df_groups.mean() - df_groups.median()) / df_groups.std()\n",
    "skew.sort(ascending=True)\n",
    "# skew.plot(kind='bar')\n",
    "print skew\n",
    "\n",
    "#Pearson Mode Skewness, Alternative\n",
    "# 3*(df_groups.mean() - df_groups.median()) / df_groups.std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108b8c950>"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11095dd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# groups = [df_all, df_basic, df_lego, df_media, df_storage, df_connect, df_social, df_sensor, df_draw]\n",
    "# group_names = ['all', 'basic', 'lego', 'media', 'storage', 'connect', 'social', 'sensor', 'draw']\n",
    "\n",
    "#new dataframe where each group is column (rows don't correspond to anything really)\n",
    "df_mult = pd.DataFrame(columns = mult_cols, index=group_names)\n",
    "\n",
    "for i in range(len(groups)):\n",
    "    group_name = group_names[i] #basic, lego\n",
    "    df_temp = groups[i] #df of specific group\n",
    "    for j in range(len(mult_cols)): #0-2\n",
    "        mult_name = mult_cols[j]\n",
    "        frac = float(df_temp[mult_name].sum()) / float(len(df_temp)) #frac of projects that has multiplier\n",
    "        df_mult.loc[group_names[i], mult_name] = frac\n",
    "\n",
    "        \n",
    "cmap = 'Paired'\n",
    "plot = df_mult.plot(kind='bar', figsize=(12.944,8), fontsize=20, colormap=cmap)\n",
    "plot.set_xlabel('Functionality Group')\n",
    "plot.set_ylabel('Proportion of Projects')\n",
    "        \n",
    "#looping through colormaps to find one that looks good in gray scale\n",
    "# qual=['Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3']        \n",
    "# for val in qual:\n",
    "#     df_mult.plot(kind='bar', figsize=(12.944,8), fontsize=20, colormap=val, title=val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at Outlier Projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#figuring out project w/ most num unique blocks\n",
    "#it's a password keeper!!! \"v2_67\"\n",
    "# max_unique = df_this['num_blocks_unique'].max()\n",
    "\n",
    "# df_max = df_this[df_this['num_blocks_unique']==max_unique]\n",
    "# df_max[['palette_groups']+agg_cols]\n",
    "#69 total\n",
    "#getting all outlier projects\n",
    "df_outlier = df_this[np.abs(df_this.num_blocks_unique-df_this.num_blocks_unique.mean())>=(3*df_this.num_blocks_unique.std())]\n",
    "# df_outlier[['project_name', 'num_components', 'num_blocks', 'num_blocks_unique', 'palette_groups', 'num_palette_groups' ]]\n",
    "# df_outlier.groupby(df_outlier.palette_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Determmining which components appear most in multiple groups\n",
    "\n",
    "#list of all columns refering to blocks or components\n",
    "# blocks = [name for name in df.columns if name[:2]==\"b_\"] #111 total\n",
    "# components = [name for name in df.columns if name[:2]==\"c_\"] #57 total\n",
    "\n",
    "# count = df[df.num_palette_groups>1][components].apply(lambda column: (column!=0).sum())\n",
    "# count.sort(ascending=False)\n",
    "# count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iqr  = 0.6745 #fraction of std deviation = 1 quartile\n",
    "# (df_groups.median()+(2.698+0.7*df_groups.std()) / (0.6745*df_groups.std())\n",
    "# df_groups.quantile(q=0.99)\n",
    "# /df_groups.quantile(q=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df[['num_palette_groups']].hist()\n",
    "# df[df.num_palette_groups>1]['num_palette_groups'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3289\n",
      "basic              1202\n",
      "drawing             501\n",
      "media               319\n",
      "connect             224\n",
      "storage             146\n",
      "media,sensor        110\n",
      "sensor,drawing       92\n",
      "sensor               91\n",
      "media,drawing        71\n",
      "social               58\n",
      "storage,connect      40\n",
      "storage,drawing      34\n",
      "media,connect        29\n",
      "lego                 28\n",
      "media,storage        26\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10ca73fd0>], dtype=object)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad63090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = df_this['palette_groups'].value_counts()\n",
    "print count.sum()\n",
    "print count.head(15)\n",
    "count.head(15).plot(kind='pie', subplots=True, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basic\t1202\t0.36546062633\n",
      "drawing\t501\t0.152325934935\n",
      "media\t319\t0.0969899665552\n",
      "connect\t224\t0.0681058072362\n",
      "storage\t146\t0.0443903922165\n",
      "media,sensor\t110\t0.0334448160535\n",
      "sensor,drawing\t92\t0.027972027972\n",
      "sensor\t91\t0.0276679841897\n",
      "media,drawing\t71\t0.0215871085436\n",
      "social\t58\t0.0176345393737\n",
      "storage,connect\t40\t0.0121617512922\n",
      "storage,drawing\t34\t0.0103374885984\n",
      "media,connect\t29\t0.00881726968683\n",
      "lego\t28\t0.00851322590453\n",
      "media,storage\t26\t0.00790513833992\n",
      "storage,connect,sensor\t25\t0.00760109455762\n",
      "media,sensor,drawing\t23\t0.00699300699301\n",
      "connect,sensor\t21\t0.0063849194284\n",
      "media,social\t19\t0.00577683186379\n",
      "storage,sensor\t19\t0.00577683186379\n",
      "storage,social\t19\t0.00577683186379\n",
      "media,storage,connect\t17\t0.00516874429918\n",
      "media,storage,drawing\t16\t0.00486470051687\n",
      "connect,social\t12\t0.00364852538766\n",
      "social,sensor\t11\t0.00334448160535\n",
      "media,storage,social,sensor\t11\t0.00334448160535\n",
      "media,storage,social\t10\t0.00304043782305\n",
      "media,connect,sensor\t8\t0.00243235025844\n",
      "media,storage,sensor,drawing\t8\t0.00243235025844\n",
      "media,connect,social\t8\t0.00243235025844\n",
      "storage,connect,social\t7\t0.00212830647613\n",
      "media,storage,connect,sensor\t7\t0.00212830647613\n",
      "media,storage,sensor\t7\t0.00212830647613\n",
      "media,storage,connect,social,sensor\t7\t0.00212830647613\n",
      "connect,drawing\t6\t0.00182426269383\n",
      "storage,connect,social,sensor\t6\t0.00182426269383\n",
      "storage,social,sensor\t4\t0.00121617512922\n",
      "connect,social,drawing\t4\t0.00121617512922\n",
      "connect,social,sensor\t4\t0.00121617512922\n",
      "connect,sensor,drawing\t4\t0.00121617512922\n",
      "media,social,sensor\t4\t0.00121617512922\n",
      "media,social,drawing\t3\t0.000912131346914\n",
      "storage,connect,drawing\t3\t0.000912131346914\n",
      "social,drawing\t3\t0.000912131346914\n",
      "storage,sensor,drawing\t3\t0.000912131346914\n",
      "media,connect,social,sensor\t3\t0.000912131346914\n",
      "media,storage,connect,drawing\t2\t0.000608087564609\n",
      "media,connect,drawing\t2\t0.000608087564609\n",
      "media,connect,sensor,drawing\t2\t0.000608087564609\n",
      "media,connect,social,sensor,drawing\t2\t0.000608087564609\n",
      "storage,connect,sensor,drawing\t1\t0.000304043782305\n",
      "media,storage,social,sensor,drawing\t1\t0.000304043782305\n",
      "social,sensor,drawing\t1\t0.000304043782305\n",
      "media,social,sensor,drawing\t1\t0.000304043782305\n",
      "media,storage,connect,social\t1\t0.000304043782305\n",
      "media,connect,social,drawing\t1\t0.000304043782305\n",
      "storage,social,drawing\t1\t0.000304043782305\n",
      "media,storage,connect,social,drawing\t1\t0.000304043782305\n",
      "58\n",
      "2971\n",
      "0.903314077227\n"
     ]
    }
   ],
   "source": [
    "#% of each group\n",
    "NUM_PROJS = len(df_this)\n",
    "# for key in count.keys:\n",
    "#     count[keys] = count[keys] / NUM_PROJS\n",
    "sum=0\n",
    "i = 0;\n",
    "for key, val in count.iteritems():\n",
    "    if i<15:\n",
    "        sum+=val\n",
    "    i=i+1\n",
    "    print key + \"\\t\" + str(val) + \"\\t\" + str(float(val)/NUM_PROJS)\n",
    "#     \"%.2f\" % a\n",
    "print i\n",
    "print sum\n",
    "print float(sum)/NUM_PROJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
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 },
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