{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_manifest = pd.read_csv('0203_contributing_merged_manifest.csv')\n",
    "topic_distributions = pd.read_csv('020125_CONTRIBUTING_file_topic_distributions.csv')\n",
    "readability_scores = pd.read_csv('020125_CONTRIBUTING_readability.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "715"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_merge = readability_scores.merge(topic_distributions, on=['filename'],how=\"inner\")\n",
    "#primary_merge = first_merge.merge(readability_scores, )\n",
    "first_merge['fvf_filepath'] = first_merge['filename']\n",
    "second_merge = first_merge.merge(merged_manifest, on=['fvf_filepath'], how=\"inner\")\n",
    "len(second_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">flesch_reading_ease</th>\n",
       "      <th colspan=\"2\" halign=\"left\">reading_time</th>\n",
       "      <th colspan=\"2\" halign=\"left\">word_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranef_grouping</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.102071</td>\n",
       "      <td>49.99</td>\n",
       "      <td>23.409286</td>\n",
       "      <td>13.720</td>\n",
       "      <td>259.635714</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.237276</td>\n",
       "      <td>53.41</td>\n",
       "      <td>29.065789</td>\n",
       "      <td>16.880</td>\n",
       "      <td>329.151703</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.432222</td>\n",
       "      <td>54.78</td>\n",
       "      <td>34.361627</td>\n",
       "      <td>23.365</td>\n",
       "      <td>394.242063</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               flesch_reading_ease        reading_time          word_count  \\\n",
       "                              mean median         mean  median        mean   \n",
       "ranef_grouping                                                               \n",
       "0                        46.102071  49.99    23.409286  13.720  259.635714   \n",
       "1                        44.237276  53.41    29.065789  16.880  329.151703   \n",
       "2                        47.432222  54.78    34.361627  23.365  394.242063   \n",
       "\n",
       "                       \n",
       "               median  \n",
       "ranef_grouping         \n",
       "0               153.0  \n",
       "1               198.0  \n",
       "2               252.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "readability_aggregate = second_merge.groupby('ranef_grouping').agg({\n",
    "    'flesch_reading_ease' : ['mean', 'median'],\n",
    "    'reading_time' : ['mean', 'median'],\n",
    "    'word_count' : ['mean', 'median'],\n",
    "})\n",
    "\n",
    "readability_aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>t0</th>\n",
       "      <th>t1</th>\n",
       "      <th>t2</th>\n",
       "      <th>t3</th>\n",
       "      <th>t4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ranef_grouping</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.093281</td>\n",
       "      <td>0.368268</td>\n",
       "      <td>0.156304</td>\n",
       "      <td>0.098748</td>\n",
       "      <td>0.283398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.157519</td>\n",
       "      <td>0.281908</td>\n",
       "      <td>0.185817</td>\n",
       "      <td>0.112195</td>\n",
       "      <td>0.262561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.202565</td>\n",
       "      <td>0.252555</td>\n",
       "      <td>0.167163</td>\n",
       "      <td>0.090678</td>\n",
       "      <td>0.287039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      t0        t1        t2        t3        t4\n",
       "                    mean      mean      mean      mean      mean\n",
       "ranef_grouping                                                  \n",
       "0               0.093281  0.368268  0.156304  0.098748  0.283398\n",
       "1               0.157519  0.281908  0.185817  0.112195  0.262561\n",
       "2               0.202565  0.252555  0.167163  0.090678  0.287039"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_aggregate = second_merge.groupby('ranef_grouping').agg({\n",
    "    't0' : ['mean'],\n",
    "    't1' : ['mean'],\n",
    "    't2' : ['mean'],\n",
    "    't3' : ['mean'],\n",
    "    't4' : ['mean']\n",
    "})\n",
    "\n",
    "topic_aggregate"
   ]
  }
 ],
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