294 lines
8.3 KiB
Plaintext
294 lines
8.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"merged_manifest = pd.read_csv('0203_contributing_merged_manifest.csv')\n",
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"topic_distributions = pd.read_csv('020125_CONTRIBUTING_file_topic_distributions.csv')\n",
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"readability_scores = pd.read_csv('020125_CONTRIBUTING_readability.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"715"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"first_merge = readability_scores.merge(topic_distributions, on=['filename'],how=\"inner\")\n",
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"#primary_merge = first_merge.merge(readability_scores, )\n",
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"first_merge['fvf_filepath'] = first_merge['filename']\n",
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"second_merge = first_merge.merge(merged_manifest, on=['fvf_filepath'], how=\"inner\")\n",
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"len(second_merge)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead tr th {\n",
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" text-align: left;\n",
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" }\n",
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"\n",
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" .dataframe thead tr:last-of-type th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th colspan=\"2\" halign=\"left\">flesch_reading_ease</th>\n",
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" <th colspan=\"2\" halign=\"left\">reading_time</th>\n",
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" <th colspan=\"2\" halign=\"left\">word_count</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>mean</th>\n",
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" <th>median</th>\n",
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" <th>mean</th>\n",
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" <th>median</th>\n",
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" <th>mean</th>\n",
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" <th>median</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ranef_grouping</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>46.102071</td>\n",
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" <td>49.99</td>\n",
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" <td>23.409286</td>\n",
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" <td>13.720</td>\n",
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" <td>259.635714</td>\n",
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" <td>153.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>44.237276</td>\n",
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" <td>53.41</td>\n",
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" <td>29.065789</td>\n",
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" <td>16.880</td>\n",
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" <td>329.151703</td>\n",
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" <td>198.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>47.432222</td>\n",
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" <td>54.78</td>\n",
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" <td>34.361627</td>\n",
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" <td>23.365</td>\n",
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" <td>394.242063</td>\n",
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" <td>252.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" flesch_reading_ease reading_time word_count \\\n",
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" mean median mean median mean \n",
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"ranef_grouping \n",
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"0 46.102071 49.99 23.409286 13.720 259.635714 \n",
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"1 44.237276 53.41 29.065789 16.880 329.151703 \n",
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"2 47.432222 54.78 34.361627 23.365 394.242063 \n",
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"\n",
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" \n",
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" median \n",
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"ranef_grouping \n",
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"0 153.0 \n",
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"1 198.0 \n",
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"2 252.0 "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"readability_aggregate = second_merge.groupby('ranef_grouping').agg({\n",
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" 'flesch_reading_ease' : ['mean', 'median'],\n",
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" 'reading_time' : ['mean', 'median'],\n",
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" 'word_count' : ['mean', 'median'],\n",
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"})\n",
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"\n",
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"readability_aggregate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead tr th {\n",
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" text-align: left;\n",
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" }\n",
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"\n",
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" .dataframe thead tr:last-of-type th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>t0</th>\n",
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" <th>t1</th>\n",
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" <th>t2</th>\n",
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" <th>t3</th>\n",
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" <th>t4</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>mean</th>\n",
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" <th>mean</th>\n",
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" <th>mean</th>\n",
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" <th>mean</th>\n",
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" <th>mean</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ranef_grouping</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.093281</td>\n",
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" <td>0.368268</td>\n",
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" <td>0.156304</td>\n",
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" <td>0.098748</td>\n",
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" <td>0.283398</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.157519</td>\n",
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" <td>0.281908</td>\n",
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" <td>0.185817</td>\n",
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" <td>0.112195</td>\n",
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" <td>0.262561</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0.202565</td>\n",
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" <td>0.252555</td>\n",
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" <td>0.167163</td>\n",
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" <td>0.090678</td>\n",
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" <td>0.287039</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" t0 t1 t2 t3 t4\n",
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" mean mean mean mean mean\n",
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"ranef_grouping \n",
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"0 0.093281 0.368268 0.156304 0.098748 0.283398\n",
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"1 0.157519 0.281908 0.185817 0.112195 0.262561\n",
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"2 0.202565 0.252555 0.167163 0.090678 0.287039"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"topic_aggregate = second_merge.groupby('ranef_grouping').agg({\n",
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" 't0' : ['mean'],\n",
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" 't1' : ['mean'],\n",
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" 't2' : ['mean'],\n",
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" 't3' : ['mean'],\n",
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" 't4' : ['mean']\n",
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"})\n",
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"\n",
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"topic_aggregate"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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}
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"nbformat": 4,
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"nbformat_minor": 2
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}
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