815 lines
21 KiB
Plaintext
815 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "247bbf84",
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"metadata": {},
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"source": [
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"# Split-apply-combine operations for tabular data"
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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": 1,
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"id": "44584190",
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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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"id": "ba193f3f",
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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 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 style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>subject_id</th>\n",
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" <th>condition_id</th>\n",
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" <th>response_time</th>\n",
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" <th>response</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>312</td>\n",
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" <td>A1</td>\n",
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" <td>0.12</td>\n",
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" <td>LEFT</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>312</td>\n",
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" <td>A2</td>\n",
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" <td>0.37</td>\n",
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" <td>LEFT</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>312</td>\n",
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" <td>C2</td>\n",
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" <td>0.68</td>\n",
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" <td>LEFT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>313</td>\n",
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" <td>A1</td>\n",
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" <td>0.07</td>\n",
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" <td>RIGHT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>313</td>\n",
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" <td>B1</td>\n",
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" <td>0.08</td>\n",
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" <td>RIGHT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>314</td>\n",
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" <td>A2</td>\n",
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" <td>0.29</td>\n",
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" <td>LEFT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>314</td>\n",
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" <td>B1</td>\n",
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" <td>0.14</td>\n",
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" <td>RIGHT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>314</td>\n",
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" <td>C2</td>\n",
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" <td>0.73</td>\n",
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" <td>RIGHT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>711</td>\n",
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" <td>A1</td>\n",
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" <td>4.01</td>\n",
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" <td>RIGHT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>712</td>\n",
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" <td>A2</td>\n",
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" <td>3.29</td>\n",
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" <td>LEFT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>713</td>\n",
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" <td>B1</td>\n",
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" <td>5.74</td>\n",
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" <td>LEFT</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>714</td>\n",
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" <td>B2</td>\n",
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" <td>3.32</td>\n",
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" <td>RIGHT</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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" subject_id condition_id response_time response\n",
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"0 312 A1 0.12 LEFT\n",
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"1 312 A2 0.37 LEFT\n",
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"2 312 C2 0.68 LEFT\n",
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"3 313 A1 0.07 RIGHT\n",
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"4 313 B1 0.08 RIGHT\n",
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"5 314 A2 0.29 LEFT\n",
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"6 314 B1 0.14 RIGHT\n",
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"7 314 C2 0.73 RIGHT\n",
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"8 711 A1 4.01 RIGHT\n",
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"9 712 A2 3.29 LEFT\n",
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"10 713 B1 5.74 LEFT\n",
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"11 714 B2 3.32 RIGHT"
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]
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},
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"execution_count": 2,
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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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"data = pd.DataFrame(\n",
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" data=[\n",
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" ['312', 'A1', 0.12, 'LEFT'],\n",
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" ['312', 'A2', 0.37, 'LEFT'],\n",
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" ['312', 'C2', 0.68, 'LEFT'],\n",
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" ['313', 'A1', 0.07, 'RIGHT'],\n",
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" ['313', 'B1', 0.08, 'RIGHT'],\n",
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" ['314', 'A2', 0.29, 'LEFT'],\n",
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" ['314', 'B1', 0.14, 'RIGHT'],\n",
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" ['314', 'C2', 0.73, 'RIGHT'],\n",
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" ['711', 'A1', 4.01, 'RIGHT'],\n",
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" ['712', 'A2', 3.29, 'LEFT'],\n",
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" ['713', 'B1', 5.74, 'LEFT'],\n",
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" ['714', 'B2', 3.32, 'RIGHT'],\n",
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" ],\n",
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" columns=['subject_id', 'condition_id', 'response_time', 'response'],\n",
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")\n",
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"data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8a239e0c",
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"metadata": {},
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"source": [
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"# Group-by"
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]
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},
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{
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"cell_type": "markdown",
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"id": "31eba91e",
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"metadata": {},
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"source": [
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"We want to compute the mean response time by condition.\n",
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"\n",
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"Let's start by doing it by hand, using for loops!"
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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": 14,
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"id": "e8331039",
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"metadata": {},
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"outputs": [],
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"source": [
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"conditions = data['condition_id'].unique()\n",
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"results_dict = {}\n",
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"for condition in conditions:\n",
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" group = data[data['condition_id'] == condition]\n",
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" results_dict[condition] = group['response_time'].mean()\n",
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"\n",
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"results = pd.DataFrame([results_dict], index=['response_time']).T"
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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": 15,
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"id": "09cb04c4",
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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 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 style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>response_time</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>A1</th>\n",
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" <td>1.400000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>A2</th>\n",
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" <td>1.316667</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C2</th>\n",
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" <td>0.705000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>B1</th>\n",
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" <td>1.986667</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>B2</th>\n",
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" <td>3.320000</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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" response_time\n",
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"A1 1.400000\n",
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"A2 1.316667\n",
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"C2 0.705000\n",
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"B1 1.986667\n",
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"B2 3.320000"
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]
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},
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"execution_count": 15,
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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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"results"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2bc09c66",
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"metadata": {},
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"source": [
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"This is a basic operation, and we would need to repeat his pattern a million times!\n",
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"\n",
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"Pandas and all other tools for tabular data provide a command for performing operations on groups."
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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": 29,
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"id": "0500cd4a",
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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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"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x14ff67a90>"
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]
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},
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"execution_count": 29,
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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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"# df.groupby(column_name) groups a DataFrame by the values in the column\n",
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"data.groupby('condition_id')"
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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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"id": "c5857c4e",
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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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"condition_id\n",
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"A1 3\n",
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"A2 3\n",
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"B1 3\n",
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"B2 1\n",
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"C2 2\n",
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"dtype: int64"
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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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"# The group-by object can by used as a DataFrame. \n",
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"# Operations are executed on each group individually, then aggregated\n",
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"data.groupby('condition_id').size()"
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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": 33,
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"id": "5c865cc1",
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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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"condition_id\n",
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"A1 1.400000\n",
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"A2 1.316667\n",
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"B1 1.986667\n",
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"B2 3.320000\n",
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"C2 0.705000\n",
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"Name: response_time, dtype: float64"
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]
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},
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"execution_count": 33,
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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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"data.groupby('condition_id')['response_time'].mean()"
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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": 36,
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"id": "615a4515",
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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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"condition_id\n",
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"A1 4.01\n",
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"A2 3.29\n",
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"B1 5.74\n",
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"B2 3.32\n",
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"C2 0.73\n",
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"Name: response_time, dtype: float64"
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]
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},
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"execution_count": 36,
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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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"data.groupby('condition_id')['response_time'].max()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b0441458",
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"metadata": {},
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"source": [
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"# Pivot tables"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3feec98d",
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"metadata": {},
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"source": [
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"We want to look at response time biases when the subjects respond LEFT vs RIGHT. In principle, we expect them to have the same response time in both cases.\n",
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"\n",
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"We compute a summary table with 1) condition_id on the rows; 2) response on the columns; 3) the average response time for all experiments with a that condition and response\n",
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"\n",
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"We can do it with `groupby`, with some table manipulation commands."
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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": 44,
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"id": "4a8a7d0d",
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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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"condition_id response\n",
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"A1 LEFT 0.120000\n",
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" RIGHT 2.040000\n",
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"A2 LEFT 1.316667\n",
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"B1 LEFT 5.740000\n",
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" RIGHT 0.110000\n",
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"B2 RIGHT 3.320000\n",
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"C2 LEFT 0.680000\n",
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" RIGHT 0.730000\n",
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"Name: response_time, dtype: float64"
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]
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},
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"execution_count": 44,
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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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"summary = data.groupby(['condition_id', 'response'])['response_time'].mean()\n",
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"summary"
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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": 45,
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"id": "e5a645e0",
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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 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 style=\"text-align: right;\">\n",
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" <th>response</th>\n",
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" <th>LEFT</th>\n",
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" <th>RIGHT</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>condition_id</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>A1</th>\n",
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" <td>0.120000</td>\n",
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" <td>2.04</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>A2</th>\n",
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" <td>1.316667</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>B1</th>\n",
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" <td>5.740000</td>\n",
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" <td>0.11</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>B2</th>\n",
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" <td>NaN</td>\n",
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" <td>3.32</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>C2</th>\n",
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" <td>0.680000</td>\n",
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" <td>0.73</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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"response LEFT RIGHT\n",
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"condition_id \n",
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"A1 0.120000 2.04\n",
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"A2 1.316667 NaN\n",
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"B1 5.740000 0.11\n",
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"B2 NaN 3.32\n",
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"C2 0.680000 0.73"
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]
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},
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"execution_count": 45,
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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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"summary.unstack(level=1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3307fcc6",
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"metadata": {},
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"source": [
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"Pandas has a command called `pivot_table` that can be used to perform this kind of operation straightforwardly."
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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": 47,
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"id": "8941edfe",
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"data": {
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"<style scoped>\n",
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" <th>response</th>\n",
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"response LEFT RIGHT\n",
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"condition_id \n",
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"A1 0.120000 2.04\n",
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"A2 1.316667 NaN\n",
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"B1 5.740000 0.11\n",
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"B2 NaN 3.32\n",
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"C2 0.680000 0.73"
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]
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}
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],
|
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"source": [
|
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"data.pivot_table(index='condition_id', columns='response', values='response_time', aggfunc='mean')"
|
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]
|
|
},
|
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{
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"cell_type": "code",
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"execution_count": 59,
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" <tr>\n",
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" <th></th>\n",
|
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" <th colspan=\"2\" halign=\"left\">mean</th>\n",
|
|
" <th colspan=\"2\" halign=\"left\">std</th>\n",
|
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" <th colspan=\"2\" halign=\"left\">count</th>\n",
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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" </tr>\n",
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|
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|
|
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|
|
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|
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|
|
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|
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|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
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],
|
|
"text/plain": [
|
|
" mean std count \n",
|
|
"response LEFT RIGHT LEFT RIGHT LEFT RIGHT\n",
|
|
"condition_id \n",
|
|
"A1 0.120000 2.04 NaN 2.786001 1.0 2.0\n",
|
|
"A2 1.316667 NaN 1.709425 NaN 3.0 NaN\n",
|
|
"B1 5.740000 0.11 NaN 0.042426 1.0 2.0\n",
|
|
"B2 NaN 3.32 NaN NaN NaN 1.0\n",
|
|
"C2 0.680000 0.73 NaN NaN 1.0 1.0"
|
|
]
|
|
},
|
|
"execution_count": 59,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"(\n",
|
|
" data\n",
|
|
" .pivot_table(\n",
|
|
" index='condition_id', \n",
|
|
" columns='response', \n",
|
|
" values='response_time', \n",
|
|
" aggfunc=['mean', 'std', 'count'],\n",
|
|
" )\n",
|
|
")"
|
|
]
|
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},
|
|
{
|
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|
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