336 lines
8.4 KiB
Plaintext
336 lines
8.4 KiB
Plaintext
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{
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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": null,
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"id": "ff3f890b",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "805d04c7",
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"id": "dcc8c9c7",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "818b8346",
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"id": "04f6ff60",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62600721",
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"id": "a770b812",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0234ccf2",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0c77c2dc",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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