519 lines
38 KiB
Plaintext
519 lines
38 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 seaborn as sns"
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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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"import os, sys\n",
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"nb_dir = os.path.split(os.getcwd())[0]\n",
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"if nb_dir not in sys.path:\n",
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" sys.path.append(nb_dir)"
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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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"source": [
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"from features.communication import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Example of feature calculation"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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" id _id timestamp device_id call_type \\\n",
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"0 1649 2 1603359870948 645ca1c1-b798-410c-a0b2-fd24d0f0186d 2 \n",
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"1 1648 1 1603359849077 645ca1c1-b798-410c-a0b2-fd24d0f0186d 2 \n",
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"2 1647 1 1603358854783 049df3f8-8541-4cf5-af2b-83f6b3f0cf4b 2 \n",
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"3 1267 5 1599242289282 d2a71262-b2cf-484b-b422-ec2a84eebd3d 2 \n",
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"4 1266 4 1599242131166 d2a71262-b2cf-484b-b422-ec2a84eebd3d 2 \n",
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"5 794 3 1588053846893 d2a71262-b2cf-484b-b422-ec2a84eebd3d 3 \n",
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"6 744 2 1587137920351 d2a71262-b2cf-484b-b422-ec2a84eebd3d 3 \n",
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"7 616 1 1585919254218 d2a71262-b2cf-484b-b422-ec2a84eebd3d 1 \n",
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"8 556 1 1585043148221 d5fb52e1-7df8-44b5-a805-8d04ca008061 1 \n",
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"\n",
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" call_duration trace participant_id \\\n",
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"0 0 040519011 21 \n",
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"1 0 +38640519011 21 \n",
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"2 0 72441dc0eb9550fcdc5a61cce9dc8bd302494680 21 \n",
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"3 0 4f345b8682824a491e57efbd4afd61e6212a9c05 21 \n",
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"4 0 4f345b8682824a491e57efbd4afd61e6212a9c05 21 \n",
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"5 0 1d705b16b9983c32d2ef1af7f150944696a23fb5 21 \n",
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"6 0 1d705b16b9983c32d2ef1af7f150944696a23fb5 21 \n",
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"7 29 1d705b16b9983c32d2ef1af7f150944696a23fb5 21 \n",
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"8 17 501cef50691bcc4f0ddc4bb5d6daa07154189d47 21 \n",
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"\n",
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" username \n",
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"0 nokia_0000003 \n",
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"1 nokia_0000003 \n",
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"2 nokia_0000003 \n",
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"3 nokia_0000003 \n",
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"4 nokia_0000003 \n",
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"5 nokia_0000003 \n",
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"6 nokia_0000003 \n",
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"7 nokia_0000003 \n",
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"8 nokia_0000003 \n"
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]
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}
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],
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"source": [
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"df_calls = get_call_data([\"nokia_0000003\"])\n",
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"print(df_calls)"
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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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" .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>call_type</th>\n",
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" <th>no_incoming</th>\n",
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" <th>no_outgoing</th>\n",
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" <th>no_missed</th>\n",
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" <th>duration_incoming</th>\n",
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" <th>duration_outgoing</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>participant_id</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>21</th>\n",
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" <td>2</td>\n",
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" <td>5</td>\n",
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" <td>2</td>\n",
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" <td>46</td>\n",
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" <td>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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"call_type no_incoming no_outgoing no_missed duration_incoming \\\n",
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"participant_id \n",
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"21 2 5 2 46 \n",
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"\n",
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"call_type duration_outgoing \n",
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"participant_id \n",
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"21 0 "
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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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"count_comms(df_calls)"
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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": 6,
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"metadata": {
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"scrolled": true
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},
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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>message_type</th>\n",
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" <th>no_received</th>\n",
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" <th>no_sent</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>participant_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>21</th>\n",
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" <td>16</td>\n",
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" <td>2</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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"message_type no_received no_sent\n",
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"participant_id \n",
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"21 16 2"
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]
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},
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"execution_count": 6,
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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_sms = get_sms_data([\"nokia_0000003\"])\n",
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"count_comms(df_sms)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Explore the whole dataset"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Call 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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import participants.query_db"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"participants_inactive_usernames = participants.query_db.get_usernames()"
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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": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_calls_inactive = get_call_data(participants_inactive_usernames)"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_calls_features = count_comms(df_calls_inactive)"
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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": 11,
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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>call_type</th>\n",
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" <th>no_incoming</th>\n",
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" <th>no_outgoing</th>\n",
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" <th>no_missed</th>\n",
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" <th>duration_incoming</th>\n",
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" <th>duration_outgoing</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>count</th>\n",
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" <td>43.000000</td>\n",
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" <td>44.000000</td>\n",
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" <td>38.000000</td>\n",
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" <td>43.000000</td>\n",
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" <td>44.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>27.604651</td>\n",
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" <td>37.727273</td>\n",
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" <td>9.105263</td>\n",
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" <td>5926.813953</td>\n",
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" <td>7220.409091</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>37.445923</td>\n",
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" <td>50.961620</td>\n",
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" <td>13.337185</td>\n",
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" <td>7140.290568</td>\n",
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" <td>11331.095182</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>89.000000</td>\n",
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" <td>2.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>6.500000</td>\n",
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" <td>6.750000</td>\n",
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" <td>2.000000</td>\n",
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" <td>924.500000</td>\n",
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" <td>823.500000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>15.000000</td>\n",
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" <td>21.000000</td>\n",
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" <td>5.000000</td>\n",
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" <td>3258.000000</td>\n",
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" <td>2491.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>33.000000</td>\n",
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" <td>37.500000</td>\n",
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" <td>9.000000</td>\n",
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" <td>8762.500000</td>\n",
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" <td>8089.500000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>196.000000</td>\n",
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" <td>258.000000</td>\n",
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" <td>66.000000</td>\n",
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" <td>31146.000000</td>\n",
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" <td>55270.000000</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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"call_type no_incoming no_outgoing no_missed duration_incoming \\\n",
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"count 43.000000 44.000000 38.000000 43.000000 \n",
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"mean 27.604651 37.727273 9.105263 5926.813953 \n",
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"std 37.445923 50.961620 13.337185 7140.290568 \n",
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"min 1.000000 1.000000 1.000000 89.000000 \n",
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"25% 6.500000 6.750000 2.000000 924.500000 \n",
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"50% 15.000000 21.000000 5.000000 3258.000000 \n",
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"75% 33.000000 37.500000 9.000000 8762.500000 \n",
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"max 196.000000 258.000000 66.000000 31146.000000 \n",
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"\n",
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"call_type duration_outgoing \n",
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"count 44.000000 \n",
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"mean 7220.409091 \n",
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"std 11331.095182 \n",
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"min 2.000000 \n",
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"25% 823.500000 \n",
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"50% 2491.000000 \n",
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"75% 8089.500000 \n",
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"max 55270.000000 "
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]
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},
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"execution_count": 11,
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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_calls_features.describe()"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"calls_number = pd.wide_to_long(\n",
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" df_calls_features.reset_index(), \n",
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" i=\"participant_id\", \n",
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" j=\"call_type\", \n",
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" stubnames=\"no\", \n",
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" sep=\"_\", \n",
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" suffix=\"\\D+\"\n",
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")"
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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": 13,
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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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"<seaborn.axisgrid.FacetGrid at 0x7f867a9bb490>"
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]
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},
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"execution_count": 13,
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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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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 658x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"sns.displot(calls_number, x=\"no\", hue=\"call_type\", binwidth=5, element=\"step\", height=8)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<seaborn.axisgrid.FacetGrid at 0x7f867a7ec8b0>"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 658x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"calls_duration = pd.wide_to_long(\n",
|
|
" df_calls_features.reset_index(), \n",
|
|
" i=\"participant_id\", \n",
|
|
" j=\"call_type\", \n",
|
|
" stubnames=\"duration\", \n",
|
|
" sep=\"_\", \n",
|
|
" suffix=\"\\D+\"\n",
|
|
")\n",
|
|
"sns.displot(calls_duration, x=\"duration\", hue=\"call_type\", multiple=\"dodge\", height=8, log_scale=(True, False))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "straw2analysis",
|
|
"language": "python",
|
|
"name": "straw2analysis"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|