814 lines
55 KiB
Plaintext
814 lines
55 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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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt"
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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()\n",
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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": 9,
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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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" }\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>13</th>\n",
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" <td>3.0</td>\n",
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" <td>21.0</td>\n",
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" <td>2.0</td>\n",
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" <td>342.0</td>\n",
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" <td>2836.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>16.0</td>\n",
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" <td>22.0</td>\n",
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" <td>11.0</td>\n",
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" <td>1873.0</td>\n",
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" <td>2789.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>3.0</td>\n",
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" <td>2.0</td>\n",
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" <td>NaN</td>\n",
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" <td>310.0</td>\n",
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" <td>19.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <td>4.0</td>\n",
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" <td>6.0</td>\n",
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" <td>3.0</td>\n",
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" <td>1963.0</td>\n",
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" <td>849.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>20.0</td>\n",
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" <td>60.0</td>\n",
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" <td>8.0</td>\n",
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" <td>5789.0</td>\n",
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" <td>17046.0</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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"13 3.0 21.0 2.0 342.0 \n",
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"14 16.0 22.0 11.0 1873.0 \n",
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"15 3.0 2.0 NaN 310.0 \n",
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"16 4.0 6.0 3.0 1963.0 \n",
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"17 20.0 60.0 8.0 5789.0 \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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"13 2836.0 \n",
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"14 2789.0 \n",
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"15 19.0 \n",
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"16 849.0 \n",
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"17 17046.0 "
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]
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},
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"execution_count": 9,
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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 = count_comms(df_calls_inactive)\n",
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"df_calls_features.head()"
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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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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" 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>47.000000</td>\n",
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" <td>48.000000</td>\n",
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" <td>42.000000</td>\n",
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" <td>47.000000</td>\n",
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" <td>48.000000</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>29.659574</td>\n",
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" <td>41.270833</td>\n",
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" <td>10.809524</td>\n",
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" <td>7222.297872</td>\n",
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" <td>8462.750000</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>37.325988</td>\n",
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" <td>50.983827</td>\n",
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" <td>14.385355</td>\n",
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" <td>8790.037189</td>\n",
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" <td>11965.518908</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>7.500000</td>\n",
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" <td>7.750000</td>\n",
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" <td>2.250000</td>\n",
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" <td>1174.000000</td>\n",
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" <td>891.750000</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>16.000000</td>\n",
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" <td>22.500000</td>\n",
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" <td>6.500000</td>\n",
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" <td>3471.000000</td>\n",
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" <td>2812.500000</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>37.000000</td>\n",
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" <td>61.250000</td>\n",
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" <td>10.750000</td>\n",
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" <td>10441.000000</td>\n",
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" <td>12758.500000</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>40232.000000</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 47.000000 48.000000 42.000000 47.000000 \n",
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"mean 29.659574 41.270833 10.809524 7222.297872 \n",
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"std 37.325988 50.983827 14.385355 8790.037189 \n",
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"min 1.000000 1.000000 1.000000 89.000000 \n",
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"25% 7.500000 7.750000 2.250000 1174.000000 \n",
|
|
"50% 16.000000 22.500000 6.500000 3471.000000 \n",
|
|
"75% 37.000000 61.250000 10.750000 10441.000000 \n",
|
|
"max 196.000000 258.000000 66.000000 40232.000000 \n",
|
|
"\n",
|
|
"call_type duration_outgoing \n",
|
|
"count 48.000000 \n",
|
|
"mean 8462.750000 \n",
|
|
"std 11965.518908 \n",
|
|
"min 2.000000 \n",
|
|
"25% 891.750000 \n",
|
|
"50% 2812.500000 \n",
|
|
"75% 12758.500000 \n",
|
|
"max 55270.000000 "
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df_calls_features.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"calls_number = pd.wide_to_long(\n",
|
|
" df_calls_features.reset_index(), \n",
|
|
" i=\"participant_id\", \n",
|
|
" j=\"call_type\", \n",
|
|
" stubnames=\"no\", \n",
|
|
" sep=\"_\", \n",
|
|
" suffix=\"\\D+\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<seaborn.axisgrid.FacetGrid at 0x195fec3f070>"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"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": [
|
|
"sns.displot(calls_number, x=\"no\", hue=\"call_type\", binwidth=5, element=\"step\", height=8)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<seaborn.axisgrid.FacetGrid at 0x19581452d60>"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"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))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Most frequent contacts by participant"
|
|
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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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"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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" id _id timestamp device_id \\\n",
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"3824 5048 241 1618926744570 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n",
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"3825 5043 236 1618912135563 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n",
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"\n",
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" call_type call_duration trace \\\n",
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"3824 2 218 ed9d4bc2d3436dedfecce58ddefbe0a14ce49ee2 \n",
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"3828 59 uploader_21880 1 38 "
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]
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},
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"execution_count": 14,
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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_inactive = enumerate_contacts(df_calls_inactive)\n",
|
|
"df_calls_inactive.tail()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_calls_frequent = df_calls_inactive.query('contact_id < 5')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='contact_id', ylabel='freq'>"
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|
]
|
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},
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|
"execution_count": 16,
|
|
"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 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"sns.boxplot(x=\"contact_id\", y=\"freq\", data=df_calls_frequent)"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.9.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|