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MaxHalford/emojis.ipynb

Created September 22, 2023 09:31
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Analyzing Slack emoji usage
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emojis.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyzing Slack emoji usage"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [],
"source": [
"import collections\n",
"import pathlib\n",
"import json\n",
"\n",
"def iter_messages(export_path):\n",
" export = pathlib.Path(export_path)\n",
" for channel in export.glob('*'):\n",
" for day in channel.glob('*.json'):\n",
" with open(day) as f:\n",
" messages = json.load(open(day))\n",
" yield from messages\n",
"\n",
"emoji_usage = []\n",
"emoji_pattern = re.compile(r':([a-z_]+):')\n",
"\n",
"for message in iter_messages('Carbonfact Slack export Mar 2 2021 - Aug 29 2023'):\n",
"\n",
" # Skip non-user messages\n",
" if (\n",
" message.get('type') != 'message' or\n",
" message.get('subtype') in {'bot_message', 'thread_broadcast'} or\n",
" message['user'] == 'USLACKBOT'\n",
" ):\n",
" continue\n",
"\n",
" # Handle message content\n",
" for emoji in re.findall(emoji_pattern, message['text']):\n",
" emoji_usage.append({\n",
" 'user': message['user'],\n",
" 'emoji_name': emoji,\n",
" 'context': 'message'\n",
" })\n",
"\n",
" # Handle message reactions\n",
" for emoji in message.get('reactions', []):\n",
" for user in emoji['users']:\n",
" emoji_usage.append({\n",
" 'user': user,\n",
" 'emoji_name': emoji['name'],\n",
" 'context': 'reaction'\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user</th>\n",
" <th>emoji_name</th>\n",
" <th>context</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>U03911L004W</td>\n",
" <td>ok_hand</td>\n",
" <td>reaction</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>U03911L004W</td>\n",
" <td>arrow_up</td>\n",
" <td>message</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>U03911L004W</td>\n",
" <td>ok_hand</td>\n",
" <td>reaction</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>U03911L004W</td>\n",
" <td>wave</td>\n",
" <td>message</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>U03911L004W</td>\n",
" <td>eyes</td>\n",
" <td>reaction</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user emoji_name context\n",
"0 U03911L004W ok_hand reaction\n",
"1 U03911L004W arrow_up message\n",
"2 U03911L004W ok_hand reaction\n",
"3 U03911L004W wave message\n",
"4 U03911L004W eyes reaction"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"emoji_usage = pd.DataFrame(emoji_usage)\n",
"emoji_usage['emoji_name'] = emoji_usage['emoji_name'].str.split('::', expand=True)[0]\n",
"emoji_usage.head()"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"reaction 20552\n",
"message 14020\n",
"Name: context, dtype: int64"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emoji_usage['context'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_0fb89\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_0fb89_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
" <th id=\"T_0fb89_level0_col1\" class=\"col_heading level0 col1\" >share</th>\n",
" <th id=\"T_0fb89_level0_col2\" class=\"col_heading level0 col2\" >cum share</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row0\" class=\"row_heading level0 row0\" >U01QJAL4FBJ</th>\n",
" <td id=\"T_0fb89_row0_col0\" class=\"data row0 col0\" >6,765</td>\n",
" <td id=\"T_0fb89_row0_col1\" class=\"data row0 col1\" >19.6%</td>\n",
" <td id=\"T_0fb89_row0_col2\" class=\"data row0 col2\" >19.6%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row1\" class=\"row_heading level0 row1\" >U02SVML185B</th>\n",
" <td id=\"T_0fb89_row1_col0\" class=\"data row1 col0\" >5,424</td>\n",
" <td id=\"T_0fb89_row1_col1\" class=\"data row1 col1\" >15.7%</td>\n",
" <td id=\"T_0fb89_row1_col2\" class=\"data row1 col2\" >35.3%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row2\" class=\"row_heading level0 row2\" >U03141B8PAN</th>\n",
" <td id=\"T_0fb89_row2_col0\" class=\"data row2 col0\" >5,036</td>\n",
" <td id=\"T_0fb89_row2_col1\" class=\"data row2 col1\" >14.6%</td>\n",
" <td id=\"T_0fb89_row2_col2\" class=\"data row2 col2\" >49.8%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row3\" class=\"row_heading level0 row3\" >U01PRD0PFGD</th>\n",
" <td id=\"T_0fb89_row3_col0\" class=\"data row3 col0\" >4,056</td>\n",
" <td id=\"T_0fb89_row3_col1\" class=\"data row3 col1\" >11.7%</td>\n",
" <td id=\"T_0fb89_row3_col2\" class=\"data row3 col2\" >61.6%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row4\" class=\"row_heading level0 row4\" >U01PDNBSK47</th>\n",
" <td id=\"T_0fb89_row4_col0\" class=\"data row4 col0\" >3,659</td>\n",
" <td id=\"T_0fb89_row4_col1\" class=\"data row4 col1\" >10.6%</td>\n",
" <td id=\"T_0fb89_row4_col2\" class=\"data row4 col2\" >72.1%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row5\" class=\"row_heading level0 row5\" >U02U3G0ULAE</th>\n",
" <td id=\"T_0fb89_row5_col0\" class=\"data row5 col0\" >3,066</td>\n",
" <td id=\"T_0fb89_row5_col1\" class=\"data row5 col1\" >8.9%</td>\n",
" <td id=\"T_0fb89_row5_col2\" class=\"data row5 col2\" >81.0%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row6\" class=\"row_heading level0 row6\" >U04790EPZPB</th>\n",
" <td id=\"T_0fb89_row6_col0\" class=\"data row6 col0\" >2,058</td>\n",
" <td id=\"T_0fb89_row6_col1\" class=\"data row6 col1\" >6.0%</td>\n",
" <td id=\"T_0fb89_row6_col2\" class=\"data row6 col2\" >87.0%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0fb89_level0_row7\" class=\"row_heading level0 row7\" >U03911L004W</th>\n",
" <td id=\"T_0fb89_row7_col0\" class=\"data row7 col0\" >1,426</td>\n",
" <td id=\"T_0fb89_row7_col1\" class=\"data row7 col1\" >4.1%</td>\n",
" <td id=\"T_0fb89_row7_col2\" class=\"data row7 col2\" >91.1%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1769d6cd0>"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(\n",
" pd.DataFrame({\n",
" 'count': emoji_usage['user'].value_counts(),\n",
" 'share': emoji_usage['user'].value_counts(normalize=True),\n",
" 'cum share': emoji_usage['user'].value_counts(normalize=True).cumsum()\n",
" })\n",
" .head(8)\n",
" .style.format({\n",
" 'count': '{:,}',\n",
" 'share': '{:.1%}',\n",
" 'cum share': '{:.1%}'\n",
" })\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_08313\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th id=\"T_08313_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
" <th id=\"T_08313_level0_col1\" class=\"col_heading level0 col1\" >share</th>\n",
" <th id=\"T_08313_level0_col2\" class=\"col_heading level0 col2\" >cum share</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row0\" class=\"row_heading level0 row0\" >+1</th>\n",
" <td id=\"T_08313_row0_col0\" class=\"data row0 col0\" >4,300</td>\n",
" <td id=\"T_08313_row0_col1\" class=\"data row0 col1\" >12.4%</td>\n",
" <td id=\"T_08313_row0_col2\" class=\"data row0 col2\" >12.4%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row1\" class=\"row_heading level0 row1\" >ok_hand</th>\n",
" <td id=\"T_08313_row1_col0\" class=\"data row1 col0\" >3,911</td>\n",
" <td id=\"T_08313_row1_col1\" class=\"data row1 col1\" >11.3%</td>\n",
" <td id=\"T_08313_row1_col2\" class=\"data row1 col2\" >23.8%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row2\" class=\"row_heading level0 row2\" >pray</th>\n",
" <td id=\"T_08313_row2_col0\" class=\"data row2 col0\" >3,352</td>\n",
" <td id=\"T_08313_row2_col1\" class=\"data row2 col1\" >9.7%</td>\n",
" <td id=\"T_08313_row2_col2\" class=\"data row2 col2\" >33.4%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row3\" class=\"row_heading level0 row3\" >slightly_smiling_face</th>\n",
" <td id=\"T_08313_row3_col0\" class=\"data row3 col0\" >2,878</td>\n",
" <td id=\"T_08313_row3_col1\" class=\"data row3 col1\" >8.3%</td>\n",
" <td id=\"T_08313_row3_col2\" class=\"data row3 col2\" >41.8%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row4\" class=\"row_heading level0 row4\" >muscle</th>\n",
" <td id=\"T_08313_row4_col0\" class=\"data row4 col0\" >1,526</td>\n",
" <td id=\"T_08313_row4_col1\" class=\"data row4 col1\" >4.4%</td>\n",
" <td id=\"T_08313_row4_col2\" class=\"data row4 col2\" >46.2%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row5\" class=\"row_heading level0 row5\" >eyes</th>\n",
" <td id=\"T_08313_row5_col0\" class=\"data row5 col0\" >1,428</td>\n",
" <td id=\"T_08313_row5_col1\" class=\"data row5 col1\" >4.1%</td>\n",
" <td id=\"T_08313_row5_col2\" class=\"data row5 col2\" >50.3%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row6\" class=\"row_heading level0 row6\" >fire</th>\n",
" <td id=\"T_08313_row6_col0\" class=\"data row6 col0\" >1,332</td>\n",
" <td id=\"T_08313_row6_col1\" class=\"data row6 col1\" >3.9%</td>\n",
" <td id=\"T_08313_row6_col2\" class=\"data row6 col2\" >54.2%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_08313_level0_row7\" class=\"row_heading level0 row7\" >white_check_mark</th>\n",
" <td id=\"T_08313_row7_col0\" class=\"data row7 col0\" >1,245</td>\n",
" <td id=\"T_08313_row7_col1\" class=\"data row7 col1\" >3.6%</td>\n",
" <td id=\"T_08313_row7_col2\" class=\"data row7 col2\" >57.8%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1695a3310>"
]
},
"execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(\n",
" pd.DataFrame({\n",
" 'count': emoji_usage['emoji_name'].value_counts(),\n",
" 'share': emoji_usage['emoji_name'].value_counts(normalize=True),\n",
" 'cum share': emoji_usage['emoji_name'].value_counts(normalize=True).cumsum()\n",
" })\n",
" .head(8)\n",
" .style.format({\n",
" 'count': '{:,}',\n",
" 'share': '{:.1%}',\n",
" 'cum share': '{:.1%}'\n",
" })\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [],
"source": [
"emoji_usage['emoji_name'] = emoji_usage['emoji_name'].replace({'+1': '✅'})"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [],
"source": [
"import prince\n",
"\n",
"tally = (\n",
" emoji_usage\n",
" .groupby(['user', 'emoji_name'])\n",
" .size()\n",
" .unstack('emoji_name', fill_value=0)\n",
")\n",
"\n",
"pca = prince.PCA().fit(tally)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" #altair-viz-8ae5e3d36bb84c6a9c9371e852c26a9e.vega-embed {\n",
" width: 100%;\n",
" display: flex;\n",
" }\n",
"\n",
" #altair-viz-8ae5e3d36bb84c6a9c9371e852c26a9e.vega-embed details,\n",
" #altair-viz-8ae5e3d36bb84c6a9c9371e852c26a9e.vega-embed details summary {\n",
" position: relative;\n",
" }\n",
"</style>\n",
"<div id=\"altair-viz-8ae5e3d36bb84c6a9c9371e852c26a9e\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-8ae5e3d36bb84c6a9c9371e852c26a9e\") {\n",
" outputDiv = document.getElementById(\"altair-viz-8ae5e3d36bb84c6a9c9371e852c26a9e\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.8.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"5.8.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"circle\", \"size\": 50}, \"encoding\": {\"tooltip\": [{\"field\": \"user\", \"type\": \"nominal\"}, {\"field\": \"component 0\", \"type\": \"quantitative\"}, {\"field\": \"component 1\", \"type\": \"quantitative\"}], \"x\": {\"axis\": {\"title\": \"component 0 \\u2014 33.48%\"}, \"field\": \"component 0\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}, \"y\": {\"axis\": {\"title\": \"component 1 \\u2014 14.90%\"}, \"field\": \"component 1\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}}, \"name\": \"view_5\"}, {\"data\": {\"name\": \"data-1ce51ec0d8d36a5f9c09040fe2623939\"}, \"mark\": {\"type\": \"square\", \"color\": \"green\", \"size\": 50}, \"encoding\": {\"tooltip\": [{\"field\": \"variable\", \"type\": \"nominal\"}], \"x\": {\"field\": \"component 0\", \"scale\": {\"zero\": false}, \"type\": \"quantitative\"}, \"y\": {\"field\": 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"text/plain": [
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],
"source": [
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