Telegram-stats-bot is a simple bot that lives in your Telegram group, logging messages to a Postgresql database and serving statistical tables and plots to users as Telegram messages.
This software is intended to be run on a server, handling updates for a bot user with a single bot per channel (multi-channel support could be added at some point if there is interest), using the excellent Python-telegram-bot library.
The bot is still in active development but at the moment, it features:
Message logging to Postgresql database with optional JSON file backup
Statistics output for users in group as Telegram messages, with optional filtering by date or limiting to the querying user. Some statistics are more useful than others, but they are mainly intended to be fun for users to play with.
Most active users
A user’s message time correlation with other users
A user’s median message time difference with other users
Message activity by hour of day
Message activity by day of week
Message activity over the week by hour and day
Message activity history
A Telegram bot token with privacy mode disabled (needed to log messages)
See here for details
Postgresql (Tested with 12.3, but there shouldn’t be anything that won’t work with 9.4 or up)
This can be on a different system than telegram-stats-bot and requires either table creation permissions on a database or database can be pre-initialized following the setup in
The easiest way to install or upgrade is with pip:
$ pip install telegram-stats-bot --upgrade
Or you can install the latest git version using poetry (installed to Python 3.7 or later):
$ git clone https://github.com/mkdryden/telegram-stats-bot.git $ poetry install
Installing directly with
setup.py should also work from the Pypi sdist (but why?).
Once installed, you can run the bot by calling the
main module with a few required arguments:
$ python -m telegram_stats_bot.main BOT_TOKEN CHAT_ID POSTGRESQL_URL
BOT_TOKEN: Your bot’s token e.g.,
CHAT_ID: The chat id to monitor (will be a large integer, possibly negative, if unknown, set to 0 and see below)
POSTGRESQL_URL: Connection information in the form:
if DB_NAME exists, there must not be tables called
user_nameswith incorrect columns
Two optional arguments exist as well:
json-path: Specifying a path here will log messages to json files in addition to the database. If only a prefix is specified, they will be saved under that prefix in your platform’s preferred app data directory. This was mostly for development purposes and is not necessary in normal use.
tz: Specify a tz database time zone string here (e.g.,
America/New_York) to return statistics queries in this time zone. (Defaults to
A complete command might look like:
$ python -m telegram_stats_bot.main --tz="America/Toronto" "110201543:AAHdqTcvCH1vGWJxfSeofSAs0K5PALDsaw" "postgresql://telegram:[email protected]/telegram_bot"
On startup, the bot will attempt to create the database and tables, if they do not already exist. If you do not know the chat’s id and have set it to 0 as mentioned above, you can send the
/chatid command inside the group, and the bot will reply with it, then restart the bot with the id. If you have forgotten to disable privacy mode, an error will be logged in the terminal.
The bot will now log all messages in the group, but will only respond to users who have sent a message that has been logged previously (and this list is only updated once an hour, so if you’re impatient, you can restart the bot after you’ve sent a message to trigger the update). You can see if messages are being logged correctly by reviewing the terminal output. You should see a line like
2020-06-04 02:08:39,212 - __main__ - INFO - 8, whenever a message is logged.
To fetch stats, simply message the bot, either inside the group being logged, or in a direct message, using the
/stats with no arguments prints the table of most active users and other statistics are available through various subcommands. All commands are documented and the built in help can be displayed with
/stats -h or
stats <subcommand> -h.
Most commands have optional arguments that change the behaviour of the output. Nearly all have:
-endfollowed by a timestamp (e.g., 2019, 2019-01, 2019-01-01, "2019-01-01 14:21") specify the range of data to fetch, otherwise all available data will be used. Either or both options can be given.
-lqueryfollowed by a lexical query (using Postgres’ tsquery syntax) limits results to matching messages.
-mecalculates statistics for the user sending the command, rather than all chat users.
Sample outputs of each available subcommand follow.
/stats counts returns a list of the most active users in the group.
User Total Messages Percent @ACoolUser 42150 7.0 @NumberOne 37370 6.2 @WinstonChurchill 32668 5.4 @AAAAAAA 32134 5.4 @WhereAreMyManners 30481 5.1 @TheWorstOfTheBest 28705 4.8
/stats hours returns a plot of message frequency for the hours of the day.
/stats days returns a plot of message frequency for the days of the week.
/stats week returns a plot of total messages over the data period by day of week and hour of day.
/stats history returns a plot of messages versus date.
/stats titles returns a plot of group titles over time.
/stats user returns basic statistics for the user.
Messages sent: 16711 Average messages per day: 12.31 First message was 1357.22 days ago. Usernames on record: 3 Average username lifetime: 452.41 days joined on 2017-10-01 16:11:08-04:00
/stats corr returns a list of users with the highest and lowest message time correlations with the requesting user.
User Correlations for @TheManWhoWasThursday HIGHEST CORRELATION: @MyGoodFriend 0.335 @Rawr 0.302 @MangesUnePoutine 0.284 @GreenBlood 0.251 @TooMuchVacuum 0.235 LOWEST CORRELATION: @Shiny 0.146 @BlueDog 0.142 @CoolCat 0.122 @EatMe 0.116 @JustPassingBy 0.106
/stats delta returns a list of users with the shortest differences in message times with the requesting user.
Median message delays for @KingLeer and: @PolyamorousPasta 00:03:23 @AggressiveArgon 00:04:43 @AdjectiveNoun 00:08:27 @SuperSalad 00:09:05 @ABoredProgrammer 00:09:06
/stats types returns a table of messages by type, comparing the requesting user with the full group.
Messages by type, @AUser vs group: type Group Count Group Percent User Count User Percent text 528813.0 88.3 13929.0 83.4 sticker 34621.0 5.8 1226.0 7.3 photo 25995.0 4.3 1208.0 7.2 animation 6983.0 1.2 274.0 1.6 video 1325.0 0.2 48.0 0.3 voice 475.0 0.1 2.0 0.0 location 252.0 0.0 2.0 0.0 video_note 84.0 0.0 1.0 0.0 audio 62.0 0.0 1.0 0.0 poll 29.0 0.0 1.0 0.0 document 1.0 0.0 1.0 0.0 Total 598640.0 100.0 16693.0 100.0
/stats words returns a table of the most commonly used lexemes
Most frequently used lexemes: Lexeme Messages Uses like 1265 1334 well 753 765 actual 628 645 make 600 619 yeah 609 609 mean 544 553 thing 473 490 realli 472 482 though 467 470 peopl 415 445 think 425 433 know 403 409 need 396 408 time 371 389 want 354 371 would 345 366 much 345 357 probabl 348 356 even 331 338 stuff 318 332
/stats random prints a random message from the database.
Telegram-stats-bot is a work in progress. New stats will be added, but no guarantees that the database structure will stay constant if Telegram’s message structure changes or I need to change something to make a new statistic work.