Telegram gives you plenty of reach, but it doesn’t give you serious analytics. Not in the official app. As developers, we don’t guess. We measure. We look at patterns. We look at what works and what doesn’t.

In this tutorial, I’ll show you how to use Python and the Telethon library to scrape your own channel’s data, calculate engagement per post, and visualize how things change over time.

Prerequisites

Make sure Python is installed along with these libraries:

pip install telethon pandas matplotlib

You’ll also need your _api_id _and _api_hash _from my.telegram.org.

The Script

You’ll connect to Telegram, fetch the latest 100 messages from your channel, and pull the view counts along with the post dates. That’s all you need to start spotting patterns.

Here’s the core script:

from telethon.sync import TelegramClient
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

# 1. Setup Connection
api_id = 'YOUR_API_ID'
api_hash = 'YOUR_API_HASH'
channel_username = 'YOUR_TARGET_CHANNEL' # e.g., 'durov'

client = TelegramClient('session_name', api_id, api_hash)

data = []

async def main():
# 2. Scrape Data
async with client:
print("Fetching data...")
async for message in client.iter_messages(channel_username, limit=100):
if message.views is not None:
data.append({
'id': message.id,
'date': message.date,
'views': message.views,
'forwards': message.forwards or 0
})

# 3. Process with Pandas
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date']).dt.date

print(f"Analyzed {len(df)} messages.")

# 4. Simple Visualization
plt.figure(figsize=(10,5))
plt.plot(df['date'], df['views'], marker='o', linestyle='-', color='b')
plt.title(f'View Growth for {channel_username}')
plt.xlabel('Date')
plt.ylabel('Views')
plt.grid(True)
plt.show()

# Run the client

with client:
client.loop.run_until_complete(main())

How to Test This Script

Here’s a common issue. If you test this on a brand new Telegram channel, your data will look flat. Zero movement. Zero changes. And that makes debugging tough. Your chart will look like a straight line.

You need fluctuation to confirm that your logic picks up spikes and dips.

When I built this script, I used the Free Telegram Views Tool from BuyMember. It let me add quick test views to new posts so I could see real changes in the graph. Super helpful for developers who want to:

• Stress test their analytics bots
• Confirm their scraping code reacts to sudden jumps
• Debug update logic when view counts refresh often

Analyzing the Results

Once you have data, organic or generated, run the script and look at the trend.

• Spikes show viral posts or successful promotion.
• Dips often happen on weekends, especially if your audience is B2B. Patterns start forming. That’s when things get interesting.

Conclusion

Using Telethon lets you build your own Telegram dashboard without paying for heavy SMM tools. You can add keyword tracking later. Or automate weekly reports. Or run sentiment checks.

If you want a Part 2 that adds sentiment analysis on top of this script, just let me know.