Python Coding engagement report
@clcoding - 652K followers on X
Measured over 40 original posts from a 30-day window, last computed on August 25, 2026.
Engagement
A typical post picks up 139 interactions against 652K followers, an engagement rate of 0.021%. Measured over 40 original posts, its engagement rate beats 44% of 3,739 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 20K times each, and 0.681% of those impressions turn into an interaction. That is about 3.13% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.9 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 17:00 UTC, and Tuesday is the busiest day of the week. Of the 40 posts sampled, 85% carry an image or video, 15% are part of a thread and 75% link out. The account's strongest tracked post pulled 555 interactions, about 4.0x its own typical post.
Measured over 40 original posts from a 30-day window, last computed on August 25, 2026.
Compared with accounts its own size
Python Coding's engagement rate beats 44% of the tracked X accounts closest to it in follower count (3,739 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 42% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.021%, Python Coding sits above the 25th percentile of the 36,134 accounts in this comparison. That places it in the below the median band, which runs 0.012% to 0.08%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.431% |
| 90th percentile | 2.11% |
| 99th percentile | 161.6% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 17:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 50K |
| 01:00 UTC | -2% | 51K |
| 02:00 UTC | -4% | 49K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 42K |
| 05:00 UTC | -4% | 41K |
| 06:00 UTC | -4% | 47K |
| 07:00 UTC | -5% | 51K |
| 08:00 UTC | -4% | 60K |
| 09:00 UTC | -3% | 68K |
| 10:00 UTC | -2% | 71K |
| 11:00 UTC | -3% | 77K |
| 12:00 UTC | -2% | 85K |
| 13:00 UTC | -2% | 93K |
| 14:00 UTC | -3% | 96K |
| 15:00 UTC | -2% | 99K |
| 16:00 UTC | -3% | 96K |
| 17:00 UTC | -2% | 89K |
| 18:00 UTC | -2% | 83K |
| 19:00 UTC | -2% | 79K |
| 20:00 UTC | -1% | 73K |
| 21:00 UTC | -1% | 65K |
| 22:00 UTC | -1% | 57K |
| 23:00 UTC | -2% | 51K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 228K |
| Monday | 0% | 281K |
| Tuesday | -2% | 270K |
| Wednesday | -1% | 249K |
| Thursday | -2% | 242K |
| Friday | -3% | 250K |
| Saturday | +3% | 225K |
Best tweets
- Aug 22, 20264.0x their median
Bayes' Rule with Python: A Tutorial Introduction to Bayesian Analysis (Free PDF) Get it Free: https://t.co/tktsWq5fij https://t.co/6HJ60omCfw
- Aug 20, 20263.2x their median
Matrix Calculus (for Machine Learning and Beyond) โ Free PDF ๐ Matrix Calculus (for Machine Learning and Beyond) ๐ 101 pages ๐ Free PDF This MIT course material covers matrix derivatives, Jacobians, gradients, Hessians, matrix factorizations, optimization, automatic differentiation, and applications in machine learning. MIT provides the complete lecture notes openly through OpenCourseWare. ๐ Read & access the free PDF: https://t.co/cknb3Chnk8
- Aug 23, 20262.7x their median
Deep Learning Methods of Mathematical Physics: Volume I โ A Comprehensive Guide to AI for Direct and Inverse Problems ๐ Free PDF ๐ 461 pages A comprehensive resource exploring how deep learning and mathematical physics can be combined to solve direct and inverse problems, with applications across scientific computing, modeling, and AI. Free PDF: https://t.co/fAJEAp0MLV
- Aug 24, 20262.4x their median
๐ Integral Calculus โ Free PDF A comprehensive 769-page resource for learning Integral Calculus from fundamentals to advanced concepts. ๐ Inside youโll explore: ๐น Definite & indefinite integrals ๐น Techniques of integration ๐น Fundamental Theorem of Calculus ๐น Applications of integration ๐น Sequences and series ๐น Practice problems and exercises ๐ 769 pages of valuable mathematics content. Free PDF: https://t.co/aH7SRJYFnS
- Aug 23, 20262.3x their median
๐ Advanced Statistics from an Elementary Point of View โ Free PDF* ๐ Author: Michael J. Panik ๐ Pages: 905 ๐ Topics: Probability, descriptive statistics, distributions, sampling, estimation, hypothesis testing, nonparametric statistics, regression, and correlation. ๎ Free PDF: https://t.co/MPECf7BJrw
- Aug 21, 20262.3x their median
Smart Package Tracker using Python 6 Python Books You Can Download for FREE! https://t.co/w0uBvFF63s https://t.co/qToWeABFyi
- Aug 25, 20261.9x their median
Complete OOP Concept Map Free Course on OOP: https://t.co/kxUqPWRwym Support: https://t.co/GLXCGweryt https://t.co/QhYsPRtvr9
- Aug 19, 20261.8x their median
๐งต Python Statements: A Beginner-Friendly Guide ๐ Every Python program is made up of statementsโinstructions that tell Python what to do. From assigning values to making decisions and repeating code, statements control how your program runs. Letโs break them down ๐ https://t.co/EvnsECSyMQ
- Aug 19, 20261.8x their median
Understanding Statistics and Experimental Design: How to Not Lie with Statistics โ Free Book Read the full post and access the book https://t.co/w6cIxVtjYU ๐ Pages: 142 A useful resource for students, researchers, data scientists, and anyone who wants to understand statistics and experimental design more effectively. The book focuses on statistical thinking, experimental design, interpreting data, and avoiding common ways statistics can be misleading. If you're learning Data Science, Machine Learning, Research Methodology, or Statistics, this can be a valuable addition to your learning resources.
- Aug 19, 20261.7x their median
๐ค Fundamentals of Machine Learning and Artificial Intelligence This resource provides a beginner-friendly introduction to AI and Machine Learning, helping readers understand how intelligent systems learn from data and make predictions. Detailed Explanation: https://t.co/Rsw6t2VkvR The key concepts include: Artificial Intelligence (AI) โ systems that perform tasks requiring human-like intelligence. Machine Learning (ML) โ algorithms that learn patterns from data rather than relying only on fixed rules. Supervised Learning โ learning from labeled data, including regression and classification. Unsupervised Learning โ discovering patterns in unlabeled data, such as clustering. Deep Learning โ using neural networks with multiple layers to solve complex problems. Model Evaluation โ understanding whether a trained model performs well on unseen data. Real-world applications โ recommendations, fraud detection, healthcare, computer vision, NLP, and more.
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 139 interactions against 652K followers, an engagement rate of 0.021%. Measured over 40 original posts, its engagement rate beats 44% of 3,739 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 20K times each, and 0.681% of those impressions turn into an interaction. That is about 3.13% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.9 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 17:00 UTC, and Tuesday is the busiest day of the week. Of the 40 posts sampled, 85% carry an image or video, 15% are part of a thread and 75% link out. The account's strongest tracked post pulled 555 interactions, about 4.0x its own typical post.
- What is Python Coding's engagement rate on X?
- Python Coding (@clcoding) has an engagement rate of 0.021%, based on the median interactions across 40 original posts from the last 30 days against 652,456 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.021%, Python Coding sits above the 25th percentile of the 36,134 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @clcoding have real engagement?
- Its engagement rate beats 44% of the tracked X accounts closest to it in follower count (3,739 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @clcoding post?
- Most posts go out around 17:00 UTC, and Tuesday is its busiest day, at roughly 1.9 posts per day across the measured window.