Peak Detection with Matplotlib — Plus a One-Page Stats Guide!

From annotated time series plots to making the right call between a T-test and a Z-test — this issue is all about clarity in analysis.

🔄 Choose the Right Statistical Test - Every Time

Using the wrong test skews your analysis. In interviews, that’s a red flag. In real work, it’s worse.

Here’s what we just released:

• ✅ When to use a T-test vs a Z-test
• ⚠️ The assumptions that can trip you up
• 🧪 Real examples in Python
• 📄 A one-page cheat sheet for quick reference

📥 Grab the guide now →

🧠 Matplotlib Data Challenge: Time Series Plot with Peak Annotation

You’ve got 5 years of monthly revenue. Now highlight the highest-performing periods — clearly and correctly.

🧩 What you’ll apply:

• plt.plot() with color="deepskyblue"
• plt.scatter() for peak with color="orangered"
• df["revenue"].idxmax() to find the spike
• Clear axis labels, rotated ticks, subtle grid
• Annotate and explain with a legend

📈 Build it from scratch →

📚 Train XGBoost in Your Browser — No Setup Needed

Point, click, run. Tune and evaluate models in a live browser environment.

⚙️ Try the no-install tool now →

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