📊 Why You’re Always Rewriting That Same Plot

Ever feel like you’re rewriting the same sns.lineplot() again… and again?

Same colors.
Same fonts.
Same tweaks.

It’s not just annoying — it’s a productivity drain.

😩 Why It Hurts

Every plot you re-style manually eats into your deep work time.
Changing fonts, adjusting labels, adding gridlines... again.

Multiply that across every project, and it adds up fast.

💡 The Fix: Reusable Plot Templates

Don’t start from scratch. Build a plot once — reuse it forever.

Here’s how:

✅ 3 Ways to Make Your Plots Reusable

1. Create a Custom Theme (Seaborn/Matplotlib)

Define your style once, apply it everywhere.

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(
    style="whitegrid",
    rc={
        "axes.titlesize": 16,
        "axes.labelsize": 14,
        "figure.figsize": (10, 6),
        "xtick.labelsize": 12,
        "ytick.labelsize": 12
    }
)

📌 Save it in a plot_config.py file. Import it into every notebook.

2. Wrap Common Plots in Functions

Turn repeated plots into reusable tools.

def plot_line(df, x, y, title):
    sns.lineplot(data=df, x=x, y=y)
    plt.title(title)
    plt.xlabel(x.title())
    plt.ylabel(y.title())
    plt.tight_layout()
    plt.show()

✅ Cleaner code
✅ No more copy-paste errors
✅ Easier collaboration

3. Use Style Contexts for Fast Switching

Apply a theme temporarily — great for specific exports.

with sns.axes_style("darkgrid"):
    sns.histplot(data=my_data, x="value")

Perfect when switching from light to dark themes for reports.

🚀 Bonus Tip: Share Your Template

If you’re on a team, save your custom styles in a shared repo.
Helps enforce consistent visuals across notebooks, presentations, and dashboards.

📊 Poll

Do you use reusable plot templates or style files?
Let us know in this quick poll.