Python Seaborn Grids

 # Seaborn — Grids

# Grid = multiple plots arranged automatically in rows and columns

# Instead of creating each plot manually, seaborn splits data by a category

# and draws one plot per group — all in one call

#

# Three grid types:

# FacetGrid → create any plot type, split by category

# PairGrid → one plot for every pair of columns (manual control)

# pairplot → shortcut for PairGrid with sensible defaults

import seaborn as sns

import matplotlib.pyplot as plt

import pandas as pd

# ── Sample data used throughout ───────────────────────────────────────────────

# 6 rows — 3 departments × 2 genders (1 male, 1 female per dept)

df = pd.DataFrame({

"dept": ["HR","HR","IT","IT","Finance","Finance"],

"gender": ["M", "F", "M", "F", "M", "F"],

"salary": [45000, 48000, 80000, 85000, 65000, 70000],

"score": [60, 65, 85, 88, 72, 75],

"age": [28, 30, 35, 38, 33, 36]

})

# ══════════════════════════════════════════════════════════════════════════════

# ── 1. FacetGrid — Draw any plot split by a category ─────────────────────────

# Step 1: Create the grid → FacetGrid(data, col= or row=)

# Step 2: Draw the plot → g.map(plot_function, "column_name")

#

# col= → one plot per unique value, arranged side by side

# row= → one plot per unique value, stacked top to bottom

# ══════════════════════════════════════════════════════════════════════════════

# ── col= — one plot per dept, side by side ────────────────────────────────────

g = sns.FacetGrid(df, col="dept") # split by dept → 3 columns

g.map(sns.histplot, "salary") # draw histplot of salary in each

plt.suptitle("Salary Distribution per Dept", y=1.02)

plt.show() # Output: 3 histograms side by side — one for HR, IT, Finance