Matplotlib — Subplots & Dashboards

• Create a grid of subplots with plt.subplots(nrows, ncols).
• Add a figure-level title with fig.suptitle.
• Tweak layouts with tight_layout and constrained_layout.
• Use GridSpec for unequal-size panels (1 big chart + 2 small).

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(10, 8))
axes[0, 0].plot([1, 2, 3], [3, 1, 2])
axes[0, 1].bar(["A", "B"], [5, 3])
axes[1, 0].scatter([1, 2, 3], [3, 5, 1])
axes[1, 1].pie([1, 2, 3])

fig.suptitle("Four chart types")
fig.tight_layout()
plt.show()

axes is a 2-D numpy array of Axes objects — same indexing as a 2-D list. axes.flat flattens it if you want to iterate.

One row, many columns

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 4))

Share axes

fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)

Shared axes are great for small multiples — same scale, same range, easy to compare.

Custom layouts with GridSpec

from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(12, 6))
gs = GridSpec(2, 3, figure=fig)
big   = fig.add_subplot(gs[:, 0:2])  # rows 0-1, cols 0-1
small1 = fig.add_subplot(gs[0, 2])    # row 0, col 2
small2 = fig.add_subplot(gs[1, 2])    # row 1, col 2

That gives you one big chart on the left and two small ones stacked on the right — a common dashboard shape.

constrained_layout — the modern alternative to tight_layout

fig, axes = plt.subplots(2, 2, constrained_layout=True)

constrained_layout=True packs subplots smartly and avoids overlapping titles/labels. Set it once on figure creation.

# dashboard.py
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

df = pd.read_csv("clean.csv", parse_dates=["date"])
df["total"] = df["quantity"] * df["price"]

fig = plt.figure(figsize=(12, 7), constrained_layout=True)
gs  = GridSpec(2, 3, figure=fig)

big = fig.add_subplot(gs[:, 0:2])
small_top = fig.add_subplot(gs[0, 2])
small_bot = fig.add_subplot(gs[1, 2])

# Big: daily revenue trend
daily = (df.groupby(df["date"].dt.date)["total"].sum()
           .rename("daily").to_frame())
daily["roll"] = daily["daily"].rolling(3).mean()
big.plot(daily.index, daily["daily"], alpha=0.4, label="daily")
big.plot(daily.index, daily["roll"], linewidth=2, label="3-day avg")
big.set_title("Daily revenue")
big.set_ylabel("RM")
big.legend()
big.grid(axis="y", linestyle="--", alpha=0.4)

# Small top: revenue by product (bar)
rev = df.groupby("product")["total"].sum().sort_values(ascending=False)
small_top.bar(rev.index, rev.values, color="#4a90e2")
small_top.set_title("Revenue by product")
small_top.set_ylabel("RM")

# Small bottom: share (pie)
small_bot.pie(rev.values, labels=rev.index, autopct="%1.0f%%",
              startangle=90, counterclock=False)
small_bot.set_title("Share")

fig.suptitle("Sales · last 30 days", fontsize=16, weight="bold")
fig.savefig("dashboard.png", dpi=200)
plt.show()

Open dashboard.png — one image with three coordinated panels and a single overarching title. Print it and pin it up; you just shipped a report.

Subplots compose stories. plt.subplots for regular grids, GridSpec for custom shapes. constrained_layout = no overlapping labels. Add a suptitle to tie the panels together. Tomorrow: the data-story project pulls it all together.