Matplotlib Cheatsheet: Figure, Axes and Plot Types Reference

# Install — core + bundled deps (numpy comes along)
pip install "matplotlib>=3.9"

# Optional but useful
pip install ipympl                          # interactive backend for Jupyter
pip install seaborn                         # statistical wrapper on top
pip install pillow                          # for image I/O via imread

# Conda — picks a sane backend for your OS
conda install -c conda-forge matplotlib

# Pick a backend before importing pyplot (only if the default fights you)
import matplotlib
matplotlib.use("Agg")                       # non-GUI: scripts, CI, servers
# matplotlib.use("TkAgg")                   # native window
# matplotlib.use("module://matplotlib_inline.backend_inline")  # Jupyter

# In Jupyter — choose one per notebook
# %matplotlib inline       # static PNGs (default)
# %matplotlib widget       # interactive (needs ipympl)

# Confirm
python -c "import matplotlib as m; print(m.__version__, m.get_backend())"

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 200)

# Single figure, 2x2 grid — shared y, constrained layout
fig, axes = plt.subplots(
    nrows=2, ncols=2, figsize=(8, 6),
    sharex=True, sharey="row",                # share within rows
    layout="constrained",                     # auto-fix overlap; replaces tight_layout
)

axes[0, 0].plot(x, np.sin(x));  axes[0, 0].set_title("sin")
axes[0, 1].plot(x, np.cos(x));  axes[0, 1].set_title("cos")
axes[1, 0].plot(x, np.tan(x));  axes[1, 0].set_title("tan"); axes[1, 0].set_ylim(-5, 5)
axes[1, 1].plot(x, x**2);       axes[1, 1].set_title("x^2")

# Uneven layout — subplot_mosaic. ASCII map is the layout.
fig, axd = plt.subplot_mosaic(
    [["top", "top"],
     ["left", "right"]],
    figsize=(8, 6),
    layout="constrained",
)
axd["top"].plot(x, np.sin(x))
axd["left"].hist(np.random.default_rng(0).standard_normal(1000), bins=30)
axd["right"].scatter(x, np.sin(x) + np.random.default_rng(0).normal(0, 0.1, 200))

# GridSpec — manual control when mosaic isn't enough
fig = plt.figure(figsize=(8, 6), layout="constrained")
gs  = fig.add_gridspec(3, 3)
ax_big   = fig.add_subplot(gs[:2, :2])
ax_right = fig.add_subplot(gs[:2, 2])
ax_bot   = fig.add_subplot(gs[2,  :])

import matplotlib.pyplot as plt
import numpy as np

# Style sheet — applies to all plots in this process
plt.style.use("seaborn-v0_8-whitegrid")     # builtin; many others available
# plt.style.available  → list them

# Or one-off rcParams overrides
plt.rcParams.update({
    "figure.dpi":      120,
    "savefig.dpi":     200,
    "font.size":       11,
    "axes.titlesize":  13,
    "axes.spines.top": False,
    "axes.spines.right": False,
    "legend.frameon":  False,
})

# Context manager — temporary style (doesn't leak)
with plt.style.context("dark_background"):
    fig, ax = plt.subplots()
    ax.plot([0, 1, 2], [0, 1, 4])

# Colors: cycle, named, hex, rgb, alpha
rng = np.random.default_rng(0)
fig, ax = plt.subplots()
for i in range(4):
    ax.plot(rng.standard_normal(50).cumsum(),
            color=f"C{i}",                  # cycle slot (C0..C9)
            linewidth=1.5, alpha=0.85,
            label=f"series {i}")
ax.legend(loc="upper left", ncols=2)

# Per-line cosmetics — short codes still work
ax.plot([0,1,2], [0,1,4], "o--", color="#ff6b6b", markersize=6, markeredgewidth=0)

# Annotate a point with an arrow
ax.annotate("peak", xy=(2, 4), xytext=(1, 3.5),
            arrowprops=dict(arrowstyle="->", color="grey"))

# Time-series chart, publication-ready — title, secondary y, formatted ticks, PDF save.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd

rng = np.random.default_rng(0)
idx = pd.date_range("2026-01-01", periods=180, freq="D")
price  = 100 + rng.standard_normal(180).cumsum()
volume = rng.integers(1_000, 5_000, 180)

fig, ax = plt.subplots(figsize=(9, 4.5), layout="constrained")
ax.plot(idx, price, color="C0", linewidth=1.5, label="Price")
ax.set_ylabel("Price (USD)")
ax.set_title("Daily price + volume, 2026 H1")

# Secondary y for volume — twinx shares the x axis
ax2 = ax.twinx()
ax2.bar(idx, volume, alpha=0.25, color="C1", label="Volume")
ax2.set_ylabel("Volume")

# Date ticks — major month, minor day
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
ax.xaxis.set_minor_locator(mdates.DayLocator())

# Merge legends from both axes
h1, l1 = ax.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
ax.legend(h1 + h2, l1 + l2, loc="upper left", frameon=False)

# Save — bbox_inches="tight" trims whitespace
fig.savefig("price_volume.pdf", bbox_inches="tight")
fig.savefig("price_volume.png", dpi=200, bbox_inches="tight")
plt.close(fig)