Matplotlib: Build Publication-Quality Charts in Python

Numbers alone rarely tell the whole story. Matplotlib is Python’s foundational plotting library — it gives you complete, fine-grained control over every element of a chart: axes, tick marks, colors, fonts, legends, and layout. Every other Python visualization library (Seaborn, Pandas plotting, Plotly) is built on or alongside Matplotlib, so learning it first means you always have an escape hatch when higher-level tools fall short.

Installation and Import

pip install matplotlib

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

pyplot is the submodule you will use for almost everything. The alias plt is universal.

Two Interfaces: plt vs. Axes

Matplotlib offers two ways to build charts.

State-based (plt.xxx) — simpler
Object-oriented (fig, ax) — recommended

Call functions directly on plt. Matplotlib manages the current figure and axes automatically. Best for single charts.

plt.plot([1, 2, 3], [4, 5, 6])
plt.title("My Chart")
plt.show()

Explicitly create a Figure and one or more Axes objects. Essential for subplots or when you need to configure individual panels. The ax.set_* versions of functions keep code clean and unambiguous.

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
ax.set_title("My Chart")
plt.show()

Both produce identical output. Use the state-based style for quick single plots and the object-oriented style for anything with multiple panels.

Line Plot

Line plots show how a value changes over a continuous axis — most commonly time.

years  = [2019, 2020, 2021, 2022, 2023, 2024, 2025]
sales  = [120,  95,   145,  210,  290,  410,  500]
profit = [30,   15,   40,   65,   92,   140,  180]

plt.figure(figsize=(9, 4))

plt.plot(years, sales,  marker="o", color="#3B82F6", linewidth=2, label="Sales (₹k)")
plt.plot(years, profit, marker="s", color="#10B981", linewidth=2, label="Profit (₹k)")

plt.title("Annual Sales and Profit Growth", fontsize=14, fontweight="bold")
plt.xlabel("Year")
plt.ylabel("Amount (₹ thousands)")
plt.legend()
plt.grid(True, linestyle="--", alpha=0.5)
plt.tight_layout()
plt.show()

Common `plot()` Styling Options

Parameter	Example values
`color`	`"blue"`, `"#3B82F6"`, `"teal"`
`linestyle`	`"-"`, `"--"`, `":"`, `"-."`
`linewidth`	`1`, `2`, `3`
`marker`	`"o"`, `"s"`, `"^"`, `"D"`, `"*"`
`alpha`	`0.0` (invisible) to `1.0` (opaque)
`label`	string shown in the legend

Bar Chart

Bar charts compare quantities across discrete categories.

courses  = ["Python", "SQL", "Machine Learning", "Git", "Data Analysis"]
students = [450, 320, 540, 180, 390]
colors   = ["#3B82F6", "#10B981", "#8B5CF6", "#F59E0B", "#EF4444"]

plt.figure(figsize=(9, 4))
bars = plt.bar(courses, students, color=colors, edgecolor="white", width=0.6)

# Add value labels on top of each bar
for bar in bars:
    plt.text(
        bar.get_x() + bar.get_width() / 2,
        bar.get_height() + 8,
        str(int(bar.get_height())),
        ha="center", va="bottom", fontsize=10
    )

plt.title("Student Enrollment by Course")
plt.ylabel("Number of Students")
plt.ylim(0, 620)
plt.tight_layout()
plt.show()

Horizontal Bar Chart

Use barh when category names are long — it prevents overlapping x-axis labels.

roles    = ["Software Engineer", "Data Scientist", "Product Manager", "UI/UX Designer"]
salaries = [110, 120, 105, 90]  # thousands

plt.figure(figsize=(8, 3))
plt.barh(roles, salaries, color="#8B5CF6")
plt.xlabel("Median Salary (₹ lakhs)")
plt.title("Median Salaries by Role")
plt.tight_layout()
plt.show()

Scatter Plot

Scatter plots reveal correlations (or lack thereof) between two continuous variables.

study_hours = [2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12]
exam_scores = [45, 52, 58, 65, 71, 75, 82, 87, 91, 95, 98]

plt.figure(figsize=(7, 4))
plt.scatter(study_hours, exam_scores,
            color="#EF4444", s=80, edgecolors="darkred", alpha=0.8)

plt.title("Study Hours vs. Exam Score")
plt.xlabel("Weekly Study Hours")
plt.ylabel("Exam Score (/100)")
plt.grid(True, linestyle=":", alpha=0.5)
plt.tight_layout()
plt.show()

Histogram

Histograms show the frequency distribution of a continuous variable by grouping values into “bins”.

rng    = np.random.default_rng(42)
scores = rng.normal(loc=70, scale=12, size=500)

plt.figure(figsize=(8, 4))
plt.hist(scores, bins=25, color="#3B82F6", edgecolor="white", alpha=0.8)

plt.axvline(scores.mean(), color="red", linestyle="--", linewidth=1.5, label=f"Mean: {scores.mean():.1f}")
plt.title("Distribution of Exam Scores (n=500)")
plt.xlabel("Score")
plt.ylabel("Frequency")
plt.legend()
plt.tight_layout()
plt.show()

The bins parameter controls granularity. Too few bins and the distribution looks flat; too many and it looks jagged. Start with 20–30 and adjust visually.

Customizing Charts

Every element of a Matplotlib chart is customizable.

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

plt.figure(figsize=(9, 4))
plt.plot(x, np.sin(x), label="sin(x)", color="royalblue",  linewidth=2)
plt.plot(x, np.cos(x), label="cos(x)", color="darkorange", linewidth=2, linestyle="--")

# Title and axis labels
plt.title("Sine and Cosine Waves", fontsize=15, fontweight="bold", pad=12)
plt.xlabel("Angle (radians)", fontsize=11)
plt.ylabel("Amplitude",       fontsize=11)

# Custom tick marks
plt.xticks([0, np.pi/2, np.pi, 3*np.pi/2, 2*np.pi],
           ["0", "π/2", "π", "3π/2", "2π"])

# Axis limits, grid, and legend
plt.xlim(0, 2 * np.pi)
plt.ylim(-1.3, 1.3)
plt.grid(True, linestyle=":", alpha=0.6)
plt.legend(loc="upper right", fontsize=10)

plt.tight_layout()
plt.show()

Subplots — Multi-Panel Figures

Use plt.subplots(rows, cols) to create a grid of charts. Switch to the object-oriented interface to address each panel independently.

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(11, 8))

x = np.linspace(0, 5, 60)

# Top-left: linear
axes[0, 0].plot(x, x, color="steelblue")
axes[0, 0].set_title("Linear: y = x")

# Top-right: quadratic
axes[0, 1].plot(x, x**2, color="seagreen")
axes[0, 1].set_title("Quadratic: y = x²")

# Bottom-left: sine wave
axes[1, 0].plot(x, np.sin(x), color="darkorange")
axes[1, 0].set_title("Sine: y = sin(x)")

# Bottom-right: exponential
axes[1, 1].plot(x, np.exp(x), color="crimson")
axes[1, 1].set_title("Exponential: y = eˣ")

for ax in axes.flat:
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    ax.grid(True, linestyle=":", alpha=0.5)

plt.suptitle("Four Mathematical Functions", fontsize=14, fontweight="bold", y=1.01)
plt.tight_layout()
plt.show()

plt.tight_layout() automatically adjusts spacing so titles and labels do not overlap between subplots. Call it just before plt.show() or plt.savefig().

Saving Charts to File

fig, ax = plt.subplots(figsize=(8, 4))
ax.plot([1, 2, 3, 4], [10, 15, 12, 18], marker="o")
ax.set_title("Sample Chart")

# Save before plt.show() — show() clears the figure
plt.savefig("chart.png", dpi=150, bbox_inches="tight")
plt.savefig("chart.pdf", bbox_inches="tight")   # vector format for print
plt.show()

Parameter	Purpose
`dpi`	Resolution in dots per inch (150+ for presentations, 300 for print)
`bbox_inches="tight"`	Crops whitespace around the figure
`transparent=True`	Transparent background (useful for slides)

Practical Example: Temperature Trend Analysis

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Simulated monthly temperature data for two cities
months = ["Jan","Feb","Mar","Apr","May","Jun",
          "Jul","Aug","Sep","Oct","Nov","Dec"]
hyderabad  = [22, 25, 30, 35, 38, 35, 30, 29, 28, 26, 23, 21]
bengaluru  = [18, 21, 24, 27, 27, 23, 21, 21, 21, 21, 18, 17]

fig, axes = plt.subplots(1, 2, figsize=(13, 5))

# ── Panel 1: Line chart — monthly trends ─────────────────────────────────────
axes[0].plot(months, hyderabad, marker="o", color="#EF4444", label="Hyderabad", linewidth=2)
axes[0].plot(months, bengaluru, marker="s", color="#3B82F6", label="Bengaluru",  linewidth=2)
axes[0].set_title("Monthly Temperature Trends")
axes[0].set_xlabel("Month")
axes[0].set_ylabel("Temperature (°C)")
axes[0].legend()
axes[0].grid(True, linestyle="--", alpha=0.4)
axes[0].tick_params(axis="x", rotation=45)

# ── Panel 2: Bar chart — annual average comparison ────────────────────────────
cities = ["Hyderabad", "Bengaluru"]
avgs   = [np.mean(hyderabad), np.mean(bengaluru)]
bars   = axes[1].bar(cities, avgs, color=["#EF4444", "#3B82F6"], width=0.4)
for bar, avg in zip(bars, avgs):
    axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.2,
                 f"{avg:.1f}°C", ha="center", fontsize=11, fontweight="bold")
axes[1].set_title("Annual Average Temperature")
axes[1].set_ylabel("Temperature (°C)")
axes[1].set_ylim(0, 35)

plt.suptitle("City Temperature Analysis", fontsize=14, fontweight="bold")
plt.tight_layout()
plt.savefig("temperature_analysis.png", dpi=150, bbox_inches="tight")
plt.show()

Next: Seaborn

Build on Matplotlib with Seaborn’s statistical charts, beautiful defaults, and native Pandas DataFrame integration.

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Browse hundreds of chart examples with full source code to find the right plot type for your data.

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Matplotlib: Build Publication-Quality Charts in Python

Installation and Import

Two Interfaces: plt vs. Axes

Line Plot

Common `plot()` Styling Options

Bar Chart

Horizontal Bar Chart

Scatter Plot

Histogram

Customizing Charts

Subplots — Multi-Panel Figures

Saving Charts to File

Practical Example: Temperature Trend Analysis

Next: Seaborn

Matplotlib gallery

​Installation and Import

​Two Interfaces: plt vs. Axes

​Line Plot

​Common plot() Styling Options

​Bar Chart

​Horizontal Bar Chart

​Scatter Plot

​Histogram

​Customizing Charts

​Subplots — Multi-Panel Figures

​Saving Charts to File

​Practical Example: Temperature Trend Analysis

Next: Seaborn

Matplotlib gallery

Installation and Import

Two Interfaces: plt vs. Axes

Line Plot

Common `plot()` Styling Options

Bar Chart

Horizontal Bar Chart

Scatter Plot

Histogram

Customizing Charts

Subplots — Multi-Panel Figures

Saving Charts to File

Practical Example: Temperature Trend Analysis