Matplotlib in Python

1. Matplotlib – Introduction

What is Matplotlib?

Matplotlib is a powerful, open-source Python library used for creating visualizations — charts, graphs, plots, and figures of all kinds. It is the foundation of data visualization in Python.

Why use Matplotlib?

• Creates professional-quality charts with just a few lines of code
• Supports dozens of chart types: line, bar, pie, scatter, histogram, and more
• Works seamlessly with Pandas and NumPy
• Highly customizable — colors, fonts, sizes, labels, everything
• Works in Jupyter Notebooks, scripts, and web applications

How to install Matplotlib:

pip install matplotlib

How to import Matplotlib:

import matplotlib.pyplot as plt

The alias plt is the universally accepted convention. You’ll see it in every Matplotlib example.

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2. Matplotlib – Pyplot

What is Pyplot?

pyplot is a module inside Matplotlib that provides a simple interface for creating plots. It works like a state machine — you build up a plot step by step, and then display it.

• Think of it as a drawing board — each function call adds something to the drawing
• plt.plot() draws the chart
• plt.show() displays it on screen

Example: Your very first plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]

plt.plot(x, y)
plt.show()

This draws a simple straight line graph with x values on the horizontal axis and y values on the vertical axis.

Example: Plot without x values (auto index)

import matplotlib.pyplot as plt

values = [3, 8, 1, 10, 5]
plt.plot(values)
plt.show()

When no x values are given, Matplotlib automatically uses 0, 1, 2, 3, … as the x-axis.

Key Pyplot functions you’ll use often:

• plt.plot() — Draw a line chart
• plt.show() — Display the chart
• plt.title() — Add a title
• plt.xlabel() / plt.ylabel() — Label the axes
• plt.savefig() — Save the chart as an image file

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3. Matplotlib – Marker

What is a Marker?

A marker is a symbol placed at each data point on your plot. By default, Matplotlib draws only a line. Adding markers makes each individual data point clearly visible.

Syntax:

plt.plot(x, y, marker="symbol")

Common Marker Symbols:

Marker Code	Shape
"o"	Circle
"*"	Star
"s"	Square
"^"	Triangle (up)
"v"	Triangle (down)
"+"	Plus sign
"x"	X mark
"D"	Diamond
"."	Small dot
"p"	Pentagon

Example: Circle marker

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(x, y, marker="o")
plt.show()

Example: Star marker

plt.plot(x, y, marker="*")
plt.show()

Example: Diamond marker

plt.plot(x, y, marker="D")
plt.show()

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4. Matplotlib – Line & Color

Customizing Line Style and Color

You can control how the line looks using the linestyle and color parameters.

Line Styles:

Code	Style
"-"	Solid line (default)
"--"	Dashed line
"-."	Dash-dot line
":"	Dotted line
"None"	No line (only markers)

Color Options:

You can use color names, short codes, or hex values:

Short Code	Color
"r"	Red
"g"	Green
"b"	Blue
"k"	Black
"w"	White
"y"	Yellow
"m"	Magenta
"c"	Cyan

Example: Dashed red line

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(x, y, linestyle="--", color="r")
plt.show()

Example: Dotted green line with star markers

plt.plot(x, y, linestyle=":", color="green", marker="*")
plt.show()

Shorthand format (marker + line + color in one string):

plt.plot(x, y, "o--r")   # circle markers, dashed line, red color
plt.plot(x, y, "*:b")    # star markers, dotted line, blue color

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5. Matplotlib – Other Marker Attributes

Beyond just choosing the marker shape, you can fine-tune its appearance further.

Key marker attributes:

• markersize (or ms) — Size of the marker
• markerfacecolor (or mfc) — Fill color of the marker
• markeredgecolor (or mec) — Border/edge color of the marker
• markeredgewidth (or mew) — Thickness of the marker border

Example: Large red-filled marker with blue border

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(
    x, y,
    marker="o",
    markersize=15,
    markerfacecolor="red",
    markeredgecolor="blue",
    markeredgewidth=2
)
plt.show()

Example: Using shorthand aliases

plt.plot(x, y, marker="D", ms=12, mfc="yellow", mec="black", mew=1.5)
plt.show()

Combining these attributes gives you full creative control over how each data point looks on your chart.

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6. Matplotlib – Other Line Attributes

Just like markers, the line connecting data points can be customized in detail.

Key line attributes:

• linewidth (or lw) — Thickness of the line
• linestyle (or ls) — Style of the line (solid, dashed, etc.)
• color (or c) — Color of the line
• alpha — Transparency (0.0 = invisible, 1.0 = fully visible)

Example: Thick blue dashed line

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(x, y, linewidth=4, linestyle="--", color="blue")
plt.show()

Example: Semi-transparent line

plt.plot(x, y, lw=3, color="green", alpha=0.4)
plt.show()

Example: Combining marker and line attributes together

plt.plot(
    x, y,
    marker="o", ms=10, mfc="orange",
    lw=2, ls="-.", color="purple", alpha=0.8
)
plt.show()

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7. Matplotlib – Labels

Why add labels?

Without labels, your chart has no context. Labels on the x-axis and y-axis tell the viewer what the data represents.

Functions:

• plt.xlabel("text") — Label for the horizontal axis
• plt.ylabel("text") — Label for the vertical axis

Example:

import matplotlib.pyplot as plt

x = [2020, 2021, 2022, 2023, 2024]
y = [100, 150, 130, 200, 180]

plt.plot(x, y, marker="o", color="blue")
plt.xlabel("Year")
plt.ylabel("Sales (in thousands)")
plt.show()

Customizing label font size and color:

plt.xlabel("Year", fontsize=14, color="darkblue")
plt.ylabel("Sales (in thousands)", fontsize=14, color="darkred")

Example with full customization:

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May"]
revenue = [5000, 7000, 6500, 9000, 8000]

plt.plot(months, revenue, marker="s", color="teal", lw=2)
plt.xlabel("Month", fontsize=13, labelpad=10)
plt.ylabel("Revenue ($)", fontsize=13, labelpad=10)
plt.show()

labelpad adds space between the axis and the label text — helpful when your tick values are long.

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8. Matplotlib – Titles & Position

Adding a Title

Every chart should have a title that explains what it’s showing.

plt.title("text")

Controlling title position:

• loc — Horizontal alignment: "left", "center" (default), "right"
• pad — Space between the title and the top of the chart

Example: Basic title

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(x, y)
plt.title("My First Chart")
plt.show()

Example: Left-aligned title

plt.title("Monthly Sales", loc="left")

Example: Right-aligned title with padding

plt.title("Revenue Report", loc="right", pad=20)

Example: Full chart with title and labels

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May"]
sales = [5000, 7000, 6500, 9000, 8000]

plt.plot(months, sales, marker="o", color="steelblue", lw=2)
plt.title("Monthly Sales Report – 2024", loc="center", pad=15)
plt.xlabel("Month")
plt.ylabel("Sales ($)")
plt.show()

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9. Matplotlib – Font Properties

Customizing Fonts

Matplotlib lets you control font style, size, weight, family, and color for titles, labels, and tick marks.

Common font parameters (used inside title/label functions):

• fontsize — Size of the text (e.g., 12, 14, 16)
• fontweight — Weight: "normal", "bold", "light"
• fontstyle — Style: "normal", "italic", "oblique"
• fontfamily — Font family: "serif", "sans-serif", "monospace"
• color — Text color

Using a fontdict for reusable font settings:

title_font = {
    "family": "serif",
    "color": "darkblue",
    "size": 20,
    "weight": "bold"
}

label_font = {
    "family": "sans-serif",
    "color": "darkred",
    "size": 14
}

Example: Applying fontdict

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

title_font = {"family": "serif", "color": "darkblue", "size": 20, "weight": "bold"}
label_font = {"family": "sans-serif", "color": "darkred", "size": 13}

plt.plot(x, y, marker="o", color="steelblue")
plt.title("Sales Performance", fontdict=title_font)
plt.xlabel("Week", fontdict=label_font)
plt.ylabel("Units Sold", fontdict=label_font)
plt.show()

Controlling tick label font size globally:

plt.tick_params(axis="both", labelsize=12)

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10. Matplotlib – Gridlines & Their Properties

What are Gridlines?

Gridlines are horizontal and vertical lines drawn across the chart background. They make it easier to read values from a chart, especially when the data points are far from the axes.

Basic usage:

plt.grid(True)    # Turn on grid
plt.grid(False)   # Turn off grid

Customizing gridlines:

• color — Color of the grid lines
• linestyle — Line style ("-", "--", ":", "-.")
• linewidth — Thickness of the grid lines
• alpha — Transparency (0 to 1)
• axis — Which grid to show: "both", "x", "y"

Example: Basic grid

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(x, y, marker="o")
plt.grid(True)
plt.show()

Example: Customized grid

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 25, 15, 40, 30]

plt.plot(x, y, marker="o", color="navy")
plt.grid(
    color="gray",
    linestyle="--",
    linewidth=0.8,
    alpha=0.6,
    axis="both"
)
plt.title("Sales Data with Gridlines")
plt.xlabel("Week")
plt.ylabel("Units")
plt.show()

Example: Show only horizontal gridlines

plt.grid(axis="y", linestyle=":", color="green", alpha=0.7)

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11. Matplotlib – Subplot

What is a Subplot?

A subplot lets you display multiple charts in a single figure window, arranged in rows and columns. This is extremely useful when you want to compare different datasets side by side.

Syntax:

plt.subplot(rows, columns, plot_number)

• rows — Number of rows in the grid
• columns — Number of columns in the grid
• plot_number — Position of the current plot (starts at 1)

Example: Two plots side by side

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y1 = [10, 25, 15, 40, 30]
y2 = [5, 15, 10, 30, 25]

plt.subplot(1, 2, 1)          # 1 row, 2 columns, position 1
plt.plot(x, y1, color="blue")
plt.title("Dataset A")

plt.subplot(1, 2, 2)          # 1 row, 2 columns, position 2
plt.plot(x, y2, color="red")
plt.title("Dataset B")

plt.suptitle("Comparison of Two Datasets")  # Overall title
plt.tight_layout()             # Prevents overlapping
plt.show()

Example: 2×2 grid of subplots

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]

plt.subplot(2, 2, 1)
plt.plot(x, [1, 4, 9, 16, 25], color="blue")
plt.title("Squares")

plt.subplot(2, 2, 2)
plt.plot(x, [1, 8, 27, 64, 125], color="green")
plt.title("Cubes")

plt.subplot(2, 2, 3)
plt.plot(x, [2, 4, 6, 8, 10], color="red")
plt.title("Doubles")

plt.subplot(2, 2, 4)
plt.plot(x, [1, 3, 5, 7, 9], color="purple")
plt.title("Odds")

plt.suptitle("Four Plots in One Figure")
plt.tight_layout()
plt.show()

Always call plt.tight_layout() when using subplots — it automatically adjusts spacing so titles and labels don’t overlap.

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12. Matplotlib – Scatter Plot

What is a Scatter Plot?

A scatter plot displays individual data points on a 2D graph without connecting them with lines. Each point represents one observation — defined by its x and y values.

When to use scatter plots:

• To see if two variables are related
• To spot outliers in data
• To visualize the distribution of a dataset
• To compare two numeric variables

Syntax:

plt.scatter(x, y)

Example: Basic scatter plot

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25, 30]
y = [20, 35, 25, 55, 45, 70]

plt.scatter(x, y)
plt.title("Hours Studied vs Exam Score")
plt.xlabel("Hours Studied")
plt.ylabel("Exam Score")
plt.show()

Each dot represents one student’s hours studied and their corresponding exam score.

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13. Matplotlib – Scatter Comparison

Comparing Two Datasets on the Same Scatter Plot

You can plot two (or more) sets of data on the same scatter plot by calling plt.scatter() multiple times. Adding a legend helps identify which points belong to which group.

Example: Comparing two student groups

import matplotlib.pyplot as plt

# Group A
x1 = [5, 10, 15, 20, 25]
y1 = [40, 55, 60, 75, 85]

# Group B
x2 = [3, 8, 12, 18, 22]
y2 = [30, 45, 50, 65, 72]

plt.scatter(x1, y1, label="Group A", color="blue")
plt.scatter(x2, y2, label="Group B", color="red")

plt.title("Study Hours vs Scores – Two Groups")
plt.xlabel("Hours Studied")
plt.ylabel("Exam Score")
plt.legend()
plt.show()

What the legend does:

• label="Group A" — assigns a name to the scatter series
• plt.legend() — displays the legend box on the chart

Example: Three datasets compared

import matplotlib.pyplot as plt

plt.scatter([1,2,3], [10,20,30], label="Batch 1", color="blue")
plt.scatter([1,2,3], [15,25,35], label="Batch 2", color="orange")
plt.scatter([1,2,3], [8, 18, 28], label="Batch 3", color="green")

plt.title("Batch Comparison")
plt.legend()
plt.show()

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14. Matplotlib – Scatter Color

Coloring Scatter Points

You can color scatter points in several ways:

• A single color for all points
• A list of different colors for each point
• A colormap (gradient of colors based on a value)

Example: Single color

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25]
y = [40, 55, 60, 75, 85]

plt.scatter(x, y, color="purple")
plt.title("Single Color Scatter")
plt.show()

Example: Different color for each point

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25]
y = [40, 55, 60, 75, 85]
colors = ["red", "blue", "green", "orange", "purple"]

plt.scatter(x, y, color=colors)
plt.title("Multi-Color Scatter")
plt.show()

Example: Colormap (automatic gradient coloring)

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25]
y = [40, 55, 60, 75, 85]
intensity = [10, 30, 50, 70, 90]   # Values that drive the color

scatter = plt.scatter(x, y, c=intensity, cmap="viridis")
plt.colorbar(scatter)              # Shows the color scale legend
plt.title("Scatter with Colormap")
plt.show()

Popular colormaps: "viridis", "plasma", "coolwarm", "Blues", "Reds", "rainbow"

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15. Matplotlib – Scatter Size & Transparency

Controlling Point Size

Use the s parameter to control the size of each scatter point. You can set one size for all points, or a list of different sizes for each point.

Controlling Transparency

Use the alpha parameter (0.0 to 1.0) to make points transparent. This is especially useful when points overlap — transparency lets you see the density of clustered points.

Example: Fixed size

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25]
y = [40, 55, 60, 75, 85]

plt.scatter(x, y, s=200)    # All points size 200
plt.title("Large Scatter Points")
plt.show()

Example: Variable sizes (bubble chart effect)

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25]
y = [40, 55, 60, 75, 85]
sizes = [50, 100, 200, 400, 600]    # Each point has a different size

plt.scatter(x, y, s=sizes, color="teal")
plt.title("Variable Size Scatter (Bubble Effect)")
plt.show()

Example: Transparency for overlapping points

import matplotlib.pyplot as plt

import random
x = [random.randint(1, 10) for _ in range(100)]
y = [random.randint(1, 10) for _ in range(100)]

plt.scatter(x, y, s=100, color="blue", alpha=0.3)
plt.title("Transparent Points – Shows Overlap Density")
plt.show()

Example: Combining size, color, and transparency

import matplotlib.pyplot as plt

x = [5, 10, 15, 20, 25]
y = [40, 55, 60, 75, 85]

plt.scatter(x, y, s=[100, 200, 300, 400, 500],
            c=["red","blue","green","orange","purple"],
            alpha=0.6)
plt.title("Size + Color + Transparency")
plt.show()

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16. Matplotlib – Bar Chart

What is a Bar Chart?

A bar chart displays data as rectangular bars. The height (or length) of each bar represents the value of that category. It’s perfect for comparing values across categories.

Syntax:

plt.bar(x, height)         # Vertical bars
plt.barh(y, width)         # Horizontal bars

Example: Vertical bar chart

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
scores = [85, 92, 78, 88, 95]

plt.bar(subjects, scores)
plt.title("Student Scores by Subject")
plt.xlabel("Subject")
plt.ylabel("Score")
plt.show()

Example: Horizontal bar chart

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
scores = [85, 92, 78, 88, 95]

plt.barh(subjects, scores)
plt.title("Student Scores by Subject (Horizontal)")
plt.xlabel("Score")
plt.show()

Use vertical bars when comparing a small number of categories. Use horizontal bars when category names are long.

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17. Matplotlib – Bar Color & Size

Customizing Bar Appearance

You can change the color, width, and edge styling of bars to make your chart more readable and visually appealing.

Key parameters:

• color — Fill color of the bars
• edgecolor — Border color of each bar
• width — Width of each bar (default is 0.8; range 0 to 1)
• linewidth — Thickness of the bar border

Example: Custom colors

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
scores = [85, 92, 78, 88, 95]

plt.bar(subjects, scores, color="steelblue", edgecolor="black")
plt.title("Scores by Subject")
plt.show()

Example: Different color for each bar

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
scores = [85, 92, 78, 88, 95]
colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7"]

plt.bar(subjects, scores, color=colors, edgecolor="black", linewidth=1.2)
plt.title("Colorful Scores Chart")
plt.show()

Example: Adjusting bar width

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
scores = [85, 92, 78, 88, 95]

plt.bar(subjects, scores, width=0.4, color="coral")   # Narrow bars
plt.title("Narrow Bar Chart")
plt.show()

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18. Matplotlib – Histogram

What is a Histogram?

A histogram shows the distribution of a dataset — how frequently values fall into different ranges (called bins). Unlike bar charts, histograms work with continuous numeric data.

• The x-axis shows the data ranges (bins)
• The y-axis shows the frequency (how many values fell in that range)
• Each bar represents one bin

Syntax:

plt.hist(data, bins=n)

Example: Basic histogram

import matplotlib.pyplot as plt

ages = [22, 25, 27, 23, 25, 28, 30, 22, 24, 26,
        29, 31, 23, 25, 27, 24, 28, 30, 22, 26]

plt.hist(ages, bins=5)
plt.title("Age Distribution")
plt.xlabel("Age")
plt.ylabel("Number of People")
plt.show()

Example: Customized histogram

import matplotlib.pyplot as plt

scores = [55, 60, 65, 70, 70, 75, 75, 80, 80, 80,
          85, 85, 85, 85, 90, 90, 92, 95, 98, 100]

plt.hist(scores, bins=8, color="steelblue", edgecolor="black", alpha=0.8)
plt.title("Exam Score Distribution")
plt.xlabel("Score")
plt.ylabel("Number of Students")
plt.grid(axis="y", alpha=0.5)
plt.show()

What bins means:

• bins=5 — divide the data into 5 equal ranges
• More bins = more detail but can look noisy
• Fewer bins = smoother but less detail
• You can also pass a list: bins=[50, 60, 70, 80, 90, 100]

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19. Matplotlib – Pie Chart

What is a Pie Chart?

A pie chart shows data as slices of a circle, where each slice represents a proportion of the total. It’s best for showing percentages or parts of a whole.

Syntax:

plt.pie(values, labels=labels)

Example: Basic pie chart

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]

plt.pie(hours, labels=subjects)
plt.title("Weekly Study Hours by Subject")
plt.show()

Example: Show percentages on slices

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]

plt.pie(hours, labels=subjects, autopct="%1.1f%%")
plt.title("Study Time Distribution")
plt.show()

• autopct="%1.1f%%" — displays percentage on each slice with 1 decimal place

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20. Matplotlib – Pie Labels, Explode & Shadow

Enhancing Pie Charts

Matplotlib lets you separate slices, add drop shadows, and customize the starting angle of the pie.

Key parameters:

• labels — List of text labels for each slice
• explode — Tuple to “pull out” slices from the center (value = distance)
• shadow — Adds a drop shadow behind the pie (True / False)
• startangle — Rotates the start of the pie (default is 0°)

Example: Exploding a slice

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]
explode = [0.1, 0, 0, 0, 0]   # Pull out the first slice (Math)

plt.pie(hours, labels=subjects, explode=explode, autopct="%1.1f%%")
plt.title("Highlighted: Math Study Time")
plt.show()

Example: With shadow and rotated start angle

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]
explode = [0.1, 0, 0, 0, 0]

plt.pie(
    hours,
    labels=subjects,
    explode=explode,
    autopct="%1.1f%%",
    shadow=True,
    startangle=90
)
plt.title("Study Time – With Shadow & Explode")
plt.show()

startangle=90 starts the pie from the top (12 o’clock position), which is often more visually natural.

Example: Exploding multiple slices

explode = [0.1, 0.1, 0, 0.05, 0]  # Pull out Math, Science, and slightly History

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21. Matplotlib – Pie Colors & Legends

Customizing Pie Chart Colors

Use the colors parameter to assign specific colors to each slice.

Example: Custom colors

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]
colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7"]

plt.pie(hours, labels=subjects, colors=colors, autopct="%1.1f%%")
plt.title("Study Hours – Custom Colors")
plt.show()

Adding a Legend

Sometimes the labels on slices are too small or crowded. You can move the labels into a legend instead.

plt.legend()                              # Default legend
plt.legend(title="Subjects")              # With a title
plt.legend(loc="upper right")            # Positioned legend

Example: Pie chart with legend (no labels on slices)

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]
colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7"]
explode = [0.05, 0, 0, 0, 0]

plt.pie(
    hours,
    labels=None,               # No labels on the slices
    colors=colors,
    explode=explode,
    autopct="%1.1f%%",
    shadow=True,
    startangle=140
)

plt.legend(subjects, title="Subjects", loc="upper right")
plt.title("Weekly Study Hours")
plt.show()

Full example combining everything:

import matplotlib.pyplot as plt

subjects = ["Math", "Science", "English", "History", "Art"]
hours = [4, 3, 2, 1, 2]
colors = ["#e74c3c", "#3498db", "#2ecc71", "#f39c12", "#9b59b6"]
explode = [0.1, 0, 0, 0, 0]

plt.pie(
    hours,
    labels=subjects,
    colors=colors,
    explode=explode,
    autopct="%1.1f%%",
    shadow=True,
    startangle=90
)

plt.title("Study Time Distribution", fontsize=15, fontweight="bold")
plt.legend(subjects, title="Subjects", loc="lower right", fontsize=9)
plt.tight_layout()
plt.show()

---

Quick Reference Table

Method / Parameter	Description	Example
import matplotlib.pyplot as plt	Import Matplotlib’s pyplot module	import matplotlib.pyplot as plt
plt.plot(x, y)	Draw a line chart	plt.plot([1,2,3], [4,5,6])
plt.show()	Display the chart	plt.show()
plt.savefig("name.png")	Save the chart as an image	plt.savefig("chart.png")
marker="o"	Circle marker at each data point	plt.plot(x, y, marker="o")
marker="*"	Star marker	plt.plot(x, y, marker="*")
marker="s"	Square marker	plt.plot(x, y, marker="s")
marker="D"	Diamond marker	plt.plot(x, y, marker="D")
linestyle="--"	Dashed line	plt.plot(x, y, linestyle="--")
linestyle=":"	Dotted line	plt.plot(x, y, linestyle=":")
linestyle="-."	Dash-dot line	plt.plot(x, y, linestyle="-.")
color="red"	Set line/element color	plt.plot(x, y, color="red")
markersize / ms	Size of markers	plt.plot(x, y, ms=12)
markerfacecolor / mfc	Fill color of marker	plt.plot(x, y, mfc="yellow")
markeredgecolor / mec	Border color of marker	plt.plot(x, y, mec="black")
markeredgewidth / mew	Border thickness of marker	plt.plot(x, y, mew=2)
linewidth / lw	Thickness of the line	plt.plot(x, y, lw=3)
alpha	Transparency (0.0–1.0)	plt.plot(x, y, alpha=0.5)
plt.xlabel("text")	Label for the x-axis	plt.xlabel("Month")
plt.ylabel("text")	Label for the y-axis	plt.ylabel("Sales")
plt.title("text")	Add a title to the chart	plt.title("My Chart")
plt.title(loc="left")	Position the title	plt.title("T", loc="right")
fontdict={...}	Apply font properties via dict	plt.title("T", fontdict=font)
fontsize	Set font size	plt.xlabel("X", fontsize=14)
fontweight="bold"	Bold text	plt.title("T", fontweight="bold")
fontstyle="italic"	Italic text	plt.xlabel("X", fontstyle="italic")
plt.grid(True)	Turn on gridlines	plt.grid(True)
plt.grid(axis="y")	Only horizontal gridlines	plt.grid(axis="y")
plt.grid(color, linestyle, linewidth, alpha)	Customize grid appearance	plt.grid(color="gray", ls="--")
plt.subplot(r, c, n)	Create a subplot at position n	plt.subplot(1, 2, 1)
plt.suptitle("text")	Overall title for all subplots	plt.suptitle("My Figure")
plt.tight_layout()	Fix overlapping subplot spacing	plt.tight_layout()
plt.scatter(x, y)	Draw a scatter plot	plt.scatter(x, y)
plt.scatter(x, y, color=[...])	Color scatter points	plt.scatter(x, y, color="blue")
plt.scatter(x, y, c=data, cmap="viridis")	Colormap-based scatter coloring	plt.scatter(x, y, c=vals, cmap="plasma")
plt.colorbar()	Display colormap scale legend	plt.colorbar(scatter)
s=value	Scatter point size	plt.scatter(x, y, s=200)
s=[list]	Variable size per point	plt.scatter(x, y, s=[50,100,200])
plt.legend()	Display legend	plt.legend()
plt.bar(x, height)	Vertical bar chart	plt.bar(subjects, scores)
plt.barh(y, width)	Horizontal bar chart	plt.barh(subjects, scores)
edgecolor	Bar border color	plt.bar(x, y, edgecolor="black")
width	Bar width (0–1)	plt.bar(x, y, width=0.4)
plt.hist(data, bins=n)	Histogram	plt.hist(ages, bins=5)
bins=[list]	Custom bin edges	bins=[50, 60, 70, 80, 90, 100]
plt.pie(values, labels=[...])	Pie chart	plt.pie(hours, labels=subjects)
autopct="%1.1f%%"	Show percentages on pie slices	plt.pie(data, autopct="%1.1f%%")
explode=(0.1, 0, 0, ...)	Pull out a pie slice	plt.pie(data, explode=explode)
shadow=True	Add shadow to pie chart	plt.pie(data, shadow=True)
startangle=90	Rotate pie start position	plt.pie(data, startangle=90)
colors=[...]	Custom colors for pie slices	plt.pie(data, colors=colors)
plt.legend(title="text", loc="upper right")	Positioned legend with title	plt.legend(title="Items")

---

Matplotlib has a lot more to offer — 3D plots, animations, heatmaps, and more. But once you’re confident with these fundamentals, you have everything you need to build clear, professional charts for any project. Practice makes perfect — try recreating charts from news articles or Kaggle datasets to sharpen your skills!