Line, Bar and Pie Charts in Python0%

Line, Bar and Pie Charts in Python

Intermediate8 min read•Updated: 2026-10-01

Line, Bar, and Pie Charts in Python

Choosing the correct visualization type is essential for communicating data insights effectively. In this guide, we dive into the three most essential chart types in Matplotlib: Line Charts (for continuous trends), Bar Charts (for categorical comparisons), and Pie Charts (for proportional composition).

1. Line Charts: Trends Over Time (`ax.plot`)

Line charts excel at displaying continuous variables over a sequential progression (such as dates, months, or timestamps):

1import matplotlib.pyplot as plt
2 
3days = [1, 2, 3, 4, 5, 6, 7]
4website_visits = [1200, 1450, 1380, 1800, 2100, 2600, 2400]
5mobile_app_visits = [800, 950, 1100, 1300, 1550, 1900, 1850]
6 
7fig, ax = plt.subplots(figsize=(8, 4.5))
8 
9# Plot multiple lines on the same axes
10ax.plot(days, website_visits, label="Website", color="#0284c7", linewidth=2, linestyle="-", marker="o")
11ax.plot(days, mobile_app_visits, label="Mobile App", color="#10b981", linewidth=2, linestyle="--", marker="s")
12 
13ax.set_title("Weekly User Traffic Comparison", fontsize=13, fontweight="bold")
14ax.set_xlabel("Day of Week")
15ax.set_ylabel("Active Sessions")
16ax.legend(loc="upper left")
17ax.grid(True, linestyle=":", alpha=0.6)
18 
19plt.tight_layout()
20plt.show()

2. Bar Charts: Categorical Comparisons (`ax.bar` & `ax.barh`)

Bar charts compare discrete numerical values across distinct nominal categories.

Vertical & Horizontal Bar Charts

1import matplotlib.pyplot as plt
2 
3languages = ["Python", "JavaScript", "TypeScript", "Java", "C++"]
4popularity_percent = [31.5, 28.0, 18.2, 12.4, 9.9]
5 
6fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.5))
7 
8# 1. Vertical Bar Chart
9bars = ax1.bar(languages, popularity_percent, color="#6366f1", width=0.6)
10ax1.set_title("Most Popular Languages (Vertical)")
11ax1.set_ylabel("Market Share (%)")
12 
13# Value annotations on top of vertical bars
14for bar in bars:
15    yval = bar.get_height()
16    ax1.text(bar.get_x() + bar.get_width()/2, yval + 0.5, f"{yval}%", ha="center", va="bottom", fontsize=9)
17 
18# 2. Horizontal Bar Chart (ax.barh)
19ax2.barh(languages, popularity_percent, color="#f59e0b", height=0.6)
20ax2.set_title("Language Adoption (Horizontal)")
21ax2.set_xlabel("Share (%)")
22 
23plt.tight_layout()
24plt.show()

Grouped Bar Charts

To display multi-series comparisons across categories, offset the X-coordinates by the bar width:

1import matplotlib.pyplot as plt
2import numpy as np
3 
4categories = ["Q1", "Q2", "Q3", "Q4"]
5sales_2025 = [45, 52, 58, 65]
6sales_2026 = [50, 62, 70, 82]
7 
8x = np.arange(len(categories))
9width = 0.35  # Bar width
10 
11fig, ax = plt.subplots(figsize=(8, 4))
12ax.bar(x - width/2, sales_2025, width, label="2025", color="#94a3b8")
13ax.bar(x + width/2, sales_2026, width, label="2026", color="#3b82f6")
14 
15ax.set_title("Quarterly Revenue Comparison (in Lakhs ₹)")
16ax.set_xticks(x)
17ax.set_xticklabels(categories)
18ax.legend()
19plt.show()

3. Pie & Donut Charts: Proportions of a Whole (`ax.pie`)

Pie charts display how individual segments contribute to 100% of a whole.

1import matplotlib.pyplot as plt
2 
3categories = ["Engineering", "Sales & Mktg", "Product", "Operations", "Customer Support"]
4budgets = [42, 25, 15, 10, 8]
5colors = ["#3b82f6", "#10b981", "#f59e0b", "#8b5cf6", "#ec4899"]
6 
7# Explode (detach) the largest slice for emphasis
8explode = (0.08, 0, 0, 0, 0)
9 
10fig, ax = plt.subplots(figsize=(6, 6))
11 
12wedges, texts, autotexts = ax.pie(
13    budgets,
14    explode=explode,
15    labels=categories,
16    autopct="%1.1f%%",
17    startangle=140,
18    colors=colors,
19    shadow=True
20)
21 
22# Customize text styling
23for at in autotexts:
24    at.set_color("white")
25    at.set_weight("bold")
26 
27ax.set_title("Annual Corporate Budget Allocation", fontsize=13, fontweight="bold")
28plt.show()

Creating a Modern Donut Chart

Add a white center circle to transform a standard pie chart into a modern donut chart:

1# Create central white circle
2centre_circle = plt.Circle((0, 0), 0.70, fc="white")
3fig.gca().add_artist(centre_circle)

4. When to Use Which Chart?

Chart Type	Best Used When...	Avoid When...
Line Chart	Showing trends, rate of change, or time-series	Categories have no logical sequence
Bar Chart	Comparing discrete categorical amounts	You have hundreds of continuous data points
Pie Chart	Showing 2-5 simple parts of a 100% total	You have >6 slices or tiny differences in size

⚠️ Common Intermediate Mistakes & Gotchas

1. Using Pie Charts with Too Many Categories

Pie charts become unreadable when displaying more than 5–6 slices. Use a horizontal bar chart for datasets with numerous categories.

2. Cluttered X-Axis Labels

When x-axis categories have long names, they overlap horizontally. Rotate them using plt.xticks(rotation=45, ha='right') for clean readability.

💻 Try It Yourself: Comparing Quarterly Revenue Bar Chart

Scenario

Create a vertical bar chart comparing revenue across 4 fiscal quarters, coloring the highest quarter differently and displaying data labels on top of each bar.

Complete Solution

1import matplotlib.pyplot as plt
2 
3quarters = ["Q1", "Q2", "Q3", "Q4"]
4revenue = [12.5, 15.8, 14.2, 19.5]  # in Lakhs INR
5colors = ["#93c5fd", "#93c5fd", "#93c5fd", "#2563eb"]  # Highlight Q4
6 
7plt.figure(figsize=(7, 4.5))
8bars = plt.bar(quarters, revenue, color=colors, width=0.55)
9 
10for bar in bars:
11    yval = bar.get_height()
12    plt.text(bar.get_x() + bar.get_width()/2.0, yval + 0.3, f"₹{yval}L", ha="center", va="bottom", fontweight="bold")
13 
14plt.title("Fiscal Year Quarterly Revenue", fontsize=13, fontweight="bold")
15plt.ylabel("Revenue (₹ Lakhs)")
16plt.ylim(0, 24)
17plt.tight_layout()
18plt.savefig("quarterly_revenue.png", dpi=150)
19print("Chart saved to 'quarterly_revenue.png'.")
20plt.close()

Expected Output

1Chart saved to 'quarterly_revenue.png'.

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

Which chart type is best suited for visualizing website traffic trends over 30 days?

Question 2

Which method on a Matplotlib Axes object creates a horizontal bar chart?

Question 3

What does the parameter autopct="%1.1f%%" accomplish in ax.pie()?

Question 4

What is the primary visual drawback of using pie charts with more than 7 or 8 categories?

Question 5

In a multi-line chart, what method must be called to display the legend mapping line colors to their respective labels?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with Customizing Graphs (13: Data Visualization Basics).

👉 Continue to Next Lesson: Customizing Graphs →

Next Lesson

Customizing Graphs

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Introduction to Matplotlib	Customizing Graphs

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

Previous Next

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Customizing Graphs

Line, Bar and Pie Charts in Python

Intermediate8 min read•Updated: 2026-10-01

Study Materials Cheatsheet

Line, Bar, and Pie Charts in Python

1. Line Charts: Trends Over Time (`ax.plot`)

Line charts excel at displaying continuous variables over a sequential progression (such as dates, months, or timestamps):

1import matplotlib.pyplot as plt
2 
3days = [1, 2, 3, 4, 5, 6, 7]
4website_visits = [1200, 1450, 1380, 1800, 2100, 2600, 2400]
5mobile_app_visits = [800, 950, 1100, 1300, 1550, 1900, 1850]
6 
7fig, ax = plt.subplots(figsize=(8, 4.5))
8 
9# Plot multiple lines on the same axes
10ax.plot(days, website_visits, label="Website", color="#0284c7", linewidth=2, linestyle="-", marker="o")
11ax.plot(days, mobile_app_visits, label="Mobile App", color="#10b981", linewidth=2, linestyle="--", marker="s")
12 
13ax.set_title("Weekly User Traffic Comparison", fontsize=13, fontweight="bold")
14ax.set_xlabel("Day of Week")
15ax.set_ylabel("Active Sessions")
16ax.legend(loc="upper left")
17ax.grid(True, linestyle=":", alpha=0.6)
18 
19plt.tight_layout()
20plt.show()

2. Bar Charts: Categorical Comparisons (`ax.bar` & `ax.barh`)

Bar charts compare discrete numerical values across distinct nominal categories.

Vertical & Horizontal Bar Charts

1import matplotlib.pyplot as plt
2 
3languages = ["Python", "JavaScript", "TypeScript", "Java", "C++"]
4popularity_percent = [31.5, 28.0, 18.2, 12.4, 9.9]
5 
6fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.5))
7 
8# 1. Vertical Bar Chart
9bars = ax1.bar(languages, popularity_percent, color="#6366f1", width=0.6)
10ax1.set_title("Most Popular Languages (Vertical)")
11ax1.set_ylabel("Market Share (%)")
12 
13# Value annotations on top of vertical bars
14for bar in bars:
15    yval = bar.get_height()
16    ax1.text(bar.get_x() + bar.get_width()/2, yval + 0.5, f"{yval}%", ha="center", va="bottom", fontsize=9)
17 
18# 2. Horizontal Bar Chart (ax.barh)
19ax2.barh(languages, popularity_percent, color="#f59e0b", height=0.6)
20ax2.set_title("Language Adoption (Horizontal)")
21ax2.set_xlabel("Share (%)")
22 
23plt.tight_layout()
24plt.show()

Grouped Bar Charts

To display multi-series comparisons across categories, offset the X-coordinates by the bar width:

1import matplotlib.pyplot as plt
2import numpy as np
3 
4categories = ["Q1", "Q2", "Q3", "Q4"]
5sales_2025 = [45, 52, 58, 65]
6sales_2026 = [50, 62, 70, 82]
7 
8x = np.arange(len(categories))
9width = 0.35  # Bar width
10 
11fig, ax = plt.subplots(figsize=(8, 4))
12ax.bar(x - width/2, sales_2025, width, label="2025", color="#94a3b8")
13ax.bar(x + width/2, sales_2026, width, label="2026", color="#3b82f6")
14 
15ax.set_title("Quarterly Revenue Comparison (in Lakhs ₹)")
16ax.set_xticks(x)
17ax.set_xticklabels(categories)
18ax.legend()
19plt.show()

3. Pie & Donut Charts: Proportions of a Whole (`ax.pie`)

Pie charts display how individual segments contribute to 100% of a whole.

1import matplotlib.pyplot as plt
2 
3categories = ["Engineering", "Sales & Mktg", "Product", "Operations", "Customer Support"]
4budgets = [42, 25, 15, 10, 8]
5colors = ["#3b82f6", "#10b981", "#f59e0b", "#8b5cf6", "#ec4899"]
6 
7# Explode (detach) the largest slice for emphasis
8explode = (0.08, 0, 0, 0, 0)
9 
10fig, ax = plt.subplots(figsize=(6, 6))
11 
12wedges, texts, autotexts = ax.pie(
13    budgets,
14    explode=explode,
15    labels=categories,
16    autopct="%1.1f%%",
17    startangle=140,
18    colors=colors,
19    shadow=True
20)
21 
22# Customize text styling
23for at in autotexts:
24    at.set_color("white")
25    at.set_weight("bold")
26 
27ax.set_title("Annual Corporate Budget Allocation", fontsize=13, fontweight="bold")
28plt.show()

Creating a Modern Donut Chart

Add a white center circle to transform a standard pie chart into a modern donut chart:

1# Create central white circle
2centre_circle = plt.Circle((0, 0), 0.70, fc="white")
3fig.gca().add_artist(centre_circle)

4. When to Use Which Chart?

Chart Type	Best Used When...	Avoid When...
Line Chart	Showing trends, rate of change, or time-series	Categories have no logical sequence
Bar Chart	Comparing discrete categorical amounts	You have hundreds of continuous data points
Pie Chart	Showing 2-5 simple parts of a 100% total	You have >6 slices or tiny differences in size

⚠️ Common Intermediate Mistakes & Gotchas

1. Using Pie Charts with Too Many Categories

Pie charts become unreadable when displaying more than 5–6 slices. Use a horizontal bar chart for datasets with numerous categories.

2. Cluttered X-Axis Labels

When x-axis categories have long names, they overlap horizontally. Rotate them using plt.xticks(rotation=45, ha='right') for clean readability.

💻 Try It Yourself: Comparing Quarterly Revenue Bar Chart

Scenario

Create a vertical bar chart comparing revenue across 4 fiscal quarters, coloring the highest quarter differently and displaying data labels on top of each bar.

Complete Solution

1import matplotlib.pyplot as plt
2 
3quarters = ["Q1", "Q2", "Q3", "Q4"]
4revenue = [12.5, 15.8, 14.2, 19.5]  # in Lakhs INR
5colors = ["#93c5fd", "#93c5fd", "#93c5fd", "#2563eb"]  # Highlight Q4
6 
7plt.figure(figsize=(7, 4.5))
8bars = plt.bar(quarters, revenue, color=colors, width=0.55)
9 
10for bar in bars:
11    yval = bar.get_height()
12    plt.text(bar.get_x() + bar.get_width()/2.0, yval + 0.3, f"₹{yval}L", ha="center", va="bottom", fontweight="bold")
13 
14plt.title("Fiscal Year Quarterly Revenue", fontsize=13, fontweight="bold")
15plt.ylabel("Revenue (₹ Lakhs)")
16plt.ylim(0, 24)
17plt.tight_layout()
18plt.savefig("quarterly_revenue.png", dpi=150)
19print("Chart saved to 'quarterly_revenue.png'.")
20plt.close()

Expected Output

1Chart saved to 'quarterly_revenue.png'.

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

Which chart type is best suited for visualizing website traffic trends over 30 days?

Question 2

Which method on a Matplotlib Axes object creates a horizontal bar chart?

Question 3

What does the parameter autopct="%1.1f%%" accomplish in ax.pie()?

Question 4

What is the primary visual drawback of using pie charts with more than 7 or 8 categories?

Question 5

In a multi-line chart, what method must be called to display the legend mapping line colors to their respective labels?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with Customizing Graphs (13: Data Visualization Basics).

👉 Continue to Next Lesson: Customizing Graphs →

Next Lesson

Customizing Graphs

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Introduction to Matplotlib	Customizing Graphs

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

Previous Next

Line, Bar, and Pie Charts in Python

1. Line Charts: Trends Over Time (ax.plot)

2. Bar Charts: Categorical Comparisons (ax.bar & ax.barh)

Vertical & Horizontal Bar Charts

Grouped Bar Charts

3. Pie & Donut Charts: Proportions of a Whole (ax.pie)

Creating a Modern Donut Chart

4. When to Use Which Chart?

⚠️ Common Intermediate Mistakes & Gotchas

1. Using Pie Charts with Too Many Categories

2. Cluttered X-Axis Labels

💻 Try It Yourself: Comparing Quarterly Revenue Bar Chart

Scenario

Complete Solution

Expected Output

Practice Quiz

🚀 What's Next?

Customizing Graphs

Related Lessons

Practice Quiz

Loading Page Content...

Line, Bar, and Pie Charts in Python

1. Line Charts: Trends Over Time (ax.plot)

2. Bar Charts: Categorical Comparisons (ax.bar & ax.barh)

Vertical & Horizontal Bar Charts

Grouped Bar Charts

3. Pie & Donut Charts: Proportions of a Whole (ax.pie)

Creating a Modern Donut Chart

4. When to Use Which Chart?

⚠️ Common Intermediate Mistakes & Gotchas

1. Using Pie Charts with Too Many Categories

2. Cluttered X-Axis Labels

💻 Try It Yourself: Comparing Quarterly Revenue Bar Chart

Scenario

Complete Solution

Expected Output

Practice Quiz

🚀 What's Next?

Customizing Graphs

Related Lessons

Practice Quiz

1. Line Charts: Trends Over Time (`ax.plot`)

2. Bar Charts: Categorical Comparisons (`ax.bar` & `ax.barh`)

3. Pie & Donut Charts: Proportions of a Whole (`ax.pie`)

1. Line Charts: Trends Over Time (`ax.plot`)

2. Bar Charts: Categorical Comparisons (`ax.bar` & `ax.barh`)

3. Pie & Donut Charts: Proportions of a Whole (`ax.pie`)