Line, Bar and Pie Charts0%
Customizing Graphs

Line, Bar and Pie Charts

Beginner12 min read•Updated: Jul 11, 2026
Study Materials

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):

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

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

Bar charts compare discrete numerical values across distinct nominal categories.

Vertical & Horizontal Bar Charts

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

Grouped Bar Charts

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

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

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

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

Python
import matplotlib.pyplot as plt
 
categories = ["Engineering", "Sales & Mktg", "Product", "Operations", "Customer Support"]
budgets = [42, 25, 15, 10, 8]
colors = ["#3b82f6", "#10b981", "#f59e0b", "#8b5cf6", "#ec4899"]
 
# Explode (detach) the largest slice for emphasis
explode = (0.08, 0, 0, 0, 0)
 
fig, ax = plt.subplots(figsize=(6, 6))
 
wedges, texts, autotexts = ax.pie(
budgets,
explode=explode,
labels=categories,
autopct="%1.1f%%",
startangle=140,
colors=colors,
shadow=True
)
 
# Customize text styling
for at in autotexts:
at.set_color("white")
at.set_weight("bold")
 
ax.set_title("Annual Corporate Budget Allocation", fontsize=13, fontweight="bold")
plt.show()

Creating a Modern Donut Chart

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

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

4. When to Use Which Chart?

Chart TypeBest Used When...Avoid When...
Line ChartShowing trends, rate of change, or time-seriesCategories have no logical sequence
Bar ChartComparing discrete categorical amountsYou have hundreds of continuous data points
Pie ChartShowing 2-5 simple parts of a 100% totalYou have >6 slices or tiny differences in size

Multiple Choice Questions

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

A. Pie chart B. Line chart C. Scatter plot D. Donut chart Answer: B Explanation: Line charts are specifically designed to illustrate continuous trends and rates of change across sequential intervals like time.

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

A. ax.hbar() B. ax.barh() C. ax.horizontal_bar() D. ax.plot_bar(horizontal=True) Answer: B Explanation: ax.barh() creates horizontal bar plots, whereas ax.bar() creates vertical bar plots.

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

A. It rounds values to the nearest integer B. It automatically formats and displays the percentage contribution on each slice with one decimal place C. It sorts the slices in descending order D. It colors each slice automatically Answer: B Explanation: autopct formats the numeric percentage label rendered inside each pie slice.

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

A. Pie charts consume too much GPU memory B. Slices become narrow, cluttered, and difficult for human eyes to compare accurately C. Matplotlib crashes with more than 5 slices D. Percentages cannot add up to 100% Answer: B Explanation: Human visual perception struggles to compare angles when there are numerous small slices; a horizontal bar chart is far more legible for many categories.

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

A. ax.show_labels() B. ax.legend() C. ax.render_key() D. ax.display_info() Answer: B Explanation: ax.legend() renders the legend box mapping line labels to their respective colors and markers.

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