Lesson 3 - Customizing Plots

Making Professional Visualizations#

You can create basic plots. Now you will learn customization—transforming plain graphs into professional, informative visualizations that communicate insights clearly.

By the end of this lesson, you will be able to:

• Remove scientific notation from axes for better readability
• Add titles and axis labels to graphs
• Customize line colors, styles, and markers
• Create professional-looking visualizations
• Combine multiple customizations effectively

A graph without labels is like a book without a title—you do not know what it is about. Professional graphs always include proper titles, labels, and styling.

Removing Scientific Notation#

In the previous lesson, we saw large numbers displayed in scientific notation (like 7.5e6). While technically correct, it is harder for most people to read.

Why This Matters#

• Scientific notation: 7.5e6 requires mental calculation
• Regular notation: 7,500,000 is instantly understandable
• Makes graphs more accessible to non-technical audiences

We can fix this with plt.ticklabel_format():

import matplotlib.pyplot as plt

# COVID-19 new cases data (January - July 2020)
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul']
new_cases = [292219, 852102, 858350, 3027042, 3633886, 7823289, 7879833]

# Create the plot
plt.plot(months, new_cases)

# Remove scientific notation from y-axis
plt.ticklabel_format(axis='y', style='plain')

plt.show()

Understanding the Code#

• axis='y' - applies to y-axis (can also use 'x' or 'both')
• style='plain' - uses regular numbers instead of scientific notation

Now the y-axis displays regular numbers that anyone can read immediately.

Adding Titles and Labels#

A professional graph needs three key text elements:

1. Title - What the graph shows
2. X-axis label - What the horizontal axis represents
3. Y-axis label - What the vertical axis represents

Adding All Three Elements#

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul']
new_cases = [292219, 852102, 858350, 3027042, 3633886, 7823289, 7879833]

plt.plot(months, new_cases)
plt.ticklabel_format(axis='y', style='plain')

# Add title and labels
plt.title('Global COVID-19 New Cases (January - July 2020)')
plt.xlabel('Month')
plt.ylabel('New Cases')

plt.show()

Best Practices for Labels#

Title:

• Should be descriptive but concise
• Summarizes what the graph shows
• Use title case for better readability

Axis Labels:

• Include units if applicable (e.g., “Temperature (Celsius)”, “Revenue ($)”)
• Keep them short and clear
• Capitalize appropriately

Example labels with units:

plt.ylabel('Temperature (°F)')
plt.ylabel('Revenue ($)')
plt.ylabel('Distance (km)')
plt.xlabel('Time (hours)')

Customizing Line Colors#

The default blue line is fine, but sometimes you want to:

• Match your company’s branding colors
• Make the line stand out more
• Use color to convey meaning (red for danger, green for growth)

You can change the color using the color parameter:

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul']
new_cases = [292219, 852102, 858350, 3027042, 3633886, 7823289, 7879833]

# Red line to show alarming trend
plt.plot(months, new_cases, color='red')
plt.ticklabel_format(axis='y', style='plain')
plt.title('Global COVID-19 New Cases (January - July 2020)')
plt.xlabel('Month')
plt.ylabel('New Cases')

plt.show()

Common Color Options#

Named colors:

• 'red', 'blue', 'green', 'orange', 'purple', 'black', 'gray'

Short codes:

• 'r' (red), 'b' (blue), 'g' (green), 'k' (black), 'm' (magenta), 'c' (cyan), 'y' (yellow)

Hex codes (for exact colors):

• '#FF5733', '#3498DB', '#2ECC71'

Tip: You can find hex color codes at colorpicker.com or similar websites.

Using Color Meaningfully#

• Red: Warnings, danger, negative trends
• Green: Growth, positive trends, success
• Blue: Neutral, professional, trust
• Orange: Attention, moderate concern
• Purple: Premium, special categories

Customizing Line Styles and Markers#

Beyond color, you can customize:

• Line style - solid, dashed, dotted
• Markers - points at each data value
• Line width - thicker or thinner lines

Dashed Line with Markers#

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul']
new_cases = [292219, 852102, 858350, 3027042, 3633886, 7823289, 7879833]

# Dashed line with circular markers
plt.plot(months, new_cases,
         color='red',
         linestyle='--',  # Dashed line
         marker='o')      # Circular markers

plt.ticklabel_format(axis='y', style='plain')
plt.title('Global COVID-19 New Cases (January - July 2020)')
plt.xlabel('Month')
plt.ylabel('New Cases')

plt.show()

Line Style Options#

Marker Options#

When to Use Markers#

• When you have few data points (< 20)
• To emphasize individual values
• To make the line more visible
• To distinguish multiple lines

Adjusting Line Width#

# Thicker dotted line with square markers
plt.plot(months, new_cases,
         color='purple',
         linestyle=':',
         marker='s',
         linewidth=2)  # Thicker line

plt.ticklabel_format(axis='y', style='plain')
plt.title('Global COVID-19 New Cases (January - July 2020)')
plt.xlabel('Month')
plt.ylabel('New Cases')

plt.show()

Line width values:

• Default: 1.0
• Thin: 0.5
• Thick: 2.0 or 3.0

Practice Exercises#

Apply customization techniques with these exercises.

Exercise 1: Sales Report#

You have monthly sales data for your company:

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [45000, 52000, 48000, 61000, 58000, 67000]

Task: Create a professional line plot with:

1. A green solid line (green = growth)
2. Title: “Monthly Sales Performance (H1 2024)”
3. X-axis label: “Month”
4. Y-axis label: “Sales ($)”
5. No scientific notation

# Your code here

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [45000, 52000, 48000, 61000, 58000, 67000]

plt.plot(months, sales, color='green')
plt.ticklabel_format(axis='y', style='plain')
plt.title('Monthly Sales Performance (H1 2024)')
plt.xlabel('Month')
plt.ylabel('Sales ($)')

plt.show()

Exercise 2: Temperature Forecast#

A weather forecast shows expected temperatures for the next week:

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
temps = [72, 75, 73, 68, 65, 67, 71]

Task: Create a line plot with:

1. Orange dashed line with circular markers
2. Title: “7-Day Temperature Forecast”
3. X-axis label: “Day of Week”
4. Y-axis label: “Temperature (°F)”

Hint: Use linestyle='--' and marker='o'

# Your code here

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
temps = [72, 75, 73, 68, 65, 67, 71]

plt.plot(days, temps,
         color='orange',
         linestyle='--',
         marker='o')
plt.title('7-Day Temperature Forecast')
plt.xlabel('Day of Week')
plt.ylabel('Temperature (°F)')

plt.show()

Exercise 3: Multiple Customizations#

Website traffic data for the past 6 months:

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
visitors = [125000, 142000, 138000, 156000, 171000, 189000]

Task: Create your most professional plot yet with:

1. Blue solid line with star markers
2. Line width of 2
3. Title: “Website Traffic Growth”
4. Proper axis labels with units
5. No scientific notation

Challenge: Try different combinations to see what looks best!

# Your code here

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
visitors = [125000, 142000, 138000, 156000, 171000, 189000]

plt.plot(months, visitors,
         color='blue',
         marker='*',
         linewidth=2)
plt.ticklabel_format(axis='y', style='plain')
plt.title('Website Traffic Growth')
plt.xlabel('Month')
plt.ylabel('Unique Visitors')

plt.show()

Summary#

You now create professional, customized visualizations. Let’s review the key concepts.

Key Concepts#

Remove Scientific Notation

• Use plt.ticklabel_format(axis='y', style='plain')
• Makes large numbers readable
• Apply to 'x', 'y', or 'both' axes

Always Add Labels

• plt.title() for the graph title
• plt.xlabel() for x-axis
• plt.ylabel() for y-axis (include units!)
• Makes graphs self-explanatory

Customize Appearance

• color= - Change line color
• linestyle= - Solid, dashed, dotted
• marker= - Add markers at data points
• linewidth= - Adjust line thickness

Professional Graphs

• Need titles, labels, and readable numbers
• Use color meaningfully
• Choose appropriate markers
• Adjust line width for emphasis

Customization Reference#

# Full customization example
plt.plot(x, y,
         color='green',        # Line color
         linestyle='--',       # Dashed line
         marker='o',           # Circular markers
         linewidth=2)          # Thicker line

plt.title('Your Title Here')
plt.xlabel('X-Axis Label')
plt.ylabel('Y-Axis Label (units)')
plt.ticklabel_format(axis='y', style='plain')
plt.show()

Important Reminders#

• Self-explanatory graphs: Anyone should understand without asking
• Use units: Always include units in axis labels
• Color with purpose: Colors should enhance meaning
• Less is more: Do not over-customize—clarity over decoration

Next Steps#

You can create professional single-line plots. In the next lesson, you will learn to plot multiple lines on the same graph, add legends, and work with real-world time series data from CSV files.

Create Publication-Quality Visualizations#

A graph should be self-explanatory. With titles, labels, and styling, your visualizations now communicate insights professionally.

Use customization to make your data speak clearly!