Summary  
This chapter covers how to use matplotlib to create and customize line plots by plotting data points, setting axes labels and a title, adding markers, colors, and gridlines, and displaying the chart.

General domain of usage  
Personal finance tracking

Tracking your freelance earnings is essential for understanding your financial health and planning for the future. Visualizing this data helps you spot trends, identify seasonal patterns, and make informed decisions about your business. As a freelancer, clear and attractive charts can quickly communicate your progress to clients or accountants. The `matplotlib` library is a powerful tool in Python that allows you to create a wide range of visualizations, including line plots that are perfect for showing how your earnings change over time.

import matplotlib.pyplot as plt

# Hardcoded data for monthly earnings
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
earnings = [1200, 1500, 1700, 1600, 1800, 2100]

# Create a line plot of earnings over months
plt.plot(months, earnings)

# Add labels and a title
plt.xlabel("Month")
plt.ylabel("Earnings ($)")
plt.title("Freelance Earnings Over Time")

# Display the plot
plt.show()

When creating a plot with `matplotlib`, you work with several key elements. The **axes** are the horizontal and vertical lines that frame your data; they represent the variables being plotted, such as months and earnings. **Labels** on the axes help viewers understand what each axis represents, making your chart easier to interpret. The **title** provides context about what the plot shows. Customizing your plot's appearance—like changing line colors, adding markers, or adjusting gridlines—can improve clarity and make your visualizations more engaging and readable.

import matplotlib.pyplot as plt

months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
earnings = [1200, 1500, 1700, 1600, 1800, 2100]

# Create a line plot with markers and gridlines
plt.plot(months, earnings, marker="o", linestyle="-", color="green")

plt.xlabel("Month")
plt.ylabel("Earnings ($)")
plt.title("Freelance Earnings Over Time")

# Add gridlines for better readability
plt.grid(True)

plt.show()

What type of plot is best for showing earnings over time?

Which matplotlib function is used to display a plot?

Why is it important to label axes and add a title to your plots?

Unlock the power of Python to streamline, automate, and enhance your freelance workflow. This course is designed for freelancers who already know basic Python syntax and want to apply their skills to real-world freelance scenarios, from automating repetitive tasks to analyzing client data and visualizing project progress.

Learn how to use Python to automate repetitive and time-consuming freelance tasks, such as time tracking, invoice generation, and email reminders.

Master the use of Python for analyzing and visualizing your freelance business data, helping you make informed decisions and present results to clients.

Explore advanced Python techniques to optimize your freelance business, including pricing analysis, workload balancing, and client communication automation.

Visualizing Earnings with matplotlib

1. What type of plot is best for showing earnings over time?

2. Which matplotlib function is used to display a plot?

3. Why is it important to label axes and add a title to your plots?