Trend lines on scatter plots in Matplotlib - Mini Project: Build & Apply

Trend lines on scatter plots

📖 Scenario: You are a data analyst working with sales data. You want to see if there is a trend between the number of hours spent on advertising and the sales revenue.

🎯 Goal: Build a scatter plot showing advertising hours vs sales revenue and add a trend line to see the relationship clearly.

📋 What You'll Learn

Create a dictionary with advertising hours and sales revenue data

Create a variable for the degree of the polynomial for the trend line

Calculate the polynomial fit and generate the trend line values

Plot the scatter plot and the trend line using matplotlib

Display the plot

💡 Why This Matters

🌍 Real World

Trend lines help businesses see if more advertising hours lead to higher sales, guiding marketing decisions.

💼 Career

Data analysts and scientists often use scatter plots with trend lines to find patterns and make predictions.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'hours': [1, 2, 3, 4, 5, 6, 7, 8] and 'revenue': [100, 150, 200, 250, 300, 350, 400, 450].

Matplotlib

# Create the sales_data dictionary with hours and revenue lists
# Your code here

Need a hint?

Use a dictionary with keys 'hours' and 'revenue' and assign the exact lists as values.

2

CONFIGURATION: Set the polynomial degree for the trend line

Create a variable called degree and set it to 1 to indicate a linear trend line.

Matplotlib

sales_data = {'hours': [1, 2, 3, 4, 5, 6, 7, 8], 'revenue': [100, 150, 200, 250, 300, 350, 400, 450]}
# Create the degree variable and set it to 1
# Your code here

Need a hint?

Set degree to 1 for a straight line trend.

3

CORE LOGIC: Calculate the trend line values

Use numpy.polyfit with sales_data['hours'], sales_data['revenue'], and degree to get the polynomial coefficients. Then use numpy.poly1d to create a polynomial function called trend_line. Finally, create a list called trend_values by applying trend_line to sales_data['hours'].

Matplotlib

sales_data = {'hours': [1, 2, 3, 4, 5, 6, 7, 8], 'revenue': [100, 150, 200, 250, 300, 350, 400, 450]}
degree = 1
import numpy as np
# Calculate polynomial coefficients
# Create polynomial function trend_line
# Calculate trend_values by applying trend_line to sales_data['hours']
# Your code here

Need a hint?

Use np.polyfit to get coefficients, then np.poly1d to create the function, then apply it to hours.

4

OUTPUT: Plot the scatter plot and trend line

Import matplotlib.pyplot as plt. Use plt.scatter to plot sales_data['hours'] vs sales_data['revenue']. Use plt.plot to plot sales_data['hours'] vs trend_values with a red line. Finally, call plt.show() to display the plot.

Matplotlib

sales_data = {'hours': [1, 2, 3, 4, 5, 6, 7, 8], 'revenue': [100, 150, 200, 250, 300, 350, 400, 450]}
degree = 1
import numpy as np
coefficients = np.polyfit(sales_data['hours'], sales_data['revenue'], degree)
trend_line = np.poly1d(coefficients)
trend_values = trend_line(sales_data['hours'])
import matplotlib.pyplot as plt
# Plot scatter plot of hours vs revenue
# Plot trend line in red
# Show the plot
# Your code here

Need a hint?

Use plt.scatter for points, plt.plot for the line, and plt.show() to display.