Multiple time series comparison in Matplotlib - Mini Project: Build & Apply

Multiple Time Series Comparison

📖 Scenario: You work as a data analyst for a small retail company. You have sales data for three different products over a week. Your manager wants to see how the sales of these products compare day by day in a single chart.

🎯 Goal: Create a line chart that compares the daily sales of three products over one week using matplotlib. You will prepare the sales data, set up the days of the week, plot all three products on the same graph with labels, and display the chart.

📋 What You'll Learn

Create a dictionary called sales_data with keys 'Product A', 'Product B', and 'Product C' and their daily sales as lists of integers.

Create a list called days with the days of the week from Monday to Sunday.

Use matplotlib.pyplot to plot the sales of all three products on the same line chart with appropriate labels and a legend.

Display the plot using plt.show().

💡 Why This Matters

🌍 Real World

Comparing multiple time series data visually helps businesses understand trends and make decisions, such as comparing sales of different products over time.

💼 Career

Data analysts and scientists often need to visualize and compare multiple data series to communicate insights clearly to stakeholders.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Product A' with sales [20, 22, 19, 24, 30, 28, 25], 'Product B' with sales [15, 18, 17, 20, 22, 21, 19], and 'Product C' with sales [10, 12, 11, 14, 16, 15, 13].

Matplotlib

# Create the sales_data dictionary with exact values
# Your code here

Hint

Use curly braces {} to create the dictionary and colons : to assign lists to each product key.

2

Create the days list

Create a list called days containing the days of the week as strings: ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'].

Matplotlib

sales_data = {
    'Product A': [20, 22, 19, 24, 30, 28, 25],
    'Product B': [15, 18, 17, 20, 22, 21, 19],
    'Product C': [10, 12, 11, 14, 16, 15, 13]
}
# Create the days list with the exact days of the week
# Your code here

Hint

Use square brackets [] to create the list and include all days as strings.

3

Plot the sales data

Import matplotlib.pyplot as plt. Use a for loop with variables product and sales to iterate over sales_data.items(). Inside the loop, plot days on the x-axis and sales on the y-axis with label product. Add a legend with plt.legend().

Matplotlib

sales_data = {
    'Product A': [20, 22, 19, 24, 30, 28, 25],
    'Product B': [15, 18, 17, 20, 22, 21, 19],
    'Product C': [10, 12, 11, 14, 16, 15, 13]
}
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

# Import matplotlib.pyplot as plt
# Use a for loop to plot each product's sales with label
# Add a legend
# Your code here

Hint

Remember to import matplotlib.pyplot as plt. Use plt.plot() inside the loop and add label=product. Call plt.legend() after the loop.

4

Display the plot

Use plt.show() to display the line chart comparing the sales of the three products over the week.

Matplotlib

import matplotlib.pyplot as plt

sales_data = {
    'Product A': [20, 22, 19, 24, 30, 28, 25],
    'Product B': [15, 18, 17, 20, 22, 21, 19],
    'Product C': [10, 12, 11, 14, 16, 15, 13]
}
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

for product, sales in sales_data.items():
    plt.plot(days, sales, label=product)

plt.legend()
# Display the plot
# Your code here

Hint

Call plt.show() to open the window with the line chart.

Practice

1. What is the main purpose of plotting multiple time series on the same graph using matplotlib?

easy

A. To make the graph load faster

B. To create separate graphs for each time series

C. To hide the data labels for clarity

D. To visually compare trends and differences between series over time

Solution

1. Step 1: Understand the purpose of multiple time series plots

   Plotting multiple lines on one graph helps us see how different series change over time side by side.

2. Step 2: Compare options with this purpose

   Only To visually compare trends and differences between series over time describes this visual comparison goal correctly.

3. Final Answer:

   To visually compare trends and differences between series over time -> Option D

4. Quick Check:

   Multiple time series comparison = Visual comparison [OK]

Hint: Multiple lines on one plot show trends side by side [OK]

Common Mistakes:

• Thinking multiple plots are created instead of one combined plot
• Ignoring the importance of labels and legends
• Believing it speeds up graph loading

2. Which of the following matplotlib code snippets correctly plots two time series series1 and series2 on the same graph with labels?

easy

A. plt.plot(series1, label='Series 1')
plt.legend()
plt.show()

B. plt.plot(series1)
plt.plot(series2)
plt.show()

C. plt.plot(series1, label='Series 1')
plt.plot(series2, label='Series 2')
plt.legend()
plt.show()

D. plt.plot(series1, label='Series 1')
plt.plot(series2, label='Series 2')
plt.show()

Solution

1. Step 1: Check for plotting both series with labels

   plt.plot(series1, label='Series 1') plt.plot(series2, label='Series 2') plt.legend() plt.show() plots both series and adds labels for each.

2. Step 2: Verify legend call to show labels

   plt.plot(series1, label='Series 1') plt.plot(series2, label='Series 2') plt.legend() plt.show() calls plt.legend() to display labels on the graph.

3. Final Answer:

   Code that plots both series with labels and shows legend -> Option C

4. Quick Check:

   Plot both + labels + legend = plt.plot(series1, label='Series 1') plt.plot(series2, label='Series 2') plt.legend() plt.show() [OK]

Hint: Always add labels and call plt.legend() to show them [OK]

Common Mistakes:

• Forgetting to call plt.legend() so labels don't appear
• Plotting only one series instead of both
• Not adding labels to each series

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
series1 = [1, 3, 2, 4]
series2 = [2, 1, 3, 5]
plt.plot(series1, label='A')
plt.plot(series2, label='B')
plt.legend()
plt.show()

medium

A. A graph with two lines labeled 'A' and 'B' showing series1 and series2

B. A graph with one line showing the sum of series1 and series2

C. A graph with two lines but no labels

D. SyntaxError due to missing import

Solution

1. Step 1: Analyze the plotting commands

   Two lines are plotted: series1 with label 'A' and series2 with label 'B'.

2. Step 2: Check legend and show calls

   Calling plt.legend() displays the labels on the graph.

3. Final Answer:

   A graph with two lines labeled 'A' and 'B' showing series1 and series2 -> Option A

4. Quick Check:

   Two labeled lines plotted = A graph with two lines labeled 'A' and 'B' showing series1 and series2 [OK]

Hint: Labels appear only if plt.legend() is called [OK]

Common Mistakes:

• Thinking lines are summed instead of plotted separately
• Forgetting plt.legend() hides labels
• Assuming syntax error without import

4. The following code is intended to plot two time series with labels, but the legend does not appear. What is the error?

import matplotlib.pyplot as plt
series1 = [1, 2, 3]
series2 = [3, 2, 1]
plt.plot(series1, label='First')
plt.plot(series2, label='Second')
plt.show()

medium

A. Missing plt.legend() call to display the legend

B. Labels are incorrectly assigned inside plt.plot()

C. plt.show() should be called before plotting

D. series2 should be plotted before series1

Solution

1. Step 1: Check for legend display code

   The code does not call plt.legend(), so labels won't show.

2. Step 2: Confirm label usage is correct

   Labels are correctly passed in plt.plot(), so no issue there.

3. Final Answer:

   Missing plt.legend() call to display the legend -> Option A

4. Quick Check:

   Legend missing without plt.legend() [OK]

Hint: Always add plt.legend() to show labels on plot [OK]

Common Mistakes:

• Assuming labels show automatically without legend call
• Thinking order of plotting affects legend display
• Calling plt.show() too early

5. You have three time series with different lengths:
series1 = [1, 2, 3, 4], series2 = [2, 3, 4], series3 = [1, 3, 5, 7, 9].
How can you correctly plot all three on the same graph to compare them over time?

hard

A. Plot all series using the length of the longest series only

B. Plot each series with its own x-axis range matching its length, then add labels and legend

C. Pad shorter series with zeros to match the longest series length before plotting

D. Plot only the shortest series to avoid errors

Solution

1. Step 1: Understand different lengths require matching x-axis

   Each series must be plotted with x values matching its length to avoid misalignment.

2. Step 2: Plot each series with its own x range and add labels

   Use range(len(series)) for x-axis, plot all, then add labels and legend.

3. Final Answer:

   Plot each series with its own x-axis range matching its length, then add labels and legend -> Option B

4. Quick Check:

   Match x-axis length per series + labels = Plot each series with its own x-axis range matching its length, then add labels and legend [OK]

Hint: Use range(len(series)) for x-axis of each series [OK]

Common Mistakes:

• Plotting all series without adjusting x-axis causes misaligned lines
• Padding with zeros changes data meaning
• Ignoring series length differences