Line plots in Data Analysis Python - Mini Project: Build & Apply

Line plots

📖 Scenario: You are a data analyst working with monthly sales data for a small store. You want to visualize how sales changed over the first six months of the year.

🎯 Goal: Create a line plot to show monthly sales trends from January to June.

📋 What You'll Learn

Create a dictionary with months as keys and sales numbers as values

Create a list of months in order

Use matplotlib to plot sales data as a line plot

Label the x-axis as 'Month' and y-axis as 'Sales'

Display the plot

💡 Why This Matters

🌍 Real World

Line plots are used to show trends over time, like sales, temperature, or stock prices.

💼 Career

Data analysts and scientists use line plots to communicate patterns and changes clearly to others.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230.

Data Analysis Python

# Your code here

Hint

Use curly braces {} to create a dictionary with month names as keys and sales numbers as values.

2

Create the list of months in order

Create a list called months with these exact values in order: 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'.

Data Analysis Python

sales_data = {'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230}
# Your code here

Hint

Use square brackets [] to create a list of months in the correct order.

3

Plot the sales data as a line plot

Import matplotlib.pyplot as plt. Create a list called sales that contains the sales numbers from sales_data in the order of months. Use plt.plot(months, sales) to create a line plot.

Data Analysis Python

sales_data = {'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230}
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
# Your code here

Hint

Use a list comprehension to get sales numbers in order. Then use plt.plot() to draw the line plot.

4

Add labels and display the plot

Use plt.xlabel('Month') and plt.ylabel('Sales') to label the axes. Then use plt.show() to display the plot.

Data Analysis Python

import matplotlib.pyplot as plt

sales_data = {'Jan': 150, 'Feb': 200, 'Mar': 180, 'Apr': 220, 'May': 210, 'Jun': 230}
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [sales_data[month] for month in months]
plt.plot(months, sales)
# Your code here

Hint

Use plt.xlabel() and plt.ylabel() to add axis labels. Then call plt.show() to display the plot window.

Practice

1. What is the main purpose of a line plot in data analysis?

easy

A. To display data as separate bars for comparison

B. To list raw data values without visualization

C. To show the distribution of data using boxes

D. To show trends or changes over time by connecting data points

Solution

1. Step 1: Understand the function of line plots

   Line plots connect points to show how values change over time or order.

2. Step 2: Compare with other plot types

   Bar plots show separate bars, box plots show distribution, and raw data listing is not a plot.

3. Final Answer:

   To show trends or changes over time by connecting data points -> Option D

4. Quick Check:

   Line plot = trends over time [OK]

Hint: Line plots connect points to show trends clearly [OK]

Common Mistakes:

• Confusing line plots with bar charts
• Thinking line plots show data distribution
• Assuming line plots display raw data only

2. Which of the following is the correct way to create a simple line plot using matplotlib in Python?

easy

A. plt.graph(x, y)

B. plt.line(x, y)

C. plt.plot(x, y)

D. plt.chart(x, y)

Solution

1. Step 1: Recall matplotlib line plot syntax

   The function to create line plots in matplotlib is plt.plot().

2. Step 2: Check other options

   Functions like plt.line(), plt.graph(), and plt.chart() do not exist in matplotlib.

3. Final Answer:

   plt.plot(x, y) -> Option C

4. Quick Check:

   matplotlib line plot = plt.plot() [OK]

Hint: Use plt.plot() to draw line plots in matplotlib [OK]

Common Mistakes:

• Using non-existent functions like plt.line()
• Confusing plot with chart or graph functions
• Forgetting to import matplotlib.pyplot as plt

3. What will be the output of the following code?

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 15, 25]
plt.plot(x, y, marker='o')
plt.show()

medium

A. A bar chart with bars at x positions 1 to 4

B. A line plot connecting points (1,10), (2,20), (3,15), (4,25) with circle markers

C. A scatter plot with only circle markers at given points

D. An error because marker='o' is invalid

Solution

1. Step 1: Understand plt.plot with marker

   The code plots a line connecting points with circle markers at each point.

2. Step 2: Differentiate from scatter and bar plots

   plt.plot with marker='o' adds markers on a line plot, not a scatter or bar chart.

3. Final Answer:

   A line plot connecting points (1,10), (2,20), (3,15), (4,25) with circle markers -> Option B

4. Quick Check:

   plt.plot + marker='o' = line with circles [OK]

Hint: plt.plot with marker='o' adds circle markers on line [OK]

Common Mistakes:

• Thinking plt.plot with marker='o' creates scatter plot
• Confusing bar chart with line plot
• Assuming marker='o' causes error

4. Identify the error in this code that tries to plot two lines on the same graph:

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [2, 4, 6]
y2 = [1, 3, 5]
plt.plot(x, y1)
plt.plot(y2, x)
plt.show()

medium

A. The second plt.plot has x and y swapped, causing incorrect plotting

B. Missing plt.show() to display the plot

C. Using plt.plot twice is not allowed

D. Lists x, y1, y2 must be numpy arrays

Solution

1. Step 1: Check the order of arguments in plt.plot

   plt.plot expects x values first, then y values. The second plot swaps these.

2. Step 2: Understand impact of swapped arguments

   Swapping causes the line to plot y2 on x-axis and x on y-axis, which is incorrect for intended comparison.

3. Final Answer:

   The second plt.plot has x and y swapped, causing incorrect plotting -> Option A

4. Quick Check:

   plt.plot(x, y) order matters [OK]

Hint: Always put x first, y second in plt.plot(x, y) [OK]

Common Mistakes:

• Swapping x and y arguments in plt.plot
• Thinking plt.plot twice overwrites the first plot
• Believing lists must be numpy arrays

5. You have daily sales data for two products over a week:

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
sales_A = [100, 120, 130, 90, 110, 115, 105]
sales_B = [80, 95, 100, 85, 90, 100, 95]

How would you create a line plot to compare both products' sales clearly with labels and a legend?

hard

A. plt.plot(days, sales_A, label='Product A')
plt.plot(days, sales_B, label='Product B')
plt.legend()
plt.show()

B. plt.plot(sales_A, days)
plt.plot(sales_B, days)
plt.show()

C. plt.bar(days, sales_A)
plt.bar(days, sales_B)
plt.show()

D. plt.scatter(days, sales_A)
plt.scatter(days, sales_B)
plt.legend()
plt.show()

Solution

1. Step 1: Plot both sales lists against days with labels

   Use plt.plot(days, sales_A, label='Product A') and similarly for sales_B to plot lines with labels.

2. Step 2: Add legend and show plot

   Call plt.legend() to display labels and plt.show() to render the plot.

3. Final Answer:

   plt.plot(days, sales_A, label='Product A') plt.plot(days, sales_B, label='Product B') plt.legend() plt.show() -> Option A

4. Quick Check:

   Use labels + legend for multiple lines [OK]

Hint: Add label= and call plt.legend() to compare lines [OK]

Common Mistakes:

• Swapping x and y in plt.plot
• Using bar or scatter plots instead of line plots
• Forgetting to add legend for clarity