Why line plots show trends in Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib plotting library.

Matplotlib

import [1] as plt

Drag options to blanks, or click blank then click option'

Amatplotlib.pyplot

Bseaborn

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like numpy or pandas for plotting.

Forgetting to import pyplot specifically.

We import matplotlib.pyplot as plt to create line plots.

2fill in blank

medium

Complete the code to plot a line graph of y values.

Matplotlib

plt.[1]([1, 2, 3, 4], [10, 20, 25, 30])
plt.show()

Drag options to blanks, or click blank then click option'

Ascatter

Bplot

Cline

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scatter' which only plots points without lines.

Using 'bar' which creates bar charts.

The plot function draws line plots connecting points.

3fill in blank

hard

Fix the error in the code to correctly label the x-axis.

Matplotlib

plt.plot([1, 2, 3], [4, 5, 6])
plt.xlabel([1])
plt.show()

Drag options to blanks, or click blank then click option'

A'x label'

Bx label

Cx_label

Dxlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing label without quotes causing a NameError.

Using wrong parameter names.

Axis labels must be strings, so use quotes around the label text.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Matplotlib

{word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong condition like word > 3 which compares strings to numbers.

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if count is positive.

Matplotlib

{ [1]: [2] for [3] in data.items() if [2] > 0 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Bcount

Cword

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using data instead of iterating over data.items().

Not converting words to uppercase for keys.

Using count without defining it in the loop.

This comprehension creates a dictionary with uppercase words as keys and their counts as values, including only positive counts.

Practice

1. Why do line plots help us see trends in data?

easy

A. Because they connect points showing how values change over time

B. Because they show data as separate bars

C. Because they only display one data point

D. Because they use colors to highlight categories

Solution

1. Step 1: Understand what a line plot does

   A line plot connects data points with lines, showing how values move from one point to the next.

2. Step 2: Relate connection to trend visibility

   By connecting points, it becomes easy to see if values go up, down, or stay steady, revealing trends.

3. Final Answer:

   Because they connect points showing how values change over time -> Option A

4. Quick Check:

   Line plots connect points = D [OK]

Hint: Line plots connect points to show change over time [OK]

Common Mistakes:

• Confusing line plots with bar charts
• Thinking line plots show only single points
• Believing colors alone show trends

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

easy

A. plt.hist([1, 2, 3], [4, 5, 6])

B. plt.bar([1, 2, 3], [4, 5, 6])

C. plt.scatter([1, 2, 3], [4, 5, 6])

D. plt.plot([1, 2, 3], [4, 5, 6])

Solution

1. Step 1: Identify the function for line plots

   In matplotlib, plt.plot() is used to create line plots.

2. Step 2: Check the options

   plt.plot([1, 2, 3], [4, 5, 6]) uses plt.plot() with x and y data, which is correct. Others use bar, scatter, or histogram functions.

3. Final Answer:

   plt.plot([1, 2, 3], [4, 5, 6]) -> Option D

4. Quick Check:

   Line plot function = plt.plot() [OK]

Hint: Use plt.plot() for line plots, not bar or scatter [OK]

Common Mistakes:

• Using plt.bar() instead of plt.plot()
• Mixing up scatter and line plot functions
• Passing wrong arguments to plot()

3. What will the following code output?

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 15, 13, 17]
plt.plot(x, y)
plt.show()

medium

A. A scatter plot with points at (1,10), (2,15), (3,13), (4,17)

B. A line plot connecting points (1,10), (2,15), (3,13), (4,17)

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

D. An error because plt.show() is missing arguments

Solution

1. Step 1: Understand plt.plot() with x and y lists

   The code uses plt.plot(x, y), which creates a line plot connecting the points given by x and y.

2. Step 2: Check plt.show() usage

   plt.show() displays the plot. No arguments are needed, so no error occurs.

3. Final Answer:

   A line plot connecting points (1,10), (2,15), (3,13), (4,17) -> Option B

4. Quick Check:

   plt.plot() + plt.show() = line plot [OK]

Hint: plt.plot() connects points; plt.show() displays plot [OK]

Common Mistakes:

• Thinking plt.plot() creates scatter plot
• Expecting bar chart from plt.plot()
• Believing plt.show() needs arguments

4. Find the error in this code that tries to plot a line:

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

medium

A. plt.show() should be called before plt.plot()

B. plt.plot() is missing parentheses

C. x and y lists have different lengths causing an error

D. No error; code runs fine

Solution

1. Step 1: Check lengths of x and y lists

   x has 3 elements, y has 2 elements. They must be the same length to plot points.

2. Step 2: Understand matplotlib error on length mismatch

   Matplotlib raises a ValueError if x and y lengths differ because it cannot pair points properly.

3. Final Answer:

   x and y lists have different lengths causing an error -> Option C

4. Quick Check:

   Equal length x,y needed = A [OK]

Hint: Check x and y lengths match before plotting [OK]

Common Mistakes:

• Ignoring length mismatch error
• Thinking plt.show() order matters
• Assuming missing parentheses cause error

5. You have daily sales data for a week: [100, 120, 115, 130, 125, 140, 150]. How does a line plot help you understand this data better than just looking at numbers?

hard

A. It connects sales points to reveal if sales are increasing or decreasing over days

B. It hides fluctuations and shows only the average sales

C. It shows the exact sales numbers as text on the plot

D. It changes sales data into categories for easier reading

Solution

1. Step 1: Understand what line plots do with sequential data

   Line plots connect points in order, showing how values rise or fall over time.

2. Step 2: Compare to just numbers

   Numbers alone don't show trends clearly; line plots make it easy to see if sales go up or down day by day.

3. Final Answer:

   It connects sales points to reveal if sales are increasing or decreasing over days -> Option A

4. Quick Check:

   Line plot shows trend over time = C [OK]

Hint: Line plots reveal ups and downs in data over time [OK]

Common Mistakes:

• Thinking line plots show exact numbers as text
• Believing line plots hide fluctuations
• Confusing line plots with category charts