Why histograms show distributions in Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library needed to create histograms.

Matplotlib

import [1] as plt

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

Apandas

Bseaborn

Cmatplotlib.pyplot

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy instead of matplotlib.pyplot

Importing pandas which is for data frames

Importing seaborn which is a different plotting library

We use matplotlib.pyplot to create histograms in Python.

2fill in blank

medium

Complete the code to create a histogram of the data list.

Matplotlib

data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
plt.[1](data)
plt.show()

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

Ahist

Bplot

Cscatter

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot which makes a line graph

Using scatter which makes a scatter plot

Using bar which makes bar charts but not histograms

The hist function creates a histogram to show data distribution.

3fill in blank

hard

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

Matplotlib

plt.hist(data)
plt.xlabel([1])
plt.show()

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

A'Values'

Bxlabel

Cx_label

DValues

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names without quotes

Using incorrect function names

Forgetting quotes around label text

The label text must be a string, so it needs quotes.

4fill in blank

hard

Fill both blanks to create a histogram with 5 bins and a title.

Matplotlib

plt.hist(data, bins=[1])
plt.title([2])
plt.show()

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

A5

B'Data Distribution'

C'Histogram'

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using too many bins like 10

Not putting the title in quotes

Confusing bins with title

Setting bins to 5 groups data into 5 parts. The title must be a string.

5fill in blank

hard

Fill all three blanks to create a histogram with green color, 8 bins, and x-axis label 'Scores'.

Matplotlib

plt.hist(data, bins=[1], color=[2])
plt.xlabel([3])
plt.show()

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

A8

B'green'

C'Scores'

D'blue'

Attempts:

3 left

💡 Hint

Common Mistakes

Using color without quotes

Using numbers instead of strings for labels

Mixing up color names

Bins is a number (8), color is a string ('green'), and xlabel is a string ('Scores').

Practice

1. What does a histogram primarily show about a dataset?

easy

A. The average value of the dataset

B. The exact values of each data point

C. The order in which data was collected

D. How data values are spread or grouped

Solution

1. Step 1: Understand histogram purpose

   A histogram groups data into ranges (bins) to show how values spread.

2. Step 2: Compare options to definition

   Only How data values are spread or grouped describes this grouping and spread, others describe different concepts.

3. Final Answer:

   How data values are spread or grouped -> Option D

4. Quick Check:

   Histogram = Data spread [OK]

Hint: Histograms show data grouping, not exact values [OK]

Common Mistakes:

• Thinking histograms show exact data points
• Confusing histogram with mean or order
• Assuming histograms show data collection sequence

2. Which of the following is the correct way to create a histogram using matplotlib for a list named data?

easy

A. plt.plot(data)

B. plt.hist(data)

C. plt.scatter(data)

D. plt.bar(data)

Solution

1. Step 1: Recall matplotlib histogram function

   The function to create histograms is plt.hist().

2. Step 2: Match options with correct function

   Only plt.hist(data) uses plt.hist(data), others create different plots.

3. Final Answer:

   plt.hist(data) -> Option B

4. Quick Check:

   Histogram function = plt.hist() [OK]

Hint: Use plt.hist() to plot histograms in matplotlib [OK]

Common Mistakes:

• Using plt.plot() instead of plt.hist()
• Confusing scatter or bar plots with histograms
• Forgetting to import matplotlib.pyplot as plt

3. Given the code:

import matplotlib.pyplot as plt
import numpy as np
data = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])
plt.hist(data, bins=4)
plt.show()

What does the histogram show about the data distribution?

medium

A. There are more data points at 1 than at 4

B. Data points are evenly spread across all values

C. Most data points are around 4

D. Data points cluster mostly at 2 and 3

Solution

1. Step 1: Count data frequency per value

   Data has 1 one, 2 twos, 3 threes, and 4 fours; highest count is 4.

2. Step 2: Understand histogram bins and heights

   With 4 bins, each value likely forms a bin; tallest bar corresponds to value 4.

3. Final Answer:

   Most data points are around 4 -> Option C

4. Quick Check:

   Highest frequency = 4 [OK]

Hint: Look for tallest bar to find where data clusters [OK]

Common Mistakes:

• Assuming equal spread without checking counts
• Confusing bin count with data values
• Ignoring frequency differences

4. What is wrong with this code snippet for plotting a histogram?

import matplotlib.pyplot as plt
data = [1, 2, 2, 3, 3, 3]
plt.hist(data, bins='five')
plt.show()

medium

A. The bins parameter should be an integer, not a string

B. plt.hist() cannot plot lists, only arrays

C. plt.show() is missing

D. Data list contains duplicate values which histograms can't handle

Solution

1. Step 1: Check bins parameter type

   Bins must be an integer or sequence, not a string like 'five'.

2. Step 2: Verify other parts of code

   Data as list is valid, plt.show() is present, duplicates are allowed.

3. Final Answer:

   The bins parameter should be an integer, not a string -> Option A

4. Quick Check:

   Bins type error = The bins parameter should be an integer, not a string [OK]

Hint: Bins must be number, not text [OK]

Common Mistakes:

• Passing string instead of integer for bins
• Thinking duplicates cause errors
• Missing plt.show() call

5. You have a dataset with ages of 1000 people ranging from 0 to 100. You want to see detailed age groups but also avoid too much noise. Which bin setting for plt.hist() best balances detail and clarity?

hard

A. bins=20 (moderate groups)

B. bins=1000 (one bin per person)

C. bins='auto' (let matplotlib decide)

D. bins=5 (very few large groups)

Solution

1. Step 1: Understand bin size impact

   Few bins (5) lose detail; too many bins (1000) cause noise.

2. Step 2: Choose balanced bin count

   20 bins give moderate detail without clutter; 'auto' can vary unpredictably.

3. Final Answer:

   bins=20 (moderate groups) -> Option A

4. Quick Check:

   Balanced bins = 20 [OK]

Hint: Choose moderate bins for clear, detailed histograms [OK]

Common Mistakes:

• Using too few bins losing detail
• Using too many bins causing noisy plots
• Relying on 'auto' without checking result