Basic histogram with plt.hist in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Histogram bin count output

What is the output of the following code snippet that creates a histogram and prints the counts of each bin?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.array([1, 2, 2, 3, 4, 5, 5, 5, 6])
counts, bins, patches = plt.hist(data, bins=5)
plt.close()
print(counts)

A [1. 2. 1. 1. 4.]

B [1. 2. 3. 2. 1.]

C [2. 1. 3. 2. 1.]

D [1. 3. 2. 2. 1.]

💡 Hint

Remember that bins divide the data range into equal parts and counts show how many data points fall into each bin.

The data ranges from 1 to 6. With 5 bins, the bins are [1,2), [2,3), [3,4), [4,5), [5,6]. The counts correspond to how many data points fall into each bin. For example, one 1 falls into the first bin and two 2s into the second (note: the last bin [5,6] is closed on the right to include the 6).

❓ data_output

intermediate

Number of bins in histogram

How many bins will the histogram have after running this code?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randint(0, 100, size=50)
plt.hist(data, bins=10)
plt.close()
print(len(plt.hist(data, bins=10)[0]))

A 9

B 50

C 10

D 11

💡 Hint

The bins parameter controls how many bins the histogram has.

When bins=10, the histogram divides the data range into exactly 10 bins, so the counts array length is 10.

❓ visualization

advanced

Effect of bin size on histogram shape

Which histogram plot best represents the data distribution when using 3 bins versus 15 bins for the same dataset?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.normal(loc=0, scale=1, size=1000)

plt.subplot(1, 2, 1)
plt.hist(data, bins=3)
plt.title('3 bins')

plt.subplot(1, 2, 2)
plt.hist(data, bins=15)
plt.title('15 bins')

plt.tight_layout()
plt.show()

A 3 bins histogram shows many peaks; 15 bins histogram is very smooth and flat.

B 3 bins histogram is very smooth and general; 15 bins histogram shows more detail and peaks.

C Both histograms look identical regardless of bin count.

D 3 bins histogram has more bars than 15 bins histogram.

💡 Hint

More bins mean more detail but can be noisy; fewer bins mean smoother but less detail.

With 3 bins, the histogram groups data broadly, showing a smooth shape. With 15 bins, it reveals more detailed distribution features like peaks and valleys.

🧠 Conceptual

advanced

Understanding histogram bin edges

Given the code below, what is the length of the bins array returned by plt.hist when bins=7?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.arange(14)
counts, bins, patches = plt.hist(data, bins=7)
plt.close()
print(len(bins))

A 8

B 7

C 6

D 14

💡 Hint

Bins array contains the edges of each bin, so it has one more element than the number of bins.

The bins array contains the edges of the bins, so if there are 7 bins, there are 8 edges.

🔧 Debug

expert

Identify the error in histogram plotting code

What error will this code produce when run?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.array([1, 2, 3, 4, 5])
plt.hist(data, bins='five')
plt.show()

A TypeError: 'str' object cannot be interpreted as an integer

B SyntaxError: invalid syntax

C No error, histogram plots successfully

D ValueError: bins must be a positive integer or sequence

💡 Hint

The bins parameter expects an integer or a sequence of bin edges, not a string.

Passing a string like 'five' to bins causes a ValueError because matplotlib expects an int or sequence for bins.

Practice

1. What does the plt.hist() function do in matplotlib?

easy

A. It creates a scatter plot of two variables.

B. It plots a line graph connecting data points.

C. It creates a histogram to show data frequency distribution.

D. It generates a pie chart to show proportions.

Solution

1. Step 1: Understand the purpose of plt.hist()

   The function plt.hist() groups data into bins and shows how often values fall into each bin.

2. Step 2: Compare with other plot types

   Line graphs connect points, scatter plots show points, and pie charts show parts of a whole, which are different from histograms.

3. Final Answer:

   It creates a histogram to show data frequency distribution. -> Option C

4. Quick Check:

   Histogram = frequency bars [OK]

Hint: Histograms show frequency counts in bars [OK]

Common Mistakes:

• Confusing histogram with line plot
• Thinking plt.hist() creates scatter plots
• Mixing histogram with pie chart

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

easy

A. plt.histogram(data); plt.show()

B. plt.plot.hist(data); plt.show()

C. plt.bar(data); plt.show()

D. plt.hist(data); plt.show()

Solution

1. Step 1: Recall the correct function name

   The correct function to create a histogram is plt.hist(), not plt.plot.hist() or plt.histogram().

2. Step 2: Confirm usage with plt.show()

   After plotting, plt.show() displays the figure. Using plt.bar() creates bar charts, not histograms.

3. Final Answer:

   plt.hist(data); plt.show() -> Option D

4. Quick Check:

   Use plt.hist() for histograms [OK]

Hint: Use plt.hist() to plot histograms, then plt.show() [OK]

Common Mistakes:

• Using plt.plot.hist() which is invalid
• Calling plt.histogram() which doesn't exist
• Using plt.bar() instead of plt.hist()

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

import matplotlib.pyplot as plt

values = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
plt.hist(values, bins=4)
plt.show()

medium

A. A histogram with 4 bars showing frequencies: 1, 2, 3, and 4

B. A histogram with 10 bars, one for each value

C. A line plot connecting the values 1 to 4

D. An error because bins must be greater than 4

Solution

1. Step 1: Understand bins parameter

   Setting bins=4 divides the data range into 4 intervals (bins).

2. Step 2: Count frequencies in each bin

   The values 1, 2, 3, and 4 fall into these bins with frequencies 1, 2, 3, and 4 respectively, so the histogram shows 4 bars with these heights.

3. Final Answer:

   A histogram with 4 bars showing frequencies: 1, 2, 3, and 4 -> Option A

4. Quick Check:

   Bins split data; bars show counts [OK]

Hint: Bins split data; bar height = count in bin [OK]

Common Mistakes:

• Thinking bins=4 means 10 bars
• Confusing histogram with line plot
• Assuming bins must be greater than data length

4. Identify the error in this code snippet that tries to plot a histogram:

import matplotlib.pyplot as plt

values = [5, 7, 8, 5, 6]
plt.hist(values, bins='five')
plt.show()

medium

A. plt.show() is missing and needed to display the plot.

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

C. plt.hist() requires a color argument.

D. The values list contains invalid data types.

Solution

1. Step 1: Check bins parameter type

   The bins argument must be an integer or sequence of numbers, not a string like 'five'.

2. Step 2: Verify other parts

   The data list is valid, color is optional, and plt.show() is present, so no other errors.

3. Final Answer:

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

4. Quick Check:

   Bins must be int or list, not string [OK]

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

Common Mistakes:

• Passing bins as a string instead of integer
• Thinking color is required
• Forgetting plt.show() (not the case here)

5. You have a dataset of exam scores ranging from 0 to 100. You want to create a histogram with 5 equal-width bins and color the bars blue. Which code snippet correctly achieves this?

hard

A. plt.hist(scores, bins=5, color='blue'); plt.show()

B. plt.hist(scores, bins=[0,20,50,70,90,100], color='blue'); plt.show()

C. plt.hist(scores, bins=5, colors='blue'); plt.show()

D. plt.hist(scores, bins=5, color=blue); plt.show()

Solution

1. Step 1: Use bins=integer for equal-width

   bins=5 automatically divides the range (0-100) into 5 equal-width bins of 20 units each: [0-20, 20-40, 40-60, 60-80, 80-100].

2. Step 2: Color the bars blue

   The color='blue' parameter sets the bar color correctly.

3. Step 3: Eliminate distractors

   B specifies explicit bins [0,20,50,70,90,100] with unequal widths (20,30,20,20,10); C uses invalid 'colors'; D has unquoted 'blue' causing NameError.

4. Final Answer:

   plt.hist(scores, bins=5, color='blue'); plt.show() -> Option A

5. Quick Check:

   bins=5 (equal width) + color='blue' [OK]

Hint: bins=5 for equal-width over data range, color='blue' [OK]

Common Mistakes:

• Using explicit bins with unequal widths
• Using 'colors' instead of 'color'
• Using color=blue without quotes