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.