Bin count and bin edges in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of bin counts using numpy histogram

What is the output of the following code that calculates bin counts for the given data array?

Matplotlib

import numpy as np

data = np.array([1, 2, 2, 3, 4, 5, 5, 5, 6])
bin_counts, bin_edges = np.histogram(data, bins=3)
print(bin_counts)

A [2 3 4]

B [3 3 3]

C [3 2 4]

D [4 3 2]

💡 Hint

Remember that numpy.histogram divides the data range into equal-width bins and counts how many values fall into each bin.

The data ranges from 1 to 6. Dividing into 3 bins gives edges roughly at [1, 2.666..., 4.333..., 6]. The counts are: 1,2,2 fall into first bin (3 items), 3,4 fall into second bin (2 items), and 5,5,5,6 fall into third bin (4 items).

❓ data_output

intermediate

Bin edges from numpy histogram

What are the bin edges returned by numpy.histogram for the data below with 4 bins?

Matplotlib

import numpy as np

data = np.array([10, 15, 20, 25, 30, 35, 40])
bin_counts, bin_edges = np.histogram(data, bins=4)
print(bin_edges)

A [10. 17.5 25. 32.5 40.]

B [10. 20. 30. 40. 50.]

C [10. 15. 20. 25. 30.]

D [10. 18. 26. 34. 42.]

💡 Hint

Bin edges split the range from min to max into equal parts.

The data ranges from 10 to 40. Dividing into 4 bins means edges at 10, 17.5, 25, 32.5, and 40.

❓ visualization

advanced

Histogram bin count visualization

Which histogram plot corresponds to the bin counts produced by the code below?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.array([2, 4, 4, 4, 5, 5, 7, 9])
plt.hist(data, bins=3)
plt.show()

A Histogram with bins: [2-3), count=1; [3-6), count=5; [6-9], count=2

B Histogram with bins: [2-5), count=5; [5-7), count=2; [7-9], count=1

C Histogram with bins: [2-4), count=3; [4-7), count=4; [7-9], count=1

D Histogram with bins: [2-4), count=4; [4-6), count=2; [6-9], count=2

💡 Hint

Check how matplotlib divides the range into bins and counts data points in each bin.

The data ranges from 2 to 9. Dividing into 3 bins gives edges roughly at [2, 4.333..., 6.666..., 9]. The counts are: 2,4,4,4 fall into first bin (4 items), 5,5 fall into second bin (2 items), 7,9 fall into third bin (2 items).

🧠 Conceptual

advanced

Effect of bin number on histogram shape

How does increasing the number of bins in a histogram affect the bin counts and bin edges?

A More bins create narrower ranges, so bin counts generally decrease and bin edges increase in number.

B More bins create wider ranges, so bin counts increase and bin edges decrease in number.

C More bins do not affect bin counts or edges; they stay the same regardless of bin number.

D More bins create fewer bins with more data points each, so bin counts increase and edges decrease.

💡 Hint

Think about splitting the data range into more parts.

Increasing bins divides the data range into more, smaller intervals. Each bin covers a smaller range, so fewer data points fall into each bin, and the number of bin edges increases.

🔧 Debug

expert

Identify the error in bin edge calculation

What error will the following code produce and why?

Matplotlib

import numpy as np

data = np.array([1, 2, 3, 4, 5])
bin_counts, bin_edges = np.histogram(data, bins=[1, 2, 3])
print(bin_edges)

A IndexError: list index out of range

B No error; outputs bin edges [1, 2, 3]

C ValueError: bins must increase monotonically

D TypeError: unsupported operand type(s) for -: 'list' and 'int'

💡 Hint

Check if the bins argument is valid as a sequence of edges.

The bins argument is a valid sequence of bin edges. The data points 1,2,3 fall into bins [1,2) and [2,3). No error occurs and bin_edges output is [1, 2, 3].