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].

Practice

1. What does the bins parameter control when creating a histogram in matplotlib?

easy

A. The number of intervals to divide the data into

B. The color of the bars in the histogram

C. The width of the plot window

D. The type of plot to display

Solution

1. Step 1: Understand the role of bins in histograms

   The bins parameter sets how many groups or intervals the data is split into for counting.

2. Step 2: Relate bins to data grouping

   Each bin counts how many data points fall within its range, so bins control these ranges.

3. Final Answer:

   The number of intervals to divide the data into -> Option A

4. Quick Check:

   Bins = intervals count [OK]

Hint: Bins split data into groups for counting [OK]

Common Mistakes:

• Confusing bins with color or style options
• Thinking bins control plot size
• Assuming bins change plot type

2. Which of the following is the correct way to get bin counts and edges using matplotlib's hist function?

easy

A. edges, counts = plt.hist(data, bins=5)

B. plt.hist(data, bins=5).counts, plt.hist(data, bins=5).edges

C. counts = plt.hist(data, bins=5)

D. counts, edges = plt.hist(data, bins=5)

Solution

1. Step 1: Recall the return values of plt.hist

   plt.hist returns a tuple: (counts, bin edges, patches).

2. Step 2: Match the correct unpacking order

   The first returned value is counts, second is edges, so counts, edges = plt.hist(...) is correct.

3. Final Answer:

   counts, edges = plt.hist(data, bins=5) -> Option D

4. Quick Check:

   Return order = counts, edges [OK]

Hint: Remember plt.hist returns counts first, then edges [OK]

Common Mistakes:

• Swapping counts and edges order
• Trying to access attributes instead of unpacking
• Assigning only one variable to plt.hist output

3. Given the code:

import matplotlib.pyplot as plt
import numpy as np
data = np.array([1, 2, 2, 3, 4, 5, 5, 5, 6])
counts, edges = plt.hist(data, bins=[1, 3, 5, 7])
print(counts)
print(edges)

What will be the output of counts and edges?

medium

A. [3. 3. 3.]
[1 3 5 7]

B. [3. 4. 2.]
[1 3 5 7]

C. [3. 2. 4.]
[1 3 5 7]

D. [3 3 3]
[1 2 3 4]

Solution

1. Step 1: Understand bins and data grouping

   Bins are [1,3), [3,5), [5,7). Count data points in each range.

2. Step 2: Count data points in each bin

   Bin [1,3): values 1,2,2 -> 3 points; Bin [3,5): values 3,4 -> 2 points; Bin [5,7): values 5,5,5,6 -> 4 points.

3. Final Answer:

   [3. 2. 4.] [1 3 5 7] -> Option C

4. Quick Check:

   Counts match bins ranges [OK]

Hint: Count data points inside each bin range [OK]

Common Mistakes:

• Miscounting points on bin edges
• Assuming bins are inclusive on both ends
• Confusing bin edges with counts

4. What is wrong with this code snippet?

import matplotlib.pyplot as plt
data = [1, 2, 3, 4, 5]
counts, edges = plt.hist(data, bins=3)
print(edges)
print(counts)

medium

A. The variable names counts and edges are swapped in print statements

B. Nothing is wrong; the code runs correctly

C. The order of unpacking is reversed; edges should come first

D. The bins parameter must be a list, not an integer

Solution

1. Step 1: Check plt.hist return values

   plt.hist returns counts first, then edges, so unpacking as counts, edges is correct.

2. Step 2: Verify bins parameter and print order

   Bins can be an integer for number of bins; printing edges then counts is valid.

3. Final Answer:

   Nothing is wrong; the code runs correctly -> Option B

4. Quick Check:

   Unpacking and bins usage are correct [OK]

Hint: Remember plt.hist returns counts first, edges second [OK]

Common Mistakes:

• Swapping counts and edges variables
• Thinking bins must be a list
• Confusing print order with unpacking order

5. You have data points: [2, 4, 4, 4, 6, 8, 10]. You want to create bins so that the first bin counts values less than 5, the second bin counts values from 5 to less than 9, and the last bin counts values 9 and above. Which bin edges should you use in plt.hist to get correct counts?

hard

A. [0, 5, 9, 11]

B. [0, 4, 9, 10]

C. [2, 5, 9, 10]

D. [1, 5, 9, 10]

Solution

1. Step 1: Define bins to match ranges

   First bin: values less than 5 -> bin edge 0 to 5; second bin: 5 to less than 9 -> 5 to 9; third bin: 9 and above -> 9 to 11 (to include 10).

2. Step 2: Choose bin edges covering all data

   Edges [0, 5, 9, 11] cover all points and match desired ranges.

3. Final Answer:

   [0, 5, 9, 11] -> Option A

4. Quick Check:

   Bins match desired value ranges [OK]

Hint: Set bin edges to cover desired value ranges fully [OK]

Common Mistakes:

• Using edges that exclude max data point
• Not covering all data points in bins
• Confusing inclusive and exclusive bin edges