Bin count and bin edges in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the number of bins for the histogram.

Matplotlib

import numpy as np

data = np.array([1, 2, 2, 3, 4, 5, 5, 5, 6])
bin_count = len(np.histogram_bin_edges(data, bins=[1])) - 1
print(bin_count)

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

A'auto'

B5

C'sqrt'

D'sturges'

Attempts:

3 left

💡 Hint

Common Mistakes

Using an integer directly as bins when expecting a method string.

Confusing bin count with number of edges.

The 'auto' option lets numpy decide the best bin edges automatically. Using it with np.histogram_bin_edges returns edges from which we calculate the bin count.

2fill in blank

medium

Complete the code to get the bin edges for the data using 4 bins.

Matplotlib

import numpy as np

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

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

A4

B3

C5

D'auto'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of an integer for fixed bin count.

Confusing bin count with number of edges.

Setting bins=4 divides the data range into 4 equal-width bins, and np.histogram_bin_edges returns the edges of these bins.

3fill in blank

hard

Fix the error in the code to correctly calculate bin edges for data with 3 bins.

Matplotlib

import numpy as np

data = np.array([10, 20, 30, 40, 50])
bin_edges = np.histogram_bin_edges(data, bins=[1])
print(bin_edges)

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

A'auto'

B'3'

C3

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the number of bins as a string instead of an integer.

Passing None which causes an error.

The bins parameter expects an integer for fixed bin count, not a string. Using 3 correctly sets the number of bins.

4fill in blank

hard

Fill both blanks to create a dictionary with bin edges as keys and counts as values.

Matplotlib

import numpy as np

data = np.array([1, 2, 2, 3, 4, 5, 5, 5, 6])
counts, edges = np.histogram(data, bins=[1])
bin_dict = {edges[i]: counts[i] for i in [2]
print(bin_dict)

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

A4

Brange(len(counts))

Crange(len(edges) - 1)

Drange(len(edges))

Attempts:

3 left

💡 Hint

Common Mistakes

Using the full length of edges which is one more than counts.

Using a string instead of an integer for bins.

Using 4 bins splits the data into 4 parts. The dictionary comprehension uses range(len(edges) - 1) because counts length is one less than edges length.

5fill in blank

hard

Fill all three blanks to create a dictionary with bin edges as keys and counts as values, using 5 bins.

Matplotlib

import numpy as np

data = np.array([2, 4, 6, 8, 10, 12, 14])
counts, edges = np.histogram(data, bins=[1])
bin_dict = {edges[i]: counts[i] for i in [2] if counts[i] [3] 0}
print(bin_dict)

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

A5

Brange(len(counts))

C>

Drange(len(edges))

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong range length for iteration.

Using a comparison operator other than '>'.

Using 5 bins divides the data into 5 parts. The dictionary comprehension iterates over counts indices and includes only bins with counts greater than zero.

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