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.