Histogram computation with np.histogram() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute the histogram counts of the data array.

NumPy

import numpy as np

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

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

A3

B4

C5

D6

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number of bins that is too small or too large for the data.

Forgetting to pass the bins parameter.

The bins parameter specifies the number of bins for the histogram. Here, 4 bins will group the data into 4 intervals.

2fill in blank

medium

Complete the code to compute histogram counts with custom bin edges.

NumPy

import numpy as np

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

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

Abin_edges

B3

C[1, 2, 3]

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a number instead of the bin edges list.

Using an incomplete list of bin edges.

Passing a list of bin edges to bins lets you define exact intervals for the histogram.

3fill in blank

hard

Fix the error in the code to correctly compute histogram counts and bin edges.

NumPy

import numpy as np

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

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

A[1, 2, 3, 4]

B[1, 2, 3, 4, 5]

C[0, 1, 2, 3, 4, 5]

D[1, 2, 3, 4, 5, 6]

Attempts:

3 left

💡 Hint

Common Mistakes

Using bin edges that do not cover the maximum data value.

Using a bin edges list with the same length as data.

The bin edges must cover the full range of data. Adding 6 as the last edge includes the last data point in the final bin.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator.

Using the word itself instead of its length.

The dictionary comprehension maps each word to its length only if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 4 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
result = {{'[1]': [2] for word in words if len(word) [3] 4}}

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as key without uppercase.

Using wrong comparison operator.

Swapping keys and values.

This dictionary comprehension creates keys as uppercase words and values as their lengths, filtering words longer than 4 characters.

Practice

1. What does the np.histogram() function in NumPy do?

easy

A. It creates a line plot of the data points.

B. It sorts the data points in ascending order.

C. It counts how many data points fall into specified bins and returns counts and bin edges.

D. It calculates the mean and median of the data points.

Solution

1. Step 1: Understand the purpose of np.histogram()

   The function divides data into bins and counts how many points fall into each bin.

2. Step 2: Identify the output of np.histogram()

   It returns two arrays: one with counts per bin and one with the bin edges.

3. Final Answer:

   It counts how many data points fall into specified bins and returns counts and bin edges. -> Option C

4. Quick Check:

   np.histogram() = counts and edges [OK]

Hint: np.histogram() returns counts and bin edges separately [OK]

Common Mistakes:

• Thinking np.histogram() sorts data
• Confusing histogram with mean calculation
• Expecting a plot from np.histogram()

2. Which of the following is the correct way to call np.histogram() to create 4 bins for data array data?

easy

A. np.histogram(data, bins=4)

B. np.histogram(4, data)

C. np.histogram(bins=data, 4)

D. np.histogram(data, bins=[4])

Solution

1. Step 1: Check the correct parameter usage for bins

   The bins parameter can be passed as a keyword argument like bins=4.

2. Step 2: Identify the correct syntax

   np.histogram(data, bins=4) is the standard and clear way to specify 4 bins.

3. Final Answer:

   np.histogram(data, bins=4) -> Option A

4. Quick Check:

   Use bins=4 to specify number of bins [OK]

Hint: Use bins=number to set bin count in np.histogram() [OK]

Common Mistakes:

• Passing bins as a list with one number
• Swapping argument order incorrectly
• Using positional argument without data first

3. What is the output of this code?

import numpy as np
data = np.array([1, 2, 2, 3, 4, 5])
counts, bins = np.histogram(data, bins=[1, 3, 5])
print(counts)

medium

A. [2 4]

B. [3 3]

C. [1 5]

D. [3 2]

Solution

1. Step 1: Understand bins and data distribution

   Bins are [1, 3) and [3, 5). Count how many data points fall into each bin.

2. Step 2: Count data points in each bin

   Bin [1,3) includes 1, 2, 2 -> 3 points; Bin [3,5) includes 3, 4 -> 2 points. Note that 5 equals the rightmost bin edge and is included in the last bin.

3. Final Answer:

   [3 3] -> Option B

4. Quick Check:

   Counts per bin = [3, 3] [OK]

Hint: Count points in each bin range carefully [OK]

Common Mistakes:

• Including right edge in left bin
• Miscounting repeated values
• Confusing bin edges with counts

4. The following code throws an error. What is the problem?

import numpy as np
data = [1, 2, 3, 4]
counts, bins = np.histogram(data, bins='five')

medium

A. The counts and bins cannot be unpacked together.

B. The data must be a NumPy array, not a list.

C. np.histogram() requires at least 5 data points.

D. The bins parameter must be an integer or sequence, not a string.

Solution

1. Step 1: Check the bins parameter type

   bins='five' is a string, but bins must be an int or a sequence of numbers.

2. Step 2: Understand np.histogram() input requirements

   Data can be list or array, and unpacking counts and bins is valid.

3. Final Answer:

   The bins parameter must be an integer or sequence, not a string. -> Option D

4. Quick Check:

   bins must be int or sequence, not string [OK]

Hint: Bins must be int or list/array, not text [OK]

Common Mistakes:

• Thinking data must be np.array
• Assuming unpacking counts and bins is invalid
• Believing minimum data length is required

5. You have data points [0, 1, 2, 3, 4, 5, 6]. You want to create a histogram with bins of width 2 starting from 0 up to 6 (inclusive). Which bins parameter should you pass to np.histogram() to get correct bin counts?

hard

A. [0, 2, 4, 6, 8]

B. [0, 2, 4, 6]

C. 3

D. [0, 1, 2, 3, 4, 5, 6]

Solution

1. Step 1: Determine bin edges for width 2 from 0 to 6 inclusive

   Bins should cover ranges: 0-2, 2-4, 4-6, and include 6, so edges are [0, 2, 4, 6, 8].

2. Step 2: Understand np.histogram bin edge behavior

   Rightmost edge is inclusive for the last bin, so to include 6, bin edges must go beyond 6.

3. Final Answer:

   [0, 2, 4, 6, 8] -> Option A

4. Quick Check:

   Bin edges must cover all data points [OK]

Hint: Add one extra bin edge beyond max data to include last point [OK]

Common Mistakes:

• Using bins that exclude the max value
• Using number of bins instead of edges
• Using bin edges with wrong intervals