Histogram computation with np.histogram() in NumPy - Mini Project: Build & Apply

Histogram computation with np.histogram()

📖 Scenario: You work in a small bakery that tracks the number of cookies sold each day. You want to understand how cookie sales are spread over a week to plan better.

🎯 Goal: Build a program that uses np.histogram() to find how many days fall into different sales ranges.

📋 What You'll Learn

Create a numpy array with daily cookie sales

Set up bins to group sales counts

Use np.histogram() to compute the histogram

Print the histogram counts

💡 Why This Matters

🌍 Real World

Histograms help businesses understand how data like sales or customer visits are spread across ranges.

💼 Career

Data scientists and analysts use histograms to summarize and visualize data distributions quickly.

1

Create the daily sales data

Create a numpy array called daily_sales with these exact values: 12, 15, 7, 10, 20, 25, 18.

NumPy

import numpy as np
# Create the numpy array daily_sales with values 12, 15, 7, 10, 20, 25, 18
# Your code here

Hint

Use np.array() to create the array with the given numbers.

2

Set up bins for sales ranges

Create a list called bins with these exact values: 0, 10, 20, 30. These will group sales into ranges 0-10, 10-20, and 20-30.

NumPy

import numpy as np
daily_sales = np.array([12, 15, 7, 10, 20, 25, 18])
# Create the bins list with values 0, 10, 20, 30
# Your code here

Hint

Bins are the edges that separate groups. Use a list with the given numbers.

3

Compute the histogram

Use np.histogram() with daily_sales and bins to compute the histogram. Save the counts in a variable called hist_counts and the bin edges in hist_bins.

NumPy

import numpy as np
daily_sales = np.array([12, 15, 7, 10, 20, 25, 18])
bins = [0, 10, 20, 30]
# Use np.histogram() to get hist_counts and hist_bins
# Your code here

Hint

Call np.histogram() with the array and bins, and unpack the result into two variables.

4

Print the histogram counts

Print the variable hist_counts to show how many days fall into each sales range.

NumPy

import numpy as np
daily_sales = np.array([12, 15, 7, 10, 20, 25, 18])
bins = [0, 10, 20, 30]
hist_counts, hist_bins = np.histogram(daily_sales, bins=bins)
# Print hist_counts
# Your code here

Hint

Use print(hist_counts) to display the counts.

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