Bin count and bin edges in Matplotlib - Mini Project: Build & Apply

Bin count and bin edges

📖 Scenario: You work as a data analyst for a small retail company. You have collected the daily sales amounts for a week. You want to understand how these sales amounts are distributed by grouping them into bins.

🎯 Goal: Build a simple Python program that uses matplotlib to calculate the count of sales amounts in each bin and the edges of these bins.

📋 What You'll Learn

Create a list called sales with the exact daily sales amounts given.

Create a variable called bins with the exact bin edges given.

Use matplotlib.pyplot.hist with bins and density=False to get the counts and bin edges.

Print the counts and bin edges exactly as shown.

💡 Why This Matters

🌍 Real World

Grouping data into bins helps businesses understand patterns, like how many sales fall into certain ranges.

💼 Career

Data analysts and scientists often use binning to summarize and visualize data distributions for reports and decision-making.

1

Create the sales data list

Create a list called sales with these exact daily sales amounts: 23, 45, 12, 67, 34, 89, 55.

Matplotlib

# Create the sales list with the exact values
# Your code here

Hint

Use square brackets to create a list and separate numbers with commas.

2

Create the bins variable

Create a list called bins with these exact bin edges: 0, 20, 40, 60, 80, 100.

Matplotlib

sales = [23, 45, 12, 67, 34, 89, 55]
# Create the bins list with the exact edges
# Your code here

Hint

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

3

Calculate counts and bin edges using matplotlib

Import matplotlib.pyplot as plt. Use plt.hist with sales and bins, and set density=False. Assign the returned counts to counts and bin edges to edges.

Matplotlib

sales = [23, 45, 12, 67, 34, 89, 55]
bins = [0, 20, 40, 60, 80, 100]
# Import matplotlib.pyplot as plt
# Use plt.hist with sales and bins, density=False
# Assign counts and edges
# Your code here

Hint

Use import matplotlib.pyplot as plt and call plt.hist with the correct arguments.

4

Print the counts and bin edges

Print the counts array and the edges array exactly as they are.

Matplotlib

import matplotlib.pyplot as plt

sales = [23, 45, 12, 67, 34, 89, 55]
bins = [0, 20, 40, 60, 80, 100]

counts, edges, _ = plt.hist(sales, bins=bins, density=False)
# Print counts and edges
# Your code here

Hint

Use two print statements: one for counts and one for edges.

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