Bin count and bin edges in Matplotlib - Step-by-Step Execution

Concept Flow - Bin count and bin edges

Start with data array

↓

Choose number of bins or bin edges

↓

Calculate bin edges

↓

Count data points in each bin

↓

Output bin counts and edges

↓

End

We start with data, decide bins, calculate edges, count points per bin, then output counts and edges.

Execution Sample

Matplotlib

import numpy as np

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

This code counts how many values fall into 3 bins and shows the bin edges.

Execution Table

Step	Action	Input/Condition	Result
1	Start with data array	values = [1, 2, 2, 3, 4, 5]	Data array ready
2	Choose number of bins	bins = 3	3 bins selected
3	Calculate bin edges	min=1, max=5, bins=3	Edges = [1.0, 2.3333, 3.6667, 5.0]
4	Count data points in bins	Values: 1,2,2,3,4,5	Counts = [3, 1, 2]
5	Output counts and edges	counts, edges	counts=[3,1,2], edges=[1.0,2.3333,3.6667,5.0]
6	End	All steps done	Execution complete

💡 All data points counted in bins, process finished

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
values	[1,2,2,3,4,5]	[1,2,2,3,4,5]	[1,2,2,3,4,5]	[1,2,2,3,4,5]
bins	Not set	3	3	3
edges	Not set	[1.0, 2.3333, 3.6667, 5.0]	[1.0, 2.3333, 3.6667, 5.0]	[1.0, 2.3333, 3.6667, 5.0]
counts	Not set	Not set	[3, 1, 2]	[3, 1, 2]

Key Moments - 2 Insights

Why are there 4 bin edges for 3 bins?

Because bin edges mark the boundaries between bins, so for N bins there are N+1 edges (see execution_table step 3).

Why does the first bin count include the value 1 but not 2.3333?

Bins include the left edge but exclude the right edge except the last bin includes both edges (see execution_table step 4).

Visual Quiz - 3 Questions

Look at the execution table, what is the bin edge at index 2 after step 3?

A 2.3333

B 5.0

C 3.6667

D 1.0

💡 Hint

Check the 'Calculate bin edges' row in execution_table, column 'Result'

At which step do we get the counts of data points in each bin?

A Step 4

B Step 3

C Step 2

D Step 5

💡 Hint

Look for 'Count data points in bins' action in execution_table

If we increase bins from 3 to 4, how does the counts array length change?

A It becomes length 5

B It becomes length 4

C It stays the same length

D It becomes length 3

💡 Hint

Counts array length equals number of bins (see variable_tracker for counts)

Concept Snapshot

np.histogram(data, bins)
- Returns counts and bin edges
- counts length = bins
- edges length = bins + 1
- bins divide data range evenly by default
- Left edge inclusive, right edge exclusive except last bin

Full Transcript

We start with a data array and decide how many bins to use. Then we calculate the bin edges that split the data range into equal parts. Next, we count how many data points fall into each bin. Finally, we output the counts and edges. For example, with data [1,2,2,3,4,5] and 3 bins, edges are [1.0, 2.3333, 3.6667, 5.0] and counts are [3,1,2]. There are always one more edges than bins. Bins include the left edge but exclude the right edge except the last bin includes both 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