Ranking charts in Matplotlib - Step-by-Step Execution

Concept Flow - Ranking charts

Prepare data with values

↓

Sort data by value descending

↓

Assign ranks based on sorted order

↓

Plot bars with ranks on y-axis

↓

Add labels and title

↓

Display chart

Ranking charts show items ordered by value, with ranks on the axis, helping compare items visually.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt

items = ['A', 'B', 'C', 'D']
values = [23, 45, 12, 37]

# Sort and rank
sorted_items = sorted(zip(items, values), key=lambda x: x[1], reverse=True)
ranks = list(range(1, len(items)+1))

# Plot
plt.barh(ranks, [v for _, v in sorted_items])
plt.yticks(ranks, [i for i, _ in sorted_items])
plt.xlabel('Value')
plt.title('Ranking Chart')
plt.show()

This code sorts items by value, assigns ranks, and plots a horizontal bar chart showing ranks on y-axis.

Execution Table

Step	Action	Data State	Result
1	Define items and values	items=['A','B','C','D'], values=[23,45,12,37]	Data ready
2	Sort items by value descending	sorted_items=[('B',45),('D',37),('A',23),('C',12)]	Items ordered by value
3	Assign ranks	ranks=[1,2,3,4]	Ranks assigned from 1 to 4
4	Plot horizontal bars	Bars plotted at ranks with values	Visual ranking chart created
5	Set y-axis labels	Y-axis labels=['B','D','A','C']	Labels show item names by rank
6	Add labels and title	X-label='Value', Title='Ranking Chart'	Chart labeled
7	Display chart	Chart shown on screen	Execution complete

💡 All steps completed, ranking chart displayed

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
items	['A','B','C','D']	['A','B','C','D']	['A','B','C','D']	['A','B','C','D']
values	[23,45,12,37]	[23,45,12,37]	[23,45,12,37]	[23,45,12,37]
sorted_items	N/A	[('B',45),('D',37),('A',23),('C',12)]	[('B',45),('D',37),('A',23),('C',12)]	[('B',45),('D',37),('A',23),('C',12)]
ranks	N/A	N/A	[1,2,3,4]	[1,2,3,4]

Key Moments - 3 Insights

Why do we sort the items before assigning ranks?

Sorting ensures the highest values get the top ranks, as shown in execution_table step 2 and 3.

Why are ranks used on the y-axis instead of item names directly?

Ranks provide a clear order visually; item names are added as labels on the y-axis after ranks (step 5).

What happens if we don't reverse sort the values?

Items would be ranked from smallest to largest, reversing the intended ranking order (see step 2 sorting).

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of sorted_items after step 2?

A [('A',23),('B',45),('C',12),('D',37)]

B [('C',12),('A',23),('D',37),('B',45)]

C [('B',45),('D',37),('A',23),('C',12)]

D [('D',37),('B',45),('C',12),('A',23)]

💡 Hint

Check the 'Data State' column for step 2 in execution_table.

At which step are ranks assigned to the items?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look for 'Assign ranks' action in execution_table.

If we remove reverse=True in sorting, how would ranks change?

A Ranks would remain the same

B Ranks would assign highest rank to smallest value

C Ranks would be random

D Ranks would assign highest rank to largest value

💡 Hint

Refer to key_moments about sorting order and ranks.

Concept Snapshot

Ranking charts:
- Sort data by value descending
- Assign ranks starting at 1
- Plot bars with ranks on y-axis
- Label y-axis with item names
- Use horizontal bars for clear ranking
- Add axis labels and title

Full Transcript

Ranking charts help us see which items have the highest values by ordering them visually. First, we prepare the data with items and their values. Then, we sort the items from highest to lowest value. After sorting, we assign ranks starting at 1 for the highest value. We plot a horizontal bar chart with ranks on the y-axis and values on the x-axis. We label the y-axis with the item names so we know which bar belongs to which item. Finally, we add labels and a title to make the chart clear. This process helps us quickly compare items by their rank and value.

Practice

1. What is the main purpose of a ranking chart in matplotlib?

easy

A. To display items ordered by their values from highest to lowest

B. To show random data points without any order

C. To plot data only on the x-axis without y-axis labels

D. To create 3D surface plots

Solution

1. Step 1: Understand ranking chart purpose

   Ranking charts are designed to show items sorted by their values, usually from highest to lowest.

2. Step 2: Compare options with definition

   Only To display items ordered by their values from highest to lowest correctly describes this purpose, while others describe unrelated chart types or features.

3. Final Answer:

   To display items ordered by their values from highest to lowest -> Option A

4. Quick Check:

   Ranking chart = ordered display [OK]

Hint: Ranking charts always sort data before plotting [OK]

Common Mistakes:

• Thinking ranking charts show unsorted data
• Confusing ranking charts with scatter plots
• Assuming ranking charts are 3D plots

2. Which of the following matplotlib code snippets correctly sorts data for a ranking chart?

easy

A. data_sorted = data.sort_values(ascending=False)

B. data_sorted = data.random_shuffle()

C. data_sorted = data.sort_index()

D. data_sorted = data.dropna()

Solution

1. Step 1: Identify sorting method for ranking

   Ranking charts require sorting values in descending order to rank from highest to lowest.

2. Step 2: Evaluate each option

   data_sorted = data.sort_values(ascending=False) uses sort_values(ascending=False) which sorts data correctly. Others either shuffle, sort by index, or drop missing values, which are unrelated.

3. Final Answer:

   data_sorted = data.sort_values(ascending=False) -> Option A

4. Quick Check:

   Sort values descending = correct sorting [OK]

Hint: Use sort_values(ascending=False) to rank highest first [OK]

Common Mistakes:

• Using sort_index instead of sort_values
• Shuffling data randomly before plotting
• Dropping data instead of sorting

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
values = [50, 20, 30]
labels = ['A', 'B', 'C']
plt.barh(labels, values)
plt.gca().invert_yaxis()
plt.show()

medium

A. A horizontal bar chart with 'C' at the top and 'A' at the bottom

B. A vertical bar chart with bars labeled A, B, C

C. A horizontal bar chart with 'A' at the top and 'C' at the bottom

D. An error because invert_yaxis() is invalid here

Solution

1. Step 1: Understand horizontal bar chart with invert_yaxis()

   The barh function plots horizontal bars with labels on y-axis. By default, y-axis starts from bottom.

2. Step 2: Effect of invert_yaxis()

   Calling invert_yaxis() flips the y-axis so the first label 'A' appears at the top, making ranking easier to read.

3. Final Answer:

   A horizontal bar chart with 'A' at the top and 'C' at the bottom -> Option C

4. Quick Check:

   invert_yaxis flips labels top-down [OK]

Hint: invert_yaxis() flips bars so top label is first [OK]

Common Mistakes:

• Thinking invert_yaxis() causes error
• Confusing horizontal with vertical bars
• Assuming labels order stays bottom-up

4. Identify the error in this ranking chart code:

import matplotlib.pyplot as plt
values = [10, 40, 30]
labels = ['X', 'Y', 'Z']
plt.barh(labels, values)
plt.show()

medium

A. plt.show() is missing

B. The bars are not sorted, so ranking is incorrect

C. barh() cannot plot horizontal bars

D. The labels list is missing one label

Solution

1. Step 1: Check if data is sorted for ranking

   The values are [10, 40, 30] but not sorted. Ranking charts require sorted data to show correct order.

2. Step 2: Confirm other parts are correct

   Labels match values count, barh is valid, and plt.show() is present. So only sorting is missing.

3. Final Answer:

   The bars are not sorted, so ranking is incorrect -> Option B

4. Quick Check:

   Ranking needs sorted data [OK]

Hint: Always sort values before plotting ranking charts [OK]

Common Mistakes:

• Ignoring sorting before plotting
• Assuming barh() plots vertical bars
• Forgetting plt.show()

5. You have a dictionary of sales data:

sales = {'Store A': 300, 'Store B': 450, 'Store C': 200, 'Store D': 450}

How can you create a ranking chart that correctly shows stores ranked by sales, with ties handled by alphabetical order, using matplotlib?

hard

A. Sort by sales ascending, then plot horizontal bars without inverting y-axis

B. Plot bars directly without sorting, then invert y-axis

C. Sort only by store name ascending, then plot vertical bars

D. Sort by sales descending, then by store name ascending, then plot horizontal bars with inverted y-axis

Solution

1. Step 1: Sort data by sales descending and store name ascending

   To handle ties, first sort by sales descending, then by store name ascending to break ties alphabetically.

2. Step 2: Plot horizontal bars and invert y-axis for ranking

   Plot sorted data with barh and call invert_yaxis() to show highest rank at top.

3. Final Answer:

   Sort by sales descending, then by store name ascending, then plot horizontal bars with inverted y-axis -> Option D

4. Quick Check:

   Sort by value desc + name asc + invert_yaxis = ranking [OK]

Hint: Sort by value desc and name asc, then invert y-axis [OK]

Common Mistakes:

• Not sorting by store name to break ties
• Plotting without sorting
• Using vertical bars without ranking logic