Time Complexity: Stacked bar charts
O(n * g)
0
0
Stacked bar charts in Matplotlib - Time & Space Complexity
Understanding Time Complexity
When creating stacked bar charts, we want to know how the time to draw the chart changes as we add more data.
How does the drawing time grow when we increase the number of bars or stacks?
Scenario Under Consideration
Analyze the time complexity of the following code snippet.
import matplotlib.pyplot as plt
labels = ['A', 'B', 'C', 'D']
values1 = [3, 5, 1, 7]
values2 = [2, 3, 4, 1]
plt.bar(labels, values1, label='Group 1')
plt.bar(labels, values2, bottom=values1, label='Group 2')
plt.legend()
plt.show()This code draws a stacked bar chart with two groups stacked on four bars.
Identify Repeating Operations
Identify the loops, recursion, array traversals that repeat.
- Primary operation: Drawing bars for each group and label.
- How many times: For each group, it loops over all labels once.
How Execution Grows With Input
As we add more bars or more groups, the number of drawing steps grows.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 bars, 2 groups | 20 drawing steps |
| 100 bars, 2 groups | 200 drawing steps |
| 1000 bars, 5 groups | 5000 drawing steps |
Pattern observation: The total drawing steps grow by multiplying the number of bars by the number of groups.
Final Time Complexity
Time Complexity: O(n * g)
This means the time to draw grows linearly with the number of bars (n) and the number of stacked groups (g).
Common Mistake
[X] Wrong: "Adding more groups does not affect drawing time much because bars are stacked."
[OK] Correct: Each group adds a full set of bars to draw, so more groups multiply the work.
Interview Connect
Understanding how drawing time grows helps you explain performance when visualizing large datasets, a useful skill in data science roles.
Self-Check
What if we used a grouped bar chart instead of stacked bars? How would the time complexity change?