Subplots for multiple charts in Pandas - Time & Space Complexity

Time Complexity: Subplots for multiple charts

O(n)

Understanding Time Complexity

When we create multiple charts using subplots in pandas, we want to know how the time to draw them changes as we add more charts.

We ask: How does the work grow when we increase the number of charts?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'A': range(100),
    'B': range(100, 200),
    'C': range(200, 300)
})

fig, axes = plt.subplots(3, 1)
for i, col in enumerate(data.columns):
    data[col].plot(ax=axes[i])
plt.show()
    

This code creates three line charts stacked vertically, one for each column in the data.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping over each column to draw a chart.
• How many times: Once for each column (number of charts).

How Execution Grows With Input

Each new chart adds roughly the same amount of work to draw it.

Input Size (n)	Approx. Operations
3 charts	3 times the work of one chart
10 charts	10 times the work of one chart
100 charts	100 times the work of one chart

Pattern observation: The work grows directly in proportion to the number of charts.

Final Time Complexity

Time Complexity: O(n)

This means if you double the number of charts, the time to draw them roughly doubles.

Common Mistake

[X] Wrong: "Drawing multiple charts together is as fast as drawing just one chart."

[OK] Correct: Each chart requires its own drawing steps, so more charts mean more work and more time.

Interview Connect

Understanding how drawing multiple charts scales helps you explain performance in data visualization tasks clearly and confidently.

Self-Check

What if we changed the code to plot all columns on the same single chart instead of separate subplots? How would the time complexity change?

Practice

1. What is the main purpose of using subplots in pandas plotting?

easy

A. To display multiple charts in one figure for easy comparison

B. To save plots as separate image files automatically

C. To change the color of a single plot

D. To increase the size of a single chart

Solution

1. Step 1: Understand what subplots do

   Subplots allow multiple charts to be shown together in one figure.

2. Step 2: Compare options

   Only To display multiple charts in one figure for easy comparison describes this purpose correctly.

3. Final Answer:

   To display multiple charts in one figure for easy comparison -> Option A

4. Quick Check:

   Subplots = multiple charts in one figure [OK]

Hint: Subplots = multiple charts in one figure [OK]

Common Mistakes:

• Thinking subplots save images automatically
• Confusing subplots with styling options
• Believing subplots only affect one chart

2. Which of the following is the correct way to create a 2x2 grid of subplots using matplotlib with pandas?

easy

A. fig, axes = plt.subplots(rows=2)

B. fig, axes = plt.subplots(4)

C. fig, axes = plt.subplots(columns=2)

D. fig, axes = plt.subplots(2, 2)

Solution

1. Step 1: Recall plt.subplots syntax

   The function plt.subplots(rows, columns) creates a grid of plots.

2. Step 2: Match the 2x2 grid

   Using plt.subplots(2, 2) creates 2 rows and 2 columns, making 4 subplots.

3. Final Answer:

   fig, axes = plt.subplots(2, 2) -> Option D

4. Quick Check:

   plt.subplots(2, 2) = 2 rows, 2 columns [OK]

Hint: Use plt.subplots(rows, columns) for grid size [OK]

Common Mistakes:

• Using plt.subplots(4) creates 4 rows, not a 2x2 grid
• Using keyword arguments incorrectly
• Confusing rows and columns parameters

3. Given the code below, what will axes be?

import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 3)
print(type(axes))

medium

A. <class 'numpy.ndarray'>

B. <class 'matplotlib.axes._subplots.AxesSubplot'>

C. <class 'list'>

D. <class 'tuple'>

Solution

1. Step 1: Understand plt.subplots output for multiple plots

   When creating multiple subplots in one row, axes is a numpy array of AxesSubplot objects.

2. Step 2: Check the printed type

   Printing type(axes) shows numpy.ndarray because axes is an array of subplot axes.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option A

4. Quick Check:

   Multiple subplots = axes is numpy array [OK]

Hint: Multiple subplots return numpy array of axes [OK]

Common Mistakes:

• Thinking axes is a single AxesSubplot object
• Assuming axes is a list or tuple
• Confusing axes with figure object

4. What is wrong with this code snippet for creating subplots?

fig, axes = plt.subplots(1)
axes[0].plot([1, 2, 3])
axes[1].plot([4, 5, 6])

medium

A. plt.subplots needs two arguments for multiple plots

B. axes is a single AxesSubplot, so indexing causes an error

C. axes is not iterable when only one argument is given

D. plot data lists must be the same length

Solution

1. Step 1: Understand plt.subplots(1) output

   plt.subplots(1) creates a single subplot (1 row, 1 column), so axes is a single AxesSubplot object, not an array.

2. Step 2: Indexing causes error

   Trying axes[0] or axes[1] on a single AxesSubplot raises TypeError: 'AxesSubplot' object is not subscriptable.

3. Final Answer:

   axes is a single AxesSubplot, so indexing causes an error -> Option B

4. Quick Check:

   plt.subplots(1) = single axes, indexing fails [OK]

Hint: Check if axes is array before indexing [OK]

Common Mistakes:

• Assuming axes is always a single plot
• Forgetting axes can be array for multiple subplots
• Confusing number of arguments with subplot shape

5. You want to plot three different pandas DataFrame columns side-by-side using subplots in one row. Which code correctly creates the subplots and plots each column separately?

hard

A. fig, axes = plt.subplots(3, 1)
for i, col in enumerate(df.columns[:3]):
df[col].plot(ax=axes[i])

B. fig, axes = plt.subplots(3)
for i, col in enumerate(df.columns[:3]):
df[col].plot(ax=axes[i])

C. fig, axes = plt.subplots(1, 3)
for i, col in enumerate(df.columns[:3]):
df[col].plot(ax=axes[i])

D. fig, axes = plt.subplots(1, 3)
for i, col in enumerate(df.columns[:3]):
df[col].plot(ax=fig[i])

Solution

1. Step 1: Determine subplot layout for side-by-side plots

   Side-by-side means 1 row and 3 columns, so plt.subplots(1, 3) is correct.

2. Step 2: Check plotting loop and axes usage

   Looping over columns and plotting on axes[i] is correct usage.

3. Step 3: Identify errors in other options

   fig, axes = plt.subplots(3, 1) for i, col in enumerate(df.columns[:3]): df[col].plot(ax=axes[i]) uses (3,1) which is vertical layout, not side-by-side. fig, axes = plt.subplots(1, 3) for i, col in enumerate(df.columns[:3]): df[col].plot(ax=fig[i]) uses fig[i] which is invalid. fig, axes = plt.subplots(3) for i, col in enumerate(df.columns[:3]): df[col].plot(ax=axes[i]) uses plt.subplots(3) which creates 3 rows and 1 column, not side-by-side.

4. Final Answer:

   fig, axes = plt.subplots(1, 3) for i, col in enumerate(df.columns[:3]): df[col].plot(ax=axes[i]) -> Option C

5. Quick Check:

   Side-by-side = plt.subplots(1, 3) + axes[i] [OK]

Hint: Side-by-side = 1 row, multiple columns in plt.subplots [OK]

Common Mistakes:

• Using wrong subplot shape for layout
• Passing fig instead of axes to plot
• Confusing rows and columns in plt.subplots