Subplots for multiple charts in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a figure and axes for subplots.

Pandas

fig, axes = plt.subplots([1])

Drag options to blanks, or click blank then click option'

A3

B2

C1

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number larger than needed creates extra empty plots.

Forgetting to import matplotlib.pyplot as plt.

The plt.subplots function creates a figure and a set of subplots. Here, 2 means two subplots in one row.

2fill in blank

medium

Complete the code to plot data on the first subplot.

Pandas

axes[[1]].plot(data['x'], data['y1'])

Drag options to blanks, or click blank then click option'

A0

B-1

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 instead of 0 causes an index error or plots on the second subplot.

Using negative indices without understanding their meaning.

Subplot axes are indexed starting at 0, so the first subplot is axes[0].

3fill in blank

hard

Fix the error in the code to set the title of the second subplot.

Pandas

axes[1].[1]('Second Chart')

Drag options to blanks, or click blank then click option'

Aset_title

Btitle

CsetTitle

Dtitles

Attempts:

3 left

💡 Hint

Common Mistakes

Using title instead of set_title causes an attribute error.

Using camelCase like setTitle is incorrect in matplotlib.

The correct method to set a title on an axes object is set_title().

4fill in blank

hard

Fill both blanks to create two subplots in one column and plot data on the second subplot.

Pandas

fig, axes = plt.subplots([1], [2])
axes[1].plot(data['x'], data['y2'])

Drag options to blanks, or click blank then click option'

A2

B1

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping rows and columns causes unexpected layout.

Using indices out of range for axes.

To create two subplots stacked vertically, use plt.subplots(2, 1). Then axes[1] is the second subplot.

5fill in blank

hard

Fill all three blanks to create a 2x2 grid of subplots, plot on the bottom right, and set its title.

Pandas

fig, axes = plt.subplots([1], [2])
axes[[3]].plot(data['x'], data['y3'])
axes[[3]].set_title('Bottom Right')

Drag options to blanks, or click blank then click option'

A2

B3

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong indices for the bottom right subplot.

Mixing up rows and columns in plt.subplots.

A 2x2 grid means 2 rows and 2 columns. The bottom right subplot is at index 3 (zero-based counting).

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