Subplots for multiple charts in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of using subplots in data visualization?

Subplots allow you to display multiple charts in a single figure, making it easier to compare different data visualizations side by side.

beginner

How do you create subplots using pandas plotting?

You can create subplots by using the 'subplots=True' parameter in the pandas plot function. This splits the data columns into separate charts arranged in a grid.

intermediate

What does the 'layout' parameter control in pandas subplots?

The 'layout' parameter controls the arrangement of subplots as a tuple (rows, columns), defining how many charts appear in each row and column.

beginner

How can you adjust the size of the entire figure containing subplots in pandas?

Use the 'figsize' parameter to set the width and height (in inches) of the whole figure that contains all subplots.

intermediate

Why might you want to share the x-axis or y-axis across subplots?

Sharing axes helps to align scales across charts, making it easier to compare data trends and values directly.

Which parameter in pandas plot function creates multiple charts in one figure?

A charts=True

B multiple=True

C subplots=True

D plots=True

The 'subplots=True' parameter tells pandas to create separate charts for each data column in one figure.

What does the 'layout=(2,2)' parameter do when plotting subplots?

A Creates 2 rows and 2 columns of subplots

B Creates 2 subplots only

C Creates 4 rows and 1 column of subplots

D Creates 1 row and 4 columns of subplots

The layout=(2,2) arranges subplots in a grid with 2 rows and 2 columns.

How do you change the size of the figure containing subplots?

A Use shape parameter

B Use size parameter

C Use scale parameter

D Use figsize parameter

The figsize parameter sets the width and height of the entire figure.

What is the benefit of sharing the y-axis in subplots?

A Makes charts colorful

B Easier to compare values across charts

C Increases the number of charts

D Removes axis labels

Sharing the y-axis aligns scales, making it easier to compare data values.

Which pandas method is commonly used to plot subplots?

A DataFrame.plot(subplots=True)

B DataFrame.show()

C DataFrame.display()

D DataFrame.graph()

DataFrame.plot with subplots=True creates multiple charts for each column.

Explain how to create multiple charts in one figure using pandas and how to arrange them.

Think about parameters controlling number and arrangement of charts.

Describe why sharing axes in subplots can be helpful when comparing data.

Consider how axis scales affect visual comparison.

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