Subplots for multiple charts in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of subplot creation with pandas

What will be the output of this code snippet that creates subplots for two line charts using pandas and matplotlib?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'A': [1, 3, 2, 4], 'B': [4, 2, 3, 1]}
df = pd.DataFrame(data)

axes = df.plot(subplots=True, layout=(2, 1), figsize=(6, 6))
plt.tight_layout()
plt.show()

print(type(axes))

A <class 'tuple'>

B <class 'list'>

C <class 'numpy.ndarray'>

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

💡 Hint

Think about what pandas returns when plotting multiple subplots.

When plotting multiple subplots, pandas returns a numpy ndarray of Axes objects, not a single Axes or list.

❓ data_output

intermediate

Number of subplot axes created

Given this DataFrame and plotting code, how many subplot axes will be created?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'X': [5, 6, 7], 'Y': [1, 2, 3], 'Z': [9, 8, 7]}
df = pd.DataFrame(data)

axes = df.plot(subplots=True, layout=(1, 3), figsize=(9, 3))
plt.close()  # Close plot to avoid display

print(len(axes.flatten()))

A 3

B 1

C 9

D 0

💡 Hint

Count the number of columns in the DataFrame.

Each column gets its own subplot when subplots=True, so 3 columns mean 3 subplot axes.

🔧 Debug

advanced

Identify the error in subplot layout code

What error will this code raise when trying to create subplots with pandas?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}
df = pd.DataFrame(data)

axes = df.plot(subplots=True, layout=(1, 2), figsize=(8, 4))
plt.show()

A IndexError: list index out of range

B TypeError: 'layout' argument must be a tuple of length 3

C No error, plots display correctly

D ValueError: Layout shape (1, 2) is too small for 3 subplots

💡 Hint

Check if the layout can fit all subplots.

The layout (1, 2) can only hold 2 subplots but 3 are needed, so pandas raises a ValueError.

❓ visualization

advanced

Effect of figsize on subplot appearance

What visual difference will you see when changing figsize from (6, 4) to (12, 8) in this subplot code?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'A': [1, 4, 2, 3], 'B': [4, 1, 3, 2]}
df = pd.DataFrame(data)

axes = df.plot(subplots=True, figsize=(6, 4))
plt.show()

A No change in subplot size or spacing

B Subplots become larger and more spaced out with figsize (12, 8)

C Subplots become smaller and closer together with figsize (12, 8)

D Subplots overlap and become unreadable

💡 Hint

Figsize controls the overall figure size in inches.

Increasing figsize makes the whole figure bigger, so subplots appear larger and have more space.

🚀 Application

expert

Creating a 2x2 grid of subplots with different chart types

Which code correctly creates a 2x2 grid of subplots from this DataFrame, plotting a line, bar, histogram, and scatter plot respectively?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = {'A': np.random.randint(1, 10, 10),
        'B': np.random.randint(10, 20, 10),
        'C': np.random.randn(10),
        'D': np.random.randn(10)}
df = pd.DataFrame(data)

A

fig, axes = plt.subplots(2, 2, figsize=(10, 8))
df['A'].plot(ax=axes[0,0], kind='line')
df['B'].plot(ax=axes[0,1], kind='bar')
df['C'].plot(ax=axes[1,0], kind='hist')
df.plot.scatter(x='C', y='D', ax=axes[1,1])
plt.tight_layout()
plt.show()

B

fig, axes = plt.subplots(2, 2)
df['A'].plot(kind='line', ax=axes[0,0])
df['B'].plot(kind='bar', ax=axes[0,1])
df['C'].plot(kind='hist', ax=axes[1,0])
df['D'].plot(kind='scatter', ax=axes[1,1])
plt.show()

C

fig, axes = plt.subplots(4, 1)
df['A'].plot(kind='line')
df['B'].plot(kind='bar')
df['C'].plot(kind='hist')
df.plot.scatter(x='C', y='D')
plt.show()

D

fig, axes = plt.subplots(2, 2)
df.plot(kind='line', ax=axes[0])
df.plot(kind='bar', ax=axes[1])
df.plot(kind='hist', ax=axes[2])
df.plot.scatter(x='C', y='D', ax=axes[3])
plt.show()

💡 Hint

Remember scatter plots require x and y arguments and axes indexing for 2D arrays.

Option A correctly creates a 2x2 grid and assigns each plot to the right axes. Scatter plot uses df.plot.scatter with x and y specified.