Bar plots in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple bar plot code

What will be the output of the following code snippet that creates a bar plot using pandas?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'fruits': ['apple', 'banana', 'cherry'], 'counts': [10, 15, 7]}
df = pd.DataFrame(data)
df.plot.bar(x='fruits', y='counts')
plt.close()  # Prevent plot display in some environments
print(df['counts'].sum())

A 15

B 32

C 10

D 3

💡 Hint

Think about what the sum of the 'counts' column is.

The code creates a DataFrame with counts 10, 15, and 7. The sum of these counts is 32, which is printed as output.

❓ data_output

intermediate

Number of bars in grouped bar plot

Given the following DataFrame and code, how many bars will the resulting grouped bar plot display?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'year': [2020, 2020, 2021, 2021], 'category': ['A', 'B', 'A', 'B'], 'value': [5, 7, 6, 8]}
df = pd.DataFrame(data)
df_pivot = df.pivot(index='year', columns='category', values='value')
df_pivot.plot.bar()
plt.close()

A 1

B 2

C 4

D 3

💡 Hint

Count the number of bars per group and the number of groups.

There are 2 years (groups) and 2 categories (bars per group), so total bars = 2 * 2 = 4.

🔧 Debug

advanced

Identify the error in bar plot code

What error will this code raise when trying to create a bar plot?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'items': ['pen', 'pencil', 'eraser'], 'counts': ['10', '15', '7']}
df = pd.DataFrame(data)
df.plot.bar(x='items', y='counts')
plt.close()

A No error, plot displays correctly

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: could not convert string to float: 'pen'

D TypeError: unhashable type: 'list'

💡 Hint

Check the data types of the 'counts' column.

The 'counts' column contains strings, but pandas and matplotlib can handle numeric strings for plotting without error.

❓ visualization

advanced

Bar plot with stacked bars

What will the stacked bar plot display when running this code?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Q1': [3, 5], 'Q2': [4, 6]}
index = ['Product A', 'Product B']
df = pd.DataFrame(data, index=index)
df.plot.bar(stacked=True)
plt.close()

A A line plot connecting Q1 and Q2 values

B A pie chart showing total sums of Q1 and Q2

C Four separate bars, two for Q1 and two for Q2

D Two bars, each divided into two stacked segments representing Q1 and Q2 values

💡 Hint

Stacked bars combine segments vertically in one bar per index.

The plot shows one bar per product with Q1 and Q2 values stacked on top of each other.

🧠 Conceptual

expert

Effect of setting figsize in bar plots

How does changing the figsize parameter affect a pandas bar plot?

A It changes the size of the entire plot area, making bars and labels larger or smaller accordingly

B It changes only the width of the bars but not the height of the plot

C It changes the color of the bars automatically based on size

D It changes the data values plotted on the bars

💡 Hint

Think about what 'figsize' means in matplotlib plots.

The figsize parameter controls the overall size of the figure, affecting how big the bars and labels appear.

Practice

1. What does a bar plot primarily help you do with data?

easy

A. Sort data in ascending order

B. Compare values across different categories visually

C. Calculate the average of numerical data

D. Show the relationship between two continuous variables

Solution

1. Step 1: Understand the purpose of a bar plot

   A bar plot uses bars to represent data values for different categories, making it easy to compare them visually.

2. Step 2: Compare with other options

   Calculating averages, sorting data, or showing relationships between continuous variables are not the main uses of bar plots.

3. Final Answer:

   Compare values across different categories visually -> Option B

4. Quick Check:

   Bar plot = Compare categories [OK]

Hint: Bar plots show category comparisons with bars [OK]

Common Mistakes:

• Confusing bar plots with scatter plots
• Thinking bar plots calculate statistics
• Assuming bar plots sort data automatically

2. Which of the following is the correct way to create a bar plot in pandas for columns 'Category' and 'Value' in DataFrame df?

easy

A. df.barplot('Category', 'Value')

B. df.plot.bar(y='Category', x='Value')

C. df.plot.bar('Value', 'Category')

D. df.plot.bar(x='Category', y='Value')

Solution

1. Step 1: Recall pandas bar plot syntax

   The correct syntax is df.plot.bar(x='Category', y='Value') where x is the label and y is the height of bars.

2. Step 2: Check other options for errors

   df.barplot('Category', 'Value') uses a non-existent method barplot, C swaps x and y incorrectly, D swaps x and y keyword arguments wrongly.

3. Final Answer:

   df.plot.bar(x='Category', y='Value') -> Option D

4. Quick Check:

   Use plot.bar with x and y named [OK]

Hint: Use df.plot.bar(x=..., y=...) to plot bars [OK]

Common Mistakes:

• Using wrong method like barplot instead of plot.bar
• Swapping x and y arguments
• Not naming x and y parameters

3. Given the code:

import pandas as pd
import matplotlib.pyplot as plt
data = {'Fruit': ['Apple', 'Banana', 'Cherry'], 'Count': [10, 15, 7]}
df = pd.DataFrame(data)
df.plot.bar(x='Fruit', y='Count')
plt.show()

What will the bar plot display?

medium

A. A line plot connecting fruit counts

B. Bars showing counts of Apple=7, Banana=10, Cherry=15

C. Bars showing counts of Apple=10, Banana=15, Cherry=7

D. An error because plt.show() is missing

Solution

1. Step 1: Understand the DataFrame content

   The DataFrame has fruits as categories and their counts: Apple=10, Banana=15, Cherry=7.

2. Step 2: Analyze the bar plot code

   Using df.plot.bar(x='Fruit', y='Count') plots bars with fruit names on x-axis and counts as bar heights.

3. Final Answer:

   Bars showing counts of Apple=10, Banana=15, Cherry=7 -> Option C

4. Quick Check:

   Bar heights = Count values [OK]

Hint: Bar heights match y column values [OK]

Common Mistakes:

• Mixing up fruit names and counts
• Thinking plt.show() is optional (it is needed to display)
• Confusing bar plot with line plot

4. What is wrong with this code snippet for creating a bar plot?

df.plot.bar('Category', 'Value')

medium

A. It will plot correctly with Category on x-axis and Value on y-axis

B. It will raise an error because x and y must be named arguments

C. It will swap x and y axes automatically

D. It will create a line plot instead of a bar plot

Solution

1. Step 1: Check pandas plot.bar argument handling

   The plot.bar() method accepts positional arguments: first for x, second for y.

2. Step 2: Verify the code

   df.plot.bar('Category', 'Value') correctly assigns x='Category' and y='Value', producing the intended bar plot.

3. Final Answer:

   It will plot correctly with Category on x-axis and Value on y-axis -> Option A

4. Quick Check:

   Positional x,y accepted in plot.bar [OK]

Hint: plot.bar accepts positional x,y args [OK]

Common Mistakes:

• Believing x,y must always be named arguments
• Expecting automatic axis swapping
• Confusing bar plot with line plot

5. You have a DataFrame df with columns 'Product', 'Sales_Q1', 'Sales_Q2'. You want to create a grouped bar plot to compare sales in Q1 and Q2 for each product. Which code correctly creates this grouped bar plot?

hard

A. df.plot.bar(x='Product', y=['Sales_Q1', 'Sales_Q2'])

B. df.plot.bar(x='Product', y='Sales_Q1')
df.plot.bar(x='Product', y='Sales_Q2')

C. df.plot.bar(x=['Sales_Q1', 'Sales_Q2'], y='Product')

D. df.plot.bar(y=['Sales_Q1', 'Sales_Q2'])

Solution

1. Step 1: Understand grouped bar plot requirements

   To compare multiple columns side-by-side per category, pass y as a list of columns and x as the category column.

2. Step 2: Analyze each option

   df.plot.bar(x='Product', y=['Sales_Q1', 'Sales_Q2']) correctly uses x='Product' and y=['Sales_Q1', 'Sales_Q2'] to create grouped bars. df.plot.bar(x='Product', y='Sales_Q1') df.plot.bar(x='Product', y='Sales_Q2') plots separately, not grouped. df.plot.bar(x=['Sales_Q1', 'Sales_Q2'], y='Product') swaps x and y incorrectly. df.plot.bar(y=['Sales_Q1', 'Sales_Q2']) misses x, so no category labels.

3. Final Answer:

   df.plot.bar(x='Product', y=['Sales_Q1', 'Sales_Q2']) -> Option A

4. Quick Check:

   Grouped bars need y as list and x as category [OK]

Hint: Use y=[col1, col2] and x=category for grouped bars [OK]

Common Mistakes:

• Plotting columns separately instead of grouped
• Swapping x and y arguments
• Not specifying x for categories