Bar plots in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a bar plot of the 'sales' column.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'product': ['A', 'B', 'C'], 'sales': [10, 20, 15]}
df = pd.DataFrame(data)
df['sales'].[1]()
plt.show()

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

Aplot.line

Bplot.bar

Cplot.scatter

Dplot.hist

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot.line instead of plot.bar

Forgetting to call the plot method

Using plot.hist which creates a histogram

The 'plot.bar' method creates a bar plot of the selected column.

2fill in blank

medium

Complete the code to set the title of the bar plot to 'Sales Data'.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'product': ['A', 'B', 'C'], 'sales': [10, 20, 15]}
df = pd.DataFrame(data)
ax = df['sales'].plot.bar()
ax.[1]('Sales Data')
plt.show()

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

Atitle

Bxlabel

Cylabel

Dlegend

Attempts:

3 left

💡 Hint

Common Mistakes

Using xlabel or ylabel instead of title

Trying to set title on plt instead of ax

Forgetting to call the method

The 'title' method sets the title of the plot.

3fill in blank

hard

Fix the error in the code to correctly plot a bar chart with product names as x-axis labels.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'product': ['A', 'B', 'C'], 'sales': [10, 20, 15]}
df = pd.DataFrame(data)
df.plot.bar(x=[1], y='sales')
plt.show()

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

A'product'

B'sales'

C'index'

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sales' as x-axis labels

Using 'index' which is default row numbers

Using a non-existent column name

The 'x' parameter should be the column with labels, which is 'product'.

4fill in blank

hard

Fill both blanks to create a horizontal bar plot with 'product' on y-axis and 'sales' on x-axis.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'product': ['A', 'B', 'C'], 'sales': [10, 20, 15]}
df = pd.DataFrame(data)
df.plot.barh(x=[1], y=[2])
plt.show()

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

A'product'

B'sales'

C'index'

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping x and y parameters

Using 'index' instead of 'product'

Using non-existent columns

For horizontal bar plots, x is the label column 'product' on y-axis, y is 'sales' on x-axis.

5fill in blank

hard

Fill all three blanks to create a bar plot with 'product' as x-axis, 'sales' as y-axis, and set the color to 'green'.

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'product': ['A', 'B', 'C'], 'sales': [10, 20, 15]}
df = pd.DataFrame(data)
ax = df.plot.bar(x=[1], y=[2], color=[3])
plt.show()

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

A'product'

B'sales'

C'green'

D'blue'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column names for x or y

Using color names not supported

Forgetting to put quotes around strings

Set x='product', y='sales', and color='green' to customize the bar plot.

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