Bar plots in Pandas - Mini Project: Build & Apply

Bar plots

📖 Scenario: You work in a small bakery. You want to see how many cakes of each type you sold last week. This will help you understand which cakes are popular.

🎯 Goal: Create a bar plot to show the number of cakes sold for each cake type.

📋 What You'll Learn

Create a pandas DataFrame with cake types and their sales.

Set a variable for the plot title.

Use pandas to create a bar plot of the sales.

Display the bar plot with the correct title.

💡 Why This Matters

🌍 Real World

Bar plots are used in business to quickly see which products sell best or which categories have the most data.

💼 Career

Data analysts and scientists use bar plots to visualize and communicate data insights clearly to teams and managers.

1

Create the sales data

Create a pandas DataFrame called sales_data with two columns: 'Cake' and 'Sold'. The 'Cake' column should have these exact values: 'Chocolate', 'Vanilla', 'Strawberry', 'Lemon'. The 'Sold' column should have these exact values: 50, 40, 30, 20.

Pandas

import pandas as pd
# Create the sales_data DataFrame with Cake and Sold columns
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set the plot title

Create a variable called plot_title and set it to the string 'Cake Sales Last Week'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Cake': ['Chocolate', 'Vanilla', 'Strawberry', 'Lemon'],
    'Sold': [50, 40, 30, 20]
})
# Create the plot_title variable
# Your code here

Hint

Just assign the string to the variable plot_title.

3

Create the bar plot

Use the plot method on sales_data to create a bar plot. Use x='Cake' and y='Sold' as arguments. Save the plot object in a variable called bar_plot.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Cake': ['Chocolate', 'Vanilla', 'Strawberry', 'Lemon'],
    'Sold': [50, 40, 30, 20]
})
plot_title = 'Cake Sales Last Week'
# Create the bar plot using sales_data.plot with x='Cake' and y='Sold'
# Your code here

Hint

Use sales_data.plot(x='Cake', y='Sold', kind='bar') and assign it to bar_plot.

4

Show the bar plot with title

Set the title of bar_plot to plot_title using set_title(). Then import matplotlib.pyplot as plt and call plt.show() to display the plot.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Cake': ['Chocolate', 'Vanilla', 'Strawberry', 'Lemon'],
    'Sold': [50, 40, 30, 20]
})
plot_title = 'Cake Sales Last Week'
bar_plot = sales_data.plot(x='Cake', y='Sold', kind='bar')
# Set the plot title and show the plot
# Your code here

Hint

Use bar_plot.set_title(plot_title) and then plt.show() to display the 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