Scatter plots in Pandas - Mini Project: Build & Apply

Scatter plots

📖 Scenario: You work as a data analyst for a small company that sells different products. You have collected data about the price and sales of each product. You want to see if there is any relationship between the price of a product and how many units it sells.

🎯 Goal: You will create a scatter plot using pandas to visualize the relationship between product prices and sales.

📋 What You'll Learn

Create a pandas DataFrame with product data

Add a variable for the size of the scatter points

Use the DataFrame to create a scatter plot with price on the x-axis and sales on the y-axis

Display the scatter plot

💡 Why This Matters

🌍 Real World

Scatter plots help visualize relationships between two numeric variables, like price and sales, to find patterns or trends.

💼 Career

Data analysts and scientists use scatter plots to explore data and communicate insights clearly to teams and decision makers.

1

Create the product data

Create a pandas DataFrame called products with these exact columns and values:
'Product': ['Pen', 'Notebook', 'Eraser', 'Pencil', 'Marker']
'Price': [1.5, 3.0, 0.5, 1.0, 2.0]
'Sales': [100, 80, 150, 120, 90]

Pandas

import pandas as pd
# Create the DataFrame called products with the exact data
# Your code here

Hint

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

2

Add a size variable for points

Create a variable called sizes that contains the Sales column from the products DataFrame multiplied by 2. This will be used to set the size of the scatter plot points.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser', 'Pencil', 'Marker'],
    'Price': [1.5, 3.0, 0.5, 1.0, 2.0],
    'Sales': [100, 80, 150, 120, 90]
})
# Create sizes variable by multiplying Sales by 2
# Your code here

Hint

Use products['Sales'] to get the sales column and multiply it by 2.

3

Create the scatter plot

Use the plot.scatter method on the products DataFrame to create a scatter plot with x='Price', y='Sales', and s=sizes to set the point sizes.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser', 'Pencil', 'Marker'],
    'Price': [1.5, 3.0, 0.5, 1.0, 2.0],
    'Sales': [100, 80, 150, 120, 90]
})
sizes = products['Sales'] * 2
# Create scatter plot with x='Price', y='Sales', and s=sizes
# Your code here

Hint

Use products.plot.scatter(x='Price', y='Sales', s=sizes) to create the scatter plot.

4

Display the scatter plot

Use import matplotlib.pyplot as plt and then call plt.show() to display the scatter plot.

Pandas

import pandas as pd
import matplotlib.pyplot as plt
products = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser', 'Pencil', 'Marker'],
    'Price': [1.5, 3.0, 0.5, 1.0, 2.0],
    'Sales': [100, 80, 150, 120, 90]
})
sizes = products['Sales'] * 2
scatter_plot = products.plot.scatter(x='Price', y='Sales', s=sizes)
# Show the plot
# Your code here

Hint

Import matplotlib.pyplot as plt and call plt.show() to display the plot window.

Practice

1. What does a scatter plot primarily show in a pandas DataFrame?

easy

A. The relationship between two numeric columns

B. The frequency of a single categorical column

C. The sum of all numeric columns

D. The average value of a column over time

Solution

1. Step 1: Understand scatter plot purpose

   A scatter plot is used to visualize how two numeric variables relate to each other by plotting points on an x and y axis.

2. Step 2: Match with options

   Only The relationship between two numeric columns describes this relationship between two numeric columns, while others describe different chart types or summaries.

3. Final Answer:

   The relationship between two numeric columns -> Option A

4. Quick Check:

   Scatter plot = relationship between two numeric columns [OK]

Hint: Scatter plots always compare two numeric columns [OK]

Common Mistakes:

• Confusing scatter plots with bar charts
• Thinking scatter plots show single column frequency
• Assuming scatter plots summarize data instead of showing relationships

2. Which of the following is the correct syntax to create a scatter plot in pandas for columns 'height' and 'weight'?

easy

A. df.plot.scatter('weight', 'height')

B. df.scatter_plot('height', 'weight')

C. df.plot.scatter(x='height', y='weight')

D. df.plot.scatter(x=height, y=weight)

Solution

1. Step 1: Recall pandas scatter plot syntax

   The correct method is df.plot.scatter with x and y as column names in quotes.

2. Step 2: Check each option

   df.plot.scatter(x='height', y='weight') uses correct method and argument format. df.scatter_plot('height', 'weight') is invalid method. df.plot.scatter('weight', 'height') swaps x and y but is still valid syntax, but question asks for 'height' as x and 'weight' as y. df.plot.scatter(x=height, y=weight) uses variables without quotes causing error.

3. Final Answer:

   df.plot.scatter(x='height', y='weight') -> Option C

4. Quick Check:

   Correct syntax uses df.plot.scatter with x and y as strings [OK]

Hint: Use df.plot.scatter(x='col1', y='col2') with quotes [OK]

Common Mistakes:

• Using method scatter_plot instead of plot.scatter
• Passing column names without quotes
• Swapping x and y columns unintentionally

3. Given the code:

import pandas as pd
import matplotlib.pyplot as plt

data = {'age': [25, 30, 35, 40], 'income': [50000, 60000, 65000, 70000]}
df = pd.DataFrame(data)
df.plot.scatter(x='age', y='income')
plt.show()

What will the scatter plot show?

medium

A. A bar chart of age vs income

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

C. A line plot connecting age and income

D. Points showing income increasing with age

Solution

1. Step 1: Understand the code

   The DataFrame has 'age' and 'income' columns. The scatter plot plots 'age' on x-axis and 'income' on y-axis.

2. Step 2: Interpret the plot type and data

   Scatter plot will show points representing each person's age and income. Income increases as age increases, so points trend upward.

3. Final Answer:

   Points showing income increasing with age -> Option D

4. Quick Check:

   Scatter plot shows points, not bars or lines [OK]

Hint: Scatter plots show points, not bars or lines [OK]

Common Mistakes:

• Confusing scatter plot with bar or line plot
• Thinking plt.show() is optional and causes error
• Misreading which column is x or y

4. What is wrong with this code snippet to create a scatter plot?

df.plot.scatter(x=age, y='income')

medium

A. Scatter plots require plt.scatter, not df.plot.scatter

B. The variable age is not defined or quoted

C. plot.scatter does not exist in pandas

D. The y parameter should be numeric, not string

Solution

1. Step 1: Check the x parameter

   The x parameter should be the column name as a string, but age is unquoted, treated as a variable.

2. Step 2: Verify other parameters and method

   y='income' is correct as a string. plot.scatter is valid pandas method. So only x parameter is wrong.

3. Final Answer:

   The variable age is not defined or quoted -> Option B

4. Quick Check:

   Column names must be strings in plot.scatter [OK]

Hint: Always quote column names in plot.scatter [OK]

Common Mistakes:

• Passing column names without quotes
• Confusing pandas plot.scatter with matplotlib scatter
• Thinking y must be numeric type, not string

5. You have a DataFrame with columns 'temperature', 'sales', and 'day'. You want to create a scatter plot showing sales vs temperature, but color points by day of the week. Which approach is correct?

hard

A. Use df.plot.scatter(x='temperature', y='sales', c=df['day'].astype('category').cat.codes)

B. Use df.plot.scatter(x='temperature', y='sales', color='day')

C. Use df.plot.scatter(x='temperature', y='sales', c='day')

D. Use df.plot.scatter(x='temperature', y='sales', c=df['day'])

Solution

1. Step 1: Understand color parameter in scatter plot

   The c parameter expects numeric values for colors. The 'day' column is categorical (strings), so it must be converted to numeric codes.

2. Step 2: Check each option's color argument

   Use df.plot.scatter(x='temperature', y='sales', c=df['day'].astype('category').cat.codes) converts 'day' to category codes, which is correct. Use df.plot.scatter(x='temperature', y='sales', color='day') uses color='day' which is invalid. Use df.plot.scatter(x='temperature', y='sales', c='day') passes string 'day' which is invalid. Use df.plot.scatter(x='temperature', y='sales', c=df['day']) passes categorical strings directly, causing error.

3. Final Answer:

   Use df.plot.scatter(x='temperature', y='sales', c=df['day'].astype('category').cat.codes) -> Option A

4. Quick Check:

   Color values must be numeric codes for categorical data [OK]

Hint: Convert categorical to numeric codes for color in scatter [OK]

Common Mistakes:

• Passing categorical strings directly to color parameter
• Using color='day' instead of numeric codes
• Not converting categories before coloring