Scatter plots in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a basic scatter plot code

What will be the output of this code snippet that creates a scatter plot using pandas and matplotlib?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'x': [1, 2, 3, 4], 'y': [10, 20, 25, 30]}
df = pd.DataFrame(data)
df.plot.scatter(x='x', y='y')
plt.show()

A A scatter plot with points at coordinates (1,10), (2,20), (3,25), (4,30) displayed

B A line plot connecting points (1,10), (2,20), (3,25), (4,30) displayed

C A bar chart with bars at x=1,2,3,4 with heights 10,20,25,30 displayed

D SyntaxError due to missing plt.scatter call

💡 Hint

Remember that df.plot.scatter creates a scatter plot, not a line or bar chart.

The code uses pandas DataFrame's plot.scatter method which creates a scatter plot with the given x and y columns. plt.show() displays the plot.

❓ data_output

intermediate

Number of points in scatter plot

Given this DataFrame and scatter plot code, how many points will appear in the scatter plot?

Pandas

import pandas as pd

data = {'height': [150, 160, 170, 180, 190], 'weight': [50, 60, 65, 80, 90]}
df = pd.DataFrame(data)
df.plot.scatter(x='height', y='weight')

A 6

B 4

C 0

D 5

💡 Hint

Each row in the DataFrame corresponds to one point in the scatter plot.

The DataFrame has 5 rows, so the scatter plot will have 5 points.

🔧 Debug

advanced

Identify the error in scatter plot code

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

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'a': [1, 2, 3], 'b': [4, 5, 6]}
df = pd.DataFrame(data)
df.plot.scatter(x='x', y='b')
plt.show()

A TypeError: unsupported operand type(s)

B ValueError: x and y must be numeric

C KeyError: 'x'

D No error, plot displays correctly

💡 Hint

Check if the DataFrame has a column named 'x'.

The DataFrame does not have a column named 'x', so trying to plot with x='x' raises a KeyError.

❓ visualization

advanced

Effect of color parameter in scatter plot

What will be the color of points in the scatter plot created by this code?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'x': [1, 2, 3], 'y': [4, 5, 6]}
df = pd.DataFrame(data)
df.plot.scatter(x='x', y='y', c='red')
plt.show()

A Points will have default blue color

B All points will be red

C Points will be green

D SyntaxError due to invalid color argument

💡 Hint

The color parameter sets the color of all points.

The color='red' argument sets all scatter plot points to red color.

🚀 Application

expert

Interpreting scatter plot with categorical color grouping

Given this DataFrame and scatter plot code, what does the color grouping represent in the plot?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'age': [25, 30, 22, 40, 35], 'income': [50000, 60000, 45000, 80000, 70000], 'gender': ['M', 'F', 'F', 'M', 'F']}
df = pd.DataFrame(data)
df.plot.scatter(x='age', y='income', c=df['gender'].map({'M': 'blue', 'F': 'pink'}))
plt.show()

A Blue points represent males, pink points represent females

B Blue points represent females, pink points represent males

C Colors represent income levels, blue for high income, pink for low income

D Colors represent age groups, blue for younger, pink for older

💡 Hint

Look at how the 'gender' column is mapped to colors.

The code maps 'M' to blue and 'F' to pink, so blue points are males and pink points are females.

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