Scatter plots in Pandas - Step-by-Step Execution

Concept Flow - Scatter plots

Start with DataFrame

↓

Select two columns

↓

Call plot.scatter(x, y)

↓

Matplotlib creates scatter plot

↓

Display plot to user

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End

Scatter plots show how two variables relate by plotting points for each pair of values.

Execution Sample

Pandas

import pandas as pd
import matplotlib.pyplot as plt

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

This code creates a scatter plot of 'x' vs 'y' from a simple DataFrame.

Execution Table

Step	Action	DataFrame State	Plot State	Output
1	Create DataFrame with columns 'x' and 'y'	{'x': [1,2,3], 'y': [4,5,6]}	No plot yet	DataFrame ready
2	Call df.plot.scatter(x='x', y='y')	DataFrame unchanged	Scatter plot object created	Plot object ready
3	Matplotlib prepares plot	DataFrame unchanged	Points plotted at (1,4), (2,5), (3,6)	Plot ready to display
4	Call plt.show()	DataFrame unchanged	Plot displayed on screen	Scatter plot visible
5	End of code	DataFrame unchanged	Plot displayed	Execution complete

💡 Plot displayed and code execution finished

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
df	None	{'x': [1,2,3], 'y': [4,5,6]}	{'x': [1,2,3], 'y': [4,5,6]}	{'x': [1,2,3], 'y': [4,5,6]}	{'x': [1,2,3], 'y': [4,5,6]}	{'x': [1,2,3], 'y': [4,5,6]}
plot	None	None	Scatter plot object	Scatter plot with points	Displayed plot	Displayed plot

Key Moments - 3 Insights

Why do we need to specify x and y columns explicitly in df.plot.scatter?

Because scatter plots need two variables to plot points on x and y axes. The execution_table step 2 shows the call with x='x' and y='y' to tell pandas which columns to use.

Does the DataFrame change after plotting?

No, the DataFrame stays the same throughout. The variable_tracker shows 'df' remains unchanged after each step.

What happens if plt.show() is not called?

The plot object is created but not displayed. Step 4 in execution_table shows plt.show() triggers the plot to appear.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what points are plotted on the scatter plot?

A (1,2), (3,4), (5,6)

B (4,1), (5,2), (6,3)

C (1,4), (2,5), (3,6)

D (None, None)

💡 Hint

Check the 'Plot State' column at step 3 in execution_table

According to variable_tracker, what is the state of 'plot' after step 2?

A Scatter plot object created

B No plot created

C Plot displayed

D DataFrame changed

💡 Hint

Look at the 'plot' row under 'After Step 2' in variable_tracker

If we omit plt.show(), what will happen according to execution_table?

A Plot will be displayed anyway

B Plot object created but not displayed

C DataFrame will change

D Code will error out

💡 Hint

Refer to step 4 in execution_table where plt.show() triggers display

Concept Snapshot

Scatter plots show points for two variables on x and y axes.
Use df.plot.scatter(x='col1', y='col2') to create.
Matplotlib draws points for each row.
Call plt.show() to display the plot.
DataFrame stays unchanged during plotting.

Full Transcript

This visual execution shows how to create a scatter plot using pandas. First, a DataFrame with two columns is created. Then, df.plot.scatter is called with x and y column names to make a scatter plot object. Matplotlib plots points for each pair of values. Finally, plt.show() displays the plot on screen. The DataFrame does not change during this process. Key moments include specifying x and y columns, understanding DataFrame immutability, and the role of plt.show(). The quizzes test understanding of plotted points, plot object state, and plot display behavior.

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