FacetGrid for multi-panel views in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a FacetGrid in data visualization?

A FacetGrid is a way to create multiple plots (panels) based on subsets of data. It helps compare patterns across different groups side by side.

beginner

How do you specify which variable to use for creating rows in a FacetGrid?

You use the row parameter when creating the FacetGrid. For example, FacetGrid(data, row='variable_name').

beginner

Which Python library provides the FacetGrid class?

The FacetGrid class is provided by the <strong>seaborn</strong> library, which is built on top of matplotlib for easier statistical plotting.

intermediate

What method do you use to map a plot type onto a FacetGrid?

You use the map() method of the FacetGrid object to specify the plot type and the variables to plot.

beginner

Why use FacetGrid instead of plotting all data in one plot?

FacetGrid helps by splitting data into smaller groups and plotting each group separately. This makes it easier to see differences and patterns that might be hidden in one combined plot.

Which library do you import to use FacetGrid?

A matplotlib.pyplot

B pandas

C numpy

D seaborn

FacetGrid is part of the seaborn library, which is designed for statistical data visualization.

What parameter controls the columns in a FacetGrid?

A col

B style

C hue

D row

The col parameter sets which variable divides the data into columns in the grid.

How do you add a scatter plot to a FacetGrid?

A Use <code>map(sns.scatterplot, 'x', 'y')</code>

B Use <code>plot.scatter('x', 'y')</code>

C Use <code>scatter('x', 'y')</code>

D Use <code>mapplot('x', 'y')</code>

You use the map() method with seaborn's scatterplot function to add scatter plots to each facet.

What does the hue parameter do in FacetGrid?

A Creates separate columns

B Colors points by a variable within each panel

C Creates separate rows

D Changes the plot type

The hue parameter colors data points differently based on a variable, within each panel.

Which method finalizes the FacetGrid plot display?

A plot()

B draw()

C show()

D None needed, it displays automatically

After mapping plots, you call plt.show() from matplotlib to display the figure.

Explain how to create a FacetGrid with two variables dividing rows and columns, and add scatter plots to each panel.

Think about how to split data by two variables and plot points in each small plot.

Describe why FacetGrid is useful for comparing data groups visually.

Imagine looking at many small windows instead of one big messy window.

Practice

1. What is the main purpose of using FacetGrid in data visualization?

easy

A. To perform statistical tests on data

B. To create a single plot with multiple lines

C. To split data into multiple panels for easier comparison

D. To clean and preprocess data before plotting

Solution

1. Step 1: Understand FacetGrid functionality

   FacetGrid is designed to split data into multiple smaller plots based on categories.

2. Step 2: Identify the main use case

   This splitting helps compare subsets of data side by side visually.

3. Final Answer:

   To split data into multiple panels for easier comparison -> Option C

4. Quick Check:

   FacetGrid = multi-panel comparison [OK]

Hint: FacetGrid = split data into panels for comparison [OK]

Common Mistakes:

• Thinking FacetGrid creates a single combined plot
• Confusing FacetGrid with data cleaning tools
• Assuming FacetGrid performs statistical analysis

2. Which of the following is the correct way to create a FacetGrid with columns based on the 'species' column in a DataFrame df using seaborn?

easy

A. sns.FacetGrid(df, split='species')

B. sns.FacetGrid(df, row='species')

C. sns.FacetGrid(df, group='species')

D. sns.FacetGrid(df, col='species')

Solution

1. Step 1: Recall FacetGrid parameters

   The parameter to split data into columns is col.

2. Step 2: Match parameter with 'species'

   Using col='species' splits the grid into columns by species.

3. Final Answer:

   sns.FacetGrid(df, col='species') -> Option D

4. Quick Check:

   Use col= for columns in FacetGrid [OK]

Hint: Use col='column_name' to split by columns [OK]

Common Mistakes:

• Using 'group' or 'split' which are invalid parameters
• Confusing row and col parameters
• Missing quotes around column name

3. Given the following code, what will be the output type of g?

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    'category': ['A', 'A', 'B', 'B'],
    'value': [10, 15, 10, 20]
})
g = sns.FacetGrid(df, col='category')

medium

A. A FacetGrid object with two column panels for categories A and B

B. A DataFrame filtered by category

C. A matplotlib Figure object

D. A dictionary of plots keyed by category

Solution

1. Step 1: Understand FacetGrid return type

   Creating a FacetGrid returns a FacetGrid object that manages multiple plot panels.

2. Step 2: Check the col parameter effect

   Using col='category' splits the grid into columns for each unique category value, here 'A' and 'B'.

3. Final Answer:

   A FacetGrid object with two column panels for categories A and B -> Option A

4. Quick Check:

   FacetGrid object = multi-panel grid [OK]

Hint: FacetGrid returns a grid object, not data or dict [OK]

Common Mistakes:

• Expecting a DataFrame instead of a FacetGrid
• Thinking it returns a matplotlib Figure directly
• Assuming it returns a dictionary of plots

4. What is wrong with this code snippet?

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    'type': ['X', 'Y', 'X', 'Y'],
    'score': [5, 7, 6, 8]
})
g = sns.FacetGrid(df, col=type)
g.map(sns.histplot, 'score')

medium

A. DataFrame is missing required columns

B. The variable 'type' is not defined; it should be a string 'type'

C. sns.histplot cannot be used with FacetGrid

D. FacetGrid cannot use 'col' parameter

Solution

1. Step 1: Check parameter usage in FacetGrid

   The col parameter expects a string with the column name, not a variable.

2. Step 2: Identify the error in code

   Using col=type without quotes causes a NameError because 'type' is not defined as a variable.

3. Final Answer:

   The variable 'type' is not defined; it should be a string 'type' -> Option B

4. Quick Check:

   Use quotes for column names in FacetGrid [OK]

Hint: Always quote column names in FacetGrid parameters [OK]

Common Mistakes:

• Using variable names without quotes for column names
• Thinking FacetGrid can't use histplot
• Assuming DataFrame columns are missing

5. You want to create a multi-panel plot showing the distribution of 'total_bill' by 'day' and 'time' from the seaborn 'tips' dataset. Which code correctly creates a FacetGrid with rows for 'time' and columns for 'day' and plots histograms of 'total_bill'?

hard

A. g = sns.FacetGrid(tips, row='time', col='day')
g.map(sns.histplot, 'total_bill')

B. g = sns.FacetGrid(tips, col='time', row='day')
g.map(sns.histplot, 'total_bill')

C. g = sns.FacetGrid(tips, group='time', split='day')
g.map(sns.histplot, 'total_bill')

D. g = sns.FacetGrid(tips, row='total_bill', col='day')
g.map(sns.histplot, 'time')

Solution

1. Step 1: Identify correct row and column parameters

   Rows should be split by 'time' and columns by 'day' as per requirement.

2. Step 2: Confirm correct mapping of histogram

   Use g.map(sns.histplot, 'total_bill') to plot histograms of 'total_bill'.

3. Step 3: Check for invalid parameters

   Options with 'group' or swapped row/col are incorrect.

4. Final Answer:

   g = sns.FacetGrid(tips, row='time', col='day') g.map(sns.histplot, 'total_bill') -> Option A

5. Quick Check:

   row='time', col='day', map histplot on 'total_bill' [OK]

Hint: Use row= for rows, col= for columns, map plot on target column [OK]

Common Mistakes:

• Swapping row and col parameters
• Using invalid parameters like group or split
• Mapping plot on wrong column