Plotting Basics with `MatPlotlib` and `Seaborn` - Answer Key

Authors

Noor Sohail

Will Gammerdinger

Published

March 15, 2026

Exercise 1

The current axis label text defaults to what we gave as input to geom_point (i.e the column headers). We can change this by adding additional layers called xlabel() and ylabel() for the x- and y-axis, respectively. Add these layers to the current plot such that the x-axis is labeled “Age (days)” and the y-axis is labeled “Mean expression”.

# Set the theme to "whitegrid"
sns.set_style("whitegrid")

# Initialize a plot with a specific size
plt.figure(figsize=(8, 6))

# Add a scatterplot layer to the plot, coloring points by genotype
sns.scatterplot(data = new_metadata,
                x="age_in_days", 
                y="mean_expression",
                hue="genotype",
                style="celltype",
                s=50)

# Change the size of the axis labels
plt.xlabel("Age (Days)", fontsize=20)
plt.ylabel("Mean expression", fontsize=20)

Use the plt.title() layer to add a plot title of your choice.

# Set the theme to "whitegrid"
sns.set_style("whitegrid")

# Initialize a plot with a specific size
plt.figure(figsize=(8, 6))

# Add a scatterplot layer to the plot, coloring points by genotype
sns.scatterplot(data = new_metadata,
                x="age_in_days", 
                y="mean_expression",
                hue="genotype",
                style="celltype",
                s=50)

# Change the size of the axis labels
plt.xlabel("Age (Days)", fontsize=20)
plt.ylabel("Mean expression", fontsize=20)
plt.title("Mean expression vs Age (Days)")

When you add the arguments loc="center" to the plt.title() function:
- What does it change?
- How many layers can be added to a plot, in your estimation?

# Set the theme to "whitegrid"
sns.set_style("whitegrid")

# Initialize a plot with a specific size
plt.figure(figsize=(8, 6))

# Add a scatterplot layer to the plot, coloring points by genotype
sns.scatterplot(data = new_metadata,
                x="age_in_days", 
                y="mean_expression",
                hue="genotype",
                style="celltype",
                s=50)

# Change the size of the axis labels
plt.xlabel("Age (Days)", fontsize=20)
plt.ylabel("Mean expression", fontsize=20)
plt.title("Mean expression vs Age (Days)", loc="center")

Try adding the layer plt.legend(loc="center right") to the end of your code. What does this do? How many layers can be added to a plot, in your estimation?

# Set the theme to "whitegrid"
sns.set_style("whitegrid")

# Initialize a plot with a specific size
plt.figure(figsize=(8, 6))

# Add a scatterplot layer to the plot, coloring points by genotype
sns.scatterplot(data = new_metadata,
                x="age_in_days", 
                y="mean_expression",
                hue="genotype",
                style="celltype",
                s=50)

# Change the size of the axis labels
plt.xlabel("Age (Days)", fontsize=20)
plt.ylabel("Mean expression", fontsize=20)
plt.title("Mean expression vs Age (Days)", loc="center")

# Change legend location
plt.legend(loc="center right")

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--- title: "Plotting Basics with `MatPlotlib` and `Seaborn` - Answer Key" author: - Noor Sohail - Will Gammerdinger date: "2026-03-15" license: "CC-BY-4.0" editor_options: markdown: wrap: 72 --- ```{r} #| label: load_libraries_data #| echo: false # Load libraries and data # Interfacing with R quarto and python futzing library(reticulate) use_condaenv("/opt/anaconda3/envs/intro_python", required = TRUE) ``` ```{python} #| label: load_libraries_data_py #| echo: false import matplotlib.pyplot as plt import seaborn as sns import pandas as pd # Load the new metadata data frame that we created in the previous lesson new_metadata = pd.read_csv("data/new_metadata.csv", index_col=0) ``` # Exercise 1 1. The current axis label text defaults to what we gave as input to `geom_point` (i.e the column headers). We can change this by **adding additional layers** called `xlabel()` and `ylabel()` for the x- and y-axis, respectively. Add these layers to the current plot such that the x-axis is labeled "Age (days)" and the y-axis is labeled "Mean expression". ```{python} #| label: exercise_1 # Set the theme to "whitegrid" sns.set_style("whitegrid") # Initialize a plot with a specific size plt.figure(figsize=(8, 6)) # Add a scatterplot layer to the plot, coloring points by genotype sns.scatterplot(data = new_metadata, x="age_in_days", y="mean_expression", hue="genotype", style="celltype", s=50) # Change the size of the axis labels plt.xlabel("Age (Days)", fontsize=20) plt.ylabel("Mean expression", fontsize=20) ``` 2. Use the `plt.title()` layer to add a plot title of your choice. ```{python} #| label: exercise_2 # Set the theme to "whitegrid" sns.set_style("whitegrid") # Initialize a plot with a specific size plt.figure(figsize=(8, 6)) # Add a scatterplot layer to the plot, coloring points by genotype sns.scatterplot(data = new_metadata, x="age_in_days", y="mean_expression", hue="genotype", style="celltype", s=50) # Change the size of the axis labels plt.xlabel("Age (Days)", fontsize=20) plt.ylabel("Mean expression", fontsize=20) plt.title("Mean expression vs Age (Days)") ``` 3. When you add the arguments `loc="center"` to the `plt.title()` function: - What does it change? - How many layers can be added to a plot, in your estimation? ```{python} #| label: exercise_3 # Set the theme to "whitegrid" sns.set_style("whitegrid") # Initialize a plot with a specific size plt.figure(figsize=(8, 6)) # Add a scatterplot layer to the plot, coloring points by genotype sns.scatterplot(data = new_metadata, x="age_in_days", y="mean_expression", hue="genotype", style="celltype", s=50) # Change the size of the axis labels plt.xlabel("Age (Days)", fontsize=20) plt.ylabel("Mean expression", fontsize=20) plt.title("Mean expression vs Age (Days)", loc="center") ``` 4. Try adding the layer `plt.legend(loc="center right")` to the end of your code. What does this do? How many layers can be added to a plot, in your estimation? ```{python} #| label: exercise_4 # Set the theme to "whitegrid" sns.set_style("whitegrid") # Initialize a plot with a specific size plt.figure(figsize=(8, 6)) # Add a scatterplot layer to the plot, coloring points by genotype sns.scatterplot(data = new_metadata, x="age_in_days", y="mean_expression", hue="genotype", style="celltype", s=50) # Change the size of the axis labels plt.xlabel("Age (Days)", fontsize=20) plt.ylabel("Mean expression", fontsize=20) plt.title("Mean expression vs Age (Days)", loc="center") # Change legend location plt.legend(loc="center right") ```