Showing static visualizations

This page is generated from a Jupyter notebook and demonstrates how to generate static visualizations with matplotlib, pandas, and seaborn.

Start by importing the packages we need:

import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns

Load the “Palmer penguins” dataset from week 2:

# Load data on Palmer penguins
penguins = pd.read_csv("https://raw.githubusercontent.com/MUSA-550-Fall-2023/week-2/main/data/penguins.csv")

# Show the first ten rows
penguins.head(n=10)    

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007
5	Adelie	Torgersen	39.3	20.6	190.0	3650.0	male	2007
6	Adelie	Torgersen	38.9	17.8	181.0	3625.0	female	2007
7	Adelie	Torgersen	39.2	19.6	195.0	4675.0	male	2007
8	Adelie	Torgersen	34.1	18.1	193.0	3475.0	NaN	2007
9	Adelie	Torgersen	42.0	20.2	190.0	4250.0	NaN	2007

A simple visualization, 3 different ways

I want to scatter flipper length vs. bill length, colored by the penguin species

Using matplotlib

# Setup a dict to hold colors for each species
color_map = {"Adelie": "#1f77b4", "Gentoo": "#ff7f0e", "Chinstrap": "#D62728"}

# Initialize the figure "fig" and axes "ax"
fig, ax = plt.subplots(figsize=(10, 6))

# Group the data frame by species and loop over each group
# NOTE: "group" will be the dataframe holding the data for "species"
for species, group_df in penguins.groupby("species"):

    # Plot flipper length vs bill length for this group
    # Note: we are adding this plot to the existing "ax" object
    ax.scatter(
        group_df["flipper_length_mm"],
        group_df["bill_length_mm"],
        marker="o",
        label=species,
        color=color_map[species],
        alpha=0.75,
        zorder=10
    )

# Plotting is done...format the axes!

## Add a legend to the axes
ax.legend(loc="best")

## Add x-axis and y-axis labels
ax.set_xlabel("Flipper Length (mm)")
ax.set_ylabel("Bill Length (mm)")

## Add the grid of lines
ax.grid(True);

How about in pandas?

DataFrames have a built-in “plot” function that can make all of the basic type of matplotlib plots!

First, we need to add a new “color” column specifying the color to use for each species type.

Use the pd.replace() function: it use a dict to replace values in a DataFrame column.

# Calculate a list of colors
color_map = {"Adelie": "#1f77b4", "Gentoo": "#ff7f0e", "Chinstrap": "#D62728"}

# Map species name to color 
penguins["color"] = penguins["species"].replace(color_map)

penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year	color
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007	#1f77b4
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007	#1f77b4
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007	#1f77b4
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007	#1f77b4
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007	#1f77b4

Now plot!

# Same as before: Start by initializing the figure and axes
fig, myAxes = plt.subplots(figsize=(10, 6))

# Scatter plot two columns, colored by third
# Use the built-in pandas plot.scatter function
penguins.plot.scatter(
    x="flipper_length_mm",
    y="bill_length_mm",
    c="color",
    alpha=0.75,
    ax=myAxes, # IMPORTANT: Make sure to plot on the axes object we created already!
    zorder=10
)

# Format the axes finally
myAxes.set_xlabel("Flipper Length (mm)")
myAxes.set_ylabel("Bill Length (mm)")
myAxes.grid(True);

Note: no easy way to get legend added to the plot in this case…

Seaborn: statistical data visualization

Seaborn is designed to plot two columns colored by a third column…

# Initialize the figure and axes
fig, ax = plt.subplots(figsize=(10, 6))

# style keywords as dict
color_map = {"Adelie": "#1f77b4", "Gentoo": "#ff7f0e", "Chinstrap": "#D62728"}
style = dict(palette=color_map, s=60, edgecolor="none", alpha=0.75, zorder=10)

# use the scatterplot() function
sns.scatterplot(
    x="flipper_length_mm",  # the x column
    y="bill_length_mm",  # the y column
    hue="species",  # the third dimension (color)
    data=penguins,  # pass in the data
    ax=ax,  # plot on the axes object we made
    **style  # add our style keywords
)

# Format with matplotlib commands
ax.set_xlabel("Flipper Length (mm)")
ax.set_ylabel("Bill Length (mm)")
ax.grid(True)
ax.legend(loc="best");