Catplot

Note

catplot = FacetGrid + stripplot/swarmplot/boxplot/violinplot/barplot/pointplot

The catplot function provides a new framework giving access to several types of plots that show a relationship between a numerical variable and one or more categorical variables, like boxplot, stripplot and so on.

Note

Scroll down to see the full plot when you cope with the task!

Task

Swipe to start coding

Set the 'white' style with the 'aliceblue' axes.facecolor.
Create the catplot using the seaborn library:

Set the x parameter equals the 'species';
Set the y parameter equals the 'body_mass_g';
Set the hue parameter equals the 'sex';
Set the row parameter equals the 'island';
Set the 'viridis' palette;
Set the alpha parameter equals 0.6;
Disable the legend_out.

Solution

import warnings

# Ignore all warnings

warnings.filterwarnings('ignore')

# Importing libraries needed

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

# Reading the file

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/66ba0c8e-8422-413c-b7e1-74bd24c61656/penguins.csv')

# Set the 'white' style with the 'aliceblue' facecolor

sns.set_style('white', {'axes.facecolor' : 'aliceblue'})

# Create a catplot

sns.catplot(# Set the x

x = 'species',

# Set the y

y = 'body_mass_g',

# Set the hue

hue = 'sex',

# Set the row

row = 'island',

# Set the palette

palette = 'viridis',

# Set the alpha

alpha = 0.6,

# Set the legend_out

legend_out = False,

# Setting the data

data = df)

# Displaying the plot

plt.show()

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Section 3. Chapter 8

single

import warnings

# Ignore all warnings

warnings.filterwarnings('ignore')

# Importing libraries needed

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

# Reading the file

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/66ba0c8e-8422-413c-b7e1-74bd24c61656/penguins.csv')

# Set the 'white' style with the 'aliceblue' facecolor

___(___, {'___.facecolor' : '___'})

# Create a catplot

___(# Set the x

___,

# Set the y

y = '___',

# Set the hue

___,

# Set the row

___ = '___',

# Set the palette

___,

# Set the alpha

___,

# Set the legend_out

legend_out = ___,

# Setting the data

data = df)

# Displaying the plot

plt.show()

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