Violinplot
A violinplot is a hybrid of a box plot and a kernel density plot, which shows peaks in the data. It is used to visualize the distribution of numerical data. Unlike a box plot that can only show summary statistics, violin plots depict summary statistics and the density of each variable.
Tâche
Swipe to start coding
- Create a violinplot using the
gvariable:
- Set the
xparameter equals the'day'; - Set the
yparameter equals the'total_bill'; - Set the
hueparameter equals'sex'; - Set the
'rocket'palette; - Set the
splitparameter; - Set the
innerparameter equals the'point'; - Set the
bwparameter equals the0.2.
- Set the title for the plot:
'Tips violinplot'.
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/tips.csv')
# Create a violinplot using the g variable
g = sns.violinplot(# Set the x
x = 'day',
# Set the y
y = 'total_bill',
# Set the hue
hue = 'sex',
# Set the palette
palette = 'rocket',
# Set the split
split = True,
# Set the inner
inner = 'point',
# Set the bw
bw = 0.2,
# Setting the data
data = df)
# Set the title for the plot
g.set_title('Tips violinplot')
# Displaying the plot
plt.show()
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Merci pour vos commentaires !
Section 3. Chapitre 4
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/tips.csv')
# Create a violinplot using the g variable
g = ___(# Set the x
___,
# Set the y
___,
# Set the hue
hue = '___',
# Set the palette
___,
# Set the split
___,
# Set the inner
inner = '___',
# Set the bw
bw = ___,
# Setting the data
data = df)
# Set the title for the plot
g.___('Tips violinplot')
# Displaying the plot
plt.show()
