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Вивчайте Swarmplot | Categorical Plot Types
Deep Dive into the seaborn Visualization

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Swarmplot

The swarmplot is similar to stripplot. Only the points are adjusted so they won't overlap, as it helps to represent the distribution of values better.

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Завдання

Swipe to start coding

  1. Set the 'whitegrid' style with the 'seashell' axes.facecolor.
  2. Create the swarmplot using the seaborn library:
  • x parameter equals the 'day';
  • Set the y parameter equals the 'total_bill';
  • Set the hue parameter equals the 'sex';
  • Set the linewidth parameter equals 1;
  • Set the size parameter equals 2;
  • Set the dodge parameter equals the True;
  • Set the 'rocket' palette.

Рішення

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')

# Set the 'whitegrid' style with the 'seashell' facecolor
sns.set_style('whitegrid', {'axes.facecolor': 'seashell'})
# Create a swarmplot
sns.swarmplot(# Set the x
x = 'day',
# Set the y
y = 'total_bill',
# Set the hue
hue = 'sex',
# Set the linewidth
linewidth = 1,
# Set the size
size = 2,
# Set the dodge
dodge = True,
# Set the palette
palette = 'rocket',
# Setting the data
data = df)

# Displaying the plot
plt.show()

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Секція 3. Розділ 2
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')

# Set the 'whitegrid' style with the 'seashell' facecolor
___('___', {___})
# Create a swarmplot
___(# Set the x
___,
# Set the y
___,
# Set the hue
___,
# Set the linewidth
___,
# Set the size
___ = 2,
# Set the dodge
dodge = ___,
# Set the palette
palette = '___',
# Setting the data
data = df)

# Displaying the plot
plt.show()

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