Section: Graphical customization | Data Visualization on Seaborn

Section outline

- Select activity Il y a différentes options visuelles disponibles s...
  
  There are various visual options available in Seaborn, including different point styles, line styles, and color palettes.
  
  Here is an example of code:
  
  Importation Code
  import pandas as pd import seaborn as sns import seaborn.objects as so import numpy as np import matplotlib.pyplot as plt from pyodide.http import open_url url_tips = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv" url_healthexp = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/healthexp.csv" tips = pd.read_csv(open_url(url_tips)) healthexp=pd.read_csv(open_url(url_healthexp))
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  Cell 2
  so.Plot(tips, x="total_bill", y="tip").add(so.Dots(marker="x"), so.Jitter(0.5)).show()
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  We can also modify the type of lines we use:
  
  Here is an example:
  
  Cell 3
  so.Plot(tips,"total_bill","tip").add(so.Lines(linestyle='--')).show()
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  We can also choose specific colors:
  
  Here is an example :
  
  Cell 4
  so.Plot(tips, "total_bill", "tip", color="sex", linestyle="sex").add(so.Lines()).scale(color={"Male": "blue","Female": "red"}, linestyle={"Male": "--","Female": (4,4)}).show()
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  We can also define colors using color palettes:
  
  And here is an example using code from earlier:
  
  Cell 5
  so.Plot(healthexp, x="Year", y="Spending_USD", color="Country").add(so.Lines(), so.Norm(where="x == x.min()", percent=True)).scale(color="rocket").label(y="Percent change in spending from 1970 baseline").show()
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