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孊ぶ FacetGrid | Multi-Plot Grids
Deep Dive into the seaborn Visualization

FacetGrid

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The time to learn the cool plots we were talking about!

A useful approach to exploring medium-dimensional data is by drawing multiple instances of the same plot on different subsets of your dataset.

FacetGrid object takes a DataFrame as input and the names of the variables that will form the grid's row, column, or hue dimensions. The variables should be categorical, and the data at each level of the variable will be used for a facet along that axis.

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

df = pd.read_csv('filename.csv')

# Creating the FacetGrid variable
g = sns.FacetGrid(df, col = 'column_name', row = 'row_name')

# The main approach for visualizing data on this grid is with the FacetGrid.map() method
g.map(sns.scatterplot, 'column_name')

plt.show()
タスク

スワむプしおコヌディングを開始

  1. Set the 'whitegrid' style with the 'cornsilk' axes.facecolor.

  2. Create a FacetGrid variable g:

  • Set the data for the FacetGrid;
  • Set the col parameter equals the 'day';
  • Set the row parameter equals the 'smoker';
  • Set the height parameter equals 3.

Implement the .map() function to build histplots:

  • Create histplots;
  • Set the 'olive' color for the histplots;
  • Add the kde parameter;
  • Disable the fill parameter;
  • Set the binwidth equals 4;
  • Display the plot.

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