<jupyter_start><jupyter_text>Análises com Variações Gráficas usado Seaborn >Importação de bibliotecas gráficas<jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline<jupyter_output><empty_output><jupyter_text>>Carregando o conjunto de dados Iris<jupyter_code>iris = sns.load_dataset('iris')
iris<jupyter_output><empty_output><jupyter_text>>Plotando a dispersão de combinações das variáveis pétala e sépala, comprimento e largura.<jupyter_code>sns.PairGrid(iris, hue='species', palette='husl').map(plt.scatter).add_legend();<jupyter_output><empty_output><jupyter_text>>Plotando a dispersão com histogramas na diagonal e outra paleta de cores.<jupyter_code>sns.PairGrid(iris, hue='species', palette='Set1').map_diag(plt.hist).map_offdiag(plt.scatter).add_legend();<jupyter_output><empty_output><jupyter_text>>Plotando apenas histogramas customizados na diagonal.<jupyter_code>sns.PairGrid(iris).map_diag(plt.hist, facecolor='yellow', edgecolor='red', linewidth=3);<jupyter_output><empty_output><jupyter_text>>Plotando uma variação<jupyter_code>sns.PairGrid(iris, hue='species', palette='coolwarm').\
                                                      map_diag(plt.hist).map_offdiag(plt.scatter, edgecolor='yellow').\
                                                      add_legend();<jupyter_output><empty_output><jupyter_text>>Plotando uma divisão de tipos<jupyter_code>sns.PairGrid(iris).map_diag(plt.hist, facecolor='purple', edgecolor='yellow', linewidth=2).\
                   map_upper(plt.scatter).map_lower(sns.kdeplot);<jupyter_output><empty_output><jupyter_text>>Mais uma variação de plotagem<jupyter_code>f, ax = plt.subplots(figsize=(15, 7))
sns.heatmap(iris.corr(), annot=True, cmap="RdYlGn");<jupyter_output><empty_output>
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