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import seaborn as sns
# %matplotlib inline

flights = sns.load_dataset("flights")
tips = sns.load_dataset("tips")

flights.head()

tips.head()

crr = tips.corr()

crr

sns.heatmap(crr, annot=True) 

sns.heatmap(crr, cmap="coolwarm", annot=True) # CMAP= Mudança de cores  -  ANNOT=Mostra os dados da correlação

flights.info()

pf = flights.pivot_table(values="passengers", index="month", columns="year") # Reorganizando uma tabela!!

pf

sns.heatmap(pf, cmap="magma", linecolor="gray", lw=1)

sns.clustermap(pf, standard_scale=1) # Este método agrupa de alguma forma, o que ele considera ser conjuntos relevantes!! standard_scale= Caso queira alguma escala!

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