[seaborn을 이용한 데이터 시각화] #01. rugplot, kdeplot, distplot, countplot

1차원 분포 플롯

1. 1차원 실수 분포 플롯

1) rugplot

• height : Draw a taller rug

  sns.rugplot(data=tips, x="total_bill", y="tip", height=.1)​

• hue : Represent a third variable with hue mapping

  sns.rugplot(data=tips, x="total_bill", y="tip", hue="time")​

• clip_on=False : Put the rug outside the axes

  sns.rugplot(data=tips, x="total_bill", y="tip", height=-.02, clip_on=False)​

• alpha : Show the density of a larger dataset using thinner lines and alpha blending

  sns.rugplot(data=diamonds, x="carat", y="price", lw=1, alpha=.005)

 

seaborn.rugplot — seaborn 0.11.1 documentation

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2) kdeplot

• bw_adjust=.2 : Use less smoothing

sns.kdeplot(data=tips, x="total_bill", bw_adjust=.2)

• cut=0 : Use more smoothing, but don’t smooth past the extreme data points

ax= sns.kdeplot(data=tips, x="total_bill", bw_adjust=5, cut=0)

• multiple="stack" : “Stack” the conditional distributions

sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="stack")

• multiple="fill" : Normalize the stacked distribution at each value in the grid

  sns.kdeplot(data=tips, x="total_bill", hue="time", multiple="fill")

• fill=True, common_norm=False, palette="crest" : Modify the appearance of the plot

  sns.kdeplot(
     data=tips, x="total_bill", hue="size",
     fill=True, common_norm=False, palette="crest",
     alpha=.5, linewidth=0,
  )

seaborn.kdeplot — seaborn 0.11.1 documentation

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3) distplot

• vertical=True : Plot the distribution on the vertical axis

ax = sns.distplot(x, vertical=True)

• color="y" : Change the color of all the plot elements

sns.set_color_codes()
ax = sns.distplot(x, color="y")

 

seaborn.distplot — seaborn 0.11.1 documentation

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2. 카운트 플롯

1) countplot

• palette="Set3" : Use a different color palette

ax = sns.countplot(x="who", data=titanic, palette="Set3")

• Use matplotlib.axes.Axes.bar() parameters to control the style

ax = sns.countplot(x="who", data=titanic,
                   facecolor=(0, 0, 0, 0),
                   linewidth=5,
                   edgecolor=sns.color_palette("dark", 3))

• catplot : Use catplot() to combine a countplot() and a FacetGrid. This allows grouping within additional categorical variables

g = sns.catplot(x="class", hue="who", col="survived",
                data=titanic, kind="count",
                height=4, aspect=.7);

seaborn.countplot — seaborn 0.11.1 documentation

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cf. 

[Python] seaborn 데이터 시각화 총정리 - GROWTH.J

 

Seaborn을 사용한 데이터 분포 시각화 — 데이터 사이언스 스쿨