Outlines

• relational plot
  • scatter plot
  • line plot
• distributional plot
  • histogram
  • density plot
• categorical data
  • barplot
  • boxplot
  • jitter plot
• others
  • pairplot
  • heatmap

Get started

• package: seaborn
• load the package

import seaborn as sns

• Seaborn is a Python data visualization library based on matplotlib.
• It provides a high-level interface for drawing attractive and informative statistical graphics.

Overview of the seaborn functionality

tips dataset

• dataset for scatter plot

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

##    total_bill   tip     sex smoker  day    time  size
## 0       16.99  1.01  Female     No  Sun  Dinner     2
## 1       10.34  1.66    Male     No  Sun  Dinner     3
## 2       21.01  3.50    Male     No  Sun  Dinner     3
## 3       23.68  3.31    Male     No  Sun  Dinner     2
## 4       24.59  3.61  Female     No  Sun  Dinner     4

• if you have trouble load the dataset, try the following code

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

relplot (scatter plot)

• scatter plot:
  • data: a dataframe
  • x: x axis
  • y: y axis
• implementation:
  • sns.relplot with kind: scatter (default)
  • sns.scatterplot

sns.relplot(data=tips,x="total_bill", y="tip", kind='scatter')
# sns.scatterplot(data=tips, x="total_bill", y="tip", )

color and style

• hue: Grouping variable that will produce elements with different colors

sns.relplot(data=tips,x="total_bill", y="tip", hue = "smoker")

• style: shape of the points

sns.relplot(data=tips,x="total_bill", y="tip", hue = "smoker", style="smoker")

size and transparency

• size: size of points

sns.relplot(data=tips,x="total_bill", y="tip", size = "size")

• alpha: transparency

sns.relplot(data=tips,x="total_bill", y="tip", size = "size", alpha = 0.5)

multiple subfigures

• col: columns

sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

scatter plot with linear regression fitting

sns.lmplot(data=tips, x="total_bill", y="tip", col="time", hue="smoker")

styles

• There are 5 built-in styles
  • darkgrid (default)
  • whitegrid
  • dark
  • white
  • ticks

sns.set_style("whitegrid")
sns.relplot(data=tips,x="total_bill", y="tip", kind='scatter')

alternative function:

sns.set_theme(style = "whitegrid")

Scaling Plots – set_context

• There are 4 built-in context (related to font size)
  • notebook (default)
  • paper
  • talk
  • poster

sns.set_style("whitegrid")
sns.set_context("talk")
sns.relplot(data=tips,x="total_bill", y="tip", kind='scatter')

alternative function:

sns.set_theme(context = "talk, style = "whitegrid")

fmri dataset

• dataset for line plot

fmri = sns.load_dataset("fmri")
fmri.head()

##   subject  timepoint event    region    signal
## 0     s13         18  stim  parietal -0.017552
## 1      s5         14  stim  parietal -0.080883
## 2     s12         18  stim  parietal -0.081033
## 3     s11         18  stim  parietal -0.046134
## 4     s10         18  stim  parietal -0.037970

fmri_sub13 = fmri[(fmri["subject"]=="s13")]
fmri_sub13_stim = fmri_sub13[(fmri_sub13["event"]=="stim")]
fmri_sub13_stim_parietal = fmri_sub13_stim[(fmri_sub13_stim["region"]=="parietal")]
fmri_sub13_stim_parietal.head()

##    subject  timepoint event    region    signal
## 0      s13         18  stim  parietal -0.017552
## 15     s13         17  stim  parietal -0.008265
## 29     s13         16  stim  parietal -0.002856
## 43     s13         15  stim  parietal -0.010971
## 57     s13         14  stim  parietal -0.033713

relplot (line plot)

• line plot:
  • data: a dataframe
  • x: x axis
  • y: y axis
• implementation:
  • sns.relplot with kind = line
  • sns.lineplot

sns.relplot(data=fmri_sub13_stim_parietal, kind="line", x="timepoint", y="signal")
# sns.lineplot(data=fmri_sub13_stim_parietal, x="timepoint", y="signal")

combine scatter and line plots

fig, ax = plt.subplots(figsize=(6, 4))
sns.scatterplot(data=fmri_sub13_stim_parietal, x="timepoint", y="signal", ax=ax)
sns.lineplot(data=fmri_sub13_stim_parietal, x="timepoint", y="signal", ax=ax)

relplot (line plot)

• line plot:
  • hue: Grouping variable that will produce elements with different colors
  • style: shape
  • size: size
  • col: columns
• color by hue

sns.relplot(data=fmri_sub13_stim, kind="line", x="timepoint", y="signal", hue="region", )

• style

sns.relplot(data=fmri_sub13, kind="line", x="timepoint", y="signal", hue="region", style = "event", )

• columns

sns.relplot(data=fmri_sub13, kind="line", x="timepoint", y="signal", hue="region", col="event", )

multiple subjects

• If multiple data exist for the same data points, the mean value and the confidence interval will be plotted

sns.relplot(data=fmri, kind="line", x="timepoint", y="signal", hue="region", col="event", )

Exercise

• based on the following fmri sub dataset, draw a spaghetti plot of signal with respect to time point for each subject

fmri_sub = fmri[(fmri["event"]=="stim") & (fmri["region"]=="parietal")]
fmri_sub.head()

##   subject  timepoint event    region    signal
## 0     s13         18  stim  parietal -0.017552
## 1      s5         14  stim  parietal -0.080883
## 2     s12         18  stim  parietal -0.081033
## 3     s11         18  stim  parietal -0.046134
## 4     s10         18  stim  parietal -0.037970

• solution

sns.relplot(data=fmri_sub, kind="line", x="timepoint", y="signal", hue="subject", )

histogram

• sns.displot
  • kind=“hist” by default
  • kind=“kde”: kernal density estimation
  • kind=“ecdf”: emperical cdf

# sns.displot(data=tips, x="total_bill")
sns.displot(data=tips, x="total_bill", col="time", kde=True)
## density function only 
## sns.displot(data=tips, x="total_bill", col="time", kind = "kde")

empirical cumulative distribution function

sns.displot(data=tips, kind="ecdf", x="total_bill", col="time", hue="smoker")

• rug: show each observation with marginal ticks

# sns.displot(data=tips, kind="ecdf", x="total_bill", col="time", hue="smoker", rug=True)

Visualizing categorical data

• Categorical scatterplots:
  • stripplot() (with kind=“strip”; the default)
  • swarmplot() (with kind=“swarm”)
• Categorical distribution plots:
  • boxplot() (with kind=“box”)
  • violinplot() (with kind=“violin”)
• Categorical estimate plots:
  • pointplot() (with kind=“point”)
  • barplot() (with kind=“bar”)
  • countplot() (with kind=“count”)

catplot (strip plot)

• strip plot: jitter plot

sns.catplot(data=tips, kind="strip", x="day", y="total_bill", hue="smoker")

sns.catplot(data=tips, kind="strip", x="day", y="total_bill", hue="smoker", dodge=True)
sns.stripplot(data=tips, x="day", y="total_bill", hue="smoker")

catplot (swarm plot)

• swarm plot

sns.catplot(data=tips, kind="swarm", x="day", y="total_bill", hue="smoker", dodge=True)

sns.catplot(data=tips, kind="swarm", x="day", y="total_bill", hue="smoker", )
sns.swarmplot(data=tips, x="day", y="total_bill", hue="smoker")

catplot (boxplot)

• boxplot
  • sns.catplot with kind = “box”
  • sns.boxplot

sns.catplot(data=tips, kind="box", x="day", y="total_bill", hue="smoker")
# sns.boxplot(data=tips, x="day", y="total_bill", hue="smoker")

overlay box plot and jitter plot

fig, ax = plt.subplots(figsize=(6, 4))
sns.stripplot(data=tips, x="day", y="total_bill", hue="smoker", dodge=True, ax=ax, legend=False)
sns.boxplot(data=tips, x="day", y="total_bill", hue="smoker", ax=ax)

catplot (violin)

• violin
  • sns.catplot with kind = “violin”
  • sns.violinplot

sns.catplot(data=tips, kind="violin", x="day", y="total_bill", hue="smoker")
# sns.violinplot(data=tips, x="day", y="total_bill", hue="smoker")

catplot (pointplot)

• pointplot: point estimate + error bar
  • sns.catplot with kind = “point”
  • sns.pointplot

sns.catplot(data=tips, kind="point", x="day", y="total_bill", hue="smoker")
# sns.pointplot(data=tips, x="day", y="total_bill", hue="smoker")

catplot (barplot)

• barplot:
  • sns.catplot with kind = “bar”
  • sns.barplot

sns.catplot(data=tips, kind="bar", x="day", y="total_bill", hue="smoker")
# sns.barplot(data=tips, x="day", y="total_bill", hue="smoker")
# sns.barplot(data=tips, x="day", y="total_bill", hue="smoker", ci=None)
# sns.barplot(data=tips, y="day", x="total_bill", hue="smoker") ## horizontal barplot

catplot (countplot)

• countplot
  • sns.catplot with kind = “count”
  • sns.countplot

sns.catplot(data=tips, kind="count", x="day", hue="smoker")
# sns.countplot(data=tips, x="day", hue="smoker")

iris dataset

• dataset for heatmap and pairplot

iris = sns.load_dataset("iris")
iris.head()

##    sepal_length  sepal_width  petal_length  petal_width species
## 0           5.1          3.5           1.4          0.2  setosa
## 1           4.9          3.0           1.4          0.2  setosa
## 2           4.7          3.2           1.3          0.2  setosa
## 3           4.6          3.1           1.5          0.2  setosa
## 4           5.0          3.6           1.4          0.2  setosa

pairplot

• to visualize the relationship between each pair of numerical variable

p1 = sns.pairplot(iris, hue="species")
#sns.pairplot(iris)
plt.show()

heatmap

iris_sub = iris.drop(columns="species")
sns.heatmap(iris_sub)

Reference

https://seaborn.pydata.org/tutorial