A violinplot is like a boxplot but it does a better job of showing the density of the data. Using the built in tips dataset, here is how to create a violinplot of the total bill by day for males and females.

```python
# import the seaborn libaray
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')

# create violinplot
# set variables for each axis
# split = True draws half of the violin for each group 
sns.violinplot(x='day', y='total_bill', data=df,
               hue='sex', split=True)
```

University of Michigan - Ann Arbor

- Stripplot
- Swarmplot
- Barplot
- Countplot
- Boxplot
- Violinplot

Examples of Seaborn Plots

https://www.geeksforgeeks.org/plotting-graph-using-seaborn-python/

Seaborn Plotting Examples

A stripplot is a scatterplot of categorical data. Using seaborn's built in iris dataset, here is how to create a stripplot of sepal length by species.

```python
# import needed modules
import matplotlib.pyplot as plt
import seaborn as sns

# use to set style of background of plot
sns.set(style="whitegrid")
 
# loading data-set
iris = sns.load_dataset('iris')
 
# plotting strip plot with seaborn
# set which variables on each axis
ax = sns.stripplot(x='species', y='sepal_length', data=iris)
 
# set the graph's title 
plt.title('Graph')
 
# function to show plot
plt.show()
```


 Seaborn Stripplot

A swarmplot and a stripplot are similar except a swarmplot will not overlap points like a stripplot does. Using seaborn's built in iris dataset, here is how to create a stripplot of sepal length by species.

```python
# import needed modules
import matplotlib.pyplot as plt
import seaborn as sns

# use to set style of background of plot
sns.set(style="whitegrid")
 
# loading data-set
iris = sns.load_dataset('iris')
 
# plotting strip plot with seaborn
# set which variables on each axis
ax = sns.swarmplot(x='species', y='sepal_length', data=iris)
 
# set the graph's title 
plt.title('Graph')
 
# function to show plot
plt.show()
```

Seaborn Swarmplot 

A barplot groups categorical data and uses some form of aggregation (mean by default) to create a visualization. Using the built in tips dataset, here is how to create a barplot of the mean total bill by sex. 

```python
# import the seaborn library
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
 
# plot barplot with seaborn
# set variables for each axis
# palette changes bar's colors
sns.barplot(x ='sex', y ='total_bill', data = df, 
            palette ='plasma')
```

Seaborn Barplot

A countplot counts number of occurences in a certain category. Using the built in tips dataset, here is how to create a countplot of sex.

```python
# import the seaborn libaray
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
 
# create countplot
sns.countplot(x ='sex', data = df)
```

Seaborn Countplot

A boxplot takes quantitative data by groups and displays it through quartiles while also showing any outliers in the data. Using the built in tips dataset, here is how to create a boxplot of total bill by day for smokers and non smokers.
```python

# import the seaborn libaray
import seaborn as sns
 
# reading the dataset
df = sns.load_dataset('tips')
 
# create boxplot
# hue changes color of boxplot based on whether smoker on not
sns.boxplot(x='day', y='total_bill', data=df, hue='smoker')
```

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