# Seaborn | Categorical Plots

Plots are basically used for visualizing the relationship between variables. Those variables can be either be completely numerical or a category like a group, class or division. This article deals with categorical variables and how they can be visualized using the **Seaborn library** provided by Python.

**Seaborn **besides being a statistical plotting library also provides some default datasets. We will be using one such default dataset called ‘tips’. The ‘tips’ dataset contains information about people who probably had food at a restaurant and whether or not they left a tip for the waiters, their gender, whether they smoke and so on.

Let us have a look at the tips dataset.

**Code**

`# import the seaborn libaray ` `import` `seaborn as sns ` ` ` `# import done to avoid warnings ` `from` `warnings ` `import` `filterwarnings ` ` ` `# reading the dataset ` `df ` `=` `sns.load_dataset(` `'tips'` `) ` ` ` `# first five entries if the dataset ` `df.head() ` |

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Now lets proceed onto the plots so that we can how we can visualize these categorical variables.

## Barplot

A **barplot** is basically used to aggregate the categorical data according to some methods and by default its the mean. It can also be understood as a visualization of the group by action. To use this plot we choose a categorical column for the x axis and a numerical column for the y axis and we see that it creates a plot taking a mean per categorical column.

**Syntax:**

barplot([x, y, hue, data, order, hue_order, …])

**Example:**

`# set the backgroud stle of the plot ` `sns.set_style(` `'darkgrid'` `) ` ` ` `# plot the graph using the default estimator mean ` `sns.barplot(x ` `=` `'sex'` `, y ` `=` `'total_bill'` `, data ` `=` `df, palette ` `=` `'plasma'` `) ` ` ` `# or ` `import` `numpy as np ` ` ` `# change the estimator from mean to standard devaition ` `sns.barplot(x ` `=` `'sex'` `, y ` `=` `'total_bill'` `, data ` `=` `df, ` ` ` `palette ` `=` `'plasma'` `, estimator ` `=` `np.std) ` |

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**Output:**

**Explanation/Analysis**

Looking at the plot we can say that the average total_bill for the male is more as compared to the female.

## Countplot

A countplot basically counts the categories and returns a count of their occurrences. It is one of the most simple plots provided by the seaborn library.

**Syntax:**

countplot([x, y, hue, data, order, …])

**Example:**

`sns.countplot(x ` `=` `'sex'` `, data ` `=` `df) ` |

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**Output:**

**Explanation/Analysis**

Looking at the plot we can say that the number of males is more than the number of females in the dataset. As it only returns the count based off a categorical column, we need to specify only the x parameter.

## Boxplot

A boxplot is sometimes known as the box and whisker plot.It shows the distribution of the quantitative data that represents the comparisons between variables. boxplot shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution i.e. the dots indicating the presence of outliers.

**Syntax:**

boxplot([x, y, hue, data, order, hue_order, …])

**Example:**

`sns.boxplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df, hue ` `=` `'smoker'` `) ` |

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**Output:**

**Explanation/Analysis –**

x takes the categorical column and y is a numerical column.Hence we can see the total bill spent each day.”hue” parameter is used to further add a categorical separation. By looking at the plot we can say that the people who do not smoke had a higher bill on Friday as compared to the people who smoked.

## Violinplot

It is similar to the boxplot except that it provides a higher, more advanced visualization and uses the kernel density estimation to give a better description about the data distribution.

**Syntax:**

violinplot([x, y, hue, data, order, …])

**Example:**

`sns.violinplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df, hue ` `=` `'sex'` `, split ` `=` `True` `) ` |

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**Output:**

**Explanation/Analysis –**

## Stripplot

It basically creates a scatter plot based on the category.

**Syntax:**

stripplot([x, y, hue, data, order, …])

**Example:**

`sns.stripplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df, ` ` ` `jitter ` `=` `True` `, hue ` `=` `'smoker'` `, dodge ` `=` `True` `) ` |

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**Output:**

**Explanation/Analysis –**

## Swarmplot

It is very similar to the stripplot except the fact that the points are adjusted so that they do not overlap.Some people also like combining the idea of a violin plot and a stripplot to form this plot. One drawback to using swarmplot is that sometimes they dont scale well to really large numbers and takes a lot of computation to arrange them. So in case we want to visualize a swarmplot properly we can plot it on top of a violinplot.

**Syntax:**

swarmplot([x, y, hue, data, order, …])

**Example:**

`sns.swarmplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df) ` |

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**Output:**

**Example:**

`sns.violinplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df) ` `sns.swarmplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df, color ` `=` `'black'` `) ` |

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**Output:**

## Factorplot

It is the most general of all these plots and provides a parameter called kind to choose the kind of plot we want thus saving us from the trouble of writing these plots separately. The kind parameter can be bar, violin, swarm etc.

**Syntax:**

sns.factorplot([x, y, hue, data, row, col, …])

**Example:**

`sns.factorplot(x ` `=` `'day'` `, y ` `=` `'total_bill'` `, data ` `=` `df, kind ` `=` `'bar'` `) ` |

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**Output:**

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