Relational plots in Seaborn – Part I

Relational plots are used for visualizing the statistical relationship between the data points. Visualization is necessary because it allows the human to see trends and patterns in the data. The process of understanding how the variables in the dataset relate each other and their relationships are termed as Statistical analysis.

Seaborn, unlike to matplotlib, also provides some default datasets. In this article, we will be using a default dataset named ‘tips’. This dataset gives information about people who had food at some restaurant and whether they left tip for waiters or not, their gender and whether they do smoke or not, and more.

Let us have a look to the dataset.

 `# importing the library ` `import` `seaborn as sns ` ` `  `# reading the dataset ` `data ``=` `sns.load_dataset(``'tips'``) ` ` `  `# printing first five entries ` `print``(data.head())`

Output :

```   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
```

To draw the relational plots seaborn provides three functions. These are:

• relplot()
• scatterplot()
• lineplot()

Seaborn.relplot()

This function provides us the access to some other different axes-level functions which shows the relationships between two variables with semantic mappings of subsets.

Syntax :

`seaborn.relplot(x=None, y=None, data=None, **kwargs) `

Parameters :

Parameter Value Use
x, y numeric Input data variables
Data Dataframe Dataset that is being used.
hue, size, style name in data; optional Grouping variable that will produce elements with different colors.
kind scatter or line; default : scatter defines the type of plot, either scatterplot() or lineplot()
row, col names of variables in data; optional Categorical variables that will determine the faceting of the grid.
col_wrap int; optional “Wrap” the column variable at this width, so that the column facets span multiple rows.
row_order, col_order lists of strings; optional Order to organize the rows and columns of the grid.
palette name, list, or dict; optional Colors to use for the different levels of the hue variable.
hue_order list; optional Specified order for the appearance of the hue variable levels.
hue_norm tuple or Normalize object; optional Normalization in data units for colormap applied to the hue variable when it is numeric.
sizes list, dict, or tuple; optional determines the size of each point in the plot.
size_order list; optional Specified order for appearance of the size variable levels
size_norm tuple or Normalize object; optional Normalization in data units for scaling plot objects when the size variable is numeric.
legend “brief”, “full”, or False; optional If “brief”, numeric hue and size variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If False, no legend data is added and no legend is drawn.
height scalar; optional Height (in inches) of each facet.
Aspect scalar; optional Aspect ratio of each facet, i.e. width/height
facet_kws dict; optional Dictionary of other keyword arguments to pass to FacetGrid.
kwargs key, value pairings Other keyword arguments are passed through to the underlying plotting function.

Example 1: Visualizing the most basic plot to show all the data points in tips dataset.

 `# importing the library ` `import` `seaborn as sns ` ` `  `# selecting style ` `sns.``set``(style ``=``"ticks"``) ` ` `  `# reading the dataset ` `tips ``=` `sns.load_dataset(``'tips'``) ` ` `  `# plotting a simple visualiation of data points ` `sns.relplot(x ``=``"total_bill"``, y ``=``"tip"``, data ``=` `tips) `

Output :

Example 2 : Grouping data points on the basis of category, here as time.

 `# importing the library ` `import` `seaborn as sns ` ` `  `# selecting style ` `sns.``set``(style ``=``"ticks"``) ` ` `  `# reading the dataset ` `tips ``=` `sns.load_dataset(``'tips'``) ` ` `  `sns.relplot(x``=``"total_bill"``, ` `            ``y``=``"tip"``, ` `            ``hue``=``"time"``, ` `            ``data``=``tips) `

Output :

Example 3 : using time and sex for determining the facet of the grid.

 `# importing the library ` `import` `seaborn as sns ` ` `  `# selecting style ` `sns.``set``(style ``=``"ticks"``) ` ` `  `# reading the dataset ` `tips ``=` `sns.load_dataset(``'tips'``) ` ` `  `sns.relplot(x``=``"total_bill"``,  ` `            ``y``=``"tip"``, ` `            ``hue``=``"day"``, ` `            ``col``=``"time"``, ` `            ``row``=``"sex"``, ` `            ``data``=``tips) `

Output :

Example 4 : using size attribute, we can see data points having different size.

 `# importing the library ` `import` `seaborn as sns ` ` `  `# selecting style ` `sns.``set``(style ``=``"ticks"``) ` ` `  `# reading the dataset ` `tips ``=` `sns.load_dataset(``'tips'``) ` ` `  `sns.relplot(x``=``"total_bill"``,  ` `            ``y``=``"tip"``, ` `            ``hue``=``"day"``, ` `            ``size``=``"size"``, ` `            ``data``=``tips) `

Output :

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