# Connected Scatterplot

A connected scatterplot is a line chart where each data point is shown by a circle or any type of marker. This section explains how to build a connected scatterplot with `Python`

, using both the `Matplotlib`

and the `Seaborn`

libraries.

## ⏱ Quick start

```
# library
import seaborn as sns
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(np.random.random((5,5)), columns=["a","b","c","d","e"])
# Default heatmap
p1 = sns.heatmap(df)
```

## ⚠️ Two types of connected scatterplot

There are two types of connected scatterplot, and it often creates confusion.

The __first__ is simply a lineplot with dots added on top of it. It takes as input 2 numeric variables only. The __second__ shows the relationship between 2 numeric variables across time. It requires 3 numeric variables as input.

Confusing? Visit data-to-viz to clarify..

## Connected scatterplot with `Seaborn`

Building a connected scatterplot with `Seaborn`

looks pretty much the same as for a line chart, so feel free to visit the related section. Here are a few examples to remind the basics and understand how to customize the markers.

## Connected scatterplot with `Matplotlib`

As for scatterplots, `Matplotlib`

will help us build a bubble plot thanks to the the `plt.scatter()`

function. This function provides a `s`

parameter allowing to pass a third variable that will be mapped to the markers size.

Cheatsheet: line customization with `Matplotlib`

and the `linestyle`

parameter.

## Connected scatterplot for 2 variables

As explained above, a connected scatterplot can also be base on 3 numeric variables. It allows to study the evolution of 2 variables (placed on the X and on the Y axis).

## Contact

👋 This document is a work by Yan Holtz. Any feedback is highly encouraged. You can fill an issue on Github, drop me a message onTwitter, or send an email pasting `yan.holtz.data`

with `gmail.com`

.