# 8 Visualizations with Python to Handle Multiple Time-Series Data

## 8 Visualizations with Python to Handle Multiple Time-Series Data

This article demonstrates 8 visualizations with Python to handle time-series data. A single-line time-series data or plot is a useful graph for expressing data with a long series. It’s a data visualization tool that displays data at different times at successive intervals. The line graph uses points that connect with lines.

## 8 Visualizations with Python to Handle Time-Series Data

The X-axis (horizontal axis) of the graph represents the values of the time. The Y-axis (vertical axis) represents the values of the variables. This time-series plot is easy to understand and straightforward. One common application of a time-series plot is understanding trends and seasonal events.

One problem with a time-series plot is too many lines make it hard to read. This brings us to the aim of this article: to describe some visualization ideas. There’s Python code samples that helps handle overlapping time series data.

## Making interactive graphs/charts

Interactive charts help zoom in on the area with overlapping lines. use Plotly to make interactive charts in Python.

## Comparing one by one with many, small time-series

Use Seaborn to do many, small time series. Here, the lines are plotted one by one and compared with the silhouette or outline of the other links.

## Changing the point of view using Facetgrid

Facetgrid can make multi-plot grids. In this case, the “month” and “year” values are represented vertically and horizontally.

## Using color with Heat Map

A heat map is used to represent the data in a 2D chart which shows the values in different colors. The time values are on the horizontal axis and the other group of variable data are on the vertical axis. The color differences are what helps in distinguishing between groups.

## Using a Radar Chart to apply angles

Using Plotly to set the angular axis on the scatter plot will give an interactive Radar chart. This Radar chart displays multifaceted data in the form of a 2D chart of quantitative variables represented on the X and Y-axis.

## Using Circular Bar Plot or Race Track Plot

This is a bar plot but in a circle. It’s plotted monthly and then a photo collage compares the process along the time. It’s a good way of getting attention but it is hard to compare between choices.

## Using Radial Plot to start from the center

This is based on the bar charts as well and it uses polar coordinates. A radial plot is useful when comparing categories that are far away from each other. It is also a good way to get attention.

## Sharing densities with overlapping densities (Ridge plot)

A ridge plot, which is also called overlapping densities, is used with many time-series data by setting an axis as a timeline. This can also be used to grab people’s attention.

View this post here https://towardsdatascience.com/.