Reporting & Dashboarding: When to use a Line Chart?

Jupyter Data
2 min readJul 18, 2017

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The best way to express your data is through a nice visual representation. But, for your representation to be effective, it is important to choose the proper visualization form.

Line charts are useful when you aim at visualizing continuous data (data which has been measured related to time, distance or another continuous variable). For example, to display the profit a company is doing over a lapse of time.

This kind of data visualization is especially relevant to:

Follow the evolution of your data: Example: I have an Excel file with data about sales, which I want to dig into. Thanks to the line chart below I can easily observe how my sales are evolving from January to December. To build my charts I am now using JetPack Data, a very intuitive tool, for effective reporting and dashboarding.

Click here to interact yourself with this line graph

Compare various data together: Line charts enable you to compare various data on the same graph: for example, here, I can easily compare the amount of sales for two selected products, VTT and Paseo.

Identify trend, cycle & pattern and relationship in your data: Thanks to my line chart I can detect trends and cycles in my data.

This line charts can be built with Excel Pivot table functions, but it may take a while…. Or you can save time, and build your own charts instantaneously from your data, using JetPack Data, the fastest visualization tool. The quick demo below shows how JetPack Data can be used.

I hope this short article can help you with line chart.

You can create your own charts, explore and visualize your own data with Jetpack Data.

Thomas.C

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Jupyter Data
Jupyter Data

Written by Jupyter Data

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