Line graph

A line graph is a type of data visualization that displays information as a series of data points, or markers, connected by straight lines. Line graphs are particularly useful for showing trends and changes in data over time or across different categories. They consist of two axes: the horizontal x-axis represents the independent variable (e.g., time, categories) and the vertical y-axis represents the dependent variable (e.g., quantities, values).

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Functional grouping: Text and data


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Common use cases for line graphs

  • Trend Analysis: Line graphs are excellent for visualizing trends in data over time. For example, they can be used to track changes in stock prices, population growth, temperature fluctuations, or sales figures over a period of time.
  • Comparing Multiple Data Sets: Line graphs can be used to compare multiple sets of data on the same graph. Each set of data is represented by a different line, making it easy to compare trends and identify patterns across different variables.
  • Forecasting and Predictive Analysis: Line graphs can be used to forecast future trends based on historical data. By analyzing the trajectory of the lines, analysts can make predictions about future outcomes and plan accordingly.
  • Performance Monitoring: Line graphs are commonly used in business and finance to monitor the performance of key metrics such as revenue, profit, customer satisfaction, or website traffic over time. They provide a visual representation of how these metrics are trending and whether they are meeting targets or benchmarks.
  • Process Improvement: Line graphs can be used to track the performance of processes or systems and identify areas for improvement. By analyzing changes in performance over time, organizations can pinpoint bottlenecks, inefficiencies, or areas where resources can be better allocated.


Use Clear and Descriptive Titles: Make sure your graph title clearly explains what the data represents.

Label Axes Properly: Clearly label both the x-axis and y-axis with appropriate units of measurement.

Include a Legend: If you have multiple lines representing different categories or variables, include a legend to explain what each line represents.

Provide Context: Provide context or background information to help readers understand the significance of the data being presented.

Keep it Simple: Avoid cluttering the graph with unnecessary information. Keep it clean and easy to interpret. Five is the  advisable maximum amount of data points.


  • Overcrowd the Graph: Avoid including too many lines or data points on the graph, as it can make it difficult to interpret. Five is the advisable maximum amount. 
  • Misleading Scales: Avoid using scales that distort the data or make the differences between data points appear larger or smaller than they actually are.
  • Omitting Labels: Don't forget to label data points or include annotations to provide additional context when necessary.
  • Inconsistent Time Intervals: If your line graph represents data over time, make sure the time intervals are consistent throughout the graph.
  • Misinterpreting Correlation as Causation: Avoid making assumptions about causation based solely on correlation displayed in the graph. Always consider other factors and context.