| | A point chart is a type of data visualization that uses individual points to represent values in a dataset.
Each point in the data set represents a coordinate along the X, Y, or Z (in the case of 3D) axis on the chart where a point is plotted. Depending on
whether the Y and Z values are automatically generated, there are variations called XY Scatter Point, and XYZ Scatter Point. Optionally the points in the
series can have distinct filling or colors if computed from a palette. This is useful when a fourth dimension of data (color) is plotted with the point chart.
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 | Point Charts display 1, 2 or 3-dimensional data as points in a Cartesian coordinate system. Values are represented by the position of the points on the chart. Point series is mainly intended to display discrete sets of independent values. However, it may also be used as an alternative to line or bar series. |
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In XYZ scatter mode, Point series may be used for presentation of data that is irregularly spread throughout 3D space using custom x and z values for the data points. |  |
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 | Multiseries XYZ scatter Point charts are suitable for representing the relative 3D distribution of several different datasets. |
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In XY scatter mode, Point series may be used for presentation of data that is irregularly spread throughout 2D space using custom x values for the data points. The point series supports a special rendering mode called clustered rendering, which allows you to render very large datasets. Clustering works by identifying densely populated regions of data, which are later visualized by a lesser number of points. This reduces the number of visual points on the screen and therefore improves performance. |  |
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 | The point series supports a special rendering mode called clustered rendering, which allows you to render very large datasets. Clustering works by identifying densely populated regions of data, which are later visualized by a lesser number of points. This reduces the number of visual points on the screen and therefore improves performance. |
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Creating an effective point chart (or scatter point chart in case more than one variable is specified) involves adhering to several best practices to ensure
clarity, accuracy, and usability. Here are some key guidelines:
Keep it simple :
Avoid adding too many data points to the chart, as this will compromise its readability. For example, while the chart can handle easily hundreds of
thousand of data points, plotting such a dataset will most likely result in a point cloud. Consider using sampling or clustering techniques to reduce the number of
visible data points.
Use consistent colors:
If a fourth dimension of data is not required, you can use colors that have sufficient contrast from the chart background - for example darker colors
on a white background or vice versa. You should also avoid close colors if you plot different data sets as this can also compromise legibility.
Choose correct axis scale:
For datasets with values that differ drastically in magnitude, consider using a logarithmic scale to present the data more concisely.
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Creating an effective point chart (or scatter point chart in case more than one variable is specified) involves adhering to several best practices to ensure
clarity, accuracy, and usability. Here are some key guidelines:
Keep it simple :
Avoid adding too many data points to the chart, as this will compromise its readability. For example, while the chart can handle easily hundreds of
thousand of data points, plotting such a dataset will most likely result in a point cloud. Consider using sampling or clustering techniques to reduce the number of
visible data points.
Use consistent colors:
If a fourth dimension of data is not required, you can use colors that have sufficient contrast from the chart background - for example darker colors
on a white background or vice versa. You should also avoid close colors if you plot different data sets as this can also compromise legibility.
Choose correct axis scale:
For datasets with values that differ drastically in magnitude, consider using a logarithmic scale to present the data more concisely.
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