Plotly allows you to generate graphs offline and save them in local machine. The plotly.offline.plot function creates a standalone HTML that is saved locally and opened inside your web browser. Use plotly.offline.iplot when working offline in a Jupyter Notebook to display the plot in the notebook. Styling Outliers¶. The example below shows how to use the boxpoints argument. If 'outliers', only the sample points lying outside the whiskers are shown. If 'suspectedoutliers', the outlier points are shown and points either less than 4Q1-3Q3 or greater than 4Q3-3Q1 are highlighted (using outliercolor). Draw a scatter plot with possibility of several semantic groupings. The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets. Image Scatter Plot for Large Datasets Produces an image scatter plot of large datasets where the colors encode the density of the points in the scatter plot. Works also with factors. A scatter chart shows the relationship between two numerical values. A bubble chart replaces data points with bubbles, with the bubble size representing an additional third data dimension. Scatter charts are a great choice: To show relationships between two numerical values.

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This chapter will give information about the three-dimensional (3D) Scatter Plot and 3D Surface Plot and how to make them with the help of Plotly.

3D Scatter Plot

A three-dimensional (3D) scatter plot is like a scatter plot, but with three variables - x, y, and z or f(x, y) are real numbers. The graph can be represented as dots in a three-dimensional Cartesian coordinate system. It is typically drawn on a two-dimensional page or screen using perspective methods (isometric or perspective), so that one of the dimensions appears to be coming out of the page.

3D scatter plots are used to plot data points on three axes in an attempt to show the relationship between three variables. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X, Y, and Z axes.

A fourth variable can be set to correspond to the color or size of the markers, thus, adding yet another dimension to the plot. The relationship between different variables is called correlation.

Scatter Plot Iplot

A Scatter3D trace is a graph object returned by go.Scatter3D() function. Mandatory arguments to this function are x, y and z each of them is a list or array object.

For example −

The output of the code is given below −

3D Surface Plot

Surface plots are diagrams of three-dimensional data. In a surface plot, each point is defined by 3 points: its latitude, longitude, and altitude (X, Y and Z). Rather than showing the individual data points, surface plots show a functional relationship between a designated dependent variable (Y), and two independent variables (X and Z). This plot is a companion plot to the contour plot.

Pandas Plot Scatter

Here, is a Python script to render simple surface plot where y array is transpose of x and z is calculated as cos(x2+y2)

Pyplot 3d Scatter

Below mentioned is the output of the code which is explained above −