3D Scatter Plot Plotly
3D Scatter Plot can plot two-dimensional graphics that can be enhanced by mapping up to three additional variables while using the semantics of hue, size, and style parameters. All the parameter control visual semantic which are used to identify the different subsets. Using redundant semantics can be helpful for making graphics more accessible. It can be created using the scatter_3d function of plotly.express class.
Example:
Python3
import plotly.express as px # Data to be plotted df = px.data.iris() # Plotting the figure fig = px.scatter_3d(df, x = 'sepal_width' , y = 'sepal_length' , z = 'petal_width' , color = 'species' ) fig.show() |
Output:
Refer to the below articles to get detailed information about the 3D scatter plot.
Plotly tutorial
Plotly library in Python is an open-source library that can be used for data visualization and understanding data simply and easily. Plotly supports various types of plots like line charts, scatter plots, histograms, box plots, etc. So you all must be wondering why Plotly is over other visualization tools or libraries. So here are some reasons :
- Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.
- It is visually attractive and can be accepted by a wide range of audiences.
- Plotly generally allows us endless customization of our graphs and makes our plot more meaningful and understandable for others.
This tutorial aims at providing you the insight about Plotly with the help of the huge dataset explaining the Plotly from basics to advance and covering all the popularly used charts.
Table of Content
- How to install Plotly?
- Package Structure of Plotly
- Getting Started
- Creating Different Types of Charts
- Heatmaps
- Error Bars
- 3D Line Plots
- 3D Scatter Plot Plotly
- 3D Surface Plots
- Interacting with the Plots
- Adding Buttons to the Plot
- Creating Sliders and Selectors to the Plot
- More Plots using Plotly
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