Error Bars
For functions representing 2D data points such as px.scatter, px.line, px.bar, etc., error bars are given as a column name which is the value of the error_x (for the error on x position) and error_y (for the error on y position). Error bars are the graphical presentation alternation of data and used on graphs to imply the error or uncertainty in a reported capacity.
Example:
Python3
import plotly.express as px # using the iris dataset df = px.data.iris() # Calculating the error field df[ "error" ] = df[ "petal_length" ] / 100 # plotting the scatter chart fig = px.scatter(df, x = "species" , y = "petal_width" , error_x = "error" , error_y = "error" ) # showing the plot fig.show() |
Output:
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