Interacting with the Plots

Plotly provides various tools for interacting with the plots such as adding dropdowns, buttons, sliders, etc. These can be created using the update menu attribute of the plot layout. Let’s see how to do all such things in detail.

Creating Dropdown Menu in Plotly

A drop-down menu is a part of the menu-button which is displayed on a screen all the time. Every menu button is associated with a Menu widget that can display the choices for that menu button when clicked on it. In plotly, there are 4 possible methods to modify the charts by using update menu method.

restyle: modify data or data attributes
relayout: modify layout attributes
update: modify data and layout attributes
animate: start or pause an animation

Example:

Python3

import plotly.graph_objects as px
import numpy as np
# creating random data through randomint
# function of numpy.random
np.random.seed(42)
 
# Data to be Plotted
random_x = np.random.randint(1, 101, 100)
random_y = np.random.randint(1, 101, 100)
 
plot = px.Figure(data=[px.Scatter(
    x=random_x,
    y=random_y,
    mode='markers',)
])
 
# Add dropdown
plot.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(
                    args=["type", "scatter"],
                    label="Scatter Plot",
                    method="restyle"
                ),
                dict(
                    args=["type", "bar"],
                    label="Bar Chart",
                    method="restyle"
                )
            ]),
            direction="down",
        ),
    ]
)
 
plot.show()

Output:

Output

In the above example we have created two graphs for the same data. These plots are accessible using the dropdown menu.

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

Interacting with the Plots

Creating Dropdown Menu in Plotly

Python3

Plotly tutorial

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