This blog post explains the steps required to create subplots and inset plots using Plotly in Python. It covers how to install the library, define data for main plot and inset plot, create a subplot grid with different sizes of each subplot, add data to main plot and inset plot, customize layout parameters such as title, axis labels etc., and display the final result.

To create subplots and inset plots using Plotly in Python, you need to follow the steps given below:

1. First, you need to install the Plotly library by running the following command in your Python terminal:

2. Once the library is installed, you need to import the required modules as shown below:

3. Next, you need to define the data for the main plot and the inset plot. For example, you can create a simple scatter plot as shown below:

4. Now, you need to create a subplot using the `make_subplots` function. This function allows you to define the dimensions of the subplot grid and the size of each subplot. For example, you can create a 2×2 subplot grid with the main plot taking up 75% of the space and the inset plot taking up 25% of the space as shown below:

5. Once the subplot has been created, you can add the data to the main plot and the inset plot by specifying the row and column numbers of the subplot as shown below:

6. Finally, you can customize the layout of the subplot by setting various parameters such as the title, axis labels, and annotations as shown below:

7. To display the subplot, you can use the `show()` function as shown below:

This is the basic process for creating subplots and inset plots using Plotly in Python. You can customize the size, shape, and layout of the subplots as per your requirement.

The basic process for creating subplots and inset plots using Plotly in Python has been outlined. It involves installing the library, importing the required modules, defining data for both main plot and inset plot, creating a subplot with `make_subplots` function, adding data to each of them by specifying row and column numbers of the subplot grid, customizing layout parameters such as title or annotations before finally displaying it with `show()` function. The size, shape and layout can be customized according to individual requirements.