change axis labels set title and figure size to plots with seaborn

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Okay, let's dive into a comprehensive tutorial on customizing your plots with Seaborn, focusing on changing axis labels, setting titles, and adjusting figure sizes. We'll cover various techniques with detailed explanations and code examples.

*I. Introduction*

Seaborn is a powerful Python data visualization library built on top of Matplotlib. It provides a high-level interface for creating aesthetically pleasing and informative statistical graphics. Customization is crucial for effective data communication, and Seaborn provides flexibility to tailor your plots to your specific needs. We'll cover fundamental aspects of customizing your Seaborn plots, making them publication-ready and easy to understand.

*II. Prerequisites*

1. *Python:* Make sure you have Python installed (ideally version 3.6 or higher).

2. *Packages:* Install the necessary packages using pip:



*III. Dataset Setup*

For our examples, we'll use a few built-in Seaborn datasets. Let's load the `iris` dataset for demonstrating basic plot customization and the `tips` dataset for more elaborate examples.



*IV. Changing Axis Labels*

Seaborn plots inherit their foundation from Matplotlib. Thus, you can use Matplotlib's functions to manipulate the axis labels. We'll explore different ways to achieve this.

*A. Using Matplotlib directly:*

This is the most common and flexible method. After creating a Seaborn plot, use `plt.xlabel()` and `plt.ylabel()` to set the axis labels.



*Explanation:*

1. We create a scatter plot using `sns.scatterplot()` with the 'sepal_length' on the x-axis and 'sepal_width' on the y-axis.

2. `plt.xlabel("Sepal Length (cm)")` sets the label for the x-axis.
3. `plt.ylabel("Sepal Width (cm)")` sets the label for the y-axis.
4. `plt.show()` displays the plot. This is crucial because Matplotlib often requires this to display the plot after making changes.

*B. Using Seaborn's `set()` function:*

Seaborn's `set()` function ...

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