A box plot graphically displays the spread of data in a dataset. You can use this tool to see important numbers and how the data is spread out.

A box plot shows a dataset's shape, range, and possible outliers. "Box-and-whisker" and "box-and-whisker-with-line" are not distinct types of boxplots. "Box-and-whisker plot" and "boxplot" are often used interchangeably. Let me clarify:

**Boxplot:**This is a used graphical representation of the distribution of a dataset. The summary shows important statistics, like the median and any unusual values.**Modified Boxplot:**It can be well-modified to display more information. One way to modify it is by adding lines from the box to the lowest and highest values instead of using whiskers.**Notched Boxplot:**A notched boxplot is a variation of the standard boxplot. It includes a "notch" in the boxes to help compare medians of different groups. The notches are well-made around the middle. Suppose two boxes' notches don't overlap. It suggests that the medians are different at a certain level of confidence.

A box plot shows data spread out, using quartiles and median values. Seaborn boxplots show how data is spread out, including quartiles and medians. If you want the plot colors to match the input color, use desaturated colors for large patches. Set dodge to True when using hue nesting to separate the points for different hue levels. We will use the Titanic dataset from the Seaborn library to create our box plots. We will use the Titanic dataset from the Seaborn Library to draw our box plots.

Here is an example of how to use the boxplot method of Seaborn:

Fig: Preview of the output that you will get on running this code from your IDE.

### Code

In this solution, we are using Pandas, Matplotlib, NumPy & Seaborn libraries.

### Instructions

__Follow the steps carefully to get the output quickly.__

- Install PyCharm Community Edition on your computer.
- Open the terminal and install the required libraries with the following commands.
- Install Seaborn - pip install seaborn.
- Install Pandas - pip install pandas.
- Install Matplotlib - pip install matplotlib.
- Install NumPy - pip install numpy.
- Create a new Python file(eg: test.py).
- Copy the snippet using the 'copy' button and paste it into that file(remove output from the code).
- Run the file using the run button.

I hope you found this helpful. I have added the link to dependent libraries, and version information in the following sections.

*I found this code snippet by searching for **'single boxplot using seaborn'* *in kandi. You can try any such use case!*

### Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

- The solution is created in PyCharm 2022.3.3.
- The solution is tested on Python 3.9.7.
- Pandas v2.0.2.
- NumPy v1.25.0.
- Matplotlib v3.7.1.
- Seaborn v0.12.2.

Using this solution, we are able to use the boxplot method of Seaborn with simple steps. This process also facilitates an easy, hassle-free approach to creating a hands-on working version of code which would help us to use the boxplot method of Seaborn.

### Dependent Libraries

matplotlibby matplotlib

matplotlib: plotting with Python

matplotlibby matplotlib

Python 17559 Version:v3.7.1 License: No License

numpyby numpy

The fundamental package for scientific computing with Python.

numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

pandasby pandas-dev

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandasby pandas-dev

Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)

seabornby mwaskom

Statistical data visualization in Python

seabornby mwaskom

Python 10797 Version:v0.12.2 License: Permissive (BSD-3-Clause)

*You can also search for any dependent libraries on kandi like **'Pandas'**, **'NumPy'**, '**Matplotlib' **and **'Seaborn'.*

**FAQ:**

**1. What is a box and whisker plot, and how is it different from a Seaborn Box Plot?**

A box plot shows how data is spread out and summarizes important information. It displays the median, quartiles, and potential outliers compactly. You can use it to group your data, spread it out, and identify unusual values.

The Seaborn library in Python creates a Seaborn Box Plot for data visualization. Seaborn is built-in on top of Matplotlib. It provides a higher-level interface for creating pleasing and informative statistical graphics.

**2. What are the steps to creating a simple box plot with Seaborn in Python?**

Creating a simple box plot using Seaborn in Python is straightforward. Seaborn is a powerful data visualization library built on top of Matplotlib. It is well-designed to make creating attractive and informative statistical graphics easier.

Here's how you can create a simple box plot using Seaborn:

- Install Seaborn
- Import Libraries
- Create Data
- Create the Box Plot
- Add Labels and Titles
- Show the Plot

**3. What is the Python Library used to Create Interactive Plots for Data Analysis?**

The data analyst uses the Python library to create interactive plots. You can make many interactive visuals, like line charts, scatter plots, and 3D plots. It provides an easy-to-use interface for generating interactive plots. It can be well-displayed in web browsers or embedded into web applications.

Another popular option is **Bokeh**, another interactive visualization library for Python. Bokeh is well-designed to create interactive, web-ready visualizations in Python code.

**4. How do I use matplotlib colors within my Seaborn Box Plot?**

Seaborn is built-in on top of Matplotlib. It provides a higher-level interface for creating attractive and informative statistical graphics. To customize the appearance of your Seaborn box plot, use Matplotlib colors. To do so, you can either use the color palettes provided by Seaborn or specify Matplotlib colors.

**5. What is the dictionary mapping that allows me to change hue levels when creating a seaborn Box Plot?**

Seaborn's **boxplot** function allows you to create box plots. Use the hue parameter to make different box plots for categories in your data. But, the **hue** parameter doesn't control the box plots' hue (color) levels. The tool separates your data into groups. It generates box plots for each group.

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