Data Visualization

⭐ Enjoying real beauty of programming ⭐

Basic information

Looking at the world today, we can say WOW to the amount of data that's being generated on a daily basis. If you're given big data in a raw format and asked to find some trends, it would be possible to see in what condition you will be at that moment 😁

-> For the rescue, there comes power - Data Visualization (💪)

With its help, we can understand almost all data since that's given in pictorial representation in an organized way.

Our friend Python, gives us enough chance by providing a plenty of libraries with various features. I haven't yet mentioned about the awesome graphs...

If you're ready, let's discover the worlds of Plotly, Seaborn, and Matplotlib 🚀

Oooh, wait for a sec 🖐️

Let's install those libraries. Are you familiar with our old friend - pip?

Matplotlib

You're already familiar with NumPy, right?

Well, matplotlib is built on NumPy arrays and it's low-level data visualization library in Python. Matplotlib includes wonderful plots, and you are having a chance of using them from now on.

Popular plots in matplotlib

• Scatter Plot - for observing relationships between variables using dots
• Line Chart - for representinf a relationship between two data X and Y on a different axis
• Bar Chart - for representing the category of data with rectangular bars with lenghts and heights that are proportional to the values which they represent
• Histogram - for representing data in the form of some groups.

More information setting up an environment with matplotlib

• Environment Setup for Matplotlib
• Using Matplotlib with Jupyter Notebook

Seaborn

Let's move a little higher!

Seaborn is a high-level interface built on top of the Matplotlib (it can be used with Matplotlib as well). It gives us an opportunity to play with beautiful design styles and color palettes to make atrractive graphs.

Using them together is an easy process. We just have to invoke the Seaborn Plotting function as normal, and then we can use Matplotlib's customization function.

ℹ️ Seaborn comes loaded with dataset such as iris, tips, etc...

Plotly

It allows us to detect any outliers or anomalies in numerous data points with the help of tool capabilities. With it, we can have more customization and it makes the graph more attractive.

Plotly also provides different interactions such as creating dropdown menu, adding buttons, creating sliders, and selectors

You can enjoy the functionalities by looking at the materials provided in the following section ✅

Thank you for your time! We're glad that you are learning with us 😉

• More about pip
• Matplotlib: Visualization with Python
• seaborn: statistical data visualization
• Getting Started with Plotly in Python

• Matplotlib Tutorials (Corey Schafer)
• Seaborn Beginner to Pro | Seaborn Tutorial for Beginners | Seaborn Playlist (Learnerea)
• Plotly Python Tutorials - data visualization in python (Data Science Tutorials)
• Plotly | YouTube Channel

• Almost all materials have been provided to you with the help of GeeksforGeeks