Data Analysis and Visualization with Python – 2

We continue to make visualizations on the Iris dataset I used in my previous article. There are 2 most frequently used libraries for data visualization. Of these libraries, matplotlib is known by many people, just as I know. In addition, our second library is seaborn. In this article, we will witness the visualization of data with the help of libraries.

🔐 You need to enter the link for the Colab link I use.

Data Visualization Libraries

1. Seaborn: Statistical Data Visualization Library

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface to draw attractive and informative statistical graphs. Visit the setup page to see how you can download the package and start using it.


We can say that the difference compared to Matplotlib is that it has more customization options.

Seaborn Samples

In the image I gave above, we see how we can visualize the data thanks to Seaborn. It is possible to display our data in many different graphics and forms.

2. Matplotlib: Visualization with Python

Matplotlib; it is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Matplotlib Logo

Matplotlib was originally written by John D. Hunter, has an active development community ever since.


Likewise, in the visual I have given here, there are visualization forms that can be made with Matplotlib.

🧷 Click on the link to view the plot, or graphics, in the Matplotlib library.

  • Line Plots: It shows the relationship between two variables in lines.

Line plots

  • Scatter Plots: As the name suggests, this relationship between two variables is shown as distributed points.

Scatter Plots

✨ I wanted to use the seaborn library to measure the relationship between the variables in the Iris data set.

Uploading Seaborn

After including the Seaborn library in our project, we provide the graph by entering various parameters. Here we have compared the relationship between sepal_length and petal_width attributes over dataframe. The cmap variable is the variable that determines the color palette we use in our chart. It can be changed upon request. The variables indicates the size of the points in the scatter chart given here as points.

Data Visulatizaton

We have come to the end of another article. Stay healthy ✨


  3. Machine Learning Days | Merve Noyan | Data Visualization | Study Jams 2 |,
  4. Matplotlib, Wikipedia, The Free Encyclopedia,








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