An Introduction Into Advanced Visualizations With Plotly Express
Visualizations can be the most important part of a data science project. Matplotlib as well as Seaborn are both very common and most of the time are the go to libraries to use when you need to visualize something. Well it’s time to introduce Plotly Express, this extension is a built-in part of the Plotly library. Plotly Express provides more than 30 functions for creating different types of figures. In this post I will discuss how to use this library as well cover it’s creatively unique functionalities.
To get started we will need to import the package, we will also need some data to work with. For this example we will work with the iris dataset. The iris dataset is the Hello World for the Data Science, so if you have started your career in Data Science and Machine Learning you will be practicing basic ML algorithms on this famous dataset. Iris dataset contains five columns such as Petal Length, Petal Width, Sepal Length, Sepal Width and Species Type. I will complete these steps in the code cell found below.
import plotly.express as px
import plotly.graph_objects as godf = px.data.iris()
df.head()
Now, I will get started discussing the implementation as well as use of scatter, line, area and bar charts that are all included in this library.
