In this tutorial, we'll try to do data visualization using Plotly. Plotly, just like Seaborn and Matplotlib is another Python library to make graphs or charts, it can make interactive, publication-quality graphs online. We are using the Pokemon dataset that have been cleaned beforehand.¶

Table of Content¶

• Plotly polar chart
• Using polar chart to compare Pokemon
  

In this post, we'll still use the Pokemon dataset (cleaned version), but we'll focus on filtering and selecting data, two really important things that we need to learn when using Pandas. Fortunately, Pandas makes it really easy for us for doing these.¶

Table of Content¶

• Selecting subset of data
• Filtering dataframe
• Finding the strongest and lowest Pokemon with idxmax/idxmin and loc method
  

In this post, we'll take a look at the Pokemon dataset that is available in Kaggle and we'll try to have fun practicing data analysis and visualization using Pandas and Seaborn. We won't be doing some advanced analysis, just a simple analysis such as counting, filtering, cleaning the data, and some basic plotting techniques¶