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How to Use Plotly for More Insightful and Interactive Data Explorations
Case Study: Dynamic EDA for Qatar World Cup Teams
This article will introduce the tool, Plotly [1], that brings data visualization and exploratory data analysis (EDA) to the next level. You can use this open source graphing library to make your notebook more aesthetic and interactive, regardless if you are a Python or R user. To install Plotly, use the command !pip install — upgrade plotly.
We will use the “Historical World Cup Win Loose Ratio Data [2]” to analyze the national teams participated in Qatar World Cup 2022. The dataset contains the win, loose and draw ratio between each “country1-country2” pair, as shown below. For example, the first row gives us the information that among 7 games played between Argentina and Australia, the ratio of wins, looses and draws by Argentina was 0.714286, 0.142857 and 0.142857 respectively.
df = pd.read_csv('/kaggle/input/qatar2022worldcupschudule/historical_win-loose-draw_ratios_qatar2022_teams.csv')
In this exercise, we will utilize box plot, bar chart, choropleth map and heatmap for data visualization and…
