1-Distribution Plots. We can compare the distribution plot in Seaborn to histograms in Matplotlib. They both offer pretty similar functionalities. Instead of frequency plots in the histogram, here we’ll plot an approximate probability density across the y-axis.

2-Pie Chart & Bar Chart. Pie Chart is generally used to analyze the data on how a numeric variable changes across different categories. In the dataset we are using, we’ll analyze how the top 4 categories in the Content Rating column is performing.

3-Scatter Plots. Up until now, we have been dealing with only a single numeric column from the dataset, like Rating, Reviews or Size, etc. But, what if we have to infer a relationship between two numeric columns, say “Rating and Size” or “Rating and Reviews”.

4-Pair Plots. Pair Plots are used when we want to see the relationship pattern among more than 3 different numeric variables. For example, let’s say we want to see how a company’s sales are affected by three different factors, in that case, pair plots will be very helpful.

Univariate → “one variable” data visualization

1.Strip Plot

2.Grouping with Strip Plot

3.Swarm Plot

4.Box and Violin Plot

Bivariate → “two variable” data visualization

1.Joint Plot

2.Density Plot

3.Pair Plot