These are some of the problems I encountered and the solutions I found when doing the project.：

The size of your plot isn’t changing because you’re using sns.displot() (a Seaborn figure-level function) after setting the figure size with plt.figure().

The Issue:

• plt.figure(figsize=(11,4)) creates a new Matplotlib figure with the specified size.
• However, sns.displot() is a figure-level Seaborn function, which means it creates its own new figure when called, ignoring any existing figures (including the one you just created with plt.figure()).

Solutions:

1. Use sns.histplot() (axes-level function) instead of sns.displot():

   plt.figure(figsize=(11, 4))
   sns.histplot(data=Rotten_Diff, kde=True)
   plt.xlabel("Rotten_Diff")
   plt.title("RT Critics Score minus RT User Score")
   plt.show()

   sns.histplot() is an axes-level function and will respect the figure size you set with plt.figure()

Key Difference:

• Figure-level functions (sns.displot, sns.catplot, sns.relplot, etc.) create their own figure and ignore plt.figure(). You control their size with height and aspect.
• Axes-level functions (sns.histplot, sns.scatterplot, sns.barplot, etc.) draw on the currently active Matplotlib axes (or create one if none exists). You can control their size with plt.figure(figsize=(width, height)).

If you want to get the top 5 values from one column and see their associated values in another column, you can use Pandas operations like nlargest() or sort_values(). Here’s how to do it:

Example Scenario:

• Suppose you have a DataFrame df with two columns:

  • column_A: The column from which you want the top 5 values.

  • column_B: The column whose associated values you want to extract.

Methods to Achieve This:

1. Using nlargest() (Recommended for Top N Values)

top_5 = df.nlargest(5, 'column_A')[['column_A', 'column_B']]

• This directly gives the rows with the 5 largest values in column_A, along with their corresponding column_B values.