EDA on IMDB most popular Films and series

IMDB is a popular website for rating films and series. I always go there if I want to watch something new, and many users trust its rankings. The data is about the most famous 6k+ Films and series on IMDB with rates. The Data is Perfect for Exploratory Data Analysis. I got this data from Kaggle.
I would like to do EDA. Want to find answers to some interesting questions. Todo, I am using Pandas and NumPy for data storage and processing, and for visualization, I am using Matplotlib and Seaborn.

One of the best sources I came across to do all the mentioned above is Data Analysis with Python: Zero to Pandas. I learned the basics of Python, Pandas, NumPy, and visualization tools like Matplotlid and Seaborn.

Downloading the Dataset

The dataset have 6178 rows and 14 columns. Column description given below.

Name: Name of the film/series

Data: Creation date

Rate: IMDB's Rate

Votes: Number of voters

Genre: Genres , Actions , Drama, Romance, etc…

Duration: Duration of the episode , film

Type: whether it's film or series

Certificate:

    TV-Y: Designed to be appropriate for all children
    TV-Y7: Suitable for ages 7 and up
    G: Suitable for General Audiences
    TV-G: Suitable for General Audiences
    PG: Parental Guidance suggested
    TV-PG: Parental Guidance suggested
    PG-13: Parents strongly cautioned. May be Inappropriate for ages 12 and under.
    TV-14: Parents strongly cautioned. May not be suitable for ages 14 and under.
    R: Restricted. May be inappropriate for ages 17 and under.
    TV-MA: For Mature Audiences. May not be suitable for ages 17 and under.
    NC-17: Inappropriate for ages 17 and under

Episodes: Number of Episodes only for series

Nudity, Violence, Profanity, Alcohol, and Frightening :How much does it have of these

Let's begin by downloading the data, and listing the files within the dataset.