COMP93211-Assignment 1 Solved

The assignment data has been extracted from a Movie dataset on Kaggle (https://www.kaggle.com/rounakbanik/the-

movies-dataset) , with some minor modification to make things interesting. The dataset is split into two CSV files credits

(https://github.com/mysilver/COMP9321-Data-Services/raw/master/20t1/credits.csv) and movies

(https://github.com/mysilver/COMP9321-Data-Services/raw/master/20t1/movies.csv) . Use the datasets to answer the following questions:

Join the two datasets based on the "id" columns in the datasets, keeping the rows as long as there is a match between the id columns of both dataset (do not concatenate the datasets).

Question 2: ( based on the dataframe created in Question-1 ) ( 0.5 Mark ) 

Keep the following columns in the resultant dataframe (remove the rest of columns from the result dataset): ' 'id', title', 'popularity', 'cast', 'crew', 'budget', 'genres', 'original_language', 'production_companies',

'production_countries', 'release_date', 'revenue', 'runtime', 'spoken_languages', 'vote_average', 'vote_count' 

Question 3: ( based on the dataframe created in Question-2 ) ( 0.5 Mark ) Set the index of the resultant dataframe as 'id'.

Drop all rows where the budget is 0

Question 5: (based on the dataframe created in Question-4) (1 Mark) 

Assume that there is a ranking scheme for movies defined by " (revenue - budget)/budget ". Add a new column for the dataframe, and name it "success_impact", and calculate it for each movie based on the given formula.

Question 6: (based on the dataframe created in Question-5) (1 Mark) 

Normalize the " popularity " column by scaling between 0 to 100. The least popular movie should be 0 and the most popular one must be 100. It is a float number.

Question 7: (based on the dataframe created in Question-6) ( 0.5 Mark ) Change the data type of the "popularity" column to (int16).

Question 8: (based on the dataframe created in Question-7) (1.5 Marks) 

Clean the "cast" column by converting the complex value (JSONs) to a comma separated value. The cleaned "cast" column should be a comma-separated value of alphabetically sorted characters (e.g., Angela, Athena, Betty, Chester Rush ) . NOTE: keep unusual characters e.g., '(uncredited)' as they are; no need for further cleansing.

Question 9: (based on the dataframe created in Question-8) (1.5 Marks) 

Return a list, containing the names of the top 10 movies according to the number of movie characters (Harry Potter! is one character! do not count the letters in the title of movies!). The first element in the list should be the movie with the most number of characters.

UPDATE: You can assume that there is no COMMA in the characters. 

Question 10 : (based on the dataframe created in Question-8) (1 Marks) 

Sort the dataframe by the release date (the most recently released movie should be first row in the dataframe)

Question 11: (based on the dataframe created in Question-10) (2 Marks) 

( 1 .5 Mark ) Plot a pie chart, showing the distribution of genres in the dataset (e.g., Family, Drama).
( 5 Mark ) Show the percentage of each genre in the pie chart. Please be noted that the following figure is just a sample and it does not reflect the real values or the list of all genres in the dataset.
UPDATE: You can add a legend to your chart if labels overlap.You can also merge the some of the infrequent labels (up to 4) and name them "other genres". 

Question 12 : (based on the dataframe created in Question-10) (2 Marks) 

(1.5 Marks) Plot a bar chart of the countries in which movies have been produced. For each county you need to show the count of movies.
(0.5 Mark) Countries should be alphabetically sorted according to their names.
Please be noted that the following figure is just a sample and it does not reflect the real values or the list of all countries in the dataset.

Question 13: (based on the dataframe created in Question-10) (2.5 Marks) 

(1.5 Marks) Plot a scatter chart with x axis being "vote_average" and y axis being "success_impact".
(0.5 Marks) Ink bubbles based on the movie language (e.g, English, French); In case of having multiple languages for the same movie, you are free to pick any one as you wish.
(0.5 Marks) Add a legend showing the name of languages and their associated colors. UPDATE: You can use both "original_language" (e.g. "en", "fr") or "spoken_languages" . 
 

Please be noted that the following figure is just a sample and it does not reflect the real values or the list of all countries in the dataset. (also the x and y axis should be swapped in the figure)

 You can download the code template from : https://raw.githubusercontent.com/mysilver/COMP9321-DataServices/master/20t1/z1111111.py (https://raw.githubusercontent.com/mysilver/COMP9321-DataServices/master/20t1/z1111111.py)

If you do not follow this structure, you will not be marked. 

You can only add codes in the specified lines (do not edit the rest of the lines):

import ast import json

import matplotlib.pyplot as plt import pandas as pd import sys import os studentid = os.path.basename(sys.modules[__name__].__file__) ################################################# # Your personal methods can be here ... ################################################# def log(question, output_df, other):

    print("--------------- {}----------------".format(question))     if other is not None:

        print(question, other)     if output_df is not None:

        print(output_df.head(5).to_string()) def question_1(movies, credits):

    """

    :param movies: the path for the movie.csv file

    :param credits: the path for the credits.csv file

    :return: df1

            Data Type: Dataframe

            Please read the assignment specs to know how to create the output dataframe     """

    #################################################

    # Your code goes here ...

    #################################################     log("QUESTION 1", output_df=df1, other=df1.shape)     return df1

...  if __name__ == "__main__":     df1 = question_1("movies.csv", "credits.csv")     df2 = question_2(df1)     df3 = question_3(df2)     df4 = question_4(df3)     df5 = question_5(df4)     df6 = question_6(df5)     df7 = question_7(df6)     df8 = question_8(df7)     movies = question_9(df8)     df10 = question_10(df8)     question_11(df10)     question_12(df10)     question_13(df10)
#################################################

# Your code goes here ... 

#################################################

 If your code does not run on CSE machines for any reasons (e.g., hard-coded file path such as C://Users/), you will be penalize at least by 5 marks. We assume that the two csv files are located in the same directory of your script, and the name is the same as the one in the template (movies.csv, and credits.csv)

 Please look at the documentation for each question method; it describes the inputs (e.g., a dataframe) and output (e.g., dataframe, list of movies) of the method.

""" 

:param df7: the dataframe created in question 7 

:return: df8 

          Data Type: Dataframe 

          Please read the assignment specs to know how to create the output dataframe

"""

Please use the same variable names as mentioned in the comments (e.g., in question 8, you are supposed to create a dataframe and name it df8

In the last three questions, you need to plot charts; please do not use "plt.show()" function to pop up charts. The code template will automatically save the chart on the disk. What you need to do is to just call the plot functions of the dataframe (e.g., df.plot.pie()). We highly recommend you go through the lab activities to know how to plot charts.