Movie Madness

Note

• This tutorial is also available on nbviewer, offering an alternative platform for your learning convenience.
• 🔥 Free Pandas Course: https://hedaro.gumroad.com/l/tqqfq

Description: You are a data analyst for a movie streaming service. You have been tasked with analyzing a dataset of movie ratings to determine which genres are the most popular among users.

The dataset contains the following columns:

• user_id: Unique identifier for each user
• movie_id: Unique identifier for each movie
• rating: Rating given by the user to the movie (on a scale of 1-5)
• genre: Genre of the movie (e.g. Action, Comedy, Drama, etc.)

Your task is to:

• Load the dataset into a Pandas DataFrame
• Group the data by genre and calculate the average rating for each genre
• Sort the results in descending order by average rating

Data:
You can use the following sample data to get started:

user_id,movie_id,rating,genre
1,101,4,Action
1,102,3,Comedy
2,101,5,Action
2,103,4,Drama
3,102,2,Comedy
3,104,5,Action

# import libraries
import pandas as pd
import sys
 
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)

Python version 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]
Pandas version 2.2.1

# let's try to copy the data using the clipboard
df = pd.read_clipboard(sep=",")
df

	user_id	movie_id	rating	genre
0	1	101	4	Action
1	1	102	3	Comedy
2	2	101	5	Action
3	2	103	4	Drama
4	3	102	2	Comedy
5	3	104	5	Action

# check the data types
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   user_id   6 non-null      int64 
 1   movie_id  6 non-null      int64 
 2   rating    6 non-null      int64 
 3   genre     6 non-null      object
dtypes: int64(3), object(1)
memory usage: 324.0+ bytes

# create groupby object
group = df.groupby('genre')
 
# calculate average rating
avg = group['rating'].mean()
avg

genre
Action    4.666667
Comedy    2.500000
Drama     4.000000
Name: rating, dtype: float64

I decided to place it all in one line. Yes, it is a bit ugly.

Here is what I did:

• I decided to merge the Series that contains the average ratings with the original dataframe via the column named genre
• I renamed the Series so the column names were clear
• I finally sorted the values descending

df.set_index('genre').merge(avg.rename('average_rating'), on='genre').sort_values('average_rating', ascending=False)

	user_id	movie_id	rating	average_rating
genre
Action	1	101	4	4.666667
Action	2	101	5	4.666667
Action	3	104	5	4.666667
Drama	2	103	4	4.000000
Comedy	1	102	3	2.500000
Comedy	3	102	2	2.500000

If all you needed to see was the averages…

avg.sort_values(ascending=False)

genre
Action    4.666667
Drama     4.000000
Comedy    2.500000
Name: rating, dtype: float64

Summary: §

This tutorial guided you through the analysis of a movie ratings dataset using Pandas. It covered loading data, grouping by genre, calculating average ratings, merging data, and sorting results.

Key Takeaways: §

• How to load data from a clipboard into a Pandas DataFrame using pd.read_clipboard()
• Understanding data types using df.info()
• Grouping data by a column (genre) using df.groupby()
• Calculating the average rating for each group using group['rating'].mean()
• Merging data from a Series into the original DataFrame using df.merge() or df.set_index().merge()
• Renaming columns using rename()
• Sorting data in descending order using sort_values()