Interpreting-and-analyzing-Netflix-Data

Hypothesis: Duration of some type of movies have declined in the recent years.

1. Loading the raw data from the CSV file

I will first import the CSV file which contains about 8000 rows and 11 columns of data. Got this data from [kaggle](https://www.kaggle.com/datasets/shivamb/netflix-shows). Then print the first 5 rows so that you can inspect it.

# Read in the CSV as a DataFrame
netflix_df = pd.read_csv("datasets/netflix_data.csv")

# Print the first five rows of the DataFrame
print(netflix_df[0:5])

	show_id	type	title	director	cast	country	date_added	release_year	duration	description	genre
0	s1	TV Show	3%	NaN	João Miguel, Bianca Comparato, Michel Gomes, R...	Brazil	August 14, 2020	2020	4	In a future where the elite inhabit an island ...	International TV
1	s2	Movie	7:19	Jorge Michel Grau	Demián Bichir, Héctor Bonilla, Oscar Serrano, ...	Mexico	December 23, 2016	2016	93	After a devastating earthquake hits Mexico Cit...	Dramas
2	s3	Movie	23:59	Gilbert Chan	Tedd Chan, Stella Chung, Henley Hii, Lawrence ...	Singapore	December 20, 2018	2011	78	When an army recruit is found dead, his fellow...	Horror Movies

2. Filtering for movies.

Okay, we have our data! Now we can dive in and start looking at movie lengths.

Or can we? Looking at the first five rows of our new DataFrame, we notice a column type. Scanning the column, it's clear there are also TV shows in the dataset! Moreover, the duration column we planned to use seems to represent different values depending on whether the row is a movie or a show (perhaps the number of minutes versus the number of seasons)?

# Subset the DataFrame for type "Movie"
netflix_df_movies_only = netflix_df[netflix_df.type=="Movie"]
# Select only the columns of interest
netflix_movies_col_subset=netflix_df_movies_only[['title','country','genre','release_year','duration']]
# Print the first five rows of the new DataFrame
print(netflix_movies_col_subset[0:5])

	title	country	genre	release_year	duration
1	7:19	Mexico	Dramas	2016	93
2	23:59	Singapore	Horror Movies	2011	78
3	9	United States	Action	2009	80
4	21	United States	Dramas	2008	123
6	122	Egypt	Horror Movies	2019	95

Creating a scatter plot of the data.

Okay, now we're getting somewhere. We've read in the raw data, selected rows of movies, and have limited our DataFrame to our columns of interest. Let's try visualizing the data again to inspect the data over a longer range of time.

# Create a figure and increase the figure size
fig = plt.figure(figsize=(12,8))

# Create a scatter plot of duration versus year
plt.scatter(netflix_movies_col_subset[["release_year"]],netflix_movies_col_subset[["duration"]])

# Create a title
plt.title('Movie Duration by Year of Release')

# Show the plot
plt.show()

4. Digging deeper

Upon further inspection, something else is going on. Some of these films are under an hour long! Let's filter our DataFrame for movies with a duration under 60 minutes and look at the genres. This might give us some insight into what is dragging down the average.

# Filter for durations shorter than 60 
lessixty=netflix_movies_col_subset['duration']
short_movies = netflix_movies_col_subset[lessixty<60]

# Print the first 20 rows of short_movies
print(short_movies[0:20])

	title	country	genre	release_year	duration
35	#Rucker50	United States	Documentaries	2016	56
55	100 Things to do Before High School	United States	Uncategorized	2014	44
67	13TH: A Conversation with Oprah Winfrey & Ava ...	NaN	Uncategorized	2017	37
101	3 Seconds Divorce	Canada	Documentaries	2018	53
146	A 3 Minute Hug	Mexico	Documentaries	2019	28

Could not print the whole data as it was a lot to print here.

Interesting! It looks as though many of the films that are under 60 minutes fall into genres such as "Children", "Stand-Up", and "Documentaries". This is a logical result, as these types of films are probably often shorter than 90 minute Hollywood blockbuster

5. Marking non-feature films

We could eliminate these rows from our DataFrame and plot the values again. But another interesting way to explore the effect of these genres on our data would be to plot them, but mark them with a different color.

In Python, there are many ways to do this, but one fun way might be to use a loop to generate a list of colors based on the contents of the genre column. Much as we did in Intermediate Python, we can then pass this list to our plotting function in a later step to color all non-typical genres in a different color!

# Define an empty list
colors = []

# Iterate over rows of netflix_movies_col_subset
for lab, row in netflix_movies_col_subset.iterrows():
    if row['genre']=="Children" :
        colors.append('red')
    elif row['genre']=="Documentaries" :
        colors.append('blue')
    elif row['genre']=="Stand-Up" :
        colors.append('green')
    else:
        colors.append('black')
        
# Inspect the first 10 values in your list        
print(colors[0:11])

['black', 'black', 'black', 'black', 'black', 'black', 'black', 'black', 'black', 'blue', 'black']

Plotting with color!

Lovely looping! We now have a colors list that we can pass to our scatter plot, which should allow us to visually inspect whether these genres might be responsible for the decline in the average duration of movies.

# Set the figure style and initalize a new figure
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12,8))

# Create a scatter plot of duration versus release_year
plt.scatter(netflix_movies_col_subset[["release_year"]],netflix_movies_col_subset[["duration"]], c=colors)

# Create a title and axis labels
plt.xlabel('Release Year')
plt.ylabel('Duration (min)')
plt.title('Movie duration by year of release')

# Show the plot
plt.show()

Well, as we suspected, non-typical genres such as children's movies and documentaries are all clustered around the bottom half of the plot. But we can't know for certain until we perform additional analyses.

# Are we certain that movies are getting shorter?
are_movies_getting_shorter = "No"